Prospective Blinded evaluation of Thermalytix, an artificial intelligence-enhanced breast thermal imaging software, correlated with radiologist-interpreted mammograms: Results of an exploratory study in Zambia

Prospective Blinded evaluation of Thermalytix, an artificial intelligence-enhanced breast thermal imaging software, correlated with radiologist-interpreted mammograms: Results of an exploratory study in Zambia | medRxiv /* */ /* */ <!-- <!-- /*! * yepnope1.5.4 * (c) WTFPL, GPLv2 */ (function(a,b,c){function d(a){return"[object Function]"==o.call(a)}function e(a){return"string"==typeof a}function f(){}function g(a){return!a||"loaded"==a||"complete"==a||"uninitialized"==a}function h(){var a=p.shift();q=1,a?a.t?m(function(){("c"==a.t?B.injectCss:B.injectJs)(a.s,0,a.a,a.x,a.e,1)},0):(a(),h()):q=0}function i(a,c,d,e,f,i,j){function k(b){if(!o&&g(l.readyState)&&(u.r=o=1,!q&&h(),l.onload=l.onreadystatechange=null,b)){"img"!=a&&m(function(){t.removeChild(l)},50);for(var d in y[c])y[c].hasOwnProperty(d)&&y[c][d].onload()}}var j=j||B.errorTimeout,l=b.createElement(a),o=0,r=0,u={t:d,s:c,e:f,a:i,x:j};1===y[c]&&(r=1,y[c]=[]),"object"==a?l.data=c:(l.src=c,l.type=a),l.width=l.height="0",l.onerror=l.onload=l.onreadystatechange=function(){k.call(this,r)},p.splice(e,0,u),"img"!=a&&(r||2===y[c]?(t.insertBefore(l,s?null:n),m(k,j)):y[c].push(l))}function j(a,b,c,d,f){return q=0,b=b||"j",e(a)?i("c"==b?v:u,a,b,this.i++,c,d,f):(p.splice(this.i++,0,a),1==p.length&&h()),this}function k(){var a=B;return a.loader={load:j,i:0},a}var l=b.documentElement,m=a.setTimeout,n=b.getElementsByTagName("script")[0],o={}.toString,p=[],q=0,r="MozAppearance"in l.style,s=r&&!!b.createRange().compareNode,t=s?l:n.parentNode,l=a.opera&&"[object Opera]"==o.call(a.opera),l=!!b.attachEvent&&!l,u=r?"object":l?"script":"img",v=l?"script":u,w=Array.isArray||function(a){return"[object Array]"==o.call(a)},x=[],y={},z={timeout:function(a,b){return b.length&&(a.timeout=b[0]),a}},A,B;B=function(a){function b(a){var a=a.split("!"),b=x.length,c=a.pop(),d=a.length,c={url:c,origUrl:c,prefixes:a},e,f,g;for(f=0;f<d;f++)g=a[f].split("="),(e=z[g.shift()])&&(c=e(c,g));for(f=0;f<b;f++)c=x[f](c);return c}function g(a,e,f,g,h){var i=b(a),j=i.autoCallback;i.url.split(".").pop().split("?").shift(),i.bypass||(e&&(e=d(e)?e:e[a]||e[g]||e[a.split("/").pop().split("?")[0]]),i.instead?i.instead(a,e,f,g,h):(y[i.url]?i.noexec=!0:y[i.url]=1,f.load(i.url,i.forceCSS||!i.forceJS&&"css"==i.url.split(".").pop().split("?").shift()?"c":c,i.noexec,i.attrs,i.timeout),(d(e)||d(j))&&f.load(function(){k(),e&&e(i.origUrl,h,g),j&&j(i.origUrl,h,g),y[i.url]=2})))}function h(a,b){function c(a,c){if(a){if(e(a))c||(j=function(){var a=[].slice.call(arguments);k.apply(this,a),l()}),g(a,j,b,0,h);else if(Object(a)===a)for(n in m=function(){var b=0,c;for(c in a)a.hasOwnProperty(c)&&b++;return b}(),a)a.hasOwnProperty(n)&&(!c&&!--m&&(d(j)?j=function(){var a=[].slice.call(arguments);k.apply(this,a),l()}:j[n]=function(a){return function(){var b=[].slice.call(arguments);a&&a.apply(this,b),l()}}(k[n])),g(a[n],j,b,n,h))}else!c&&l()}var h=!!a.test,i=a.load||a.both,j=a.callback||f,k=j,l=a.complete||f,m,n;c(h?a.yep:a.nope,!!i),i&&c(i)}var i,j,l=this.yepnope.loader;if(e(a))g(a,0,l,0);else if(w(a))for(i=0;i (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];var j=d.createElement(s);var dl=l!='dataLayer'?'&l='+l:'';j.src='//www.googletagmanager.com/gtm.js?id='+i+dl;j.type='text/javascript';j.async=true;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-P4HH5NV'); Skip to main content Home About Submit ALERTS / RSS Search for this keyword Advanced Search Prospective Blinded evaluation of Thermalytix, an artificial intelligence-enhanced breast thermal imaging software, correlated with radiologist-interpreted mammograms: Results of an exploratory study in Zambia Maurice Mwale , Mutinta S. Nteeni , Peter Mwaba , Mercy N. Chipamp doi: https://doi.org/10.1101/2025.01.12.25320093 Maurice Mwale 1 University of the Witwatersrand , Johannesburg, South Africa Find this author on Google Scholar Find this author on PubMed Search for this author on this site For correspondence: maurice.mwale{at}wits.ac.za Mutinta S. Nteeni 2 Levy Mwanawasa University Teaching Hospital , Lusaka Province, Zambia Find this author on Google Scholar Find this author on PubMed Search for this author on this site Peter Mwaba 3 Lusaka Apex Medical University , Lusaka Province, Zambia Find this author on Google Scholar Find this author on PubMed Search for this author on this site Mercy N. Chipamp 4 Cancer Diseases Hospital , Lusaka Province, Zambia Find this author on Google Scholar Find this author on PubMed Search for this author on this site Abstract Full Text Info/History Metrics Data/Code Preview PDF Abstract Background While mammography is commonly used for breast cancer detection, its widespread implementation in resource-constrained nations is challenging. Artificial intelligence-based Thermalytix is a low-cost, portable, radiation-free, automated test for breast cancer detection in women of all ages. Although used in India, the efficacy of Thermalytix has not been tested in an African population. Objectives To assess the agreement and correlation coefficient of Thermalytix output with radiologist-reported mammography, in a Zambian tertiary care population. Methodology In