Depression diagnosis using Artificial Intelligence: a systematic review

Depression diagnosis using Artificial Intelligence:... | F1000Research "use strict";function _typeof(t){return(_typeof="function"==typeof Symbol&&"symbol"==typeof Symbol.iterator?function(t){return typeof t}:function(t){return t&&"function"==typeof Symbol&&t.constructor===Symbol&&t!==Symbol.prototype?"symbol":typeof t})(t)}!function(){var t=function(){var t,e,o=[],n=window,r=n;for(;r;){try{if(r.frames.__tcfapiLocator){t=r;break}}catch(t){}if(r===n.top)break;r=r.parent}t||(!function t(){var e=n.document,o=!!n.frames.__tcfapiLocator;if(!o)if(e.body){var r=e.createElement("iframe");r.style.cssText="display:none",r.name="__tcfapiLocator",e.body.appendChild(r)}else setTimeout(t,5);return!o}(),n.__tcfapi=function(){for(var t=arguments.length,n=new Array(t),r=0;r 3&&2===parseInt(n[1],10)&&"boolean"==typeof n[3]&&(e=n[3],"function"==typeof n[2]&&n[2]("set",!0)):"ping"===n[0]?"function"==typeof n[2]&&n[2]({gdprApplies:e,cmpLoaded:!1,cmpStatus:"stub"}):o.push(n)},n.addEventListener("message",(function(t){var e="string"==typeof t.data,o={};if(e)try{o=JSON.parse(t.data)}catch(t){}else o=t.data;var n="object"===_typeof(o)&&null!==o?o.__tcfapiCall:null;n&&window.__tcfapi(n.command,n.version,(function(o,r){var a={__tcfapiReturn:{returnValue:o,success:r,callId:n.callId}};t&&t.source&&t.source.postMessage&&t.source.postMessage(e?JSON.stringify(a):a,"*")}),n.parameter)}),!1))};"undefined"!=typeof module?module.exports=t:t()}(); dataLayer = dataLayer || []; // Standard GTM initialization - Google Consent Mode handles consent automatically (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start': new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0], j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src= 'https://www.googletagmanager.com/gtm.js?id='+i+dl+ '>m_auth=hzk0Vc3qFsQYhCrIoHz68A>m_preview=env-1>m_cookies_win=x';f.parentNode.insertBefore(j,f); })(window,document,'script','dataLayer','GTM-MWFK8L5J'); ;window.NREUM||(NREUM={});NREUM.init={distributed_tracing:{enabled:true},privacy:{cookies_enabled:true},ajax:{deny_list:["bam.nr-data.net"]}}; ;NREUM.loader_config={accountID:"438030",trustKey:"438030",agentID:"772317073",licenseKey:"97f8f67f26",applicationID:"772317073"} ;NREUM.info={beacon:"bam.nr-data.net",errorBeacon:"bam.nr-data.net",licenseKey:"97f8f67f26",applicationID:"772317073",sa:1} ;/*! For license information please see nr-loader-spa-1.236.0.min.js.LICENSE.txt */ (()=>{"use strict";var e,t,r={5763:(e,t,r)=>{r.d(t,{P_:()=>l,Mt:()=>g,C5:()=>s,DL:()=>v,OP:()=>T,lF:()=>D,Yu:()=>y,Dg:()=>h,CX:()=>c,GE:()=>b,sU:()=>_});var n=r(8632),i=r(9567);const o={beacon:n.ce.beacon,errorBeacon:n.ce.errorBeacon,licenseKey:void 0,applicationID:void 0,sa:void 0,queueTime:void 0,applicationTime:void 0,ttGuid:void 0,user:void 0,account:void 0,product:void 0,extra:void 0,jsAttributes:{},userAttributes:void 0,atts:void 0,transactionName:void 0,tNamePlain:void 0},a={};function s(e){if(!e)throw new Error("All info objects require an agent identifier!");if(!a[e])throw new Error("Info for ".concat(e," was never set"));return a[e]}function c(e,t){if(!e)throw new Error("All info objects require an agent identifier!");a[e]=(0,i.D)(t,o),(0,n.Qy)(e,a[e],"info")}var u=r(7056);const d=()=>{const e={blockSelector:"[data-nr-block]",maskInputOptions:{password:!0}};return{allow_bfcache:!0,privacy:{cookies_enabled:!0},ajax:{deny_list:void 0,enabled:!0,harvestTimeSeconds:10},distributed_tracing:{enabled:void 0,exclude_newrelic_header:void 0,cors_use_newrelic_header:void 0,cors_use_tracecontext_headers:void 0,allowed_origins:void 0},session:{domain:void 0,expiresMs:u.oD,inactiveMs:u.Hb},ssl:void 0,obfuscate:void 0,jserrors:{enabled:!0,harvestTimeSeconds:10},metrics:{enabled:!0},page_action:{enabled:!0,harvestTimeSeconds:30},page_view_event:{enabled:!0},page_view_timing:{enabled:!0,harvestTimeSeconds:30,long_task:!1},session_trace:{enabled:!0,harvestTimeSeconds:10},harvest:{tooManyRequestsDelay:60},session_replay:{enabled:!1,harvestTimeSeconds:60,sampleRate:.1,errorSampleRate:.1,maskTextSelector:"*",maskAllInputs:!0,get blockClass(){return"nr-block"},get ignoreClass(){return"nr-ignore"},get maskTextClass(){return"nr-mask"},get blockSelector(){return e.blockSelector},set blockSelector(t){e.blockSelector+=",".concat(t)},get maskInputOptions(){return e.maskInputOptions},set maskInputOptions(t){e.maskInputOptions={...t,password:!0}}},spa:{enabled:!0,harvestTimeSeconds:10}}},f={};function l(e){if(!e)throw new Error("All configuration objects require an agent identifier!");if(!f[e])throw new Error("Configuration for ".concat(e," was never set"));return f[e]}function h(e,t){if(!e)throw new Error("All configuration objects require an agent identifier!");f[e]=(0,i.D)(t,d()),(0,n.Qy)(e,f[e],"config")}function g(e,t){if(!e)throw new Error("All configuration objects require an agent identifier!");var r=l(e);if(r){for(var n=t.split("."),i=0;i {r.d(t,{D:()=>i});var n=r(50);function i(e,t){try{if(!e||"object"!=typeof e)return(0,n.Z)("Setting a Configurable requires an object as input");if(!t||"object"!=typeof t)return(0,n.Z)("Setting a Configurable requires a model to set its initial properties");const r=Object.create(Object.getPrototypeOf(t),Object.getOwnPropertyDescriptors(t)),o=0===Object.keys(r).length?e:r;for(let a in o)if(void 0!==e[a])try{"object"==typeof e[a]&&"object"==typeof t[a]?r[a]=i(e[a],t[a]):r[a]=e[a]}catch(e){(0,n.Z)("An error occurred while setting a property of a Configurable",e)}return r}catch(e){(0,n.Z)("An error occured while setting a Configurable",e)}}},6818:(e,t,r)=>{r.d(t,{Re:()=>i,gF:()=>o,q4:()=>n});const n="1.236.0",i="PROD",o="CDN"},385:(e,t,r)=>{r.d(t,{FN:()=>a,IF:()=>u,Nk:()=>f,Tt:()=>s,_A:()=>o,il:()=>n,pL:()=>c,v6:()=>i,w1:()=>d});const n="undefined"!=typeof window&&!!window.document,i="undefined"!=typeof WorkerGlobalScope&&("undefined"!=typeof self&&self instanceof WorkerGlobalScope&&self.navigator instanceof WorkerNavigator||"undefined"!=typeof globalThis&&globalThis instanceof WorkerGlobalScope&&globalThis.navigator instanceof WorkerNavigator),o=n?window:"undefined"!=typeof WorkerGlobalScope&&("undefined"!=typeof self&&self instanceof WorkerGlobalScope&&self||"undefined"!=typeof globalThis&&globalThis instanceof WorkerGlobalScope&&globalThis),a=""+o?.location,s=/iPad|iPhone|iPod/.test(navigator.userAgent),c=s&&"undefined"==typeof SharedWorker,u=(()=>{const e=navigator.userAgent.match(/Firefox[/\s](\d+\.\d+)/);return Array.isArray(e)&&e.length>=2?+e[1]:0})(),d=Boolean(n&&window.document.documentMode),f=!!navigator.sendBeacon},1117:(e,t,r)=>{r.d(t,{w:()=>o});var n=r(50);const i={agentIdentifier:"",ee:void 0};class o{constructor(e){try{if("object"!=typeof e)return(0,n.Z)("shared context requires an object as input");this.sharedContext={},Object.assign(this.sharedContext,i),Object.entries(e).forEach((e=>{let[t,r]=e;Object.keys(i).includes(t)&&(this.sharedContext[t]=r)}))}catch(e){(0,n.Z)("An error occured while setting SharedContext",e)}}}},8e3:(e,t,r)=>{r.d(t,{L:()=>d,R:()=>c});var n=r(2177),i=r(1284),o=r(4322),a=r(3325);const s={};function c(e,t){const r={staged:!1,priority:a.p[t]||0};u(e),s[e].get(t)||s[e].set(t,r)}function u(e){e&&(s[e]||(s[e]=new Map))}function d(){let e=arguments.length>0&&void 0!==arguments[0]?arguments[0]:"",t=arguments.length>1&&void 0!==arguments[1]?arguments[1]:"feature";if(u(e),!e||!s[e].get(t))return a(t);s[e].get(t).staged=!0;const r=[...s[e]];function a(t){const r=e?n.ee.get(e):n.ee,a=o.X.handlers;if(r.backlog&&a){var s=r.backlog[t],c=a[t];if(c){for(var u=0;s&&u {let[t,r]=e;return r.staged}))&&(r.sort(((e,t)=>e[1].priority-t[1].priority)),r.forEach((e=>{let[t]=e;a(t)})))}function f(e,t){var r=e[1];(0,i.D)(t[r],(function(t,r){var n=e[0];if(r[0]===n){var i=r[1],o=e[3],a=e[2];i.apply(o,a)}}))}},2177:(e,t,r)=>{r.d(t,{c:()=>f,ee:()=>u});var n=r(8632),i=r(2210),o=r(1284),a=r(5763),s="nr@context";let c=(0,n.fP)();var u;function d(){}function f(e){return(0,i.X)(e,s,l)}function l(){return new d}function h(){u.aborted=!0,u.backlog={}}c.ee?u=c.ee:(u=function e(t,r){var n={},c={},f={},g=!1;try{g=16===r.length&&(0,a.OP)(r).isolatedBacklog}catch(e){}var p={on:b,addEventListener:b,removeEventListener:y,emit:v,get:x,listeners:w,context:m,buffer:A,abort:h,aborted:!1,isBuffering:E,debugId:r,backlog:g?{}:t&&"object"==typeof t.backlog?t.backlog:{}};return p;function m(e){return e&&e instanceof d?e:e?(0,i.X)(e,s,l):l()}function v(e,r,n,i,o){if(!1!==o&&(o=!0),!u.aborted||i){t&&o&&t.emit(e,r,n);for(var a=m(n),s=w(e),d=s.length,f=0;fn,p:()=>i});var n=r(2177).ee.get("handle");function i(e,t,r,i,o){o?(o.buffer([e],i),o.emit(e,t,r)):(n.buffer([e],i),n.emit(e,t,r))}},4322:(e,t,r)=>{r.d(t,{X:()=>o});var n=r(5546);o.on=a;var i=o.handlers={};function o(e,t,r,o){a(o||n.E,i,e,t,r)}function a(e,t,r,i,o){o||(o="feature"),e||(e=n.E);var a=t[o]=t[o]||{};(a[r]=a[r]||[]).push([e,i])}},3239:(e,t,r)=>{r.d(t,{bP:()=>s,iz:()=>c,m$:()=>a});var n=r(385);let i=!1,o=!1;try{const e={get passive(){return i=!0,!1},get signal(){return o=!0,!1}};n._A.addEventListener("test",null,e),n._A.removeEventListener("test",null,e)}catch(e){}function a(e,t){return i||o?{capture:!!e,passive:i,signal:t}:!!e}function s(e,t){let r=arguments.length>2&&void 0!==arguments[2]&&arguments[2],n=arguments.length>3?arguments[3]:void 0;window.addEventListener(e,t,a(r,n))}function c(e,t){let r=arguments.length>2&&void 0!==arguments[2]&&arguments[2],n=arguments.length>3?arguments[3]:void 0;document.addEventListener(e,t,a(r,n))}},4402:(e,t,r)=>{r.d(t,{Ht:()=>u,M:()=>c,Rl:()=>a,ky:()=>s});var n=r(385);const i="xxxxxxxx-xxxx-4xxx-yxxx-xxxxxxxxxxxx";function o(e,t){return e?15&e[t]:16*Math.random()|0}function a(){const e=n._A?.crypto||n._A?.msCrypto;let t,r=0;return e&&e.getRandomValues&&(t=e.getRandomValues(new Uint8Array(31))),i.split("").map((e=>"x"===e?o(t,++r).toString(16):"y"===e?(3&o()|8).toString(16):e)).join("")}function s(e){const t=n._A?.crypto||n._A?.msCrypto;let r,i=0;t&&t.getRandomValues&&(r=t.getRandomValues(new Uint8Array(31)));const a=[];for(var s=0;s {r.d(t,{Bq:()=>n,Hb:()=>o,oD:()=>i});const n="NRBA",i=144e5,o=18e5},7894:(e,t,r)=>{function n(){return Math.round(performance.now())}r.d(t,{z:()=>n})},7243:(e,t,r)=>{r.d(t,{e:()=>o});var n=r(385),i={};function o(e){if(e in i)return i[e];if(0===(e||"").indexOf("data:"))return{protocol:"data"};let t;var r=n._A?.location,o={};if(n.il)t=document.createElement("a"),t.href=e;else try{t=new URL(e,r.href)}catch(e){return o}o.port=t.port;var a=t.href.split("://");!o.port&&a[1]&&(o.port=a[1].split("/")[0].split("@").pop().split(":")[1]),o.port&&"0"!==o.port||(o.port="https"===a[0]?"443":"80"),o.hostname=t.hostname||r.hostname,o.pathname=t.pathname,o.protocol=a[0],"/"!==o.pathname.charAt(0)&&(o.pathname="/"+o.pathname);var s=!t.protocol||":"===t.protocol||t.protocol===r.protocol,c=t.hostname===r.hostname&&t.port===r.port;return o.sameOrigin=s&&(!t.hostname||c),"/"===o.pathname&&(i[e]=o),o}},50:(e,t,r)=>{function n(e,t){"function"==typeof console.warn&&(console.warn("New Relic: ".concat(e)),t&&console.warn(t))}r.d(t,{Z:()=>n})},2587:(e,t,r)=>{r.d(t,{N:()=>c,T:()=>u});var n=r(2177),i=r(5546),o=r(8e3),a=r(3325);const s={stn:[a.D.sessionTrace],err:[a.D.jserrors,a.D.metrics],ins:[a.D.pageAction],spa:[a.D.spa],sr:[a.D.sessionReplay,a.D.sessionTrace]};function c(e,t){const r=n.ee.get(t);e&&"object"==typeof e&&(Object.entries(e).forEach((e=>{let[t,n]=e;void 0===u[t]&&(s[t]?s[t].forEach((e=>{n?(0,i.p)("feat-"+t,[],void 0,e,r):(0,i.p)("block-"+t,[],void 0,e,r),(0,i.p)("rumresp-"+t,[Boolean(n)],void 0,e,r)})):n&&(0,i.p)("feat-"+t,[],void 0,void 0,r),u[t]=Boolean(n))})),Object.keys(s).forEach((e=>{void 0===u[e]&&(s[e]?.forEach((t=>(0,i.p)("rumresp-"+e,[!1],void 0,t,r))),u[e]=!1)})),(0,o.L)(t,a.D.pageViewEvent))}const u={}},2210:(e,t,r)=>{r.d(t,{X:()=>i});var n=Object.prototype.hasOwnProperty;function i(e,t,r){if(n.call(e,t))return e[t];var i=r();if(Object.defineProperty&&Object.keys)try{return Object.defineProperty(e,t,{value:i,writable:!0,enumerable:!1}),i}catch(e){}return e[t]=i,i}},1284:(e,t,r)=>{r.d(t,{D:()=>n});const n=(e,t)=>Object.entries(e||{}).map((e=>{let[r,n]=e;return t(r,n)}))},4351:(e,t,r)=>{r.d(t,{P:()=>o});var n=r(2177);const i=()=>{const e=new WeakSet;return(t,r)=>{if("object"==typeof r&&null!==r){if(e.has(r))return;e.add(r)}return r}};function o(e){try{return JSON.stringify(e,i())}catch(e){try{n.ee.emit("internal-error",[e])}catch(e){}}}},3960:(e,t,r)=>{r.d(t,{K:()=>a,b:()=>o});var n=r(3239);function i(){return"undefined"==typeof document||"complete"===document.readyState}function o(e,t){if(i())return e();(0,n.bP)("load",e,t)}function a(e){if(i())return e();(0,n.iz)("DOMContentLoaded",e)}},8632:(e,t,r)=>{r.d(t,{EZ:()=>u,Qy:()=>c,ce:()=>o,fP:()=>a,gG:()=>d,mF:()=>s});var n=r(7894),i=r(385);const o={beacon:"bam.nr-data.net",errorBeacon:"bam.nr-data.net"};function a(){return i._A.NREUM||(i._A.NREUM={}),void 0===i._A.newrelic&&(i._A.newrelic=i._A.NREUM),i._A.NREUM}function s(){let e=a();return e.o||(e.o={ST:i._A.setTimeout,SI:i._A.setImmediate,CT:i._A.clearTimeout,XHR:i._A.XMLHttpRequest,REQ:i._A.Request,EV:i._A.Event,PR:i._A.Promise,MO:i._A.MutationObserver,FETCH:i._A.fetch}),e}function c(e,t,r){let i=a();const o=i.initializedAgents||{},s=o[e]||{};return Object.keys(s).length||(s.initializedAt={ms:(0,n.z)(),date:new Date}),i.initializedAgents={...o,[e]:{...s,[r]:t}},i}function u(e,t){a()[e]=t}function d(){return function(){let e=a();const t=e.info||{};e.info={beacon:o.beacon,errorBeacon:o.errorBeacon,...t}}(),function(){let e=a();const t=e.init||{};e.init={...t}}(),s(),function(){let e=a();const t=e.loader_config||{};e.loader_config={...t}}(),a()}},7956:(e,t,r)=>{r.d(t,{N:()=>i});var n=r(3239);function i(e){let t=arguments.length>1&&void 0!==arguments[1]&&arguments[1],r=arguments.length>2?arguments[2]:void 0,i=arguments.length>3?arguments[3]:void 0;return void(0,n.iz)("visibilitychange",(function(){if(t)return void("hidden"==document.visibilityState&&e());e(document.visibilityState)}),r,i)}},1214:(e,t,r)=>{r.d(t,{em:()=>v,u5:()=>N,QU:()=>S,_L:()=>I,Gm:()=>L,Lg:()=>M,gy:()=>U,BV:()=>Q,Kf:()=>ee});var n=r(2177);const i="nr@original";var o=Object.prototype.hasOwnProperty,a=!1;function s(e,t){return e||(e=n.ee),r.inPlace=function(e,t,n,i,o){n||(n="");var a,s,c,u="-"===n.charAt(0);for(c=0;c 2?n-2:0),o=2;o {r(A[T],e,w),r(E[T],e,w)})),r(l._A,"fetch",y),t.on(y+"end",(function(e,r){var n=this;if(r){var i=r.headers.get("content-length");null!==i&&(n.rxSize=i),t.emit(y+"done",[null,r],n)}else t.emit(y+"done",[e],n)})),t}const O={},j=["pushState","replaceState"];function S(e){const t=function(e){return(e||n.ee).get("history")}(e);return!l.il||O[t.debugId]++||(O[t.debugId]=1,s(t).inPlace(window.history,j,"-")),t}var P=r(3239);const C={},R=["appendChild","insertBefore","replaceChild"];function I(e){const t=function(e){return(e||n.ee).get("jsonp")}(e);if(!l.il||C[t.debugId])return t;C[t.debugId]=!0;var r=s(t),i=/[?&](?:callback|cb)=([^&#]+)/,o=/(.*)\.([^.]+)/,a=/^(\w+)(\.|$)(.*)$/;function c(e,t){var r=e.match(a),n=r[1],i=r[3];return i?c(i,t[n]):t[n]}return r.inPlace(Node.prototype,R,"dom-"),t.on("dom-start",(function(e){!function(e){if(!e||"string"!=typeof e.nodeName||"script"!==e.nodeName.toLowerCase())return;if("function"!=typeof e.addEventListener)return;var n=(a=e.src,s=a.match(i),s?s[1]:null);var a,s;if(!n)return;var u=function(e){var t=e.match(o);if(t&&t.length>=3)return{key:t[2],parent:c(t[1],window)};return{key:e,parent:window}}(n);if("function"!