{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,31]],"date-time":"2026-08-31T05:18:16Z","timestamp":1788153496859,"version":"build-2803163510"},"reference-count":26,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,8,31]],"date-time":"2026-08-31T00:00:00Z","timestamp":1788134400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100004479","name":"Natural Science Foundation of Jiangxi Province","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100004479","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Med."],"abstract":"<jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Traditional PCOS assessment relies on the integration of clinical history, biochemical evaluation, and pelvic ultrasonography, which may limit the efficiency of preliminary screening in some clinical settings. This study aimed to develop an AI-assisted multimodal screening model by integrating tongue-image phenotypes with structured clinical, endocrine, and metabolic variables for PCOS risk assessment.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>We collected a multimodal dataset comprising standardized tongue images and corresponding demographic, anthropometric, reproductive, endocrine, and metabolic variables. First, a pre-trained deep learning model was utilized to extract objective tongue features, including color, texture, and coating characteristics. Subsequently, a cross-modal feature fusion framework was designed to integrate these digital tongue phenotypes with clinical data. Machine learning classifiers were employed to construct the binary classification model.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>The experimental results demonstrated that the proposed cross-modal fusion model outperformed single-modal approaches (using either tongue images or clinical data alone). The model achieved an Area Under the Curve (AUC) of 89.20%, and a Sensitivity of 85.96%. Feature importance analysis revealed that specific tongue color parameters and menstrual cycle irregularities were the most significant predictors, consistent with clinical findings.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>The integration of tongue-image phenotypes with structured clinical, endocrine, and metabolic variables showed promising performance for PCOS screening in the internally validated cohort. The proposed model should be considered an adjunctive screening and risk-stratification tool rather than a replacement for routine clinical assessment and established diagnostic procedures.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.3389\/fmed.2026.1858582","type":"journal-article","created":{"date-parts":[[2026,8,31]],"date-time":"2026-08-31T04:28:03Z","timestamp":1788150483000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["A cross-modal feature fusion model integrating tongue image phenotypes and accessible endocrine profiles for the screening of polycystic ovary syndrome"],"prefix":"10.3389","volume":"13","author":[{"given":"Chang","family":"Liu","sequence":"first","affiliation":[{"name":"Department of Hospitality and Business Management, Technological and Higher Education Institute of Hong Kong","place":["Hong Kong, Hong Kong SAR, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yuwei","family":"Luo","sequence":"additional","affiliation":[{"name":"School of Advanced Engineering, Great Bay University","place":["Dongguan, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tianzi","family":"Chen","sequence":"additional","affiliation":[{"name":"Basic Medical School of Naval Medical University, Naval Medical University","place":["Shanghai, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jie","family":"You","sequence":"additional","affiliation":[{"name":"Department of Information and Software Engineering, East China JiaoTong University","place":["Nanchang, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jinping","family":"Wang","sequence":"additional","affiliation":[{"name":"Department of Traditional Chinese Medicine, Jiangsu Kunshan Hospital of Integrated Traditional Chinese and Western Medicine","place":["Kunshan, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2026,8,31]]},"reference":[{"key":"B1","doi-asserted-by":"publisher","first-page":"16057","DOI":"10.1038\/nrdp.2016.57","article-title":"Polycystic ovary syndrome","volume":"2","author":"Azziz","year":"2016","journal-title":"Nat Rev Dis Primers"},{"key":"B2","doi-asserted-by":"publisher","first-page":"100228","DOI":"10.1016\/j.endmts.2025.100228","article-title":"The last update on polycystic ovary syndrome (PCOS), diagnosis criteria, and novel treatment","volume":"17","author":"Ghafari","year":"2025","journal-title":"Endocr Metab Sci"},{"key":"B3","doi-asserted-by":"publisher","first-page":"19","DOI":"10.1016\/j.fertnstert.2003.10.004","article-title":"Revised 2003 consensus on diagnostic criteria and long-term health risks related to polycystic ovary syndrome","volume":"81","author":"Fauser","year":"2004","journal-title":"Fertil Steril"},{"key":"B4","doi-asserted-by":"publisher","first-page":"4","DOI":"10.1111\/cen.14421","article-title":"Obesity and Polycystic Ovary Syndrome","volume":"95","author":"Barber","year":"2021","journal-title":"Clin Endocrinol"},{"key":"B5","doi-asserted-by":"publisher","first-page":"251","DOI":"10.1111\/cen.13795","article-title":"Recommendations from the international evidence-based guideline for the assessment and management of polycystic ovary syndrome","volume":"89","author":"Teede","year":"2018","journal-title":"Clin Endocrinol"},{"key":"B6","article-title":"A unified approach to interpreting model predictions","author":"Lundberg","year":"2017"},{"key":"B7","doi-asserted-by":"publisher","DOI":"10.1109\/ISBI61048.2026.11515937","article-title":"FocusSDF: boundary-aware learning for medical image segmentation via signed