October 2023, 169 women were evaluated with both Thermalytix and standard mammography at Maina Soko Military Hospital at Lusaka. Thermalytix uses advanced machine learning algorithms to interpret breast thermal scans and generates a quantitative score indicating the likelihood of malignancy. All women underwent both tests, with results blinded both ways. Subsequently the Spearman correlation coefficient and level of agreement between Thermalytix output and BIRADs scoring from radiologist-interpreted mammography was calculated. Results 144 women with complete data were analysed in this report, with median age of 50 years (53.5% postmenopausal, 65.3% asymptomatic). Six women were assessed as mammography test positive and 138 as mammography negative; in these, the correlation between Thermalytix and mammography using Spearman test of rank correlation was 0.9 [very strong], and using the US FDA recommended test of agreement, positive agreement was obtained in 83.3%. Conclusion Demonstrating a very strong correlation and level of agreement with mammography, along with its good sensitivity, specificity and negative predictive value in previous clinical trials, Thermalytix has the potential to be an additional tool in the early detection of breast cancer in Zambia. 1. Introduction Globally, breast cancer (BC) is the leading cause of cancer-related mortality among women ( 1 ). In Zambia, BC is the second leading cause of cancer mortality after cervical cancer, with an estimated incidence rate in 2022 of 20.0 per 100,000 women ( 2 ) and with observed mortality of 9.5 per 100,000 women ( 3 ). Thus, over 50% of women diagnosed with BC in Zambia will succumb to the disease. The principal factor driving this high disease-attributable mortality is the late stage at diagnosis prevalent in Zambia, reflecting delays in detection, diagnosis and treatment ( 4 ). Sixty percent of Zambian participants enrolled in the African Breast Cancer Disparities and Outcomes study ( 5 ) presented with late-stage disease, a trend consistent across various countries in Africa and other developing countries. The World Health Organization (WHO) recommends population-based mammographic screening only in high-resource settings with well-funded, coordinated health systems ( 6 ). In low and middle-income countries (LMICs), the absence of sustainable financing, cultural barriers, and limited availability of medical equipment and healthcare workers [HCW] make systematic mammographic screening challenging to implement. For instance in the entire country of Zambia (with 10 million women population) there are only 16 mammography machines, 10 in public hospitals and 6 in private hospitals ( 7 ), with most located in second-level and third-level referral hospitals, thus excluding the majority rural population. Equally relevant is the limited availability of trained radiologists that results in mammography reporting times of up to 1 month ( 8 ). Further, in Africa, as in other emerging LMICs, BC occurs at a relatively younger age, when breast density is higher ( 9 ), further reducing the efficacy of mammographic screening. There are also socio-cultural barriers to screening ( 10 ) in Africa, such as fear of screening test results, lack of knowledge about the disease and screening, distance to the screening clinic, embarrassment to undergo screening, lack of support or permission from husbands to undergo screening, and high cost of screening. Thus most of the 10 million eligible women in Zambia do not receive regular breast screening which currently consists of only opportunistic approaches with contact points such as government hospitals, private clinics, and mobile screening camps. For countries with limited resources, the WHO suggests a combination of clinical history, clinical breast examination (CBE), and diagnostic breast ultrasound. ( 6 ). However, a recent systematic review by Ngan et al ( 11 ) reported that the efficacy of CBE for breast cancer screening remains uncertain, with no consistent evidence demonstrating its ability to down-stage disease, or reduce mortality rates. While a cluster-randomised trial in India that used intensive CBE training demonstrated a non-significant 15% reduction in breast cancer mortality ( 12 ), a recent Cochrane review reported by Kenyan researchers ( 13 ) found limited improvements in CBE detection rates even with training of HCWs in CBE. Also, screening rates using CBE have remained poor across African nations. We believe that this is the rationale to study other effective and affordable approaches to breast cancer detection. Further, while announcing the Global Breast Cancer Initiative in the year 2021 ( 14 ), the WHO emphasised that any breast cancer screening program that incorporates community awareness and mobilization, primary care-based screening, enhanced referral, and tracking processes to facilitate timely diagnosis and treatment is likely to be feasible, and effective in LMIC countries. For screening to be appropriate, it must be acceptable, equitable, accessible, sustainable, and economically efficient for the target population. ( 15 ). Thermalytix is one such breast screening tool attempting to bridge this gap in early detection of BC. It is a low-cost, non-invasive, portable, radiation-free, and artificial intelligence (AI) based test