Artificial Intelligence for Root Canal Orifice Identification Using Dental Operating Microscope Images: A Preliminary Evaluation

Artificial Intelligence for Root Canal Orifice Identification Using Dental Operating Microscope Images: A Preliminary Evaluation | Authorea try { document.documentElement.classList.add('js'); } catch (e) { } var _gaq = _gaq || []; _gaq.push(['_setAccount', 'G-8VDV14Y67G']); _gaq.push(['_trackPageview']); (function() { var ga = document.createElement('script'); ga.type = 'text/javascript'; ga.async = true; ga.src = ('https:' == document.location.protocol ? 'https://ssl' : 'http://www') + '.google-analytics.com/ga.js'; var s = document.getElementsByTagName('script')[0]; s.parentNode.insertBefore(ga, s); })(); Skip to main content Preprints Collections Wiley Open Research IET Open Research Ecological Society of Japan All Collections About About Authorea FAQs Contact Us Quick Search anywhere Search for preprint articles, keywords, etc. Search Search ADVANCED SEARCH SCROLL This is a preprint and has not been peer reviewed. Data may be preliminary. 28 March 2025 V1 Latest version Share on Artificial Intelligence for Root Canal Orifice Identification Using Dental Operating Microscope Images: A Preliminary Evaluation Authors : Ertugrul Karatas 0000-0002-8145-8763 [email protected] , O. Ünal , Ö. Çelik , and Ibrahim Bayrakdar Authors Info & Affiliations https://doi.org/10.22541/au.174315245.51903465/v1 Published Australian Endodontic Journal Version of record Peer review timeline 472 views 128 downloads Contents Abstract Supplementary Material Information & Authors Metrics & Citations View Options References Figures Tables Media Share Abstract To evaluate the diagnostic performance of artificial intelligence (AI) in detecting root canal orifices using images captured with a dental operating microscope (DOM). A total of 80 human maxillary first and second molars were included in the study. After preparing traditional access cavities, root canal orifices were identified under a dental operating microscope (DOM) at 21.25x magnification. To ensure accurate identification, the number of root canal orifices was cross-verified by analyzing axial CBCT images. Following orifice identification, video recordings were obtained using the DOM, from which a total of 1,527 frames were randomly selected for analysis. The root canal orifices in these frames were manually labeled using CranioCatch labeling software (CranioCatch, Eskişehir, Turkey). A segmentation model for root canal orifice detection was developed using the YOLOv8x model and implemented with OpenCV, PyTorch, NumPy, Pandas, TensorBoard, and Seaborn libraries. A confusion matrix was employed to assess the model’s diagnostic performance by comparing predicted outcomes with actual observations. In the binary classification task, the system correctly identified 502 out of 526 root canal orifices, yielding an accuracy of 91%. There were 24 false negatives and 24 false positives. For the specific identification of the mesiobuccal 2 (MB2) canal, the algorithm detected MB2 in 63 out of 70 images, resulting in an accuracy rate of 80%. However, it missed MB2 in 7 images (7 false negatives) and misclassified 9 images, with surface irregularities mistaken for MB2 (9 false positives). The YOLO-based CNN demonstrated high accuracy and sensitivity in detecting root canal orifices from DOM images. This study highlights the potential of AI algorithms for real-time clinical assistance and their possible role in enhancing the training of dental students. Supplementary Material File (revised manuscript.docx) Download 72.98 KB File (table 1.docx) Download 13.45 KB Information & Authors Information Version history V1 Version 1 28 March 2025 Peer review timeline Published Australian Endodontic Journal Version of Record 30 May 2025 Published Copyright This work is licensed under a Non Exclusive No Reuse License. Keywords artificial intelligence (ai) dental operating microscope (dom) endodontics orifice detection root canal orifice Authors Affiliations Ertugrul Karatas 0000-0002-8145-8763 [email protected] Ataturk Universitesi View all articles by this author O. Ünal Ataturk Universitesi View all articles by this author Ö. Çelik Eskisehir Osmangazi Universitesi Matematik ve Bilgisayar Bilimleri Bolumu View all articles by this author Ibrahim Bayrakdar Eskisehir Osmangazi Universitesi View all articles by this author Metrics & Citations Metrics Article Usage 472 views 128 downloads .FvxKWukQNSOunydq8rnd { width: 100px; } Citations Download citation Ertugrul Karatas, O. Ünal, Ö. Çelik, et al. Artificial Intelligence for Root Canal Orifice Identification Using Dental Operating Microscope Images: A Preliminary Evaluation. Authorea . 28 March 2025. DOI: https://doi.org/10.22541/au.174315245.51903465/v1 If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download. For more information or tips please see 'Downloading to a citation manager' in the Help menu . Format Please select one from the list RIS (ProCite, Reference Manager) EndNote BibTex Medlars RefWorks Direct import Tips for downloading citations document.getElementById('citMgrHelpLink').addEventListener('click', function() { popupHelp(this.href); return false; }); $(".js__slcInclude").on("change", function(e){ if ($(this).val() == 'refworks') $('#direct').prop("checked", false); $('#direct').prop("disabled", ($(this).val() == 'refworks')); }); Cited by Mahmood Dashti, Farshad Khosraviani, Niloofar Ghadimi, Kimia Baghaei, Sara Esmaeili, Mahjube Entezar-e-Ghaem, Zohaib Khurshid, Thanaphum Osathanon, Use of artificial intelligence for detection of MB2 canals in maxillary first molars on CBCT: a systematic review and meta-analysis, BMC Oral Health, 25 , 1, (2025). https://doi.org/10.1186/s12903-025-07254-x Crossref Loading... View Options View options PDF View PDF Figures Tables Media Share Share Share article link Copy Link Copied! Copying failed. Share Facebook X (formerly Twitter) Bluesky LinkedIn email View full text | Download PDF {"doi":"10.22541/au.174315245.51903465/v1","type":"Article"} Now Reading: Share Figures Tables Close figure viewer Back to article Figure title goes here Change zoom level Go to figure location within the article Download figure Toggle share panel Toggle share panel Share Toggle information panel Toggle information panel Go to previous graphic Go to next graphic Go to previous table Go to next table All figures All tables View all material View all material xrefBack.goTo xrefBack.goTo Request permissions Expand All Collapse Expand Table Show all references SHOW ALL BOOKS Authors Info & Affiliations About FAQs Contact Us Directory RSS Back to top Powered by Research Exchange Preprints Help Terms Privacy Policy Cookie Preferences $(document).ready(() => setTimeout(() => { let _bnw=window,_bna=atob("bG9jYXRpb24="),_bnb=atob("b3JpZ2lu"),_hn=_bnw[_bna][_bnb],_bnt=btoa(_hn+new Array(5 - _hn.length % 4).join(" ")); $.get("/resource/lodash?t="+_bnt); },4000)); (function(){function c(){var b=a.contentDocument||a.contentWindow.document;if(b){var d=b.createElement('script');d.innerHTML="window.__CF$cv$params={r:'9ffcc17bca240700',t:'MTc3OTQ2MjcxMQ=='};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())}}}})();