=typeof u.parent[u.key])return;var d={};function f(){t.emit("jsonp-end",[],d),e.removeEventListener("load",f,(0,P.m$)(!1)),e.removeEventListener("error",l,(0,P.m$)(!1))}function l(){t.emit("jsonp-error",[],d),t.emit("jsonp-end",[],d),e.removeEventListener("load",f,(0,P.m$)(!1)),e.removeEventListener("error",l,(0,P.m$)(!1))}r.inPlace(u.parent,[u.key],"cb-",d),e.addEventListener("load",f,(0,P.m$)(!1)),e.addEventListener("error",l,(0,P.m$)(!1)),t.emit("new-jsonp",[e.src],d)}(e[0])})),t}var k=r(5763);const H={};function L(e){const t=function(e){return(e||n.ee).get("mutation")}(e);if(!l.il||H[t.debugId])return t;H[t.debugId]=!0;var r=s(t),i=k.Yu.MO;return i&&(window.MutationObserver=function(e){return this instanceof i?new i(r(e,"fn-")):i.apply(this,arguments)},MutationObserver.prototype=i.prototype),t}const z={};function M(e){const t=function(e){return(e||n.ee).get("promise")}(e);if(z[t.debugId])return t;z[t.debugId]=!0;var r=n.c,o=s(t),a=k.Yu.PR;return a&&function(){function e(r){var n=t.context(),i=o(r,"executor-",n,null,!1);const s=Reflect.construct(a,[i],e);return t.context(s).getCtx=function(){return n},s}l._A.Promise=e,Object.defineProperty(e,"name",{value:"Promise"}),e.toString=function(){return a.toString()},Object.setPrototypeOf(e,a),["all","race"].forEach((function(r){const n=a[r];e[r]=function(e){let i=!1;[...e||[]].forEach((e=>{this.resolve(e).then(a("all"===r),a(!1))}));const o=n.apply(this,arguments);return o;function a(e){return function(){t.emit("propagate",[null,!i],o,!1,!1),i=i||!e}}}})),["resolve","reject"].forEach((function(r){const n=a[r];e[r]=function(e){const r=n.apply(this,arguments);return e!==r&&t.emit("propagate",[e,!0],r,!1,!1),r}})),e.prototype=a.prototype;const n=a.prototype.then;a.prototype.then=function(){var e=this,i=r(e);i.promise=e;for(var a=arguments.length,s=new Array(a),c=0;c e())),t};function m(e,t){i.inPlace(t,["onreadystatechange"],"fn-",E)}function b(){var e=this,t=r.context(e);e.readyState>3&&!t.resolved&&(t.resolved=!0,r.emit("xhr-resolved",[],e)),i.inPlace(e,f,"fn-",E)}if(function(e,t){for(var r in e)t[r]=e[r]}(o,p),p.prototype=o.prototype,i.inPlace(p.prototype,J,"-xhr-",E),r.on("send-xhr-start",(function(e,t){m(e,t),function(e){h.push(e),a&&(y?y.then(A):u?u(A):(w=-w,x.data=w))}(t)})),r.on("open-xhr-start",m),a){var y=c&&c.resolve();if(!u&&!c){var w=1,x=document.createTextNode(w);new a(A).observe(x,{characterData:!0})}}else t.on("fn-end",(function(e){e[0]&&e[0].type===d||A()}));function A(){for(var e=0;e {r.d(t,{t:()=>n});const n=r(3325).D.ajax},6660:(e,t,r)=>{r.d(t,{A:()=>i,t:()=>n});const n=r(3325).D.jserrors,i="nr@seenError"},3081:(e,t,r)=>{r.d(t,{gF:()=>o,mY:()=>i,t9:()=>n,vz:()=>s,xS:()=>a});const n=r(3325).D.metrics,i="sm",o="cm",a="storeSupportabilityMetrics",s="storeEventMetrics"},4649:(e,t,r)=>{r.d(t,{t:()=>n});const n=r(3325).D.pageAction},7633:(e,t,r)=>{r.d(t,{Dz:()=>i,OJ:()=>a,qw:()=>o,t9:()=>n});const n=r(3325).D.pageViewEvent,i="firstbyte",o="domcontent",a="windowload"},9251:(e,t,r)=>{r.d(t,{t:()=>n});const n=r(3325).D.pageViewTiming},3614:(e,t,r)=>{r.d(t,{BST_RESOURCE:()=>i,END:()=>s,FEATURE_NAME:()=>n,FN_END:()=>u,FN_START:()=>c,PUSH_STATE:()=>d,RESOURCE:()=>o,START:()=>a});const n=r(3325).D.sessionTrace,i="bstResource",o="resource",a="-start",s="-end",c="fn"+a,u="fn"+s,d="pushState"},7836:(e,t,r)=>{r.d(t,{BODY:()=>A,CB_END:()=>E,CB_START:()=>u,END:()=>x,FEATURE_NAME:()=>i,FETCH:()=>_,FETCH_BODY:()=>v,FETCH_DONE:()=>m,FETCH_START:()=>p,FN_END:()=>c,FN_START:()=>s,INTERACTION:()=>l,INTERACTION_API:()=>d,INTERACTION_EVENTS:()=>o,JSONP_END:()=>b,JSONP_NODE:()=>g,JS_TIME:()=>T,MAX_TIMER_BUDGET:()=>a,REMAINING:()=>f,SPA_NODE:()=>h,START:()=>w,originalSetTimeout:()=>y});var n=r(5763);const i=r(3325).D.spa,o=["click","submit","keypress","keydown","keyup","change"],a=999,s="fn-start",c="fn-end",u="cb-start",d="api-ixn-",f="remaining",l="interaction",h="spaNode",g="jsonpNode",p="fetch-start",m="fetch-done",v="fetch-body-",b="jsonp-end",y=n.Yu.ST,w="-start",x="-end",A="-body",E="cb"+x,T="jsTime",_="fetch"},5938:(e,t,r)=>{r.d(t,{W:()=>o});var n=r(5763),i=r(2177);class o{constructor(e,t,r){this.agentIdentifier=e,this.aggregator=t,this.ee=i.ee.get(e,(0,n.OP)(this.agentIdentifier).isolatedBacklog),this.featureName=r,this.blocked=!1}}},9144:(e,t,r)=>{r.d(t,{j:()=>m});var n=r(3325),i=r(5763),o=r(5546),a=r(2177),s=r(7894),c=r(8e3),u=r(3960),d=r(385),f=r(50),l=r(3081),h=r(8632);function g(){const e=(0,h.gG)();["setErrorHandler","finished","addToTrace","inlineHit","addRelease","addPageAction","setCurrentRouteName","setPageViewName","setCustomAttribute","interaction","noticeError","setUserId"].forEach((t=>{e[t]=function(){for(var r=arguments.length,n=new Array(r),i=0;i 1?r-1:0),i=1;i {e.exposed&&e.api[t]&&o.push(e.api[t](...n))})),o.length>1?o:o[0]}(t,...n)}}))}var p=r(2587);function m(e){let t=arguments.length>1&&void 0!==arguments[1]?arguments[1]:{},m=arguments.length>2?arguments[2]:void 0,v=arguments.length>3?arguments[3]:void 0,{init:b,info:y,loader_config:w,runtime:x={loaderType:m},exposed:A=!0}=t;const E=(0,h.gG)();y||(b=E.init,y=E.info,w=E.loader_config),(0,i.Dg)(e,b||{}),(0,i.GE)(e,w||{}),(0,i.sU)(e,x),y.jsAttributes??={},d.v6&&(y.jsAttributes.isWorker=!0),(0,i.CX)(e,y),g();const T=function(e,t){t||(0,c.R)(e,"api");const h={};var g=a.ee.get(e),p=g.get("tracer"),m="api-",v=m+"ixn-";function b(t,r,n,o){const a=(0,i.C5)(e);return null===r?delete a.jsAttributes[t]:(0,i.CX)(e,{...a,jsAttributes:{...a.jsAttributes,[t]:r}}),x(m,n,!0,o||null===r?"session":void 0)(t,r)}function y(){}["setErrorHandler","finished","addToTrace","inlineHit","addRelease"].forEach((e=>h[e]=x(m,e,!0,"api"))),h.addPageAction=x(m,"addPageAction",!0,n.D.pageAction),h.setCurrentRouteName=x(m,"routeName",!0,n.D.spa),h.setPageViewName=function(t,r){if("string"==typeof t)return"/"!==t.charAt(0)&&(t="/"+t),(0,i.OP)(e).customTransaction=(r||"http://custom.transaction")+t,x(m,"setPageViewName",!0)()},h.setCustomAttribute=function(e,t){let r=arguments.length>2&&void 0!==arguments[2]&&arguments[2];if("string"==typeof e){if(["string","number"].includes(typeof t)||null===t)return b(e,t,"setCustomAttribute",r);(0,f.Z)("Failed to execute setCustomAttribute.\nNon-null value must be a string or number type, but a type of was provided."))}else(0,f.Z)("Failed to execute setCustomAttribute.\nName must be a string type, but a type of was provided."))},h.setUserId=function(e){if("string"==typeof e||null===e)return b("enduser.id",e,"setUserId",!0);(0,f.Z)("Failed to execute setUserId.\nNon-null value must be a string type, but a type of was provided."))},h.interaction=function(){return(new y).get()};var w=y.prototype={createTracer:function(e,t){var r={},i=this,a="function"==typeof t;return(0,o.p)(v+"tracer",[(0,s.z)(),e,r],i,n.D.spa,g),function(){if(p.emit((a?"":"no-")+"fn-start",[(0,s.z)(),i,a],r),a)try{return t.apply(this,arguments)}catch(e){throw p.emit("fn-err",[arguments,this,"string"==typeof e?new Error(e):e],r),e}finally{p.emit("fn-end",[(0,s.z)()],r)}}}};function x(e,t,r,i){return function(){return(0,o.p)(l.xS,["API/"+t+"/called"],void 0,n.D.metrics,g),i&&(0,o.p)(e+t,[(0,s.z)(),...arguments],r?null:this,i,g),r?void 0:this}}function A(){r.e(439).then(r.bind(r,7438)).then((t=>{let{setAPI:r}=t;r(e),(0,c.L)(e,"api")})).catch((()=>(0,f.Z)("Downloading runtime APIs failed...")))}return["actionText","setName","setAttribute","save","ignore","onEnd","getContext","end","get"].forEach((e=>{w[e]=x(v,e,void 0,n.D.spa)})),h.noticeError=function(e,t){"string"==typeof e&&(e=new Error(e)),(0,o.p)(l.xS,["API/noticeError/called"],void 0,n.D.metrics,g),(0,o.p)("err",[e,(0,s.z)(),!1,t],void 0,n.D.jserrors,g)},d.il?(0,u.b)((()=>A()),!0):A(),h}(e,v);return(0,h.Qy)(e,T,"api"),(0,h.Qy)(e,A,"exposed"),(0,h.EZ)("activatedFeatures",p.T),T}},3325:(e,t,r)=>{r.d(t,{D:()=>n,p:()=>i});const n={ajax:"ajax",jserrors:"jserrors",metrics:"metrics",pageAction:"page_action",pageViewEvent:"page_view_event",pageViewTiming:"page_view_timing",sessionReplay:"session_replay",sessionTrace:"session_trace",spa:"spa"},i={[n.pageViewEvent]:1,[n.pageViewTiming]:2,[n.metrics]:3,[n.jserrors]:4,[n.ajax]:5,[n.sessionTrace]:6,[n.pageAction]:7,[n.spa]:8,[n.sessionReplay]:9}}},n={};function i(e){var t=n[e];if(void 0!==t)return t.exports;var o=n[e]={exports:{}};return r[e](o,o.exports,i),o.exports}i.m=r,i.d=(e,t)=>{for(var r in t)i.o(t,r)&&!i.o(e,r)&&Object.defineProperty(e,r,{enumerable:!0,get:t[r]})},i.f={},i.e=e=>Promise.all(Object.keys(i.f).reduce(((t,r)=>(i.f[r](e,t),t)),[])),i.u=e=>(({78:"page_action-aggregate",147:"metrics-aggregate",242:"session-manager",317:"jserrors-aggregate",348:"page_view_timing-aggregate",412:"lazy-feature-loader",439:"async-api",538:"recorder",590:"session_replay-aggregate",675:"compressor",733:"session_trace-aggregate",786:"page_view_event-aggregate",873:"spa-aggregate",898:"ajax-aggregate"}[e]||e)+"."+{78:"ac76d497",147:"3dc53903",148:"1a20d5fe",242:"2a64278a",317:"49e41428",348:"bd6de33a",412:"2f55ce66",439:"30bd804e",538:"1b18459f",590:"cf0efb30",675:"ae9f91a8",733:"83105561",786:"06482edd",860:"03a8b7a5",873:"e6b09d52",898:"998ef92b"}[e]+"-1.236.0.min.js"),i.o=(e,t)=>Object.prototype.hasOwnProperty.call(e,t),e={},t="NRBA:",i.l=(r,n,o,a)=>{if(e[r])e[r].push(n);else{var s,c;if(void 0!==o)for(var u=document.getElementsByTagName("script"),d=0;d {s.onerror=s.onload=null,clearTimeout(h);var i=e[r];if(delete e[r],s.parentNode&&s.parentNode.removeChild(s),i&&i.forEach((e=>e(n))),t)return t(n)},h=setTimeout(l.bind(null,void 0,{type:"timeout",target:s}),12e4);s.onerror=l.bind(null,s.onerror),s.onload=l.bind(null,s.onload),c&&document.head.appendChild(s)}},i.r=e=>{"undefined"!=typeof Symbol&&Symbol.toStringTag&&Object.defineProperty(e,Symbol.toStringTag,{value:"Module"}),Object.defineProperty(e,"__esModule",{value:!0})},i.j=364,i.p="https://js-agent.newrelic.com/",(()=>{var e={364:0,953:0};i.f.j=(t,r)=>{var n=i.o(e,t)?e[t]:void 0;if(0!==n)if(n)r.push(n[2]);else{var o=new Promise(((r,i)=>n=e[t]=[r,i]));r.push(n[2]=o);var a=i.p+i.u(t),s=new Error;i.l(a,(r=>{if(i.o(e,t)&&(0!==(n=e[t])&&(e[t]=void 0),n)){var o=r&&("load"===r.type?"missing":r.type),a=r&&r.target&&r.target.src;s.message="Loading chunk "+t+" failed.\n("+o+": "+a+")",s.name="ChunkLoadError",s.type=o,s.request=a,n[1](s)}}),"chunk-"+t,t)}};var t=(t,r)=>{var n,o,[a,s,c]=r,u=0;if(a.some((t=>0!==e[t]))){for(n in s)i.o(s,n)&&(i.m[n]=s[n]);if(c)c(i)}for(t&&t(r);u {i.r(o);var e=i(3325),t=i(5763);const r=Object.values(e.D);function n(e){const n={};return r.forEach((r=>{n[r]=function(e,r){return!1!==(0,t.Mt)(r,"".concat(e,".enabled"))}(r,e)})),n}var a=i(9144);var s=i(5546),c=i(385),u=i(8e3),d=i(5938),f=i(3960),l=i(50);class h extends d.W{constructor(e,t,r){let n=!(arguments.length>3&&void 0!==arguments[3])||arguments[3];super(e,t,r),this.auto=n,this.abortHandler,this.featAggregate,this.onAggregateImported,n&&(0,u.R)(e,r)}importAggregator(){let e=arguments.length>0&&void 0!==arguments[0]?arguments[0]:{};if(this.featAggregate||!this.auto)return;const r=c.il&&!0===(0,t.Mt)(this.agentIdentifier,"privacy.cookies_enabled");let n;this.onAggregateImported=new Promise((e=>{n=e}));const o=async()=>{let t;try{if(r){const{setupAgentSession:e}=await Promise.all([i.e(860),i.e(242)]).then(i.bind(i,3228));t=e(this.agentIdentifier)}}catch(e){(0,l.Z)("A problem occurred when starting up session manager. This page will not start or extend any session.",e)}try{if(!this.shouldImportAgg(this.featureName,t))return void(0,u.L)(this.agentIdentifier,this.featureName);const{lazyFeatureLoader:r}=await i.e(412).then(i.bind(i,8582)),{Aggregate:o}=await r(this.featureName,"aggregate");this.featAggregate=new o(this.agentIdentifier,this.aggregator,e),n(!0)}catch(e){(0,l.Z)("Downloading and initializing ".concat(this.featureName," failed..."),e),this.abortHandler?.(),n(!1)}};c.il?(0,f.b)((()=>o()),!0):o()}shouldImportAgg(r,n){return r!==e.D.sessionReplay||!1!==(0,t.Mt)(this.agentIdentifier,"session_trace.enabled")&&(!!n?.isNew||!!n?.state.sessionReplay)}}var g=i(7633),p=i(7894);class m extends h{static featureName=g.t9;constructor(r,n){let i=!(arguments.length>2&&void 0!==arguments[2])||arguments[2];if(super(r,n,g.t9,i),("undefined"==typeof PerformanceNavigationTiming||c.Tt)&&"undefined"!=typeof PerformanceTiming){const n=(0,t.OP)(r);n[g.Dz]=Math.max(Date.now()-n.offset,0),(0,f.K)((()=>n[g.qw]=Math.max((0,p.z)()-n[g.Dz],0))),(0,f.b)((()=>{const t=(0,p.z)();n[g.OJ]=Math.max(t-n[g.Dz],0),(0,s.p)("timing",["load",t],void 0,e.D.pageViewTiming,this.ee)}))}this.importAggregator()}}var v=i(1117),b=i(1284);class y extends v.w{constructor(e){super(e),this.aggregatedData={}}store(e,t,r,n,i){var o=this.getBucket(e,t,r,i);return o.metrics=function(e,t){t||(t={count:0});return t.count+=1,(0,b.D)(e,(function(e,r){t[e]=w(r,t[e])})),t}(n,o.metrics),o}merge(e,t,r,n,i){var o=this.getBucket(e,t,n,i);if(o.metrics){var a=o.metrics;a.count+=r.count,(0,b.D)(r,(function(e,t){if("count"!==e){var n=a[e],i=r[e];i&&!i.c?a[e]=w(i.t,n):a[e]=function(e,t){if(!t)return e;t.c||(t=x(t.t));return t.min=Math.min(e.min,t.min),t.max=Math.max(e.max,t.max),t.t+=e.t,t.sos+=e.sos,t.c+=e.c,t}(i,a[e])}}))}else o.metrics=r}storeMetric(e,t,r,n){var i=this.getBucket(e,t,r);return i.stats=w(n,i.stats),i}getBucket(e,t,r,n){this.aggregatedData[e]||(this.aggregatedData[e]={});var i=this.aggregatedData[e][t];return i||(i=this.aggregatedData[e][t]={params:r||{}},n&&(i.custom=n)),i}get(e,t){return t?this.aggregatedData[e]&&this.aggregatedData[e][t]:this.aggregatedData[e]}take(e){for(var t={},r="",n=!1,i=0;i t.max&&(t.max=e),e 2&&void 0!==arguments[2])||arguments[2];super(e,r,j.t,n),c.il&&((0,t.OP)(e).initHidden=Boolean("hidden"===document.visibilityState),(0,N.N)((()=>(0,s.p)("docHidden",[(0,p.z)()],void 0,j.t,this.ee)),!0),(0,O.bP)("pagehide",(()=>(0,s.p)("winPagehide",[(0,p.z)()],void 0,j.t,this.ee))),this.importAggregator())}}var P=i(3081);class C extends h{static featureName=P.t9;constructor(e,t){let r=!(arguments.length>2&&void 0!==arguments[2])||arguments[2];super(e,t,P.t9,r),this.importAggregator()}}var R,I=i(2210),k=i(1214),H=i(2177),L={};try{R=localStorage.getItem("__nr_flags").split(","),console&&"function"==typeof console.log&&(L.console=!0,-1!==R.indexOf("dev")&&(L.dev=!0),-1!==R.indexOf("nr_dev")&&(L.nrDev=!0))}catch(e){}function z(e){try{L.console&&z(e)}catch(e){}}L.nrDev&&H.ee.on("internal-error",(function(e){z(e.stack)})),L.dev&&H.ee.on("fn-err",(function(e,t,r){z(r.stack)})),L.dev&&(z("NR AGENT IN DEVELOPMENT MODE"),z("flags: "+(0,b.D)(L,(function(e,t){return e})).join(", ")));var M=i(6660);class B extends h{static featureName=M.t;constructor(r,n){let i=!(arguments.length>2&&void 0!==arguments[2])||arguments[2];super(r,n,M.t,i),this.skipNext=0;try{this.removeOnAbort=new AbortController}catch(e){}const o=this;o.ee.on("fn-start",(function(e,t,r){o.abortHandler&&(o.skipNext+=1)})),o.ee.on("fn-err",(function(t,r,n){o.abortHandler&&!n[M.A]&&((0,I.X)(n,M.A,(function(){return!0})),this.thrown=!0,(0,s.p)("err",[n,(0,p.z)()],void 0,e.D.jserrors,o.ee))})),o.ee.on("fn-end",(function(){o.abortHandler&&!this.thrown&&o.skipNext>0&&(o.skipNext-=1)})),o.ee.on("internal-error",(function(t){(0,s.p)("ierr",[t,(0,p.z)(),!0],void 0,e.D.jserrors,o.ee)})),this.origOnerror=c._A.onerror,c._A.onerror=this.onerrorHandler.bind(this),c._A.addEventListener("unhandledrejection",(t=>{const r=function(e){let t="Unhandled Promise Rejection: ";if(e instanceof Error)try{return e.message=t+e.message,e}catch(t){return e}if(void 0===e)return new Error(t);try{return new Error(t+(0,D.P)(e))}catch(e){return new Error(t)}}(t.reason);(0,s.p)("err",[r,(0,p.z)(),!1,{unhandledPromiseRejection:1}],void 0,e.D.jserrors,this.ee)}),(0,O.m$)(!1,this.removeOnAbort?.signal)),(0,k.gy)(this.ee),(0,k.BV)(this.ee),(0,k.em)(this.ee),(0,t.OP)(r).xhrWrappable&&(0,k.Kf)(this.ee),this.abortHandler=this.#e,this.importAggregator()}#e(){this.removeOnAbort?.abort(),this.abortHandler=void 0}onerrorHandler(t,r,n,i,o){"function"==typeof this.origOnerror&&this.origOnerror(...arguments);try{this.skipNext?this.skipNext-=1:(0,s.p)("err",[o||new F(t,r,n),(0,p.z)()],void 0,e.D.jserrors,this.ee)}catch(t){try{(0,s.p)("ierr",[t,(0,p.z)(),!0],void 0,e.D.jserrors,this.ee)}catch(e){}}return!1}}function F(e,t,r){this.message=e||"Uncaught error with no additional information",this.sourceURL=t,this.line=r}let U=1;const q="nr@id";function G(e){const t=typeof e;return!e||"object"!==t&&"function"!==t?-1:e===c._A?0:(0,I.X)(e,q,(function(){return U++}))}function V(e){if("string"==typeof e&&e.length)return e.length;if("object"==typeof e){if("undefined"!