for the detection of breast cancer in women of all ages and densities ( 16 ). Thermalytix automatically generates a quantitative score, indicating the presence of a possible malignancy by using state of the art machine learning algorithms and innovative medically interpretable thermal and vasculature-based radiomic features that closely represent the actual tissue-level metabolic and vascular activities observed on thermal imaging ( 17 ). The authors were interested in evaluating Thermalytix in view of the existing clinical evidence of Thermalytix:- It has demonstrated promising results in hospital-based prospective efficacy trials conducted in India. In one study, Thermalytix showed 10% non-inferiority compared to mammography in symptomatic women ( 18 ). Another study reported sensitivity and specificity of 100% and 92.41%, respectively, for asymptomatic women, and 89.85% and 82.39%, respectively, for symptomatic women ( 19 ). Another study in women with diverse breast tissue composition, including both dense and fatty breasts reported an overall sensitivity of 95.2% and specificity of 88.6% ( 20 ). Community based studies conducted in India have demonstrated its feasibility and efficacy for population level breast cancer screening ( 21 , 22 ) over 15096 women with a cancer detection rate of over 3 times that of similar studies in the region that used CBE. It also has shown excellent patient satisfaction rating ( 23 ) as Thermalytix is a contactless test done in complete privacy of the individual. Thermalytix has European CE MDD Mark with the following indication-‘it is approved to be used for screening and early diagnosis of breast cancer in women above 18 years in an environment where patient care is provided by healthcare personnel. Women who are found to be suspicious for malignancy by Thermalytix® should be referred for a confirmatory diagnostic test. Primary diagnostic and patient management decisions are made by a qualified healthcare professional’. This was the background with which the authors sought to test Thermalytix in an African population with the primary goal-of evaluating the correlation between the output of automated Thermalytix with radiologist reported mammography in women in Zambia. A strong correlation would then guide our decision towards adopting this screening technology to complement the current clinical pathway for breast cancer detection that exists in Zambia. 2. Materials and Methods 2.1 Study Protocol and Population This study was conducted between 10th October 2023 and 27th October 2023 at Maina Soko Military Hospital in Lusaka, Zambia. Women between 30 to 80 years, both asymptomatic and symptomatic, who had been referred for standard mammography, were recruited to this study aimed at evaluating the correlation coefficient and level of agreement between the output of Thermalytix with mammography. An ethics waiver was obtained from the Lusaka Apex Medical University Bio-medical Research Ethics Committee (LAMUBREC). Written informed consent was obtained from all consenting participants. All procedures were performed in compliance with relevant local laws and institutional guidelines and were conducted in accordance with the Helsinki Declaration as revised in 2013. All participants underwent both thermal imaging with Thermalytix analysis and mammography [ Figure 1 ]. Thermal imaging was performed before mammography to avoid image interference from breast compression. Download figure Open in new tab Figure 1: Schematic chart of the planned study Imaging for the Thermalytix test was conducted by a hospital nurse after a formal two-day training program. The Thermalytix patient report was generated by the software automatically, as described later in section 2.3. The mammogram was obtained by the hospital mammography technician who had completed a postgraduate diploma in mammography. The interpretation of mammography images were conducted by an experienced breast radiologist at the hospital. Both the Thermalytix and mammography test results were kept blinded to each other until the time of analysis. At this point, with participants recruited, the study concluded and analysis was performed. Further diagnostic imaging was guided by the treating physician following standard practice guidelines, and was not influenced by the Thermalytix output. This follow-up data was not available for this initial exploratory study. 2.3 Description of Thermalytix Screening To perform thermal screening for Thermalytix test, infrastructure used included: an infrared camera [here, FLIR E54 thermal camera] with a spatial resolution of 320×240 and temperature sensitivity of 50 mK, a laptop connected to Thermalytix web-services, a portable air-cooler and an internet connection with minimum speed 2 MBPS. A private enclosure was created using curtains, so that the participant was neither seen nor touched at any time during the test [ Figure 2A/2B ]. Download figure Open in new tab Figure 2: Left panel:-a) Thermalytix includes a portable thermal sensor, a laptop and cloud-based AI software. b) Screening is conducted inside an enclosure created with cloth curtains providing privacy. Throughout the entire process of screening, the technician is seated outside the enclosure. c) Within the both, are 1. a rotatable stool, 2. a portable air cooler, 