{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,24]],"date-time":"2026-09-24T16:14:21Z","timestamp":1790266461078,"version":"4.1.0"},"reference-count":30,"publisher":"MDPI AG","issue":"15","license":[{"start":{"date-parts":[[2025,8,3]],"date-time":"2025-08-03T00:00:00Z","timestamp":1754179200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Cancers"],"abstract":"<jats:p>Background\/Objectives: The integration of artificial intelligence (AI) in medical imaging is rapidly advancing, yet its application in gynecologic use remains limited. This proof-of-concept study presents the development and validation of a convolutional neural network (CNN) designed to automatically detect and classify endometrial polyps. Methods: A multicenter dataset (n = 3) comprising 65 hysteroscopies was used, yielding 33,239 frames and 37,512 annotated objects. Still frames were extracted from full-length videos and annotated for the presence of histologically confirmed polyps. A YOLOv1-based object detection model was used with a 70\u201320\u201310 split for training, validation, and testing. Primary performance metrics included recall, precision, and mean average precision at an intersection over union (IoU) \u2265 0.50 (mAP50). Frame-level classification metrics were also computed to evaluate clinical applicability. Results: The model achieved a recall of 0.96 and precision of 0.95 for polyp detection, with a mAP50 of 0.98. At the frame level, mean recall was 0.75, precision 0.98, and F1 score 0.82, confirming high detection and classification performance. Conclusions: This study presents a CNN trained on multicenter, real-world data that detects and classifies polyps simultaneously with high diagnostic and localization performance, supported by explainable AI features that enhance its clinical integration and technological readiness. Although currently limited to binary classification, this study demonstrates the feasibility and potential of AI to reduce diagnostic subjectivity and inter-observer variability in hysteroscopy. Future work will focus on expanding the model\u2019s capabilities to classify a broader range of endometrial pathologies, enhance generalizability, and validate performance in real-time clinical settings.<\/jats:p>","DOI":"10.3390\/cancers17152559","type":"journal-article","created":{"date-parts":[[2025,8,4]],"date-time":"2025-08-04T11:54:51Z","timestamp":1754308491000},"page":"2559","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Artificial Intelligence and Hysteroscopy: A Multicentric Study on Automated Classification of Pleomorphic Lesions"],"prefix":"10.3390","volume":"17","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0340-0830","authenticated-orcid":false,"given":"Miguel","family":"Mascarenhas","sequence":"first","affiliation":[{"name":"Department of Gastroenterology, S\u00e3o Jo\u00e3o University Hospital, 4200-319 Porto, Portugal"},{"name":"WGO Gastroenterology and Hepatology Training Center, 4200-319 Porto, Portugal"},{"name":"Faculty of Medicine, University of Porto, 4200-319 Porto, Portugal"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Carla","family":"Peixoto","sequence":"additional","affiliation":[{"name":"Department of Gynecology, S\u00e3o Jo\u00e3o University Hospital, 4200-319 Porto, Portugal"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ricardo","family":"Freire","sequence":"additional","affiliation":[{"name":"Department of Gynecology, Ambulat\u00f3rio M\u00e9dico de Especialidades Barradas, S\u00e3o Paulo 6479200, Brazil"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Joao","family":"Cavaco Gomes","sequence":"additional","affiliation":[{"name":"Department of Gynecology, S\u00e3o Jo\u00e3o University Hospital, 4200-319 Porto, Portugal"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9427-5635","authenticated-orcid":false,"given":"Pedro","family":"Cardoso","sequence":"additional","affiliation":[{"name":"Department of Gastroenterology, S\u00e3o Jo\u00e3o University Hospital, 4200-319 Porto, Portugal"},{"name":"WGO Gastroenterology and Hepatology Training Center, 4200-319 Porto, Portugal"},{"name":"Faculty of Medicine, University of Porto, 4200-319 Porto, Portugal"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7906-6130","authenticated-orcid":false,"given":"In\u00eas","family":"Castro","sequence":"additional","affiliation":[{"name":"Department of Gynecology, Centro Materno-Infantil do Norte Dr. Albino Aroso (CMIN), Santo Ant\u00f3nio University Hospital, 4050-651 Porto, Portugal"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Miguel","family":"Martins","sequence":"additional","affiliation":[{"name":"Department of Gastroenterology, S\u00e3o Jo\u00e3o University Hospital, 4200-319 Porto, Portugal"},{"name":"WGO Gastroenterology and Hepatology Training Center, 4200-319 Porto, Portugal"},{"name":"Faculty of Medicine, University of Porto, 4200-319 Porto, Portugal"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5890-7049","authenticated-orcid":false,"given":"Francisco","family":"Mendes","sequence":"additional","affiliation":[{"name":"Department of Gastroenterology, S\u00e3o Jo\u00e3o University Hospital, 4200-319 Porto, Portugal"},{"name":"WGO Gastroenterology and Hepatology Training Center, 4200-319 Porto, Portugal"},{"name":"Faculty of Medicine, University of Porto, 4200-319 Porto, Portugal"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Joana","family":"Mota","sequence":"additional","affiliation":[{"name":"Department