{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,18]],"date-time":"2026-08-18T04:36:11Z","timestamp":1787027771084,"version":"build-2736575974"},"reference-count":58,"publisher":"MDPI AG","issue":"9","license":[{"start":{"date-parts":[[2026,5,5]],"date-time":"2026-05-05T00:00:00Z","timestamp":1777939200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"The Medical Research Agency, Poland","award":["KPOD.07.07-IW.07-0157\/24"],"award-info":[{"award-number":["KPOD.07.07-IW.07-0157\/24"]}]},{"award":["KPOD.07.07-IW.07-0157\/24"],"award-info":[{"award-number":["KPOD.07.07-IW.07-0157\/24"]}],"id":[{"id":"https:\/\/ror.org\/026nedj88","id-type":"ROR","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Cancers"],"abstract":"<jats:p>Background: Accurate colposcopy assessment is essential for detection of cervical precancerous lesions. However, the diagnostic performance depends heavily on the examiner\u2019s experience and subjective interpretation. Recent advances in artificial intelligence offer new opportunities for automated image analysis. In particular, deep learning models have shown promise for colposcopy image analysis intended for precancer screening. However, their limited interpretability restricts their translational value in clinical gynecology. This study leverages a custom dataset of expert-annotated digital colposcopic images to quantify the diagnostic strengths of these architectures. Methods: A comparative analysis was provided of the well-established convolutional neural network YOLOv11 and the modern transformer-based RF-DETR architecture, both of which were trained for the segmentation of 10 distinct classes. Target objects included anatomical structures, medical instruments and colposcopic findings. Results: Our results demonstrate that the YOLO architecture provides better performance for anatomical structures, whereas the RF-DERT reaches higher scores for colposcopy findings. These findings demonstrate transformer architecture superiority in more nuanced segmentation of clinically relevant findings, providing an interesting framework for computer-aided decision support systems in colposcopy. Conclusions: Both YOLOv11 and RF-DETR enable effective segmentation of colposcopic images, with performance dependent on class size and characteristics. Best results were achieved for large anatomical structures, while small and underrepresented findings remained challenging due to class imbalance. YOLO offers greater stability and efficiency, whereas RF-DETR performs better on more complex cases. Limitations include data imbalance, variable image quality, and annotation inconsistencies; future work should address these issues to improve generalization.<\/jats:p>","DOI":"10.3390\/cancers18091485","type":"journal-article","created":{"date-parts":[[2026,5,5]],"date-time":"2026-05-05T13:23:37Z","timestamp":1777987417000},"page":"1485","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Artificial Intelligence-Assisted Colposcopy: Deep Learning Multi-Class Segmentation of Anatomical Structures and Pathological Findings for Cervical Cancer Screening"],"prefix":"10.3390","volume":"18","author":[{"given":"Marcin","family":"Jurczak","sequence":"first","affiliation":[{"name":"Proacta SA, Srebrna 16, 00-810 Warsaw, Poland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"\u0141ukasz","family":"Charzewski","sequence":"additional","affiliation":[{"name":"Proacta SA, Srebrna 16, 00-810 Warsaw, Poland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Beata","family":"Go\u017ali\u0144ska","sequence":"additional","affiliation":[{"name":"Proacta SA, Srebrna 16, 00-810 Warsaw, Poland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2656-6892","authenticated-orcid":false,"given":"Pawe\u0142","family":"Albrycht","sequence":"additional","affiliation":[{"name":"Proacta SA, Srebrna 16, 00-810 Warsaw, Poland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kacper","family":"Kobus","sequence":"additional","affiliation":[{"name":"Proacta SA, Srebrna 16, 00-810 Warsaw, Poland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5853-8661","authenticated-orcid":false,"given":"Artur","family":"Ludwin","sequence":"additional","affiliation":[{"name":"University Center for Women\u2019s and Newborn\u2019s Health, Medical University of Warsaw, 1\/3 Starynkiewicza Sq, 02-015 Warsaw, Poland"},{"name":"1st Department of Obstetrics and Gynaecology, Medical University of Warsaw, 1\/3 Starynkiewicza Sq, 02-015 Warsaw, Poland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zoulikha","family":"Jabiry-Zieniewicz","sequence":"additional","affiliation":[{"name":"University Center for Women\u2019s and Newborn\u2019s Health, Medical University of Warsaw, 1\/3 Starynkiewicza Sq, 02-015 Warsaw, Poland"},{"name":"1st Department of Obstetrics and Gynaecology, Medical University of Warsaw, 1\/3 Starynkiewicza Sq, 02-015 Warsaw, Poland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sylwester","family":"Kominek","sequence":"additional","affiliation":[{"name":"University Center for Women\u2019s and Newborn\u2019s Health, Medical University of Warsaw, 1\/3 Starynkiewicza Sq, 02-015 Warsaw, Poland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Grzegorz","family":"Basi\u0144ski","sequence":"additional","affiliation":[{"name":"University Center for Women\u2019s and Newborn\u2019s Health, Medical University of Warsaw, 1\/3 Starynkiewicza Sq, 02-015 Warsaw, Poland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Bartosz","family":"Korzeb","sequence":"additional","affiliation":[{"name":"University Center for Women\u2019s and Newborn\u2019s Health, Medical University of Warsaw, 1\/3 Starynkiewicza Sq, 02-015 Warsaw, Poland"},{"name":"1st Department of Obstetrics and Gynaecology, Medical University of Warsaw, 1\/3 Starynkiewicza Sq, 02-015 Warsaw, Poland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2279-2956","authenticated-orcid":false,"given":"Barbara Ewa","family":"Sucho\u0144ska","sequence":"additional","affiliation":[{"name":"University Center for Women\u2019s and Newborn\u2019s Health, Medical University of Warsaw, 1\/3 Starynkiewicza Sq, 02-015 Warsaw, Poland"},{"name":"1st Department of Obstetrics and Gynaecology, Medical University of Warsaw, 1\/3 Starynkiewicza Sq, 02-015 Warsaw, Poland"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,5,5]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"e191","DOI":"10.1016\/S2214-109X(19)30482-6","article-title":"Estimates of Incidence and Mortality of Cervical Cancer in 2018: A Worldwide Analysis","volume":"8","author":"Arbyn","year":"2020","journal-title":"Lancet Glob. 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