3. foot markings to guide positioning of the women, 4. the thermal camers. d). The items needed to conduct screening may be folded in this manner for transportation-1. Camera case, 2. foldable screen structure with curtains, 3. Camera tripod, 4. Laptop. Right panel: depiction of the scoring system of the Thermalytix system. As depicted in Figure 2C , the entire system is foldable allowing transport without a specialised vehicle. Setting up the screening merely involves creating the enclosure with collapsible aluminum rods and curtains, and setting up the camera, air-cooler and laptop and takes 10 minutes. As shown in Figure 2B , the screening is easily performed in any room that is available. The enclosure is 6 feet by 6 feet which ensures sufficient area for a patient to sit on a stool and rotate 180 degrees comfortably. The camera is placed 3 feet from the patient (sitting on the stool). The Thermalytix software runs on any standard android laptop. The camera operates via a standard electricity socket, and has battery back-up. The camera system requires no maintenance (only annual calibration). The technician ensures that the participant is seated and is comfortable before leaving the screening area. The rest of the process is performed with the participant behind the closed curtains and the technician outside. From outside the enclosure, the technician asks the participant to remove their upper clothing to expose the body above the waist. The woman then sits in front of the infrared camera and air cooler for around 5 minutes. During this time, basic history related to breast disease, personal and family history of cancer is obtained, and chest wall cooling is monitored continuously by the technician using the software. When thermal equilibrium is achieved, the tool prompts the technician to facilitate the capture of optimal data. The technician then directs the woman to use foot markings on the ground to obtain proper orientation for imaging and five thermal images are obtained, from the neck to the abdomen-frontal, left-oblique, left-lateral, right-oblique, and right-lateral. These images are uploaded to the cloud-based Thermalytix software, where pre-trained AI algorithms extract salient vascular and thermal radiomics features. These radiomic features are analysed by Thermalytix machine learning classifiers to generate instant patient reports with quantitative scores [ Figure 2D ]. An overall score called B-Score, analogous to BIRADS, indicates the likelihood of malignancy. To ensure ease of comprehension for all educational levels of HCW, a colour-coded Thermalytix output is also generated with a scale depicted in Figure 2D (for more information of the scoring system, please refer Bansal et al ( 21 ). Women categorised as test positive are advised to undergo further confirmatory diagnostic work-up. The entire process starting from patient preparation to report generation takes approximately 12-15 minutes per person. 2.3 Scoring methodology For mammography, as per general clinical practice, test positivity was considered to ACR BIRADS 4 and 5 as per the radiologist’s report, while test negativity included BIRADS 1, 2, and 3. BI-RADS 0 were deemed inconclusive and excluded from the analysis. The test positivity of Thermalytix was defined as B-scores of 3, 4, and 5, while test negativity included B-scores of 1 and 2. Cases with a B-score of 0 were classified as inconclusive and excluded from the analysis. 2.4 Statistical analysis Data analysis was performed using SPSS (IBM SPSS v26.0.0.0). The extent of correlation between Thermalytix B-Score and mammographic BIRADS was performed by two tests: Spearman’s Correlation Coefficient( ϱ ) which is a test of rank correlation measuring the strength of association between two ranked variables and US FDA recommended test of agreement between a new test and a non-reference standard [24]. In the absence of a true reference standard, measures like sensitivity and specificity cannot be calculated. 2.5 Funding This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors 3. Results Between October 10, 2023, to October 27, 2023, 163 women were recruited at Maina Soko Military Hospital in Lusaka, Zambia to evaluate the correlation of Thermalytix and mammography. Of these, 25 participants (15.3%) were excluded from the final analysis due to the following reasons [ Figure 1 ]: in 6 women, either Thermalytix or mammography was not performed; in 7 cases, mammography reports were unavailable; in 7 cases, mammographic scoring was inconclusive; and in 5 women, Thermalytix scoring was inconclusive. 