of Gastroenterology, S\u00e3o Jo\u00e3o University Hospital, 4200-319 Porto, Portugal"},{"name":"WGO Gastroenterology and Hepatology Training Center, 4200-319 Porto, Portugal"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Maria Jo\u00e3o","family":"Almeida","sequence":"additional","affiliation":[{"name":"Department of Gastroenterology, S\u00e3o Jo\u00e3o University Hospital, 4200-319 Porto, Portugal"},{"name":"WGO Gastroenterology and Hepatology Training Center, 4200-319 Porto, Portugal"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Fabiana","family":"Silva","sequence":"additional","affiliation":[{"name":"Department of Gynecology, Ambulat\u00f3rio M\u00e9dico de Especialidades Barradas, S\u00e3o Paulo 6479200, Brazil"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Luis","family":"Gutierres","sequence":"additional","affiliation":[{"name":"Department of Gynecology, Ambulat\u00f3rio M\u00e9dico de Especialidades Barradas, S\u00e3o Paulo 6479200, Brazil"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7574-7630","authenticated-orcid":false,"given":"Bruno","family":"Mendes","sequence":"additional","affiliation":[{"name":"Department of Mechanical Engineering, Faculty of Engineering, University of Porto, 4200-465 Porto, Portugal"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jo\u00e3o","family":"Ferreira","sequence":"additional","affiliation":[{"name":"Department of Mechanical Engineering, Faculty of Engineering, University of Porto, 4200-465 Porto, Portugal"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Teresa","family":"Mascarenhas","sequence":"additional","affiliation":[{"name":"Department of Gynecology, S\u00e3o Jo\u00e3o University Hospital, 4200-319 Porto, Portugal"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Rosa","family":"Zulmira","sequence":"additional","affiliation":[{"name":"Department of Gynecology, Centro Materno-Infantil do Norte Dr. Albino Aroso (CMIN), Santo Ant\u00f3nio University Hospital, 4050-651 Porto, Portugal"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2025,8,3]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Pinto-Coelho, L. (2023). How Artificial Intelligence Is Shaping Medical Imaging Technology: A Survey of Innovations and Applications. Bioengineering, 10.","DOI":"10.20944\/preprints202311.1366.v1"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"2","DOI":"10.14366\/usg.22063","article-title":"Applications of artificial intelligence in obstetrics","volume":"42","author":"Kim","year":"2023","journal-title":"Ultrasonography"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"04155","DOI":"10.7189\/jogh.14.04155","article-title":"Global burden of gynaecological cancers in 2022 and projections to 2050","volume":"14","author":"Zhu","year":"2024","journal-title":"J. Glob. Health"},{"key":"ref_4","first-page":"394","article-title":"Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries","volume":"68","author":"Bray","year":"2018","journal-title":"CA A Cancer J. Clin."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Wilson, E.M., Eskander, R.N., and Binder, P.S. (2024). Recent Therapeutic Advances in Gynecologic Oncology: A Review. Cancers, 16.","DOI":"10.3390\/cancers16040770"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"978","DOI":"10.1097\/AOG.0000000000005321","article-title":"Trends in Uterine Cancer Mortality in the United States: A 50-Year Population-Based Analysis","volume":"142","author":"Somasegar","year":"2023","journal-title":"Obstet. Gynecol."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Anca-Stanciu, M.-B., Manu, A., Olinca, M.V., Coroleuc\u0103, C., Comanda\u0219u, D.-E., Coroleuca, C.A., Maier, C., and Bratila, E. (2025). Comprehensive Review of Endometrial Cancer: New Molecular and FIGO Classification and Recent Treatment Changes. J. Clin. Med., 14.","DOI":"10.3390\/jcm14041385"},{"key":"ref_8","first-page":"413","article-title":"ACOG practice bulletin, clinical management guidelines for obstetrician-gynecologists, number 65, August 2005: Management of endometrial cancer","volume":"106","author":"Gynecologists","year":"2005","journal-title":"Obstet. Gynecol."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"383","DOI":"10.1002\/ijgo.14923","article-title":"FIGO staging of endometrial cancer: 2023","volume":"162","author":"Berek","year":"2023","journal-title":"Int. J. Gynecol. Obstet."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"146","DOI":"10.1016\/j.ogrm.2013.03.007","article-title":"An overview of hysteroscopy and hysteroscopic surgery","volume":"23","author":"Stocker","year":"2013","journal-title":"Obstet. Gynaecol. Reprod. Med."},{"key":"ref_11","first-page":"e86","article-title":"Outpatient Hysteroscopy: (Green-top Guideline no. 59)","volume":"131","author":"Smith","year":"2024","journal-title":"BJOG"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"499","DOI":"10.1097\/AOG.0000000000004898","article-title":"Implementation of Office Hysteroscopy for the Evaluation and Treatment of Intrauterine Pathology","volume":"140","author":"Orlando","year":"2022","journal-title":"Obstet. Gynecol."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"160","DOI":"10.1016\/j.media.2011.06.008","article-title":"Hysteroscopy video summarization and browsing by estimating the physician\u2019s attention on video segments","volume":"16","author":"Scharcanski","year":"2012","journal-title":"Med. Image Anal."