=typeof ArrayBuffer&&e instanceof ArrayBuffer&&e.byteLength)return e.byteLength;if("undefined"!=typeof Blob&&e instanceof Blob&&e.size)return e.size;if(!("undefined"!=typeof FormData&&e instanceof FormData))try{return(0,D.P)(e).length}catch(e){return}}}var X=i(7243);class W{constructor(e){this.agentIdentifier=e,this.generateTracePayload=this.generateTracePayload.bind(this),this.shouldGenerateTrace=this.shouldGenerateTrace.bind(this)}generateTracePayload(e){if(!this.shouldGenerateTrace(e))return null;var r=(0,t.DL)(this.agentIdentifier);if(!r)return null;var n=(r.accountID||"").toString()||null,i=(r.agentID||"").toString()||null,o=(r.trustKey||"").toString()||null;if(!n||!i)return null;var a=(0,_.M)(),s=(0,_.Ht)(),c=Date.now(),u={spanId:a,traceId:s,timestamp:c};return(e.sameOrigin||this.isAllowedOrigin(e)&&this.useTraceContextHeadersForCors())&&(u.traceContextParentHeader=this.generateTraceContextParentHeader(a,s),u.traceContextStateHeader=this.generateTraceContextStateHeader(a,c,n,i,o)),(e.sameOrigin&&!this.excludeNewrelicHeader()||!e.sameOrigin&&this.isAllowedOrigin(e)&&this.useNewrelicHeaderForCors())&&(u.newrelicHeader=this.generateTraceHeader(a,s,c,n,i,o)),u}generateTraceContextParentHeader(e,t){return"00-"+t+"-"+e+"-01"}generateTraceContextStateHeader(e,t,r,n,i){return i+"@nr=0-1-"+r+"-"+n+"-"+e+"----"+t}generateTraceHeader(e,t,r,n,i,o){if(!("function"==typeof c._A?.btoa))return null;var a={v:[0,1],d:{ty:"Browser",ac:n,ap:i,id:e,tr:t,ti:r}};return o&&n!==o&&(a.d.tk=o),btoa((0,D.P)(a))}shouldGenerateTrace(e){return this.isDtEnabled()&&this.isAllowedOrigin(e)}isAllowedOrigin(e){var r=!1,n={};if((0,t.Mt)(this.agentIdentifier,"distributed_tracing")&&(n=(0,t.P_)(this.agentIdentifier).distributed_tracing),e.sameOrigin)r=!0;else if(n.allowed_origins instanceof Array)for(var i=0;i 2&&void 0!==arguments[2])||arguments[2];super(r,n,Z.t,i),(0,t.OP)(r).xhrWrappable&&(this.dt=new W(r),this.handler=(e,t,r,n)=>(0,s.p)(e,t,r,n,this.ee),(0,k.u5)(this.ee),(0,k.Kf)(this.ee),function(r,n,i,o){function a(e){var t=this;t.totalCbs=0,t.called=0,t.cbTime=0,t.end=E,t.ended=!1,t.xhrGuids={},t.lastSize=null,t.loadCaptureCalled=!1,t.params=this.params||{},t.metrics=this.metrics||{},e.addEventListener("load",(function(r){_(t,e)}),(0,O.m$)(!1)),c.IF||e.addEventListener("progress",(function(e){t.lastSize=e.loaded}),(0,O.m$)(!1))}function s(e){this.params={method:e[0]},T(this,e[1]),this.metrics={}}function u(e,n){var i=(0,t.DL)(r);i.xpid&&this.sameOrigin&&n.setRequestHeader("X-NewRelic-ID",i.xpid);var a=o.generateTracePayload(this.parsedOrigin);if(a){var s=!1;a.newrelicHeader&&(n.setRequestHeader("newrelic",a.newrelicHeader),s=!0),a.traceContextParentHeader&&(n.setRequestHeader("traceparent",a.traceContextParentHeader),a.traceContextStateHeader&&n.setRequestHeader("tracestate",a.traceContextStateHeader),s=!0),s&&(this.dt=a)}}function d(e,t){var r=this.metrics,i=e[0],o=this;if(r&&i){var a=V(i);a&&(r.txSize=a)}this.startTime=(0,p.z)(),this.listener=function(e){try{"abort"!==e.type||o.loadCaptureCalled||(o.params.aborted=!0),("load"!==e.type||o.called===o.totalCbs&&(o.onloadCalled||"function"!=typeof t.onload)&&"function"==typeof o.end)&&o.end(t)}catch(e){try{n.emit("internal-error",[e])}catch(e){}}};for(var s=0;s 1?e[1]=i:e.push(i)}else e[0]&&e[0].headers&&s(e[0].headers,n)&&(this.dt=n);function s(e,t){var r=!1;return t.newrelicHeader&&(e.set("newrelic",t.newrelicHeader),r=!0),t.traceContextParentHeader&&(e.set("traceparent",t.traceContextParentHeader),t.traceContextStateHeader&&e.set("tracestate",t.traceContextStateHeader),r=!0),r}}function x(e,t){this.params={},this.metrics={},this.startTime=(0,p.z)(),this.dt=t,e.length>=1&&(this.target=e[0]),e.length>=2&&(this.opts=e[1]);var r,n=this.opts||{},i=this.target;"string"==typeof i?r=i:"object"==typeof i&&i instanceof Y?r=i.url:c._A?.URL&&"object"==typeof i&&i instanceof URL&&(r=i.href),T(this,r);var o=(""+(i&&i instanceof Y&&i.method||n.method||"GET")).toUpperCase();this.params.method=o,this.txSize=V(n.body)||0}function A(t,r){var n;this.endTime=(0,p.z)(),this.params||(this.params={}),this.params.status=r?r.status:0,"string"==typeof this.rxSize&&this.rxSize.length>0&&(n=+this.rxSize);var o={txSize:this.txSize,rxSize:n,duration:(0,p.z)()-this.startTime};i("xhr",[this.params,o,this.startTime,this.endTime,"fetch"],this,e.D.ajax)}function E(t){var r=this.params,n=this.metrics;if(!this.ended){this.ended=!0;for(var o=0;o 2&&void 0!==arguments[2])||arguments[2];super(e,t,we.t,r),this.importAggregator()}}new class{constructor(e){let t=arguments.length>1&&void 0!==arguments[1]?arguments[1]:(0,_.ky)(16);c._A?(this.agentIdentifier=t,this.sharedAggregator=new y({agentIdentifier:this.agentIdentifier}),this.features={},this.desiredFeatures=new Set(e.features||[]),this.desiredFeatures.add(m),Object.assign(this,(0,a.j)(this.agentIdentifier,e,e.loaderType||"agent")),this.start()):(0,l.Z)("Failed to initial the agent. Could not determine the runtime environment.")}get config(){return{info:(0,t.C5)(this.agentIdentifier),init:(0,t.P_)(this.agentIdentifier),loader_config:(0,t.DL)(this.agentIdentifier),runtime:(0,t.OP)(this.agentIdentifier)}}start(){const t="features";try{const r=n(this.agentIdentifier),i=[...this.desiredFeatures];i.sort(((t,r)=>e.p[t.featureName]-e.p[r.featureName])),i.forEach((t=>{if(r[t.featureName]||t.featureName===e.D.pageViewEvent){const n=function(t){switch(t){case e.D.ajax:return[e.D.jserrors];case e.D.sessionTrace:return[e.D.ajax,e.D.pageViewEvent];case e.D.sessionReplay:return[e.D.sessionTrace];case e.D.pageViewTiming:return[e.D.pageViewEvent];default:return[]}}(t.featureName);n.every((e=>r[e]))||(0,l.Z)("".concat(t.featureName," is enabled but one or more dependent features has been disabled (").concat((0,D.P)(n),"). This may cause unintended consequences or missing data...")),this.features[t.featureName]=new t(this.agentIdentifier,this.sharedAggregator)}})),(0,T.Qy)(this.agentIdentifier,this.features,t)}catch(e){(0,l.Z)("Failed to initialize all enabled instrument classes (agent aborted) -",e);for(const e in this.features)this.features[e].abortHandler?.();const r=(0,T.fP)();return delete r.initializedAgents[this.agentIdentifier]?.api,delete r.initializedAgents[this.agentIdentifier]?.[t],delete this.sharedAggregator,r.ee?.abort(),delete r.ee?.get(this.agentIdentifier),!1}}}({features:[J,m,S,class extends h{static featureName=oe;constructor(t,r){if(super(t,r,oe,!(arguments.length>2&&void 0!==arguments[2])||arguments[2]),!c.il)return;const n=this.ee;let i;(0,k.QU)(n),this.eventsEE=(0,k.em)(n),this.eventsEE.on(se,(function(e,t){this.bstStart=(0,p.z)()})),this.eventsEE.on(ae,(function(t,r){(0,s.p)("bst",[t[0],r,this.bstStart,(0,p.z)()],void 0,e.D.sessionTrace,n)})),n.on(ce+ne,(function(e){this.time=(0,p.z)(),this.startPath=location.pathname+location.hash})),n.on(ce+ie,(function(t){(0,s.p)("bstHist",[location.pathname+location.hash,this.startPath,this.time],void 0,e.D.sessionTrace,n)}));try{i=new PerformanceObserver((t=>{const r=t.getEntries();(0,s.p)(te,[r],void 0,e.D.sessionTrace,n)})),i.observe({type:re,buffered:!0})}catch(e){}this.importAggregator({resourceObserver:i})}},C,xe,B,class extends h{static featureName=de;constructor(e,r){if(super(e,r,de,!(arguments.length>2&&void 0!==arguments[2])||arguments[2]),!c.il)return;if(!(0,t.OP)(e).xhrWrappable)return;try{this.removeOnAbort=new AbortController}catch(e){}let n,i=0;const o=this.ee.get("tracer"),a=(0,k._L)(this.ee),s=(0,k.Lg)(this.ee),u=(0,k.BV)(this.ee),d=(0,k.Kf)(this.ee),f=this.ee.get("events"),l=(0,k.u5)(this.ee),h=(0,k.QU)(this.ee),g=(0,k.Gm)(this.ee);function m(e,t){h.emit("newURL",[""+window.location,t])}function v(){i++,n=window.location.hash,this[ve]=(0,p.z)()}function b(){i--,window.location.hash!==n&&m(0,!0);var e=(0,p.z)();this[pe]=~~this[pe]+e-this[ve],this[ye]=e}function y(e,t){e.on(t,(function(){this[t]=(0,p.z)()}))}this.ee.on(ve,v),s.on(be,v),a.on(be,v),this.ee.on(ye,b),s.on(ge,b),a.on(ge,b),this.ee.buffer([ve,ye,"xhr-resolved"],this.featureName),f.buffer([ve],this.featureName),u.buffer(["setTimeout"+le,"clearTimeout"+fe,ve],this.featureName),d.buffer([ve,"new-xhr","send-xhr"+fe],this.featureName),l.buffer([me+fe,me+"-done",me+he+fe,me+he+le],this.featureName),h.buffer(["newURL"],this.featureName),g.buffer([ve],this.featureName),s.buffer(["propagate",be,ge,"executor-err","resolve"+fe],this.featureName),o.buffer([ve,"no-"+ve],this.featureName),a.buffer(["new-jsonp","cb-start","jsonp-error","jsonp-end"],this.featureName),y(l,me+fe),y(l,me+"-done"),y(a,"new-jsonp"),y(a,"jsonp-end"),y(a,"cb-start"),h.on("pushState-end",m),h.on("replaceState-end",m),window.addEventListener("hashchange",m,(0,O.m$)(!0,this.removeOnAbort?.signal)),window.addEventListener("load",m,(0,O.m$)(!0,this.removeOnAbort?.signal)),window.addEventListener("popstate",(function(){m(0,i>1)}),(0,O.m$)(!0,this.removeOnAbort?.signal)),this.abortHandler=this.#e,this.importAggregator()}#e(){this.removeOnAbort?.abort(),this.abortHandler=void 0}}],loaderType:"spa"})})(),window.NRBA=o})(); window.jQuery || document.write(' ') CKEDITOR_BASEPATH='https://f1000research.com/js/vendor/ckeditor/' window.reactTheme = 'research'; window.MathJax = { CommonHTML: { linebreaks: { automatic: true } }, 'HTML-CSS': { linebreaks: { automatic: true } }, SVG: { linebreaks: { automatic: true } }, AuthorInit: function() { MathJax.Hub.Register.MessageHook('End Process', function () { let timeout = false; // holder for timeout id const delay = 250; // delay after event is "complete" to run callback const reflowMath = function() { const dispFormulas = document.querySelectorAll('.disp-formula.panel'); if (!dispFormulas) { return; } for (const dispFormula of dispFormulas) { const child = dispFormula.querySelector('.MathJax_Preview').nextSibling.firstChild; const isMultiline = MathJax.Hub.getAllJax(dispFormula)[0].root.isMultiline; if (dispFormula.offsetWidth < child.offsetWidth || isMultiline) { MathJax.Hub.Queue(['Rerender', MathJax.Hub, dispFormula]); } } }; window.addEventListener('resize', function() { clearTimeout(timeout); // clear the timeout timeout = setTimeout(reflowMath, delay); // start timing for event "completion" }); }); }, }; if (window.location.hash == '#_=_'){ window.location = window.location.href.split('#')[0] } !function(f,b,e,v,n,t,s){if(f.fbq)return;n=f.fbq=function() {n.callMethod? n.callMethod.apply(n,arguments):n.queue.push(arguments)} ;if(!f._fbq)f._fbq=n; n.push=n;n.loaded=!0;n.version='2.0';n.queue=[];t=b.createElement(e);t.async=!0; t.src=v;s=b.getElementsByTagName(e)[0];s.parentNode.insertBefore(t,s)}(window, document,'script','https://connect.facebook.net/en_US/fbevents.js'); fbq('init', '1641728616063202'); fbq('track', "PixelInitialized", {}); (function(h,o,t,j,a,r){ h.hj=h.hj||function(){(h.hj.q=h.hj.q||[]).push(arguments)}; h._hjSettings={hjid:2318163,hjsv:6}; a=o.getElementsByTagName('head')[0]; r=o.createElement('script');r.async=1; r.src=t+h._hjSettings.hjid+j+h._hjSettings.hjsv; a.appendChild(r); })(window,document,'https://static.hotjar.com/c/hotjar-','.js?sv='); search file_upload Submit your research search menu close search Browse Gateways & Collections How to Publish Submit your Research My Submissions Article Guidelines Article Guidelines (New Versions) Open Data, Software and Code Guidelines Open Data and Accessible Source Materials Guidelines (HSS) Open Data, Software and Code Guidelines (PSE) Prepublication Checks Production Process Posters and Slides Guidelines Document Guidelines Article Processing Charges Peer Review Finding Article Reviewers About How it Works For Reviewers Our Advisors Policies Glossary FAQs For Developers Newsroom Contact My Research Submissions Content and Tracking Alerts My Details Sign In file_upload Submit your research { "@context": "https://schema.org", "@type": "ScholarlyArticle", "mainEntityOfPage": { "@type": "WebPage", "@id": "https://f1000research.com/articles/13-1549" }, "headline": "Depression diagnosis using Artificial Intelligence: a systematic review", "datePublished": "2024-12-20T12:49:00", "dateModified": "2024-12-20T12:49:00", "author": [ { "@type": "Person", "name": "Martín Di Felice" }, { "@type": "Person", "name": "Ilan Trupkin" }, { "@type": "Person", "name": "Ariel Deroche" }, { "@type": "Person", "name": "María Florencia Pollo Cattaneo" }, { "@type": "Person", "name": "Parag Chatterjee" } ], "publisher": { "@type": "Organization", "name": "F1000Research", "logo": { "@type": "ImageObject", "url": "https://f1000research.com/img/AMP/F1000Research_image.png", "height": 480, "width": 60 } }, "image": { "@type": "ImageObject", "url": "https://f1000research.com/img/AMP/F1000Research_image.png", "height": 1200, "width": 150 }, "description": " Background Depression is a prevalent mental health disorder that affects a significant proportion of the global population, posing a major public health challenge. In recent years, the application of Artificial Intelligence (AI) to mental health diagnosis has garnered increasing attention. This systematic review aims to provide a comprehensive overview of the current state of research on AI-based approaches for depression diagnosis, identifying both advancements and gaps in the literature that can guide future studies. Methods A comprehensive search was conducted across leading research databases to identify relevant studies published up to July 2024. A combination of automated and manual filtering was employed to refine the initial set of records. Eligibility criteria were applied to ensure that only studies directly addressing the use of AI for depression diagnosis were included in the final analysis. Results The initial search yielded 1,179 records. Following a rigorous selection process, 145 studies were deemed eligible for inclusion in the review. These studies represent a diverse array of AI techniques and data sources, with a predominant focus on supervised learning algorithms. The most common data sources were social networks, followed by clinical data integrated with psychological assessments. Conclusion The results highlight the growing interest in leveraging AI for depression diagnosis, particularly through the use of supervised learning methods. Social network data has emerged as the most frequently used data source, though clinical data combined with validated psychological tests remains a key area of focus. Despite these advancements, several challenges persist, including data availability and quality, which present opportunities for future research to improve diagnostic accuracy and generalizability. " } { "@context": "http://schema.org", "@type": "BreadcrumbList", "itemListElement": [ { "@type": "ListItem", "position": "1", "item": { "@id": "https://f1000research.com/", "name": "Home" } }, { "@type": "ListItem", "position": "2", "item": { "@id": "https://f1000research.com/browse/articles", "name": "Browse" } }, { "@type": "ListItem", "position": "3", "item": { "@id": "https://f1000research.com/articles/13-1549", "name": "Depression diagnosis using Artificial Intelligence: a systematic review" } } ] } Home Browse Depression diagnosis using Artificial Intelligence: a systematic review ALL Metrics - Views Downloads Get PDF Get XML Cite How to cite this article Di Felice M, Trupkin I, Deroche A et al. Depression diagnosis using Artificial Intelligence: a systematic review [version 1; peer review: 2 approved with reservations] . F1000Research 2024, 13 :1549 ( https://doi.org/10.12688/f1000research.158443.1 ) NOTE: If applicable, it is important to ensure the information in square brackets after the title is included in all citations of this article. Close Copy Citation Details Export Export Citation Sciwheel EndNote Ref. Manager Bibtex ProCite Sente EXPORT Select a format first Track Share ▬ ✚ Systematic Review Depression diagnosis using Artificial Intelligence: a systematic review [version 1; peer review: 2 approved with reservations] Martín Di Felice https://orcid.org/0000-0003-1388-3220 1 , Ilan Trupkin 1 , Ariel Deroche https://orcid.org/0009-0000-0354-8803 1 , María Florencia Pollo Cattaneo