3.1 Demographics of study population Among the 144 women who were considered for the analysis, the median age was 50 years (IQR 11 years), with 51.3% aged less than 50 years. The majority were postmenopausal (53.5%) and asymptomatic (65.3%). Demographic data are presented in Table 1 . View this table: View inline View popup Table 1: Demographics of the women screened 3.2 Correlation between Thermalytix B Scores and Mammographic BIRADS Scores The Spearman coefficient ( ϱ ) was 0.90, indicating a very strong positive correlation between the scores generated by Thermalytix and those obtained by mammography [ Table 3 ], while using the US-FDA recommended test of agreement, positive agreement between the scores were obtained in 83.3% [ Table 3 ]. Calculations are presented in the Supplementary file. View this table: View inline View popup Table 2: Contingency Table of the outputs of Thermalytix and Mammography View this table: View inline View popup Download powerpoint Table 3: Interpretation of Correlation and Agreement Scores D. Discussion While Thermalytix had been earlier evaluated and compared with standard radiological imaging in an Asian population, this is the first experience of the usage of Thermalytix in an African population. In this study conducted in Zambia, Thermalytix demonstrated strong to very strong positive correlation and 83.3% positive agreement with radiologist-reported mammography with radiologist-reported mammography. This result presents a promise to enable equitable care. In the African continent, around 85.2% of countries belong to LMICs economies, where most residents rely on underfunded public health care systems, and have limited access to clinical expertise, or to secondary or tertiary health centers. Therefore, affordable AI based tools may prove beneficial in triaging patients at the primary care level itself before they present to secondary or tertiary care centres, thereby improving the efficiency of specialists even in secondary care and easing the case burden caused by a worsening health provider-to-patient ratio ( 25 ). Although CBE as a screening measure is being practised as a national program in Zambia and in other African countries, we feel that the bulk of the literature on CBE published thus far only highlights its many limitations. Ngan et al ( 11 ) has reported an overview of systematic reviews of CBE, including 11 reviews, and reported that population coverage rates in screening programs based on CBE are suboptimal and the efficacy of CBE in down-staging disease or in reducing mortality remains unproven. This is likely due to a combination of factors The performance of CBE is entirely dependent on the skill of the examiner, with no standardisation of technique, and no guidelines for optimal reporting ( 26 ). Since the most sensitive technique has not been established, published clinical results report variable sensitivity of CBE of 40% to 69%, with a pooled sensitivity of 54·1% ( 11 ). To be useful, effective CBE requires a trained and diligent community HCW whose competence is trusted by the community. However, in most of Africa, the HCW has many roles, is overburdened ( 27 ) and in the face of competing healthcare priorities does not prioritise cancer screening. Of note, a recent Cochrane review found limited improvements in CBE detection rates even with imparting of training to HCWs in CBE, again implying a need to strengthen any CBE-based screening service ( 13 ). Since Thermalytix is an computer-based automated test, there is no variability in analysis with minimal dependency on the skill of the technician. Sociocultural barriers represent the most significant barrier to breast screening in most studies ( 10 ) in both high-income and LMICs and are not lessened by CBE. Cultural norms of modesty such as embarrassment, shame on disrobing, husband’s disapproval, and certain religious beliefs prevent women from accessing services requiring undressing in front of an HCW. Since Thermalytix is a completely privacy conscious test where no one sees or touches the woman during the test, this socio-cultural barrier is being addressed by the Thermalytix test. We believe that Thermalytix would be useful as an additional tool towards the early detection of breast cancer especially in situations where mammography is unavailable. We anticipate benefits for public health utilisation as the system is low cost, lightweight, portable, and requires minimal physical infrastructure and thus may be feasible in diverse healthcare settings. Further it is non-invasive, non-contact, radiation-free, and does not require breast compression, making the user experience highly acceptable for patients. The AI-based automation of Thermalytix with its easy-to-use user interface allows the entire process from image capture to report generation to be performed even by a semi-skilled HCW at the point of care. The software mandates automated image quality checks thus ensuring standardised image capture. As compared to CBE which is a subjective and qualitative assessment, the use of Thermalytix also allows objective and quantitative analysis of any abnormality detected, improving the chance of improved cancer detection rates. In the last decade, developments in radiomics-based AI algorithms have been applied to breast thermal images, enabling interpretation of the minute temperature variations not with the human eye but with computer-aided thermal image interpretation ( 17 ). These machine learning algorithms may eliminate the subjectivity of the manual interpretation process through automated quantitative analysis, wherein the interpretation process becomes more objective, more quantitative and score based. This approach is being explored at various centres across the world for screening and detection of breast cancer, especially where other imaging modalities, like mammography are not universally available ( 28 ). The recent advancement of AI into primary health may be a transformative event with potential to mitigate healthcare disparities ( 29 ). These