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"736","DOI":"10.1080\/014436104100009394","article-title":"Clinical pathway for the evaluation of postmenopausal bleeding with an emphasis on endometrial cancer detection","volume":"24","author":"Moodley","year":"2004","journal-title":"J. Obstet. Gynaecol."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"35","DOI":"10.1007\/s10397-007-0337-y","article-title":"Outcomes of endometrial-polyp treatment with hysteroscopy in operating room","volume":"5","year":"2008","journal-title":"Gynecol. Surg."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"e35465","DOI":"10.2196\/35465","article-title":"Contributions of Artificial Intelligence Reported in Obstetrics and Gynecology Journals: Systematic Review","volume":"24","author":"Dhombres","year":"2022","journal-title":"J. Med. Internet Res."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"596","DOI":"10.1016\/j.ygyno.2022.07.024","article-title":"A systematic review on the use of artificial intelligence in gynecologic imaging\u2014Background, state of the art, and future directions","volume":"166","author":"Shrestha","year":"2022","journal-title":"Gynecol. Oncol."},{"key":"ref_18","first-page":"e7124","article-title":"Artificial Intelligence: A New Paradigm in Obstetrics and Gynecology Research and Clinical Practice","volume":"12","author":"Iftikhar","year":"2020","journal-title":"Cureus"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Brand\u00e3o, M., Mendes, F., Martins, M., Cardoso, P., Macedo, G., Mascarenhas, T., and Mascarenhas Saraiva, M. (2024). Revolutionizing Women\u2019s Health: A Comprehensive Review of Artificial Intelligence Advancements in Gynecology. J. Clin. Med., 13.","DOI":"10.3390\/jcm13041061"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"7942501","DOI":"10.1155\/2016\/7942501","article-title":"Objects Classification by Learning-Based Visual Saliency Model and Convolutional Neural Network","volume":"2016","author":"Li","year":"2016","journal-title":"Comput. Intell. Neurosci."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"53","DOI":"10.1186\/s40537-021-00444-8","article-title":"Review of deep learning: Concepts, CNN architectures, challenges, applications, future directions","volume":"8","author":"Alzubaidi","year":"2021","journal-title":"J. Big Data"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"49","DOI":"10.4028\/p-kzQ3XE","article-title":"Review of Deep Learning Using Convolutional Neural Network Model","volume":"3","author":"Kurniawan","year":"2024","journal-title":"Eng. Headw."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"1211","DOI":"10.1055\/a-1950-5694","article-title":"Expected value of artificial intelligence in gastrointestinal endoscopy: European Society of Gastrointestinal Endoscopy (ESGE) Position Statement","volume":"54","author":"Messmann","year":"2022","journal-title":"Endoscopy"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Redmon, J., Divvala, S., Girshick, R., and Farhadi, A. (2015). You Only Look Once: Unified, Real-Time Object Detection. arXiv.","DOI":"10.1109\/CVPR.2016.91"},{"key":"ref_25","first-page":"2825","article-title":"Scikit-learn: Machine Learning in Python","volume":"12","author":"Pedregosa","year":"2011","journal-title":"J. Mach. Learn. Res."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"3005","DOI":"10.1109\/IEMBS.2006.259811","article-title":"Texture-based classification of hysteroscopy images of the endometrium","volume":"2006","author":"Neofytou","year":"2006","journal-title":"Conf. Proc. IEEE Eng. Med. Biol. Soc."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"859","DOI":"10.1007\/s11517-013-1058-1","article-title":"Classification of hysteroscopical images using texture and vessel descriptors","volume":"51","author":"Vlachokosta","year":"2013","journal-title":"Med. Biol. Eng. Comput."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Takahashi, Y., Sone, K., Noda, K., Yoshida, K., Toyohara, Y., Kato, K., Inoue, F., Kukita, A., Taguchi, A., and Nishida, H. (2021). Automated system for diagnosing endometrial cancer by adopting deep-learning technology in hysteroscopy. PLoS ONE, 16.","DOI":"10.1371\/journal.pone.0248526"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"10","DOI":"10.1186\/s12967-020-02660-x","article-title":"Deep learning model for classifying endometrial lesions","volume":"19","author":"Zhang","year":"2021","journal-title":"J. Transl. Med."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"2478473","DOI":"10.1080\/07853890.2025.2478473","article-title":"Visualized hysteroscopic artificial intelligence fertility assessment system for endometrial injury: An image-deep-learning study","volume":"57","author":"Li","year":"2025","journal-title":"Ann. Med."}],"container-title":["Cancers"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-6694\/17\/15\/2559\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,9]],"date-time":"2025-10-09T18:22:02Z","timestamp":1760034122000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-6694\/17\/15\/2559"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,8,3]]},"references-count":30,"journal-issue":{"issue":"15","published-online":{"date-parts":[[2025,8]]}},"alternative-id":["cancers17152559"],"URL":"https:\/\/doi.org\/10.3390\/cancers17152559","relation":{},"ISSN":["2072-6694"],"issn-type":[{"value":"2072-6694","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,8,3]]}}}