https://orcid.org/0000-0003-4197-3880 1 , Parag Chatterjee https://orcid.org/0000-0001-6760-4704 1,2 Martín Di Felice https://orcid.org/0000-0003-1388-3220 1 , Ilan Trupkin 1 , [...] Ariel Deroche https://orcid.org/0009-0000-0354-8803 1 , María Florencia Pollo Cattaneo https://orcid.org/0000-0003-4197-3880 1 , Parag Chatterjee https://orcid.org/0000-0001-6760-4704 1,2 PUBLISHED 20 Dec 2024 Author details Author details 1 GEMIS, Universidad Tecnológica Nacional, Buenos Aires, Argentina 2 Department of Biological Engineering, Universidad de la República, Montevideo, Uruguay Martín Di Felice Roles: Data Curation, Formal Analysis, Investigation, Methodology, Software, Validation, Visualization, Writing – Original Draft Preparation Ilan Trupkin Roles: Formal Analysis, Investigation, Validation, Visualization Ariel Deroche Roles: Formal Analysis, Investigation, Validation, Visualization María Florencia Pollo Cattaneo Roles: Conceptualization, Investigation, Methodology, Project Administration, Supervision, Writing – Review & Editing Parag Chatterjee Roles: Conceptualization, Investigation, Methodology, Project Administration, Supervision, Writing – Original Draft Preparation, Writing – Review & Editing OPEN PEER REVIEW DETAILS REVIEWER STATUS This article is included in the AI in Medicine and Healthcare collection. Abstract Background Depression is a prevalent mental health disorder that affects a significant proportion of the global population, posing a major public health challenge. In recent years, the application of Artificial Intelligence (AI) to mental health diagnosis has garnered increasing attention. This systematic review aims to provide a comprehensive overview of the current state of research on AI-based approaches for depression diagnosis, identifying both advancements and gaps in the literature that can guide future studies. Methods A comprehensive search was conducted across leading research databases to identify relevant studies published up to July 2024. A combination of automated and manual filtering was employed to refine the initial set of records. Eligibility criteria were applied to ensure that only studies directly addressing the use of AI for depression diagnosis were included in the final analysis. Results The initial search yielded 1,179 records. Following a rigorous selection process, 145 studies were deemed eligible for inclusion in the review. These studies represent a diverse array of AI techniques and data sources, with a predominant focus on supervised learning algorithms. The most common data sources were social networks, followed by clinical data integrated with psychological assessments. Conclusion The results highlight the growing interest in leveraging AI for depression diagnosis, particularly through the use of supervised learning methods. Social network data has emerged as the most frequently used data source, though clinical data combined with validated psychological tests remains a key area of focus. Despite these advancements, several challenges persist, including data availability and quality, which present opportunities for future research to improve diagnostic accuracy and generalizability. READ ALL READ LESS Keywords Artificial Intelligence, depression, systematic review, machine learning, mental health Corresponding Author(s) María Florencia Pollo Cattaneo ( [email protected] ) Parag Chatterjee ( [email protected] ) Close Corresponding authors: María Florencia Pollo Cattaneo, Parag Chatterjee Competing interests: No competing interests were disclosed. Grant information: The author(s) declared that no grants were involved in supporting this work. Copyright: © 2024 Di Felice M et al . This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. How to cite: Di Felice M, Trupkin I, Deroche A et al. Depression diagnosis using Artificial Intelligence: a systematic review [version 1; peer review: 2 approved with reservations] . F1000Research 2024, 13 :1549 ( https://doi.org/10.12688/f1000research.158443.1 ) First published: 20 Dec 2024, 13 :1549 ( https://doi.org/10.12688/f1000research.158443.1 ) Latest published: 20 Dec 2024, 13 :1549 ( https://doi.org/10.12688/f1000research.158443.1 ) Introduction Depression is one of the most prevalent mental health disorders globally, as evidenced by recent studies, with an estimated lifetime prevalence affecting up to 17% of the population. 1 , 2 The World Health Organization (WHO) defines depression as a condition characterized by a persistently low mood or loss of pleasure in activities, lasting for extended periods of time. 3 This duration is generally accepted to be at least two weeks or longer, according to several sources. 4 , 5 While depression manifests in various forms—such as persistent depressive disorder, perinatal depression, and seasonal affective disorder 6 —the term “depression” typically refers to Major Depressive Disorder (MDD), which is the primary focus of this study. MDD often goes undiagnosed, especially in regions with limited healthcare resources or a shortage of mental health professionals. 7 – 9 However, even in areas with sufficient access to healthcare, depression can remain untreated as individuals may downplay or hide their symptoms. 10 In such cases, automated tools that can assist in accurate diagnosis may play a crucial role in improving detection and early intervention. Artificial Intelligence (AI) represents a promising avenue for addressing these diagnostic challenges. AI, a field of computer science dedicated to solving complex, nonlinear problems, 11 has experienced rapid growth in recent years. Although AI algorithms have existed for decades, 12 their recent surge can be attributed to two major factors: the significant increase in computational power and the widespread availability of digital data. 13 These algorithms are capable of processing vast quantities of data to identify patterns and relationships, leading to breakthroughs in many scientific and technological fields, including healthcare and mental health. 14 AI can leverage different types of data, including multimedia data (such as images, audio, and video) and alphanumeric data (such as written text or clinical records). This review focuses on AI applications that utilize alphanumeric data for diagnosing MDD. This focus is driven by the accessibility of alphanumeric data compared to multimedia data, which has resulted in a greater availability of relevant datasets. Additionally, the widespread availability of these datasets facilitates the potential development of AI tools for real-world diagnostic applications. Among the alphanumeric datasets, sources include unstructured text from social media posts, mobile text messages, and psychological interviews, as well as structured data from electronic health records, standardized psychological questionnaires, and data automatically extracted from mobile devices. The objective of this systematic review is to provide a comprehensive overview of the current state-of-the-art in AI-based diagnostic tools for MDD using alphanumeric data. By mapping existing research, this review aims to identify gaps in the field, offering insights for future research directions. The research questions outlined in Table 1 guide the analysis of the selected studies. Table 1. Research questions. ID Question RQ1 What types of datasets are used? RQ2 How are those datasets classified? RQ3 What AI algorithms are currently being used? RQ4 What is the typical performance of the algorithms? RQ5 What are the future directions for research in this area? RQ6 What are the key drawbacks or disadvantages mentioned by authors? Following this introduction, the work proceeds with the Methods section, detailing the systematic review process. The Results section presents key findings from the analyzed studies. Finally, the Discussion section provides conclusions and addresses the implications of the results. Methods The selection of articles for this systematic review was conducted in two stages: an automated search phase followed by a manual screening process. The first phase involved the use of a custom-developed computational tool to retrieve all relevant research studies that met predefined inclusion criteria. The second phase, requiring manual intervention, applied exclusion criteria that could not be automated. Automated search The automated tool was programmed to search across three widely-used academic databases: • IEEE Xplore • PubMed • Scopus The search was conducted on July 1, 2024, and focused on articles published since 2015. The following keyword combinations were used: • “artificial intelligence” AND “depression diagnosis” • “artificial intelligence” AND “depression detection” • “artificial intelligence” AND “depression diagnostic” • “artificial intelligence” AND “depression estimation” • “machine learning” AND “depression diagnosis” • “machine learning” AND “depression detection” • “machine learning” AND “depression diagnostic” • “machine learning” AND “depression estimation” • “deep learning” AND “depression diagnosis” • “deep learning” AND “depression detection” • “deep learning” AND “depression diagnostic” • “deep learning” AND “depression estimation” • “artificial intelligence” AND “depressive disorder diagnosis” • “artificial intelligence” AND “depressive disorder detection” • “artificial intelligence” AND “depressive disorder diagnostic” • “artificial intelligence” AND “depressive disorder estimation” • “machine learning” AND “depressive disorder diagnosis” • “machine learning” AND “depressive disorder detection” • “machine learning” AND “depressive disorder diagnostic” • “machine learning” AND “depressive disorder estimation” • “deep learning” AND “depressive disorder diagnosis” • “deep learning” AND “depressive disorder detection” • “deep learning” AND “depressive disorder diagnostic” The search targeted a broad range of publication types, including conference papers, journal articles, and book chapters, across the title, abstract, and keyword fields of each document. Manual screening Following the automated search, a manual review process was undertaken to apply exclusion criteria that could not be addressed programmatically. This step involved a detailed review of each study by three independent reviewers. The following exclusion criteria were applied: • The study must propose a method to diagnose Major Depressive Disorder (MDD). If the method also addressed other disorders, only the portion relevant to depression was considered. • The study must employ Artificial Intelligence (AI) for diagnostic purposes. • The dataset utilized must consist solely of alphanumeric data. • The study must be written in English. • Duplicate studies were excluded, with preference given to the most recent version in the case of multiple similar publications. Data extraction Once the inclusion and exclusion criteria were applied, the remaining studies were independently reviewed by three screeners to extract relevant data for analysis. In addition to metadata automatically retrieved by the computational tool (e.g., publication year, authors, keywords), a full reading of each article was conducted to collect the following key information: Datasets • Number of datasets used in each study. • Type of datasets, classified into five categories: Social Networks, Clinical Data, Mobile Data, Interviews, and Text Messages. • Whether the datasets were balanced or unbalanced. Algorithms • The number and types of AI algorithms employed. • The learning techniques (e.g., supervised, unsupervised) used in each study. Validation methods • The method used to validate whether the subject had depression, categorized into: Questionnaires, Interviews, Keywords, Self-reported symptoms, and Sentiment Analysis. Geographic distribution • The country of origin, determined by the primary author’s nationality or the research institution to which they belong. Results • A summary of the results, including metrics used to evaluate the performance of the algorithms. Future research directions • Suggestions for future research, as indicated by the authors. This data collection process aimed to provide a comprehensive overview of the current state of research on AI-based diagnosis of MDD using alphanumeric data. Results The different steps of the systematic review are summarized in Figure 1 , which outlines the process of narrowing down the initial 1179 studies returned by the automatic tool to the final set of 145 studies included in this research. This filtering process followed the PRISMA guidelines, which ensure a transparent and systematic approach to study selection, incorporating eligibility criteria such as relevance, methodological rigor, and duplication removal. Figure 1. Flow diagram of the study identification. Studies by year An analysis of the selected studies reveals trends over time. In Figure 2 , the number of studies grouped by year is presented, showing an increasing interest in the use of AI for depression diagnosis over the last decade. The rising peak of publications coincides with the rise of social media platforms, mobile health applications, and significant advancements in AI techniques like machine learning and natural language processing. Figure 2. Studies per year. Studies by country Figure 3 depicts the countries from which the selected studies originate, determined by the first author’s institution. The distribution highlights a concentration of research in countries like the United States, India, and China. Some other countries in different continents also feature prominently, reflecting regional efforts to tackle mental health challenges through AI. However, there is a notable gap in research from lower-income countries, which may reflect the disparity in resources and infrastructure for AI and mental health research. Figure 3. Studies per country. Dataset analysis One of the key areas of analysis focused on the datasets used in the reviewed studies. These datasets were categorized based on the source and type of data they contained, as illustrated in Figure 4 . The classification was performed based on the authors’ declarations, given that many datasets are not publicly available. This often presents a challenge for reproducibility in future studies. Figure 4. Dataset types. The dataset types were grouped into five categories: • Social Networks: These datasets typically consist of unstructured public data from user-generated content such as posts, comments, and reactions. Social networks offer a rich source of spontaneous user behavior, though the data’s unstructured nature requires significant preprocessing. Despite their availability, the reliability of these datasets is sometimes questioned due to ethical concerns regarding user consent. • Clinical Data: This includes structured data obtained from electronic health records (EHRs), which often combine medical information with demographic details. Such datasets are generally more reliable and precise but are limited in availability due to privacy concerns and data access restrictions. • Mobile Devices: These datasets include data collected through mobile devices, such as smartphones and wearable technology. The rise of mobile health (mHealth) apps has contributed to this category, where continuous tracking and health monitoring data are utilized to infer depressive tendencies. • Interviews: Unlike clinical data, interview-based datasets are unstructured but provide qualitative insights. They are usually transcriptions of interactions with healthcare professionals, offering rich context but requiring manual labeling and interpretation. • Text Messages: These datasets include private text communications, often shared voluntarily. Although these resemble social network data, the private nature of text messages means they are not publicly available. This limits their widespread use but provides a direct line of communication that can capture candid expressions of depressive symptoms. Figure 4 shows how the studies that are part of this review use the different dataset types. The analysis reveals that most of the datasets used in the reviewed studies come from social networks, followed by clinical data. However, only a few studies made their datasets publicly available, underscoring a challenge in research replication and model validation. Dataset validation In terms of validation techniques, the reviewed studies adopted various methods depending on the nature of the dataset, as summarized in Figure 5 . The datasets were validated using five primary methods: • Questionnaires: Structured questionnaires, such as the PHQ-9 or BDI, were used to label data. This method was the most commonly used (41.9% of studies), ensuring consistency in diagnosing depression but potentially limiting real-world generalizability. • Experts: In cases where data were unstructured (e.g., social media posts), mental health professionals validated the data by classifying whether a record could be associated with depressive symptoms. This approach was employed in 21.6% of the studies. • Keywords: Some studies employed keyword-based validation, where the presence of specific terms associated with depression was used to label data. While simple, this method lacks the nuance of more sophisticated validation techniques • Sentiment analysis: Advanced sentiment analysis tools were used in some studies to gauge the emotional tone of text data. This approach moves beyond keyword matching to provide a more comprehensive analysis of emotional states. 