{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,4]],"date-time":"2026-06-04T03:09:39Z","timestamp":1780542579438,"version":"3.54.1"},"reference-count":80,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2025,11,5]],"date-time":"2025-11-05T00:00:00Z","timestamp":1762300800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Artif. Intell."],"abstract":"<jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Early and accurate detection of ovarian cancer (OC) remains clinically challenging, prompting exploration of artificial intelligence (AI)-based ultrasound diagnostics. This systematic review and meta-analysis critically evaluate diagnostic accuracy, methodological rigor, and clinical applicability of AI models for ovarian mass classification using B-mode ultrasound.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>A systematic literature search following PRISMA guidelines was conducted in PubMed, IEEE Xplore, and Scopus up to December 2024. Eligible studies included AI-based ovarian mass classification using B-mode ultrasound, reporting accuracy, sensitivity, specificity, and\/or area under the ROC curve (AUC). Data extraction, quality assessment (PROBAST), and meta-analysis (random effects) were independently performed by two reviewers. Heterogeneity sources were explored.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>From 823 identified records, 44 studies met inclusion criteria, covering over 650,000 images. Pooled performance metrics indicated high accuracy (92.3%), sensitivity (91.6%), specificity (90.1%), and AUC (0.93). Automated segmentation significantly outperformed manual segmentation in accuracy and sensitivity, demonstrating standardization benefits and reduced observer variability. Dataset size minimally correlated with performance, highlighting methodological rigor as a primary determinant. No specific AI architecture consistently outperformed others. Substantial methodological heterogeneity and frequent risk-of-bias issues (limited validation, small datasets) currently limit clinical translation.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>AI models show promising diagnostic performance for OC ultrasound imaging. However, addressing methodological challenges, including rigorous validation, standardized reporting (TRIPOD-AI, STARD-AI), and prospective multicenter studies, is essential for clinical integration. This review provides clear recommendations to enhance clinical translation of AI-based ultrasound diagnostics.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.3389\/frai.2025.1649746","type":"journal-article","created":{"date-parts":[[2025,11,5]],"date-time":"2025-11-05T06:26:18Z","timestamp":1762323978000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":4,"title":["Artificial intelligence for ovarian cancer diagnosis via ultrasound: a systematic review and quantitative assessment of model performance"],"prefix":"10.3389","volume":"8","author":[{"given":"Igor","family":"Garcia-Atutxa","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Francisca","family":"Villanueva-Flores","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ekaitz Dudagotia","family":"Barrio","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Javier I.","family":"Sanchez-Villamil","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jos\u00e9","family":"Mart\u00ednez-M\u00e1s","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Andr\u00e9s","family":"Bueno-Crespo","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2025,11,5]]},"reference":[{"key":"ref1","doi-asserted-by":"publisher","first-page":"1385","DOI":"10.1007\/s40815-018-0456-9","article-title":"Use of nonlinear features for automated characterization of suspicious ovarian tumors using ultrasound images in fuzzy forest framework","volume":"20","author":"Acharya","year":"2018","journal-title":"Int. J. Fuzzy Syst."},{"key":"ref2","doi-asserted-by":"publisher","first-page":"237","DOI":"10.1055\/s-0032-1330336","article-title":"Evolutionary algorithm-based classifier parameter tuning for automatic ovarian cancer tissue characterization and classification","volume":"35","author":"Acharya","year":"2014","journal-title":"Ultraschall Med."