distance supervision","author":"Shafique","year":"2025","journal-title":"arXiv"},{"key":"B8","doi-asserted-by":"publisher","first-page":"3135","DOI":"10.1002\/mp.17618","article-title":"Multi-scale dual attention embedded U-shaped network for accurate segmentation of coronary vessels in digital subtraction angiography","volume":"52","author":"Deng","year":"2025","journal-title":"Med Phys"},{"key":"B9","doi-asserted-by":"publisher","first-page":"1106625","DOI":"10.3389\/fendo.2023.1106625","article-title":"Application of machine learning and artificial intelligence in the diagnosis and classification of polycystic ovarian syndrome: a systematic review","volume":"14","author":"Barrera","year":"2023","journal-title":"Front Endocrinol"},{"key":"B10","doi-asserted-by":"publisher","first-page":"1486","DOI":"10.1109\/TENSYMP50017.2020.9230932","article-title":"Diagnosis of polycystic ovary syndrome using machine learning algorithms[C]\/\/2020 IEEE region 10 symposium (TENSYMP)","author":"Bharati","year":"2020"},{"key":"B11","doi-asserted-by":"publisher","first-page":"33638","DOI":"10.1038\/s41598-025-10453-9","article-title":"Oehlschlaeger. A machine learning approach for non-invasive PCOS diagnosis from ultrasound and clinical features","volume":"15","author":"Agirsoy","year":"2025","journal-title":"Sci Rep"},{"key":"B12","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.90","article-title":"Deep residual learning for image recognition","author":"He","year":"2016"},{"key":"B13","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-01234-2_1","article-title":"Cbam: Convolutional block attention module","author":"Woo","year":"2018"},{"key":"B14","article-title":"Attention is all you need","author":"Vaswani","year":"2017"},{"key":"B15","doi-asserted-by":"publisher","first-page":"1559389","DOI":"10.3389\/fphys.2025.1559389","article-title":"Deep learning-based automated tongue analysis system for assisted Chinese medicine diagnosis","volume":"16","author":"Chen","year":"2025","journal-title":"Front Physiol"},{"key":"B16","doi-asserted-by":"publisher","first-page":"5080","DOI":"10.1038\/s41598-024-55695-1","article-title":"Source analysis and health risk assessment of polycyclic aromatic hydrocarbon (PAHs) in total suspended particulate matter (TSP) from Bengbu, China","volume":"14","author":"Wu","year":"2024","journal-title":"Sci Rep"},{"key":"B17","doi-asserted-by":"publisher","first-page":"981","DOI":"10.1210\/er.2011-1034","article-title":"Insulin resistance and the Polycystic Ovary Syndrome revisited: an update on mechanisms and implications","volume":"33","author":"Diamanti-Kandarakis","year":"2012","journal-title":"Endocr Rev"},{"key":"B18","doi-asserted-by":"publisher","first-page":"561","year":"2017","DOI":"10.1109\/BIBM.2017.8217710"},{"key":"B19","doi-asserted-by":"publisher","first-page":"9977","DOI":"10.1038\/s41598-026-40718-w","article-title":"A multimodal feature fusion with deep representation learning approach for polycystic ovary syndrome diagnosis using ultrasound images","volume":"16","author":"Kranthi","year":"2026","journal-title":"Sci Rep"},{"key":"B20","doi-asserted-by":"publisher","first-page":"9686697","DOI":"10.1155\/2023\/9686697","article-title":"A deep learning fusion approach to diagnosis the polycystic ovary syndrome (PCOS)","volume":"2023","author":"Alamoudi","year":"2023","journal-title":"Appl Comput Intell Soft Comput"},{"key":"B21","doi-asserted-by":"publisher","first-page":"285","DOI":"10.1186\/s12866-025-04014-3","article-title":"Characteristics of tongue images and tongue coating bacteria in patients with colorectal cancer","volume":"25","author":"Zheng","year":"2025","journal-title":"BMC Microbiol"},{"key":"B22","doi-asserted-by":"publisher","first-page":"107103","DOI":"10.1016\/j.rineng.2025.107103","article-title":"A novel hybrid CNN-CBAM-GRU method for intrusion detection in modern networks","volume":"28","author":"Hosseini","year":"2025","journal-title":"Results Eng"},{"key":"B23","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s13048-024-01583-1","article-title":"Development of machine learning models for diagnostic biomarker identification and immune cell infiltration analysis in PCOS","volume":"18","author":"Chen","year":"2025","journal-title":"J Ovarian Res"},{"key":"B24","doi-asserted-by":"publisher","first-page":"1442732","DOI":"10.3389\/fmicb.2025.1442732","article-title":"Cross-population tongue image features and tongue coating microbiome changes in the evolution of colorectal cancer","volume":"16","author":"Liu","year":"2025","journal-title":"Front Microbiol"},{"key":"B25","doi-asserted-by":"publisher","first-page":"84","DOI":"10.1186\/s13020-025-01126-w","article-title":"Study on a traditional Chinese medicine constitution recognition model using tongue image characteristics and deep learning: a prospective dual-center investigation","volume":"20","author":"Liu","year":"2025","journal-title":"Chin Med"},{"key":"B26","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2017.324","article-title":"Focal loss for dense object detection","author":"Lin","year":"2017"}],"container-title":["Frontiers in Medicine"],"original-title":[],"link":[{"URL":"https:\/\/www.frontiersin.org\/articles\/10.3389\/fmed.2026.1858582\/full","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,8,31]],"date-time":"2026-08-31T04:28:04Z","timestamp":1788150484000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.frontiersin.org\/articles\/10.3389\/fmed.2026.1858582\/full"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,8,31]]},"references-count":26,"alternative-id":["10.3389\/fmed.2026.1858582"],"URL":"https:\/\/doi.org\/10.3389\/fmed.2026.1858582","relation":{},"ISSN":["2296-858X"],"issn-type":[{"value":"2296-858X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,8,31]]},"article-number":"1858582"}}