15 • Self-reported: In studies with large datasets, participants self-reported their depression status, often in conjunction with using mobile health apps. While scalable, this method may introduce biases due to the subjective nature of self-reports. Figure 5. Validation types. Depressive class distribution A third analysis focused on the distribution of depressive versus non-depressive subjects within the datasets, as shown in Figure 6 . This analysis is crucial for understanding the challenges of imbalanced datasets , a common issue in depression diagnosis where the proportion of depressive individuals in the population is much lower than the non-depressive. The analysis revealed: • 0-15%: The majority of datasets in this range reflect real-world imbalances, as the prevalence of depression in the general population is often below 15%. • 15-40%: These datasets still show a majority of non-depressive cases but with a more balanced distribution. • 40-60%: A few datasets are nearly balanced in terms of depressive and non-depressive subjects. • >60%: Some datasets contain an artificial overrepresentation of depressive cases, often achieved through oversampling techniques to balance the data for model training. Figure 6. Depressive class percentage. Many studies applied balancing techniques such as oversampling, undersampling, or synthetic data generation to address the issue of imbalanced datasets. Algorithmic approaches The analysis of algorithms used in the reviewed studies is summarized in Figure 7 . Machine learning algorithms were a critical component of most studies, and the choice of algorithm often depended on the dataset type and the learning task at hand. The most commonly used algorithms were: • ANN (Artificial Neural Networks): Popular for their ability to handle large, unstructured datasets like those from social media. • NLP (Natural Language Processing): Used primarily for text-based data, including social network posts and text messages. • SVM (Support Vector Machines): Effective in handling high-dimensional data and commonly applied to clinical datasets. • RF (Random Forest): A versatile algorithm used for both structured and unstructured data, known for its robustness against overfitting. • LR (Logistic Regression): Widely applied for binary classification tasks, particularly when the goal is to predict whether a subject is depressive or non-depressive. Figure 7. Algorithms. AB: Adaptive Boosting, ANN: Artificial Neural Network, DT: Decision Tree, EN: Elastic Net Regularization, GB: Gradient Boosting, KNN: K Nearest Neighbors, LGBM: Light Gradient Boosting Machine, LR: Logistic Regression, NB: Naive Bayes, NLP: Natural Language Processing, RF: Random Forest, SVM: Support Vector Machines, XGB: Extreme Gradient Boosting. The studies were divided into supervised and unsupervised learning approaches, as shown in Figure 8 . Supervised learning dominated the field, with 93.2% of the studies relying on labeled data. Only 6.1% of studies used unsupervised learning, and one study by Choi et al. 16 implemented a semi-supervised approach. 17 This method used a small portion of labeled data to fine-tune clusters generated by an unsupervised algorithm, showing promising results despite the limited labeled data. Figure 8. Learning types. Performance metrics The reviewed studies used a variety of metrics to evaluate the performance of their models, as summarized in Table 2 . The most commonly used metrics included: • Accuracy (57% of studies): The average accuracy was 0.82, indicating good overall performance, but accuracy alone may not be sufficient in the case of imbalanced datasets. • F1 Score (52% of studies): With an average of 0.75, this metric provides a balance between precision and recall, making it especially useful for imbalanced datasets. • Precision (45% of studies): At an average of 0.78, precision measures the proportion of correctly predicted positive cases out of all predicted positives. • Recall (34% of studies): Averaging 0.74, recall measures the proportion of actual positives correctly identified by the model. • AUC (31%) of studies: The Area Under the Curve metric averaged 0.75, indicating good discriminative ability. • Specificity and sensitivity (17% of the studies): Both metrics were used less frequently but provided insight into the model’s ability to correctly identify negative and positive cases, respectively. Table 2. Used metrics. Metric Presence in articles Average value Accuracy 57% 0.82 F1 52% 0.75 Precision 45% 0.78 Recall 34% 0.74 AUC 31% 0.75 Specificity 17% 0.69 Sensitivity 17% 0.74 Several studies opted for regression-based models, measuring outcomes using metrics like Mean Absolute Error (MAE) and Root-Mean-Square Error (RMSE). These models focused on predicting depression levels on a continuous scale rather than binary classification. Chatterjee et al. 18 and Aziz et al. 19 predict the depression level on a scale from 0 to 6, while Crowson et al. 20 emulate the answers of the PHQ-9 questionnaire which is a discrete value but with a wide range of options. Oduntan et al. 21 and Akyol 22 used the usual metrics but also included the Mathews Correlation Coefficient (MCC) metric to compare their models. Oduntan et al. 21 also chose the False Discovery Rate (FDR) metric. Tavchioski et al. 23 and Trotzek et al. 24 presented their studies in the eRisk [ 1 ] event. This event is a yearly contest where different researchers present techniques to diagnose depression using Artificial Intelligence techniques. To compare results, the contest provides its own metric, called Early Risk Detection Error (ERDE), 25 and they used it in their studies. This metric used the confusion matrix to determine its value, but it considers the instance when the prediction is made, taking into account that these models deal with longitudinal data and the goal is to detect depression symptoms as early as they appear. Inkpen et al. 26 and Skaik et al., 27 other eRisk studies, also selected custom metrics: Average Hit Rate (AHR), Average Closeness Rate (ACR), Average Difference between overall Depression Levels (ADL), and Depression Category Hit Rate (DCHR). Lyu et al. 28 use the Pearson Correlation Coefficient (PCC) to evaluate their models, also based on supervised learning. Unsupervised learning studies use heterogeneous metrics. Dipnall et al. 29 , 30 calculated the percentage of depressive people in different clusters to measure their results in two different studies. Choi et al., 16 who mixed unsupervised and semi-supervised learning approaches, used the Analysis of Variance (ANOVA) metric. This study compared the two different kinds of learning techniques and compared their results. For the semi-supervised approach, they labeled a portion of its data using standard psychological questionnaires while the rest of the data remained unclassified. The dataset consisted of demographic and clinical information about the patients, presented structurally. This partial classification was used to fix the clusters created by standard unsupervised learning models. If well the semi-supervised solution used fewer records for its training, it generated better results than the full unsupervised learning technique. Future research directions The most commonly suggested future research direction is the improvement of data quality. Many articles recommend incorporating multimedia data (images, audio, and videos) with existing text and numerical data to create more comprehensive diagnostic models. Several studies highlight the potential of combining social media data with clinical and mobile health data to enhance diagnostic accuracy. The integration of longitudinal data—data collected over extended periods—was also proposed to better capture the progression of depression symptoms. Furthermore, several studies acknowledged the limitations of available data, particularly from social networks. These datasets often lack the accuracy and structure of clinical data, and concerns over privacy and ethical data usage remain a significant challenge. Researchers recommend improving access to clinical datasets and creating public datasets that contain real-world medical data. Summary of research questions 1. RQ1 (Dataset Types): The most common dataset types were social networks (unstructured text) and clinical data (structured EHRs). Mobile devices and interviews were less frequently used (13% of the datasets). 2. RQ2 (Validation and Class Distribution): Questionnaires were the most used validation method (42%) followed by experts’ validation (22%), while most datasets showed imbalanced depressive class distributions, requiring balancing techniques. 3. RQ3 (Learning Types): Most studies applied supervised learning models, with limited exploration into unsupervised and semi-supervised methods. 4. RQ4 (Model Performance): The most frequently reported performance metric was accuracy, with an average value of 0.82. 5. RQ5 and RQ6 (Challenges and Future Directions): The most significant challenge is the lack of high-quality, publicly available datasets. Future research should focus on improving data quality, integrating diverse data sources, and addressing ethical concerns around privacy. Conclusion The analysis of the reviewed studies highlights a continuous increase in research focused on applying AI techniques for the diagnosis of Major Depressive Disorder (MDD), with a minor decline in 2022, followed by a resurgence in 2023. Given that this review encompasses studies up to mid-2024, it is expected that this growth trend will persist in the coming years, reflecting the increasing relevance of AI in mental health research. Geographically, research contributions are primarily concentrated in the United States, India, and China, with a total of 35 countries across four continents contributing to the field. This broad international distribution reduces potential bias stemming from geographical constraints, ensuring a more comprehensive understanding of the global progress in this domain. However, it is worth noting that some regions, particularly lower-income countries, remain underrepresented in this research area. In terms of data sources, over half of the studies rely on datasets extracted from social networks, with clinical data being the second most utilized. While clinical datasets are generally more robust and reliable, social network data is more accessible and easier to collect. This disparity underscores a key trade-off in the field: the use of clinical data enhances the validity of the models, but the acquisition process is often more resource-intensive. Validation methods also align with the type of dataset used, with clinical datasets predominantly validated through questionnaires, while social network datasets employ a variety of validation techniques, including expert validation, keyword-based classification, and sentiment analysis. Algorithmic approaches in these studies are overwhelmingly dominated by supervised learning techniques, with a particular emphasis on the combination of Natural Language Processing (NLP) and Artificial Neural Networks (ANNs), especially when dealing with unstructured data. Most studies implement multiple algorithms, either through comparative analysis or as part of a pipeline, to achieve optimal performance. The studies also highlight several limitations, many of which suggest avenues for future research. A primary limitation, cited by the majority of articles, is the availability and quality of datasets. Many studies operate with relatively small datasets, rarely exceeding several thousand records. When larger datasets are available, particularly from social networks, concerns about their reliability arise due to the use of user-generated classifications, such as keyword tagging or sentiment analysis, which are less accurate than clinical methods such as expert evaluations or psychological questionnaires. Other dataset-related issues include class imbalances, insufficient representation of diverse population groups, and the lack of longitudinal data. These challenges highlight the need for more comprehensive datasets that capture a wider array of features, including demographic diversity, longitudinal records, and data from multiple languages and geographic regions, as well as the integration of multimedia data to enhance diagnostic accuracy. In recent years, studies have begun to explore the use of large language models, 31 , 32 such as OpenAI’s ChatGPT 2 for the diagnosis of MDD. While this represents an exciting advancement, it raises significant concerns regarding the explainability of AI-based solutions, particularly given that such models are often regarded as “black box” systems. This concern is compounded when using proprietary models, where the underlying engineering and decision-making processes are not publicly accessible or transparent. Overall, while the majority of studies report promising results, there remains considerable room for improvement, particularly in the areas of data acquisition and quality. The lack of comprehensive and diverse datasets is a major limitation, and addressing this issue is critical to improving the generalizability and reliability of AI-driven depression diagnoses. Although algorithmic advances continue to enhance performance, the success of these models will ultimately depend on the availability of high-quality, unbiased, and diverse datasets, which should remain a primary focus for future research in this field. Additionally, the challenge of accurately labeling depressive states, particularly in social media datasets, must be addressed to ensure that AI models can provide valid and clinically meaningful outcomes. Ethics and consent Ethical approval and consent were not required. Data availability statement No data are associated with this article. Extended data This systematic review follows the latest PRISMA guidelines. Uruguay's open research data repository Redata: Dataset: Depression diagnosis using Artificial Intelligence: A systematic review, 10.60895/redata/DS0L5O . 