systems improve primary healthcare in a variety of areas, including by improving risk prediction, supporting clinical decision making, increasing the accuracy and timeliness of diagnosis, optimising resource allocation and reducing work-burden of the primary healthcare workforce. Of note, technologies utilising AI for image analysis generally alleviates shortages in skilled personnel and affordable systems and address the constraints of availability of medical equipment. Already, AI algorithms are in use for interpreting medical images in X-rays, MRIs, and CT scans, early detection of diabetic retinopathy and diagnostic imaging of the cervix ( 30 ). We acknowledge certain limitations of the study. Firstly, the sample population was small, and hence may not be representative of all the subsets of women encountered in Zambia. Secondly, this study was designed to compare the correlation and agreement between Thermalytix and mammography; in the absence of a true reference standard we are unable to comment on the sensitivity and specificity for Thermalytix from this small study 5. Conclusion This study demonstrates the feasibility of using Thermalytix in an ethnically African population in Zambia and demonstrates very strong positive correlation, and good positive agreement between Thermalytix and mammography. This initial experience has prompted us to plan for a larger study using Thermalytix towards widespread adoption for breast cancer screening in Zambia. Data Availability All data produced in the present work are contained in the manuscript Ethics Approval An ethics waiver has been obtained for this study from the Lusaka Apex Medical University Bio-medical Research Ethics Committee (LAMUBREC). Declaration of Interest Maurice Mwale, Mutinta Siachami Nteeni, Peter Mwaba and Mercy Nachalwe Chipamp have no direct or indirect conflict of interest. Supplement A: Calculation of Spearman’s Coefficient Calculation View this table: View inline View popup Download powerpoint The sum of the differences in ranks squared . The following formula was then used Substitute the sum of information and the number of scores variable ( n ) into the formula: Therefore, the Spearman coefficient is 0.900 . [Statistics without maths using SPSS for Windows. Authors: Christine P. Dancey, John Reidy … Print Book, English, 2004. Edition: 3rd ed ISBN, 013124941X, 9780131249417] Supplement B: Test of Agreement between a new test and a non-reference standard View this table: View inline View popup Download powerpoint https://tools.westgard.com/two-by-two-contingency.shtml [see reference 24] Acknowledgment We thank Niramai Health Analytix, Bangalore, India for providing the thermal camera and Thermalytix tool for this study. Footnotes Competing interests: All authors declare no financial or non-financial competing interests. References 1. ↵ Bray F , Laversanne M , Sung H , Ferlay J , Siegel RL , Soerjomataram I , et al. Global cancer statistics 2022: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries . CA Cancer J Clin . 2024 ; 74 ( 3 ): 229 – 63 . DOI: 10.3322/caac.21834 OpenUrl CrossRef 2. ↵ Ferlay J , Ervik M , Lam F , Laversanne M , Colombet M , Mery L , Piñeros M , Znaor A , Soerjomataram I , Bray F ( 2024 ). Global Cancer Observatory: Cancer Today . Lyon, France : International Agency for Research on Cancer . Available from: https://gco.iarc.who.int/today , accessed [ 20 Nov 2024 ]. 3. ↵ Kalubula M , Shen H , Makasa M , Liu L. Epidemiology of Cancers in Zambia: A significant variation in Cancer incidence and prevalence across the nation . Malawi Med J 2021 Sep ; 33 ( 3 ): 186 – 95 . DOI: 10.4314/mmj.v33i3.6 OpenUrl CrossRef PubMed 4. ↵ Cabanes A , Kapambwe S , Citonje-Msadabwe S , Parham GP , Lishimpi K , Cruz TA , et al. Challenges, opportunities, and priorities for advancing breast cancer control in Zambia: A consultative meeting on breast cancer control . J Glob Oncol 2019 ( 5 ): 1 – 7 . DOI: 10.1200/JGO.18.00222 OpenUrl CrossRef 5. ↵ McKenzie F , Zietsman A , Galukande M , Anele A , Adisa C , Parham G , et al. Drivers of advanced stage at breast cancer diagnosis in the multicountry African breast cancer – disparities in outcomes (ABC-DO) study . Int J Cancer 2018 Apr 15; 142 ( 8 ): 1568 – 79 . DOI: 10.1002/ijc.31187 OpenUrl CrossRef PubMed 6. ↵ El Saghir NS , Adebamowo CA , Anderson BO , Carlson RW , Bird PA , Corbex M , et al. Breast cancer management in low resource countries (LRCs): consensus statement from the Breast Health Global Initiative . Breast 2011 Apr ; 20 Suppl 2 : S3 – 11 . DOI: 10.1016/j.breast.2011.02.006 OpenUrl CrossRef PubMed 7. ↵ Bwanga O , Chanda E , Sindaza N , Nachalwe MC . Mammography Services in Zambia . Int J Med Rev . 2021 Mar 1; 8 ( 1 ): 12 – 9 . DOI: 10.30491/ijmr.2020.239681.1124 OpenUrl CrossRef 8. ↵ Mbewe C , Chanda-Kapata P , Sunkutu-Sichizya V , Lambwe N , Yakovlyeva N , Chirwa M , et al. An audit of licenced Zambian diagnostic imaging equipment and personnel . Pan Afr Med J . 2020 May 22; 36 : 32 . doi: 10.11604/pamj.2020.36.32.21043 OpenUrl CrossRef PubMed 9. ↵ Adeoye PA . Epidemiology of Breast Cancer in Sub-Saharan Africa . In: Breast Cancer Updates. IntechOpen ; 2023 DOI: 10.5772/intechopen.109361 OpenUrl CrossRef 10. ↵ Sarmah N , Sibiya MN , Khoza TE . The Sociocultural Influences on Breast Cancer Screening among Rural African Women in South Africa . Int J Environ Res Public Health . 