{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,8]],"date-time":"2026-04-08T04:56:56Z","timestamp":1775624216179,"version":"3.50.1"},"reference-count":22,"publisher":"MDPI AG","issue":"10","license":[{"start":{"date-parts":[[2024,5,20]],"date-time":"2024-05-20T00:00:00Z","timestamp":1716163200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["JCM"],"abstract":"<jats:p>Background\/Objectives: Proficient colposcopy is crucial for the adequate management of cervical cancer precursor lesions; nonetheless its limitations may impact its cost-effectiveness. The development of artificial intelligence models is experiencing an exponential growth, particularly in image-based specialties. The aim of this study is to develop and validate a Convolutional Neural Network (CNN) for the automatic differentiation of high-grade (HSIL) from low-grade dysplasia (LSIL) in colposcopy. Methods: A unicentric retrospective study was conducted based on 70 colposcopy exams, comprising a total of 22,693 frames. Among these, 8729 were categorized as HSIL based on histopathology. The total dataset was divided into a training (90%, n = 20,423) and a testing set (10%, n = 2270), the latter being used to evaluate the model\u2019s performance. The main outcome measures included sensitivity, specificity, accuracy, positive predictive value (PPV), negative predictive value (NPV), and the area under the receiving operating curve (AUC-ROC). Results: The sensitivity was 99.7% and the specificity was 98.6%. The PPV and NPV were 97.8% and 99.8%, respectively. The overall accuracy was 99.0%. The AUC-ROC was 0.98. The CNN processed 112 frames per second. Conclusions: We developed a CNN capable of differentiating cervical cancer precursors in colposcopy frames. The high levels of accuracy for the differentiation of HSIL from LSIL may improve the diagnostic yield of this exam<\/jats:p>","DOI":"10.3390\/jcm13103003","type":"journal-article","created":{"date-parts":[[2024,5,20]],"date-time":"2024-05-20T06:31:58Z","timestamp":1716186718000},"page":"3003","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":10,"title":["Artificial Intelligence and Colposcopy: Automatic Identification of Cervical Squamous Cell Carcinoma Precursors"],"prefix":"10.3390","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0340-0830","authenticated-orcid":false,"given":"Miguel","family":"Mascarenhas","sequence":"first","affiliation":[{"name":"Precision Medicine Unit, Department of Gastroenterology, S\u00e3o Jo\u00e3o University Hospital, Alameda Professor Hern\u00e2ni Monteiro, 4200-427 Porto, Portugal"},{"name":"Faculty of Medicine, University of Porto, Alameda Professor Hern\u00e2ni Monteiro, 4200-427 Porto, Portugal"}]},{"given":"In\u00eas","family":"Alenco\u00e3o","sequence":"additional","affiliation":[{"name":"Department of Gynecology, Centro Materno-Infantil do Norte Dr. Albino Aroso (CMIN), Santo Ant\u00f3nio University Hospital, Largo da Maternidade J\u00falio Dinis, 4050-061 Porto, Portugal"}]},{"given":"Maria Jo\u00e3o","family":"Carinhas","sequence":"additional","affiliation":[{"name":"Department of Gynecology, Centro Materno-Infantil do Norte Dr. Albino Aroso (CMIN), Santo Ant\u00f3nio University Hospital, Largo da Maternidade J\u00falio Dinis, 4050-061 Porto, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0484-4804","authenticated-orcid":false,"given":"Miguel","family":"Martins","sequence":"additional","affiliation":[{"name":"Precision Medicine Unit, Department of Gastroenterology, S\u00e3o Jo\u00e3o University Hospital, Alameda Professor Hern\u00e2ni Monteiro, 4200-427 Porto, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9427-5635","authenticated-orcid":false,"given":"Pedro","family":"Cardoso","sequence":"additional","affiliation":[{"name":"Precision Medicine Unit, Department of Gastroenterology, S\u00e3o Jo\u00e3o University Hospital, Alameda Professor Hern\u00e2ni Monteiro, 4200-427 Porto, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5890-7049","authenticated-orcid":false,"given":"Francisco","family":"Mendes","sequence":"additional","affiliation":[{"name":"Precision Medicine Unit, Department of Gastroenterology, S\u00e3o Jo\u00e3o University Hospital, Alameda Professor Hern\u00e2ni Monteiro, 4200-427 Porto, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0009-0009-8523-874X","authenticated-orcid":false,"given":"Joana","family":"Fernandes","sequence":"additional","affiliation":[{"name":"Faculty of Engineering, University of Porto, Rua Dr. Roberto Frias, 4200-065 Porto, Portugal"}]},{"given":"Jo\u00e3o","family":"Ferreira","sequence":"additional","affiliation":[{"name":"Faculty of Engineering, University of Porto, Rua Dr. Roberto Frias, 4200-065 Porto, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9387-9872","authenticated-orcid":false,"given":"Guilherme","family":"Macedo","sequence":"additional","affiliation":[{"name":"Precision Medicine Unit, Department of Gastroenterology, S\u00e3o Jo\u00e3o University Hospital, Alameda Professor Hern\u00e2ni Monteiro, 4200-427 Porto, Portugal"},{"name":"Faculty of Medicine, University of Porto, Alameda Professor Hern\u00e2ni Monteiro, 4200-427 Porto, Portugal"}]},{"given":"Rosa","family":"Zulmira Macedo","sequence":"additional","affiliation":[{"name":"Department of Gynecology, Centro Materno-Infantil do Norte Dr. Albino Aroso (CMIN), Santo Ant\u00f3nio University Hospital, Largo da Maternidade J\u00falio Dinis, 4050-061 Porto, Portugal"}]}],"member":"1968","published-online":{"date-parts":[[2024,5,20]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"e197","DOI":"10.1016\/S2214-109X(22)00501-0","article-title":"Global estimates of incidence and mortality of cervical cancer in 2020: A baseline analysis of the WHO Global Cervical Cancer Elimination Initiative","volume":"11","author":"Singh","year":"2023","journal-title":"Lancet Glob. 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