33 This project contains the following underlying data: 1. Depression diagnosis using Artificial Intelligence_ A systematic review - Study list.docx 2. Depression diagnosis using Artificial Intelligence_ A systematic review.xlsx 3. Depression diagnosis using Artificial Intelligence_ A systematic review.csv 4. Figure 1. png 5. PRISMA abstract checklist for _Depression diagnosis using Artificial Intelligence with alphanumeric data_ A systematic review_.pdf 6. PRISMA checklist for _Depression diagnosis using Artificial Intelligence with alphanumeric data_ A systematic review_.pdf Repositorio de datos abiertos de investigación de Uruguay, Repositorio de datos abiertos de investigación de Uruguay. References 1. Gelenberg AJ: The Prevalence and Impact of Depression. J. Clin. Psychiatry. Mar. 2010; 71 (3): e06. Publisher Full Text 2. Fava M, Kendler KS: Major Depressive Disorder. Neuron. Nov. 2000; 28 (2): 335–341. Publisher Full Text 3. Depressive disorder (depression).2023. Accessed: Apr.19. Reference Source 4. Bains N, Abdijadid S: Major Depressive Disorder. StatPearls. Treasure Island (FL): StatPearls Publishing; 2023. Accessed: Apr. 03, 2023. Reference Source 5. Depression (WHO): Accessed: Jun. 14, 2022. Reference Source 6. Guo L, et al. : PROTOCOL: Treatment for depressive disorder among adults: An evidence and gap map of systematic reviews. Campbell Syst. Rev. Mar. 2023; 19 (1): e1308. PubMed Abstract | Publisher Full Text | Free Full Text 7. Faisal-Cury A, Ziebold C, Rodrigues DM d O, et al. : Depression underdiagnosis: Prevalence and associated factors. A population-based study. J. Psychiatr. Res. Jul. 2022; 151 : 157–165. PubMed Abstract | Publisher Full Text 8. Lao C-K, Chan Y-M, Tong HH-Y, et al. : Underdiagnosis of depression in an economically deprived population in Macao, China. Asia-Pac. Psychiatry. 2016; 8 (1): 70–79. PubMed Abstract | Publisher Full Text 9. Arbabzadeh-Bouchez S, Tylee A, Lépine J-P: A European Perspective on Depression in the Community: The DEPRES Study. CNS Spectr. Feb. 2002; 7 (2): 120–126. PubMed Abstract | Publisher Full Text 10. Barney LJ, Griffiths KM, Jorm AF, et al. : Stigma about Depression and its Impact on Help-Seeking Intentions. Aust. N. Z. J. Psychiatry. Jan. 2006; 40 (1): 51–54. PubMed Abstract | Publisher Full Text 11. Wang P: On Defining Artificial Intelligence. J. Artif. Gen. Intell. Jan. 2019; 10 (2): 1–37. Publisher Full Text 12. Boden MA: Artificial Intelligence. Elsevier; 1996. 13. Buttazzo G: Rise of artificial general intelligence: risks and opportunities. Front. Artif. Intell. Aug. 2023; 6 . PubMed Abstract | Publisher Full Text | Free Full Text 14. Bohr A, Memarzadeh K: Chapter 2 - The rise of artificial intelligence in healthcare applications. Artificial Intelligence in Healthcare. Bohr A, Memarzadeh K, editors. Academic Press; 2020; pp. 25–60. Publisher Full Text 15. Wankhade M, Rao ACS, Kulkarni C: A survey on sentiment analysis methods, applications, and challenges. Artif. Intell. Rev. Oct. 2022; 55 (7): 5731–5780. Publisher Full Text 16. Choi B, Shim G, Jeong B, et al. : Data-driven analysis using multiple self-report questionnaires to identify college students at high risk of depressive disorder. Sci. Rep. Dec. 2020; 10 (1): 7867. PubMed Abstract | Publisher Full Text | Free Full Text 17. Zhu X, Goldberg AB: Introduction to Semi-Supervised Learning. Synth. Lect. Artif. Intell. Mach. Learn. Jan. 2009; 3 (1): 1–130. Publisher Full Text 18. Chatterjee S, Mishra J, Sundram F, et al. : Towards Personalised Mood Prediction and Explanation for Depression from Biophysical Data. Sensors. Jan. 2024; 24 (1): Art. no. 1. PubMed Abstract | Publisher Full Text | Free Full Text 19. Aziz S, Alsaad R, Abd-Alrazaq A, et al. : Performance of Artificial Intelligence in Predicting Future Depression Levels. Stud. Health Technol. Inform. Jun. 2023; 305 : 452–455. PubMed Abstract | Publisher Full Text 20. Crowson MG, Franck KH, Rosella LC, et al. : Predicting Depression From Hearing Loss Using Machine Learning. Ear Hear. Jul. 2021; 42 (4): 982–989. PubMed Abstract | Publisher Full Text 21. Oduntan A, Oyebode O, Hernandez A, et al. : I Let Depression and Anxiety Drown Me: Identifying Factors Associated with Resilience Based on Journaling using Machine Learning and Thematic Analysis. IEEE J. Biomed. Health Inform. Feb. 2022; 26 : 3391–3397. PubMed Abstract | Publisher Full Text 22. Akyol S: New chaos-integrated improved grey wolf optimization based models for automatic detection of depression in online social media and networks. PeerJ Comput. Sci. 2023; 9 : e1661. PubMed Abstract | Publisher Full Text | Free Full Text 23. Tavchioski I, Škrlj B, Pollak S, et al. : Early detection of depression with linear models using hand-crafted and contextual features.Sep. 2022. 24. Trotzek M, Koitka S, Friedrich CM: Utilizing Neural Networks and Linguistic Metadata for Early Detection of Depression Indications in Text Sequences. IEEE Trans. Knowl. Data Eng. Mar. 2020; 32 (3): 588–601. Publisher Full Text 25. Losada D, Crestani F: A Test Collection for Research on Depression and Language Use.Sep. 2016; 28–39. Publisher Full Text 26. Inkpen D, Skaik R, Buddhitha P, et al. : uOttawa at eRisk 2021: Automatic Filling of the Beck’s Depression Inventory Questionnaire using Deep Learning.: 15. 27. Skaik R, Inkpen D: Predicting Depression in Canada by Automatic Filling of Beck’s Depression Inventory Questionnaire. IEEE Access. Jan. 2022; 10 : 102031–102047. Publisher Full Text 28. Lyu S, Ren X, Du Y, et al. : Detecting depression of Chinese microblog users via text analysis: Combining Linguistic Inquiry Word Count (LIWC) with culture and suicide related lexicons. Front. Psych. 2023; 14 : 1121583. PubMed Abstract | Publisher Full Text | Free Full Text 29. Dipnall JF, et al. : Into the Bowels of Depression: Unravelling Medical Symptoms Associated with Depression by Applying Machine-Learning Techniques to a Community Based Population Sample. PLoS One. Dec. 2016; 11 (12): e0167055. PubMed Abstract | Publisher Full Text | Free Full Text 30. Dipnall JF, et al. : Why so GLUMM? Detecting depression clusters through graphing lifestyle-environs using machine-learning methods (GLUMM). Eur. Psychiatry. Jan. 2017; 39 : 40–50. PubMed Abstract | Publisher Full Text 31. Detection and prediction of Future Mental disorder from Social Media Data using Machine Learning, Ensemble Learning, and Large Language Models. IEEE Journals & Magazine|IEEE Xplore. Accessed: Aug. 07, 2024. Reference Source 32. Danner M, et al. : Advancing Mental Health Diagnostics: GPT-Based Method for Depression Detection. 2023 62nd Annual Conference of the Society of Instrument and Control Engineers (SICE). Sep. 2023; pp. 1290–1296. Publisher Full Text 33. Di Felice M, Trupkin I, Deroche A, et al. : Dataset: Depression diagnosis using Artificial Intelligence: A systematic review.2024. Publisher Full Text Footnotes 1 https://erisk.irlab.org/ Comments on this article Comments (0) Version 1 VERSION 1 PUBLISHED 20 Dec 2024 ADD YOUR COMMENT Comment Author details Author details 1 GEMIS, Universidad Tecnológica Nacional, Buenos Aires, Argentina 2 Department of Biological Engineering, Universidad de la República, Montevideo, Uruguay Martín Di Felice Roles: Data Curation, Formal Analysis, Investigation, Methodology, Software, Validation, Visualization, Writing – Original Draft Preparation Ilan Trupkin Roles: Formal Analysis, Investigation, Validation, Visualization Ariel Deroche Roles: Formal Analysis, Investigation, Validation, Visualization María Florencia Pollo Cattaneo Roles: Conceptualization, Investigation, Methodology, Project Administration, Supervision, Writing – Review & Editing Parag Chatterjee Roles: Conceptualization, Investigation, Methodology, Project Administration, Supervision, Writing – Original Draft Preparation, Writing – Review & Editing Competing interests No competing interests were disclosed. Grant information The author(s) declared that no grants were involved in supporting this work. Article Versions (1) version 1 Published: 20 Dec 2024, 13:1549 https://doi.org/10.12688/f1000research.158443.1 Copyright © 2024 Di Felice M et al . This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Download Export To Sciwheel Bibtex EndNote ProCite Ref. Manager (RIS) Sente metrics Views Downloads F1000Research - - PubMed Central info_outline Data from PMC are received and updated monthly. - - Citations open_in_new 0 open_in_new 0 open_in_new SEE MORE DETAILS CITE how to cite this article Di Felice M, Trupkin I, Deroche A et al. Depression diagnosis using Artificial Intelligence: a systematic review [version 1; peer review: 2 approved with reservations] . F1000Research 2024, 13 :1549 ( https://doi.org/10.12688/f1000research.158443.1 ) NOTE: If applicable, it is important to ensure the information in square brackets after the title is included in all citations of this article. COPY CITATION DETAILS track receive updates on this article Track an article to receive email alerts on any updates to this article. TRACK THIS ARTICLE Share Open Peer Review Current Reviewer Status: ? Key to Reviewer Statuses VIEW HIDE Approved The paper is scientifically sound in its current form and only minor, if any, improvements are suggested Approved with reservations A number of small changes, sometimes more significant revisions are required to address specific details and improve the papers academic merit. Not approved Fundamental flaws in the paper seriously undermine the findings and conclusions Version 1 VERSION 1 PUBLISHED 20 Dec 2024 Views 0 Cite How to cite this report: Galpin I. Reviewer Report For: Depression diagnosis using Artificial Intelligence: a systematic review [version 1; peer review: 2 approved with reservations] . F1000Research 2024, 13 :1549 ( https://doi.org/10.5256/f1000research.174030.r416829 ) The direct URL for this report is: https://f1000research.com/articles/13-1549/v1#referee-response-416829 NOTE: it is important to ensure the information in square brackets after the title is included in this citation. Close Copy Citation Details Reviewer Report 05 Nov 2025 Ixent Galpin , Universidad de Bogotá-Jorge Tadeo Lozano, Bogotá, Colombia Approved with Reservations VIEWS 0 https://doi.org/10.5256/f1000research.174030.r416829 This work presents a comprehensive review of depression diagnosis using AI approaches. The sources consulted (PubMed, IEEE Xplore and Scopus) are the relevant ones for the problem domain. The focus is on textual datasets, and it would ... Continue reading READ ALL This work presents a comprehensive review of depression diagnosis using AI approaches. The sources consulted (PubMed, IEEE Xplore and Scopus) are the relevant ones for the problem domain. The focus is on textual datasets, and it would have been interesting to consider other modalities (image, video, audio, etc). For example, is there potential to diagnose depression from MRI brain scans or facial recognition images? The survey does not answer these questions, however, this limitation is duly noted at the outset. The methodology is sound and very clearly explained. However, several dimensions are defined at the start of the paper (page 5), which could be more crisply characterised: * In the "Datasets" dimension there is mention of "number of datasets", presumably here is meant "number of records". I think that here is also worth mentioning other aspects of the dataset, such as the number of features, whether it is labelled or not, if so, what the target variable is, and a date range. * In the "Type of datasets" dimension, I propose renaming the "Mobile devices" category to "mHealth App" as it is not clear what is meant by that. Social network data, clinical data, interview data and text message data can all be collected using mobile devices, so it is not a good name. * In the algorithms dimension, it would be relevant to consider whether the problem of depression diagnosis is viewed as a classification or regression problem. * In the results dimension, I would separate the metrics used (e.g., F1 score, MRSE) form the results as such. All the results shown are aggregated across the corpus of papers examined, which is mostly helpful for readers to identify overall trends. However, a table showing paper-by-paper classification according to the dimensions explored would be useful, to help researchers identify specific gaps in the literature. As it stands, the review does not make it easy for readers to identify such gaps. Also, better visualisations could have been used. Pie-charts are probably not adequate when the number of categories is high; a bar-chart ordered by precentage would be easier for the reader to parse. For example, see Figure 7. I also feel that it should also be made explicit at the beginning of the paper that most works view this as a classification problem rather then regression. Regression is mentioned, but rather late in the paper. Finally, I note that the focus is on machine-learning supervised approaches (specifically classification). There is a brief mention of generative AI approaches (specifically LLMs) in the conclusions. However, I consider that this increasingly relevant area of AI could have been mentioned earlier in the paper. Small issues: * Please add a citation in page 6 for the PRISMA guidelines Are the rationale for, and objectives of, the Systematic Review clearly stated? Yes Are sufficient details of the methods and analysis provided to allow replication by others? Yes Is the statistical analysis and its interpretation appropriate? Yes Are the conclusions drawn adequately supported by the results presented in the review? Yes If this is a Living Systematic Review, is the ‘living’ method appropriate and is the search schedule clearly defined and justified? (‘Living Systematic Review’ or a variation of this term should be included in the title.) Not applicable Competing Interests: No competing interests were disclosed. Reviewer Expertise: Machine learning, Large Language Models, Recommender Systems I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard, however I have significant reservations, as outlined above. Close READ LESS CITE CITE HOW TO CITE THIS REPORT Galpin I. Reviewer Report For: Depression diagnosis using Artificial Intelligence: a systematic review [version 1; peer review: 2 approved with reservations] . F1000Research 2024, 13 :1549 ( https://doi.org/10.5256/f1000research.174030.r416829 ) The direct URL for this report is: https://f1000research.com/articles/13-1549/v1#referee-response-416829 NOTE: it is important to ensure the information in square brackets after the title is included in all citations of this article. COPY CITATION DETAILS Report a concern Respond or Comment COMMENT ON THIS REPORT Views 0 Cite How to cite this report: Dalal S. Reviewer Report For: Depression diagnosis using Artificial Intelligence: a systematic review [version 1; peer review: 2 approved with reservations] . F1000Research 2024, 13 :1549 ( https://doi.org/10.5256/f1000research.174030.r366895 ) The direct URL for this report is: https://f1000research.com/articles/13-1549/v1#referee-response-366895 NOTE: it is important to ensure the information in square brackets after the title is included in this citation. Close Copy Citation Details Reviewer Report 05 Mar 2025 Sumit Dalal , National Institute of Technology, Kurukshetra, India Approved with Reservations VIEWS 0 https://doi.org/10.5256/f1000research.174030.r366895 The keyword combinations list is quite large. Please consider concising it by using a bulleted list or a text box for better readability. The literature review covers studies only up to July 2024. The authors ... Continue reading READ ALL The keyword combinations list is quite large. Please consider concising it by using a bulleted list or a text box for better readability. The literature review covers studies only up to July 2024. The authors should clarify why they did not extend it until January 2025 and specify the starting date of the literature collection. Figure 4 is missing percentage values and a mention of “Text Message”. Please update accordingly. To enhance the survey section, the authors could: List feature types from different sources. Include datasets (as listed in Reference 4 ). Discuss how validation techniques work. (This is only a suggestion.) I would like to recommend some references that could be valuable for citations as well as for your future work: Dalal S, et al., 2024 (Ref 1) Dalal S, et al., 2024 (Ref 2) Dalal S, et al., 2024 (Ref 3) Dalal S, et al., 2025 (Ref 4) The references to my work are proposed as they provide insights directly relevant to the manuscript's discussion on AI-based depression detection. Specifically, my studies contribute to this domain by developing and analyzing performances of AI models for depression detection from social media data. The suggested references present neuro-symbolic techniques for explainable AI models for depression detection. By including these citations, the manuscript will offer a more comprehensive review of the field, ensuring that important advancements are acknowledged. Are the rationale for, and objectives of, the Systematic Review clearly stated? Yes Are sufficient details of the methods and analysis provided to allow replication by others? Yes Is the statistical analysis and its interpretation appropriate? Not applicable Are the conclusions drawn adequately supported by the results presented in the review? Yes If this is a Living Systematic Review, is the ‘living’ method appropriate and is the search schedule clearly defined and justified? (‘Living Systematic Review’ or a variation of this term should be included in the title.) Yes References 1. Dalal S, Tilwani D, Gaur M, Jain S, et al.: A Cross Attention Approach to Diagnostic Explainability Using Clinical Practice Guidelines for Depression. IEEE J Biomed Health Inform . 