2023 Nov 1; 20 ( 21 ): 7005 . doi: 10.3390/ijerph20217005 . OpenUrl CrossRef PubMed 11. ↵ Ngan TT , Nguyen NTQ , Van Minh H , Donnelly M , O’Neill C. Effectiveness of clinical breast examination as a ‘stand-alone’ screening modality: an overview of systematic reviews . BMC Cancer . 2020 ; 20 ( 1 ): 1070 . doi: 10.1186/s12885-020-07521-w OpenUrl CrossRef PubMed 12. ↵ Mittra I , Mishra GA , Dikshit RP , Gupta S , Kulkarni VY , Shaikh HKA , et al. Effect of screening by clinical breast examination on breast cancer incidence and mortality after 20 years: prospective, cluster randomised controlled trial in Mumbai . BMJ 2021 Feb 24 ; 372 : n256 . doi: 10.1136/bmj.n256 . OpenUrl Abstract / FREE Full Text 13. ↵ Sayed S , Ngugi AK , Nwosu N , Mutebi MC , Ochieng P , Mwenda AS , et al. Training health workers in clinical breast examination for early detection of breast cancer in low- and middle-income countries . Cochrane Database Syst Rev 2023 Apr 18 2023 ( 4 ): CD012515 . doi: 10.1002/14651858.CD012515.pub2 . OpenUrl CrossRef 14. ↵ Anderson BO , Ilbawi AM , Fidarova E , Weiderpass E , Stevens L , Abdel-Wahab M , et al. The Global Breast Cancer Initiative: a strategic collaboration to strengthen health care for non-communicable diseases . Lancet Oncol . 2021 May ; 22 ( 5 ): 578 – 81 . doi: 10.1016/S1470-2045(21)00071-1 . OpenUrl CrossRef PubMed 15. ↵ Shah SC , Kayamba V , Peek RM Jr . , Heimburger D. Cancer Control in Low- and Middle-Income Countries: Is It Time to Consider Screening? J Glob Oncol . 2019 Mar ; 5 : 1 – 8 . doi: 10.1200/JGO.18.00200 . OpenUrl CrossRef PubMed 16. ↵ Malik A Kakileti ST , Manjunath G , Madhu H , Ramprakash HV . Advances in breast thermography . New Perspectives in Breast Imaging . Malik A (ed): IntechOpen , London . 2017 Oct 4: 91 – 103 . DOI: 10.5772/intechopen.69198 OpenUrl CrossRef 17. ↵ Shrivastava R , Kakileti ST , Manjunath G. Thermal Radiomics for Improving the Interpretability of Breast Cancer Detection from Thermal Images . In: Artificial Intelligence over Infrared Images for Medical Applications and Medical Image Assisted Biomarker Discovery: First MICCAI Workshop, AIIIMA 2022, and First MICCAI Workshop, MIABID 2022, Held in Conjunction with MICCAI 2022, Singapore, September 18 and 22, 2022, Proceedings . Berlin, Heidelberg : Springer-Verlag ; 2022 . p. 3 – 9 . doi: 10.1007/978-3-031-19660-7_1 OpenUrl CrossRef 18. ↵ Kakileti ST , Madhu HJ , Krishnan L , Manjunath G , Sampangi S , Ramprakash HV . Observational Study to Evaluate the Clinical Efficacy of Thermalytix for Detecting Breast Cancer in Symptomatic and Asymptomatic Women . JCO Glob Oncol . 2020 Oct ; 6 : 1472 – 80 . doi: 10.1200/GO.20.00168 . OpenUrl CrossRef PubMed 19. ↵ Singh A , Bhat V , Sudhakar S , Namachivayam A , Gangadharan C , Pulchan C , et al. Multicentric study to evaluate the effectiveness of Thermalytix as compared with standard screening modalities in subjects who show possible symptoms of suspected breast cancer . BMJ Open 2021 ; 11 ( 10 ): e052098 . doi: 10.1136/bmjopen-2021-052098 . OpenUrl Abstract / FREE Full Text 20. ↵ Bansal R , Collison S , Krishnan L , Aggarwal B , Vidyasagar M , Kakileti ST , et al. A prospective evaluation of breast thermography enhanced by a novel machine learning technique for screening breast abnormalities in a general population of women presenting to a secondary care hospital . Front Artif Intell 2023 ; 5 : 1050803 . doi: 10.3389/frai.2022.1050803 OpenUrl CrossRef PubMed 21. ↵ Manjunath G , Madhu H , Buggi N , Suggaiah A , Muthanna R , Gangadharan C , et al. Feasibility and outcomes of using a novel artificial intelligence enhanced breast thermography technique, Thermalytix, in screening for breast abnormalities at primary health centres at the community level in South India . Int J Community Med Public Health . 2022 ; 9 ( 12 ): 4634 – 40 . doi: 10.18203/2394-6040.ijcmph20223225 OpenUrl CrossRef 22. ↵ Ramprasad J , Krishnan L , Gangadharan C , Deshpande G , Madhu H , Kakileti ST , et al. Performance of artificial intelligence-based breast cancer screening in a community setting: a real-world evaluation study . Lancet Oncol . 2022 ; 23 : S20 . doi: 10.1016/S1470-2045(22)00419-3 OpenUrl CrossRef 23. ↵ Davalagi SB , Palicheralu BS , Murthy SSN , Hurlihal S. Acceptance of artificial intelligence (AI)-based screening for breast health in urban slums of central Karnataka, India – SWOC analysis . J Fam Med Prim Care 2022 ; 11 ( 10 ): 6023 – 8 . doi: 10.4103/jfmpc.jfmpc_143_22 OpenUrl CrossRef 24. U.S. Food and Drug Administration . 2007 . Guidance for industry and FDA staff: statistical guidance on reporting results from studies evaluating diagnostic tests . U.S. Food and Drug Administration , Silver Spring, MD : https://www.fda.gov/downloads/medicaldevices/deviceregulationandguidance/guidancedocuments/ucm071287.pdf . [Google Scholar] 25. ↵ Akingbola , A. , Abiodun , A. , Ojo , O. , Jessica , O.U. , & Alao , U.H. ( 2024 ). Artificial Intelligence And Cancer Care In Africa . Journal of Medicine, Surgery, and Public Health . Volume 3 , 2024 , doi: 10.1016/j.glmedi.2024.100132 . OpenUrl CrossRef 26. ↵ Lohani KR , Srivastava A , Jeyapradha DA , Ranjan P , Dhar A , Kataria K , et al. ‘Dial of a Clock’ Search Pattern for Clinical Breast Examination . J Surg Res . 