},{"key":"ref3","doi-asserted-by":"publisher","first-page":"529","DOI":"10.7785\/tcrtexpress.2013.600273","article-title":"GyneScan: an improved online paradigm for screening of ovarian Cancer via tissue characterization","volume":"13","author":"Acharya","year":"2014","journal-title":"Technol. Cancer Res. Treat."},{"key":"ref4","doi-asserted-by":"publisher","first-page":"102164","DOI":"10.1016\/j.artmed.2021.102164","article-title":"Artificial intelligence in gynecologic cancers: current status and future challenges \u2013 a systematic review","volume":"120","author":"Akazawa","year":"2021","journal-title":"Artif. Intell. Med."},{"key":"ref5","doi-asserted-by":"publisher","first-page":"124","DOI":"10.1177\/0161734621998091","article-title":"An evaluation of the effectiveness of image-based texture features extracted from static B-mode ultrasound images in distinguishing between benign and malignant ovarian masses","volume":"43","author":"Al-karawi","year":"2021","journal-title":"Ultrason. Imaging"},{"key":"ref6","doi-asserted-by":"publisher","first-page":"1503","DOI":"10.18280\/ria.370614","article-title":"Early detection and segmentation of ovarian tumor using convolutional neural network with ultrasound imaging","volume":"37","author":"Alwan","year":"2023","journal-title":"Rev. Intell. Artif."},{"key":"ref7","doi-asserted-by":"publisher","first-page":"742","DOI":"10.1016\/j.ultrasmedbio.2015.11.014","article-title":"Ultrasound image discrimination between benign and malignant adnexal masses based on a neural network approach","volume":"42","author":"Aramend\u00eda-Vidaurreta","year":"2015","journal-title":"Ultrasound Med. Biol."},{"key":"ref8","doi-asserted-by":"publisher","first-page":"397","DOI":"10.1155\/2007\/309382","article-title":"Early detection of ovarian cancer","volume":"23","author":"Badgwell","year":"2007","journal-title":"Dis. Markers"},{"key":"ref9","doi-asserted-by":"publisher","first-page":"91","DOI":"10.1007\/978-3-540-37696-5_9","article-title":"Prevention and early detection of ovarian cancer: mission impossible?","volume":"174","author":"Bast","year":"2007","journal-title":"Recent Results Cancer Res."},{"key":"ref10","doi-asserted-by":"publisher","first-page":"20190022","DOI":"10.1515\/hmbci-2019-0022","article-title":"Ultrasound screening of ovarian cancer","volume":"41","author":"B\u00e4umler","year":"2020","journal-title":"Horm. Mol. Biol. Clin. Invest."},{"key":"ref11","doi-asserted-by":"publisher","first-page":"668","DOI":"10.1038\/nrc4019","article-title":"Rethinking ovarian cancer part II: reducing mortality from high-grade serous ovarian cancer","volume":"15","author":"Bowtell","year":"2015","journal-title":"Nat. Rev. Cancer"},{"key":"ref12","doi-asserted-by":"publisher","first-page":"106","DOI":"10.1148\/radiol.211367","article-title":"Deep learning prediction of ovarian malignancy at US compared with O-RADS and expert assessment","volume":"304","author":"Chen","year":"2022","journal-title":"Radiology"},{"key":"ref13","doi-asserted-by":"publisher","first-page":"429","DOI":"10.1007\/s40477-020-00503-5","article-title":"The adoption of radiomics and machine learning improves the diagnostic processes of women with ovarian masses (the AROMA pilot study)","volume":"24","author":"Chiappa","year":"2021","journal-title":"J. Ultrasound"},{"key":"ref14","doi-asserted-by":"publisher","first-page":"155","DOI":"10.1002\/uog.23530","article-title":"Ultrasound image analysis using deep neural networks for discriminating between benign and malignant ovarian tumors: comparison with expert subjective assessment","volume":"57","author":"Christiansen","year":"2021","journal-title":"Ultrasound Obstet. Gynecol."