2024; PP . PubMed Abstract | Publisher Full Text 2. S, Dalal S, Jain M, Dave: Deep Knowledge-Infusion For Explainable Depression Detection. https://arxiv.org/abs/2409.02122 . 2024. Publisher Full Text 3. Dalal S, Jain S, Dave M: Convolution Neural Network Having Multiple Channels with Own Attention Layer for Depression Detection from Social Data. New Generation Computing . 2024; 42 (1): 135-155 Publisher Full Text 4. Dalal S, Jain S, Dave M: Review of Advancements in Depression Detection Using Social Media Data. IEEE Transactions on Computational Social Systems . 2025; 12 (1): 77-100 Publisher Full Text Competing Interests: No competing interests were disclosed. Reviewer Expertise: Explainable AI, Neuro-Symbolic AI, Prompt Engineering I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard, however I have significant reservations, as outlined above. Close READ LESS CITE CITE HOW TO CITE THIS REPORT Dalal S. Reviewer Report For: Depression diagnosis using Artificial Intelligence: a systematic review [version 1; peer review: 2 approved with reservations] . F1000Research 2024, 13 :1549 ( https://doi.org/10.5256/f1000research.174030.r366895 ) The direct URL for this report is: https://f1000research.com/articles/13-1549/v1#referee-response-366895 NOTE: it is important to ensure the information in square brackets after the title is included in all citations of this article. COPY CITATION DETAILS Report a concern Respond or Comment COMMENT ON THIS REPORT Comments on this article Comments (0) Version 1 VERSION 1 PUBLISHED 20 Dec 2024 ADD YOUR COMMENT Comment keyboard_arrow_left keyboard_arrow_right Open Peer Review Reviewer Status info_outline Alongside their report, reviewers assign a status to the article: Approved The paper is scientifically sound in its current form and only minor, if any, improvements are suggested Approved with reservations A number of small changes, sometimes more significant revisions are required to address specific details and improve the papers academic merit. Not approved Fundamental flaws in the paper seriously undermine the findings and conclusions Reviewer Reports Invited Reviewers 1 2 Version 1 20 Dec 24 read read Sumit Dalal , National Institute of Technology, Kurukshetra, India Ixent Galpin , Universidad de Bogotá-Jorge Tadeo Lozano, Bogotá, Colombia Comments on this article All Comments (0) Add a comment Sign up for content alerts Sign Up You are now signed up to receive this alert Browse by related subjects keyboard_arrow_left Back to all reports Reviewer Report 0 Views copyright © 2025 Galpin I. This is an open access peer review report distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. 05 Nov 2025 | for Version 1 Ixent Galpin , Universidad de Bogotá-Jorge Tadeo Lozano, Bogotá, Colombia 0 Views copyright © 2025 Galpin I. This is an open access peer review report distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. format_quote Cite this report speaker_notes Responses (0) Approved With Reservations info_outline Alongside their report, reviewers assign a status to the article: Approved The paper is scientifically sound in its current form and only minor, if any, improvements are suggested Approved with reservations A number of small changes, sometimes more significant revisions are required to address specific details and improve the papers academic merit. Not approved Fundamental flaws in the paper seriously undermine the findings and conclusions This work presents a comprehensive review of depression diagnosis using AI approaches. The sources consulted (PubMed, IEEE Xplore and Scopus) are the relevant ones for the problem domain. The focus is on textual datasets, and it would have been interesting to consider other modalities (image, video, audio, etc). For example, is there potential to diagnose depression from MRI brain scans or facial recognition images? The survey does not answer these questions, however, this limitation is duly noted at the outset. The methodology is sound and very clearly explained. However, several dimensions are defined at the start of the paper (page 5), which could be more crisply characterised: * In the "Datasets" dimension there is mention of "number of datasets", presumably here is meant "number of records". I think that here is also worth mentioning other aspects of the dataset, such as the number of features, whether it is labelled or not, if so, what the target variable is, and a date range. * In the "Type of datasets" dimension, I propose renaming the "Mobile devices" category to "mHealth App" as it is not clear what is meant by that. Social network data, clinical data, interview data and text message data can all be collected using mobile devices, so it is not a good name. * In the algorithms dimension, it would be relevant to consider whether the problem of depression diagnosis is viewed as a classification or regression problem. * In the results dimension, I would separate the metrics used (e.g., F1 score, MRSE) form the results as such. All the results shown are aggregated across the corpus of papers examined, which is mostly helpful for readers to identify overall trends. However, a table showing paper-by-paper classification according to the dimensions explored would be useful, to help researchers identify specific gaps in the literature. As it stands, the review does not make it easy for readers to identify such gaps. Also, better visualisations could have been used. Pie-charts are probably not adequate when the number of categories is high; a bar-chart ordered by precentage would be easier for the reader to parse. For example, see Figure 7. I also feel that it should also be made explicit at the beginning of the paper that most works view this as a classification problem rather then regression. Regression is mentioned, but rather late in the paper. Finally, I note that the focus is on machine-learning supervised approaches (specifically classification). There is a brief mention of generative AI approaches (specifically LLMs) in the conclusions. However, I consider that this increasingly relevant area of AI could have been mentioned earlier in the paper. Small issues: * Please add a citation in page 6 for the PRISMA guidelines Are the rationale for, and objectives of, the Systematic Review clearly stated? Yes Are sufficient details of the methods and analysis provided to allow replication by others? Yes Is the statistical analysis and its interpretation appropriate? Yes Are the conclusions drawn adequately supported by the results presented in the review? Yes If this is a Living Systematic Review, is the ‘living’ method appropriate and is the search schedule clearly defined and justified? (‘Living Systematic Review’ or a variation of this term should be included in the title.) Not applicable Competing Interests No competing interests were disclosed. Reviewer Expertise Machine learning, Large Language Models, Recommender Systems I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard, however I have significant reservations, as outlined above. reply Respond to this report Responses (0) Galpin I. Peer Review Report For: Depression diagnosis using Artificial Intelligence: a systematic review [version 1; peer review: 2 approved with reservations] . F1000Research 2024, 13 :1549 ( https://doi.org/10.5256/f1000research.174030.r416829) NOTE: it is important to ensure the information in square brackets after the title is included in this citation. The direct URL for this report is: https://f1000research.com/articles/13-1549/v1#referee-response-416829 keyboard_arrow_left Back to all reports Reviewer Report 0 Views copyright © 2025 Dalal S. This is an open access peer review report distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. 05 Mar 2025 | for Version 1 Sumit Dalal , National Institute of Technology, Kurukshetra, India 0 Views copyright © 2025 Dalal S. This is an open access peer review report distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. format_quote Cite this report speaker_notes Responses (0) Approved With Reservations info_outline Alongside their report, reviewers assign a status to the article: Approved The paper is scientifically sound in its current form and only minor, if any, improvements are suggested Approved with reservations A number of small changes, sometimes more significant revisions are required to address specific details and improve the papers academic merit. Not approved Fundamental flaws in the paper seriously undermine the findings and conclusions The keyword combinations list is quite large. Please consider concising it by using a bulleted list or a text box for better readability. The literature review covers studies only up to July 2024. The authors should clarify why they did not extend it until January 2025 and specify the starting date of the literature collection. Figure 4 is missing percentage values and a mention of “Text Message”. Please update accordingly. To enhance the survey section, the authors could: List feature types from different sources. Include datasets (as listed in Reference 4 ). Discuss how validation techniques work. (This is only a suggestion.) I would like to recommend some references that could be valuable for citations as well as for your future work: Dalal S, et al., 2024 (Ref 1) Dalal S, et al., 2024 (Ref 2) Dalal S, et al., 2024 (Ref 3) Dalal S, et al., 2025 (Ref 4) The references to my work are proposed as they provide insights directly relevant to the manuscript's discussion on AI-based depression detection. Specifically, my studies contribute to this domain by developing and analyzing performances of AI models for depression detection from social media data. The suggested references present neuro-symbolic techniques for explainable AI models for depression detection. By including these citations, the manuscript will offer a more comprehensive review of the field, ensuring that important advancements are acknowledged. Are the rationale for, and objectives of, the Systematic Review clearly stated? Yes Are sufficient details of the methods and analysis provided to allow replication by others? Yes Is the statistical analysis and its interpretation appropriate? Not applicable Are the conclusions drawn adequately supported by the results presented in the review? Yes If this is a Living Systematic Review, is the ‘living’ method appropriate and is the search schedule clearly defined and justified? (‘Living Systematic Review’ or a variation of this term should be included in the title.) Yes References 1. Dalal S, Tilwani D, Gaur M, Jain S, et al.: A Cross Attention Approach to Diagnostic Explainability Using Clinical Practice Guidelines for Depression. IEEE J Biomed Health Inform . 2024; PP . PubMed Abstract | Publisher Full Text 2. S, Dalal S, Jain M, Dave: Deep Knowledge-Infusion For Explainable Depression Detection. https://arxiv.org/abs/2409.02122 . 2024. Publisher Full Text 3. Dalal S, Jain S, Dave M: Convolution Neural Network Having Multiple Channels with Own Attention Layer for Depression Detection from Social Data. New Generation Computing . 2024; 42 (1): 135-155 Publisher Full Text 4. Dalal S, Jain S, Dave M: Review of Advancements in Depression Detection Using Social Media Data. IEEE Transactions on Computational Social Systems . 2025; 12 (1): 77-100 Publisher Full Text Competing Interests No competing interests were disclosed. Reviewer Expertise Explainable AI, Neuro-Symbolic AI, Prompt Engineering I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard, however I have significant reservations, as outlined above. reply Respond to this report Responses (0) Dalal S. Peer Review Report For: Depression diagnosis using Artificial Intelligence: a systematic review [version 1; peer review: 2 approved with reservations] . F1000Research 2024, 13 :1549 ( https://doi.org/10.5256/f1000research.174030.r366895) NOTE: it is important to ensure the information in square brackets after the title is included in this citation. The direct URL for this report is: https://f1000research.com/articles/13-1549/v1#referee-response-366895 Alongside their report, reviewers assign a status to the article: Approved - the paper is scientifically sound in its current form and only minor, if any, improvements are suggested Approved with reservations - A number of small changes, sometimes more significant revisions are required to address specific details and improve the papers academic merit. Not approved - fundamental flaws in the paper seriously undermine the findings and conclusions Adjust parameters to alter display View on desktop for interactive features Includes Interactive Elements View on desktop for interactive features Competing Interests Policy Provide sufficient details of any financial or non-financial competing interests to enable users to assess whether your comments might lead a reasonable person to question your impartiality. Consider the following examples, but note that this is not an exhaustive list: Examples of 'Non-Financial Competing Interests' Within the past 4 years, you have held joint grants, published or collaborated with any of the authors of the selected paper. You have a close personal relationship (e.g. parent, spouse, sibling, or domestic partner) with any of the authors. You are a close professional associate of any of the authors (e.g. scientific mentor, recent student). You work at the same institute as any of the authors. You hope/expect to benefit (e.g. favour or employment) as a result of your submission. You are an Editor for the journal in which the article is published. Examples of 'Financial Competing Interests' You expect to receive, or in the past 4 years have received, any of the following from any commercial organisation that may gain financially from your submission: a salary, fees, funding, reimbursements. You expect to receive, or in the past 4 years have received, shared grant support or other funding with any of the authors. You hold, or are currently applying for, any patents or significant stocks/shares relating to the subject matter of the paper you are commenting on. Stay Updated Sign up for content alerts and receive a weekly or monthly email with all newly published articles Register with F1000Research Already registered? Sign in Not now, thanks close PLEASE NOTE If you are an AUTHOR of this article, please check that you signed in with the account associated with this article otherwise we cannot automatically identify your role as an author and your comment