2021 Apr ; 260 : 10 – 9 . doi: 10.1016/j.jss.2020.11.043 . OpenUrl CrossRef PubMed 27. ↵ Ahmat A , Okoroafor SC , Kazanga I , Asamani JA , Millogo JJS , Illou MMA , et al. The health workforce status in the WHO African Region: findings of a cross-sectional study . BMJ Glob Health 2022 ; 7 ( Suppl 1 ): e008317 . doi: 10.1136/bmjgh-2021-008317 OpenUrl CrossRef PubMed 28. ↵ Mashekova , A. , Zhao , Y. , Ng , E. Y. K. , Zarikas , V. , Fok , S. C. & Mukhmetov , O. ( 2022 ). Early detection of the breast cancer using infrared technology – a comprehensive review . Thermal Science and Engineering Progress , 27 , 101142 -. doi: 10.1016/j.tsep.2021.101142 OpenUrl CrossRef 29. ↵ Bajwa J , Munir U , Nori A , Williams B. Artificial intelligence in healthcare: transforming the practice of medicine . Future Healthc J . 2021 ; 8 ( 2 ): e188 – 94 . doi: 10.7861/fhj.2021-0095 OpenUrl Abstract / FREE Full Text 30. ↵ Andigema AS , Cyrielle NNT , Danaëlle MKL , Ekwelle E. Transforming African Healthcare with AI: Paving the Way for Improved Health Outcomes . J Collab Healthc Transl Med . 2024 ; 7 ( 1 ): 1 – 8 . doi: 10.47739/2333-7125/1046 OpenUrl CrossRef View the discussion thread. Back to top Previous Next Posted January 13, 2025. Download PDF Data/Code Email Thank you for your interest in spreading the word about medRxiv. NOTE: Your email address is requested solely to identify you as the sender of this article. Your Email * Your Name * Send To * Enter multiple addresses on separate lines or separate them with commas. You are going to email the following Prospective Blinded evaluation of Thermalytix, an artificial intelligence-enhanced breast thermal imaging software, correlated with radiologist-interpreted mammograms: Results of an exploratory study in Zambia Message Subject (Your Name) has forwarded a page to you from medRxiv Message Body (Your Name) thought you would like to see this page from the medRxiv website. Your Personal Message CAPTCHA This question is for testing whether or not you are a human visitor and to prevent automated spam submissions. Share Prospective Blinded evaluation of Thermalytix, an artificial intelligence-enhanced breast thermal imaging software, correlated with radiologist-interpreted mammograms: Results of an exploratory study in Zambia Maurice Mwale , Mutinta S. Nteeni , Peter Mwaba , Mercy N. Chipamp medRxiv 2025.01.12.25320093; doi: https://doi.org/10.1101/2025.01.12.25320093 Share This Article: Copy Citation Tools Prospective Blinded evaluation of Thermalytix, an artificial intelligence-enhanced breast thermal imaging software, correlated with radiologist-interpreted mammograms: Results of an exploratory study in Zambia Maurice Mwale , Mutinta S. Nteeni , Peter Mwaba , Mercy N. Chipamp medRxiv 2025.01.12.25320093; doi: https://doi.org/10.1101/2025.01.12.25320093 Citation Manager Formats BibTeX Bookends EasyBib EndNote (tagged) EndNote 8 (xml) Medlars Mendeley Papers RefWorks Tagged Ref Manager RIS Zotero Tweet Widget Facebook Like Google Plus One Subject Area Radiology and Imaging Subject Areas All Articles Addiction Medicine (568) Allergy and Immunology (863) Anesthesia (297) Cardiovascular Medicine (4421) Dentistry and Oral Medicine (443) Dermatology (382) Emergency Medicine (606) Endocrinology (including Diabetes Mellitus and Metabolic Disease) (1507) Epidemiology (15212) Forensic Medicine (30) Gastroenterology (1121) Genetic and Genomic Medicine (6581) Geriatric Medicine (667) Health Economics (996) Health Informatics (4520) Health Policy (1366) Health Systems and Quality Improvement (1611) Hematology (539) HIV/AIDS (1264) Infectious Diseases (except HIV/AIDS) (15906) Intensive Care and Critical Care Medicine (1103) Medical Education (620) Medical Ethics (144) Nephrology (667) Neurology (6580) Nursing (345) Nutrition (998) Obstetrics and Gynecology (1141) Occupational and Environmental Health (956) Oncology (3324) Ophthalmology (970) Orthopedics (369) Otolaryngology (420) Pain Medicine (435) Palliative Medicine (129) Pathology (663) Pediatrics (1689) Pharmacology and Therapeutics (691) Primary Care Research (710) Psychiatry and Clinical Psychology (5433) Public and Global Health (9212) Radiology and Imaging (2193) Rehabilitation Medicine and Physical Therapy (1368) Respiratory Medicine (1194) Rheumatology (593) Sexual and Reproductive Health (709) Sports Medicine (529) Surgery (709) Toxicology (99) Transplantation (288) Urology (265) (function(){function c(){var b=a.contentDocument||a.contentWindow.document;if(b){var d=b.createElement('script');d.innerHTML="window.__CF$cv$params={r:'9ff5a507a9448e2e',t:'MTc3OTM4ODE0NQ=='};var a=document.createElement('script');a.src='/cdn-cgi/challenge-platform/scripts/jsd/main.js';document.getElementsByTagName('head')[0].appendChild(a);";b.getElementsByTagName('head')[0].appendChild(d)}}if(document.body){var a=document.createElement('iframe');a.height=1;a.width=1;a.style.position='absolute';a.style.top=0;a.style.left=0;a.style.border='none';a.style.visibility='hidden';document.body.appendChild(a);if('loading'!==document.readyState)c();else if(window.addEventListener)document.addEventListener('DOMContentLoaded',c);else{var e=document.onreadystatechange||function(){};document.onreadystatechange=function(b){e(b);'loading'!==document.readyState&&(document.onreadystatechange=e,c())}}}})();