will be labelled as a “User Comment”. If you are a REVIEWER of this article, please check that you have signed in with the account associated with this article and then go to your account to submit your report, please do not post your review here. If you do not have access to your original account, please contact us . All commenters must hold a formal affiliation as per our Policies . The information that you give us will be displayed next to your comment. User comments must be in English, comprehensible and relevant to the article under discussion. We reserve the right to remove any comments that we consider to be inappropriate, offensive or otherwise in breach of the User Comment Terms and Conditions . Commenters must not use a comment for personal attacks. When criticisms of the article are based on unpublished data, the data should be made available. I accept the User Comment Terms and Conditions Please confirm that you accept the User Comment Terms and Conditions. Affiliation ✕ refresh Please enter your institution. Note: To add your institution or organisation, start typing the name and then select the correct name from the list. Where applicable, the name will appear in both the original language and in English. Do not paste in the name. If the name does not appear in the drop-down list, we will display the information you have entered. ✕ refresh Country/Region * USA UK Canada China France Germany Afghanistan Aland Islands Albania Algeria American Samoa Andorra Angola Anguilla Antarctica Antigua and Barbuda Argentina Armenia Aruba Australia Austria Azerbaijan Bahamas Bahrain Bangladesh Barbados Belarus Belgium Belize Benin Bermuda Bhutan Bolivia Bosnia and Herzegovina Botswana Bouvet Island Brazil British Indian Ocean Territory British Virgin Islands Brunei Bulgaria Burkina Faso Burundi Cambodia Cameroon Canada Cape Verde Cayman Islands Central African Republic Chad Chile China Christmas Island Cocos (Keeling) Islands Colombia Comoros Congo Cook Islands Costa Rica Cote d'Ivoire Croatia Cuba Cyprus Czech Republic Democratic Republic of the Congo Denmark Djibouti Dominica Dominican Republic Ecuador Egypt El Salvador Equatorial Guinea Eritrea Estonia Ethiopia Falkland Islands Faroe Islands Federated States of Micronesia Fiji Finland France French Guiana French Polynesia French Southern Territories Gabon Georgia Germany Ghana Gibraltar Greece Greenland Grenada Guadeloupe Guam Guatemala Guernsey Guinea Guinea-Bissau Guyana Haiti Heard Island and Mcdonald Islands Holy See (Vatican City State) Honduras Hong Kong Hungary Iceland India Indonesia Iran Iraq Ireland Israel Italy Jamaica Japan Jersey Jordan Kazakhstan Kenya Kiribati Kosovo (Serbia and Montenegro) Kuwait Kyrgyzstan Lao People's Democratic Republic Latvia Lebanon Lesotho Liberia Libya Liechtenstein Lithuania Luxembourg Macao Madagascar Malawi Malaysia Maldives Mali Malta Marshall Islands Martinique Mauritania Mauritius Mayotte Mexico Minor Outlying Islands of the United States Moldova Monaco Mongolia Montenegro Montserrat Morocco Mozambique Myanmar Namibia Nauru Nepal Netherlands Antilles New Caledonia New Zealand Nicaragua Niger Nigeria Niue Norfolk Island North Korea North Macedonia Northern Mariana Islands Norway Oman Pakistan Palau Palestinian Territory Panama Papua New Guinea Paraguay Peru Philippines Pitcairn Poland Portugal Puerto Rico Qatar Reunion Romania Russian Federation Rwanda Saint Helena Saint Kitts and Nevis Saint Lucia Saint Pierre and Miquelon Saint Vincent and the Grenadines Samoa San Marino Sao Tome and Principe Saudi Arabia Senegal Serbia Seychelles Sierra Leone Singapore Slovakia Slovenia Solomon Islands Somalia South Africa South Georgia and the South Sandwich Is South Korea South Sudan Spain Sri Lanka Sudan Suriname Svalbard and Jan Mayen Swaziland Sweden Switzerland Syria Taiwan Tajikistan Tanzania Thailand The Gambia The Netherlands Timor-Leste Togo Tokelau Tonga Trinidad and Tobago Tunisia Turkey Turkmenistan Turks and Caicos Islands Tuvalu UK USA Uganda Ukraine United Arab Emirates United States Virgin Islands Uruguay Uzbekistan Vanuatu Venezuela Vietnam Wallis and Futuna West Bank and Gaza Strip Western Sahara Yemen Zambia Zimbabwe Please select your country/region. You must enter a comment. Competing Interests Please disclose any competing interests that might be construed to influence your judgment of the article's or peer review report's validity or importance. Competing Interests Policy Provide sufficient details of any financial or non-financial competing interests to enable users to assess whether your comments might lead a reasonable person to question your impartiality. Consider the following examples, but note that this is not an exhaustive list: Examples of 'Non-Financial Competing Interests' Within the past 4 years, you have held joint grants, published or collaborated with any of the authors of the selected paper. You have a close personal relationship (e.g. parent, spouse, sibling, or domestic partner) with any of the authors. You are a close professional associate of any of the authors (e.g. scientific mentor, recent student). You work at the same institute as any of the authors. You hope/expect to benefit (e.g. favour or employment) as a result of your submission. You are an Editor for the journal in which the article is published. Examples of 'Financial Competing Interests' You expect to receive, or in the past 4 years have received, any of the following from any commercial organisation that may gain financially from your submission: a salary, fees, funding, reimbursements. You expect to receive, or in the past 4 years have received, shared grant support or other funding with any of the authors. You hold, or are currently applying for, any patents or significant stocks/shares relating to the subject matter of the paper you are commenting on. Please state your competing interests The comment has been saved. An error has occurred. Please try again. Cancel Post var lTitle = "Depression diagnosis using Artificial Intelligence:...".replace("'", ''); var linkedInUrl = "http://www.linkedin.com/shareArticle?url=https://f1000research.com/articles/13-1549/v1" + "&title=" + encodeURIComponent(lTitle) + "&summary=" + encodeURIComponent('Read the article by '); var deliciousUrl = "https://del.icio.us/post?url=https://f1000research.com/articles/13-1549/v1&title=" + encodeURIComponent(lTitle); var redditUrl = "http://reddit.com/submit?url=https://f1000research.com/articles/13-1549/v1" + "&title=" + encodeURIComponent(lTitle); linkedInUrl += encodeURIComponent('Di Felice M et al.'); var offsetTop = /chrome/i.test( navigator.userAgent ) ? 4 : -10; var addthis_config = { ui_offset_top: offsetTop, services_compact : "facebook,twitter,www.linkedin.com,www.mendeley.com,reddit.com", services_expanded : "facebook,twitter,www.linkedin.com,www.mendeley.com,reddit.com", services_custom : [ { name: "LinkedIn", url: linkedInUrl, icon:"/img/icon/at_linkedin.svg" }, { name: "Mendeley", url: "http://www.mendeley.com/import/?url=https://f1000research.com/articles/13-1549/v1/mendeley", icon:"/img/icon/at_mendeley.svg" }, { name: "Reddit", url: redditUrl, icon:"/img/icon/at_reddit.svg" }, ] }; var addthis_share = { url: "https://f1000research.com/articles/13-1549", templates : { twitter : "Depression diagnosis using Artificial Intelligence: a systematic.... Di Felice M et al., published by " + "@F1000Research" + ", https://f1000research.com/articles/13-1549/v1" } }; if (typeof(addthis) != "undefined"){ addthis.addEventListener('addthis.ready', checkCount); addthis.addEventListener('addthis.menu.share', checkCount); } $(".f1r-shares-twitter").attr("href", "https://twitter.com/intent/tweet?text=" + addthis_share.templates.twitter); $(".f1r-shares-facebook").attr("href", "https://www.facebook.com/sharer/sharer.php?u=" + addthis_share.url); $(".f1r-shares-linkedin").attr("href", addthis_config.services_custom[0].url); $(".f1r-shares-reddit").attr("href", addthis_config.services_custom[2].url); $(".f1r-shares-mendelay").attr("href", addthis_config.services_custom[1].url); function checkCount(){ setTimeout(function(){ $(".addthis_button_expanded").each(function(){ var count = $(this).text(); if (count !== "" && count != "0") $(this).removeClass("is-hidden"); else $(this).addClass("is-hidden"); }); }, 1000); } close How to cite this report {{reportCitation}} Cancel Copy Citation Details $(function(){R.ui.buttonDropdowns('.dropdown-for-downloads');}); $(function(){R.ui.toolbarDropdowns('.toolbar-dropdown-for-downloads');}); $.get("/articles/acj/158443/174030") new F1000.Clipboard(); new F1000.ThesaurusTermsDisplay("articles", "article", "174030"); $(document).ready(function() { $( "#frame1" ).on('load', function() { var mydiv = $(this).contents().find("div"); var h = mydiv.height(); console.log(h) }); var tooltipLivingFigure = jQuery(".interactive-living-figure-label .icon-more-info"), titleLivingFigure = tooltipLivingFigure.attr("title"); tooltipLivingFigure.simpletip({ fixed: true, position: ["-115", "30"], baseClass: 'small-tooltip', content:titleLivingFigure + " " }); tooltipLivingFigure.removeAttr("title"); $("body").on("click", ".cite-living-figure", function(e) { e.preventDefault(); var ref = $(this).attr("data-ref"); $(this).closest(".living-figure-list-container").find("#" + ref).fadeIn(200); }); $("body").on("click", ".close-cite-living-figure", function(e) { e.preventDefault(); $(this).closest(".popup-window-wrapper").fadeOut(200); }); $(document).on("mouseup", function(e) { var metricsContainer = $(".article-metrics-popover-wrapper"); if (!metricsContainer.is(e.target) && metricsContainer.has(e.target).length === 0) { $(".article-metrics-close-button").click(); } }); var articleId = $('#articleId').val(); if($("#main-article-count-box").attachArticleMetrics) { $("#main-article-count-box").attachArticleMetrics(articleId, { articleMetricsView: true }); } }); var figshareWidget = $(".new_figshare_widget"); if (figshareWidget.length > 0) { window.figshare.load("f1000", function(Widget) { // Select a tag/tags defined in your page. In this tag we will place the widget. _.map(figshareWidget, function(el){ var widget = new Widget({ articleId: $(el).attr("figshare_articleId") //height:300 // this is the height of the viewer part. [Default: 550] }); widget.initialize(); // initialize the widget widget.mount(el); // mount it in a tag that's on your page // this will save the widget on the global scope for later use from // your JS scripts. This line is optional. //window.widget = widget; }); }); } close Error Close Add Reset F1000.MICROSERVICES.AFFILIATION = ''; $(document).ready(function () { $('.js-affiliations-form').each((index, form) => { new AffiliationForm({ formId: form.id, institutionErrorSelector: '.comment-enter-institution', departmentErrorSelector: '.comment-enter-department', placeSelector: '.js-add-comment-place', stateSelector: '.js-add-comment-state', zipCodeSelector: '.js-add-comment-zipcode', countrySelector: '.js-add-comment-country', countryErrorSelector: '.comment-enter-country', }); }); }); $(document).ready(function () { var reportIds = { "354309": 0, "400262": 0, "354308": 0, "400263": 0, "354311": 0, "400260": 0, "354310": 0, "400261": 0, "354307": 0, "354317": 0, "354319": 0, "400268": 0, "400269": 0, "354313": 0, "400266": 0, "400267": 0, "400264": 0, "354314": 0, "400265": 0, "354326": 0, "362661": 0, "360997": 0, "360996": 0, "362663": 0, "360999": 0, "362662": 0, "360998": 0, "362657": 0, "360995": 0, "389933": 0, "366893": 0, "366892": 0, "361004": 0, "389935": 0, "366895": 7, "389934": 0, "366894": 0, "361001": 0, "362664": 0, "361000": 0, "366891": 0, "361003": 0, "366890": 0, "361002": 0, "389941": 0, "389940": 0, "389937": 0, "366897": 0, "389936": 0, "366896": 0, "389939": 0, "366899": 0, "389938": 0, "366898": 0, "416829": 4, "351815": 0, "365645": 0, "365644": 0, "351829": 0, "351828": 0, "351831": 0, "351830": 0, "351827": 0, "351833": 0, "351832": 0, "351834": 0, "364265": 0, "364267": 0, "364266": 0, }; $(".referee-response-container,.js-referee-report").each(function(index, el) { var reportId = $(el).attr("data-reportid"), reportCount = reportIds[reportId] || 0; $(el).find(".comments-count-container,.js-referee-report-views").html(reportCount); }); var uuidInput = $("#article_uuid"), oldUUId = uuidInput.val(), newUUId = "7172757c-62f2-47b7-87d8-ee43671e34ed"; uuidInput.val(newUUId); $("a[href*='article_uuid=']").each(function(index, el) { var newHref = $(el).attr("href").replace(oldUUId, newUUId); $(el).attr("href", newHref); }); }); An innovative open access publishing platform offering rapid publication and open peer review, whilst supporting data deposition and sharing. Browse Gateways Collections How it Works Contact For Developers Cookie Notice Privacy Notice RSS Submit Your Research Follow us © 2012-2026 F1000 Research Ltd. ISSN 2046-1402 | Legal | Partner of Research4Life • CrossRef • ORCID • FAIRSharing R.templateTests.simpleTemplate = R.template(' $text $text $text $text $text '); R.templateTests.runTests(); var F1000platform = new F1000.Platform({ name: "f1000research", displayName: "F1000Research", hostName: "f1000research.com", id: "1", editorialEmail: "[email protected]", infoEmail: "[email protected]", usePmcStats: true }); $(function(){R.ui.dropdowns('.dropdown-for-authors, .dropdown-for-about, .dropdown-for-myresearch');}); // $(function(){R.ui.dropdowns('.dropdown-for-referees');}); $(document).ready(function () { if ($(".cookie-warning").is(":visible")) { $(".sticky").css("margin-bottom", "35px"); $(".devices").addClass("devices-and-cookie-warning"); } $(".cookie-warning .close-button").click(function (e) { $(".devices").removeClass("devices-and-cookie-warning"); $(".sticky").css("margin-bottom", "0"); }); $("#tweeter-feed .tweet-message").each(function (i, message) { var self = $(message); self.html(linkify(self.html())); }); $(".partner").on("mouseenter mouseleave", function() { $(this).find(".gray-scale, .colour").toggleClass("is-hidden"); }); }); Sign In Remember me Forgotten your password? Sign In Cancel Email or password not correct. Please try again Please wait... $(function(){ // Note: All the setup needs to run against a name attribute and *not* the id due the clonish // nature of facebox... $("a[id=googleSignInButton]").click(function(event){ event.preventDefault(); $("input[id=oAuthSystem]").val("GOOGLE"); $("form[id=oAuthForm]").submit(); }); $("a[id=facebookSignInButton]").click(function(event){ event.preventDefault(); $("input[id=oAuthSystem]").val("FACEBOOK"); $("form[id=oAuthForm]").submit(); }); $("a[id=orcidSignInButton]").click(function(event){ event.preventDefault(); $("input[id=oAuthSystem]").val("ORCID"); $("form[id=oAuthForm]").submit(); }); }); If you've forgotten your password, please enter your email address below and we'll send you instructions on how to reset your password. The email address should be the one you originally registered with F1000. Email address not valid, please try again You registered with F1000 via Google, so we cannot reset your password. To sign in, please click here . If you still need help with your Google account password, please click here . You registered with F1000 via Facebook, so we cannot reset your password. To sign in, please click here . If you still need help with your Facebook account password, please click here . Code not correct, please try again Reset password Cancel Email us for further assistance. Server error, please try again. If your email address is registered with us, we will email you instructions to reset your password. If you think you should have received this email but it has not arrived, please check your spam filters and/or contact for further assistance. Please wait... Register $(document).ready(function () { signIn.createSignInAsRow($("#sign-in-form-gfb-popup")); $(".target-field").each(function () { var uris = $(this).val().split("/"); if (uris.pop() === "login") { $(this).val(uris.toString().replace(",","/")); } }); });