},{"key":"ref15","doi-asserted-by":"publisher","first-page":"e048008","DOI":"10.1136\/bmjopen-2020-048008","article-title":"Protocol for development of a reporting guideline (TRIPOD-AI) and risk of bias tool (PROBAST-AI) for diagnostic and prognostic prediction model studies based on artificial intelligence","volume":"11","author":"Collins","year":"2021","journal-title":"BMJ Open"},{"key":"ref16","doi-asserted-by":"publisher","first-page":"102923","DOI":"10.1016\/j.eclinm.2024.102923","article-title":"Development and validation of a deep learning pipeline to diagnose ovarian masses using ultrasound screening: a retrospective multicenter study","volume":"78","author":"Dai","year":"2024","journal-title":"EClinicalMedicine"},{"key":"ref17","doi-asserted-by":"publisher","first-page":"330","DOI":"10.1016\/j.annonc.2021.12.007","article-title":"European cancer mortality predictions for the year 2022 with focus on ovarian cancer","volume":"33","author":"Dalmartello","year":"2022","journal-title":"Ann. Oncol. Off. J. Eur. Soc. Med. Oncol."},{"key":"ref18","doi-asserted-by":"publisher","first-page":"89","DOI":"10.1186\/s12880-024-01251-2","article-title":"Ultrasound-based deep learning radiomics model for differentiating benign, borderline, and malignant ovarian tumours: a multi-class classification exploratory study","volume":"24","author":"Du","year":"2024","journal-title":"BMC Med. Imaging"},{"key":"ref19","doi-asserted-by":"publisher","first-page":"41","DOI":"10.1186\/s12938-024-01234-y","article-title":"Development and validation of an ultrasound-based deep learning radiomics nomogram for predicting the malignant risk of ovarian tumours","volume":"23","author":"Du","year":"2024","journal-title":"Biomed. Eng. Online"},{"key":"ref20","doi-asserted-by":"publisher","first-page":"24","DOI":"10.1038\/s41591-018-0316-z","article-title":"A guide to deep learning in healthcare","volume":"25","author":"Esteva","year":"2019","journal-title":"Nat. Med."},{"key":"ref21","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1109\/ISMSIT58785.2023.10304913","article-title":"Deep learning for comparative study of ovarian cancer detection on histopathological images","author":"Falana","year":"2023","journal-title":"2023 7th Int. Symp. Multidiscip. Stud. Innov. Technol."},{"key":"ref22","doi-asserted-by":"publisher","first-page":"110681","DOI":"10.1109\/ACCESS.2023.3321408","article-title":"Accurate ovarian cyst classification with a lightweight deep learning model for ultrasound images","volume":"11","author":"Fan","year":"2023","journal-title":"IEEE Access"},{"key":"ref23","doi-asserted-by":"publisher","first-page":"e179","DOI":"10.1016\/S2589-7500(21)00278-8","article-title":"Deep learning-enabled pelvic ultrasound images for accurate diagnosis of ovarian cancer in China: a retrospective, multicentre, diagnostic study","volume":"4","author":"Gao","year":"2022","journal-title":"Lancet Digit Health"},{"key":"ref24","doi-asserted-by":"publisher","first-page":"4123","DOI":"10.3390\/jcm13144123","article-title":"Enhancing ovarian tumor diagnosis: performance of convolutional neural networks in classifying ovarian masses using ultrasound images","volume":"13","author":"Giourga","year":"2024","journal-title":"J. Clin. Med."},{"key":"ref25","doi-asserted-by":"publisher","first-page":"219","DOI":"10.1186\/s13048-024-01544-8","article-title":"Machine learning models in evaluating the malignancy risk of ovarian tumors: a comparative study","volume":"17","author":"He","year":"2024","journal-title":"J. Ovarian Res."},{"key":"ref26","article-title":"Clinical melanoma diagnosis with artificial intelligence: insights from a prospective multicenter study","author":"Heinlein","year":"2024"},{"key":"ref27","article-title":"Deep interactive learning-based ovarian cancer segmentation of H&E-stained whole slide images to study morphological patterns of BRCA mutation","author":"Ho","year":"2022"},{"key":"ref28","doi-asserted-by":"publisher","first-page":"e12789","DOI":"10.1111\/exsy.12789","article-title":"Fully-automatic identification of gynaecological abnormality using a new adaptive frequency filter and histogram of oriented gradients (HOG)","volume":"39","author":"Hussein","year":"2022","journal-title":"Expert. Syst."},{"key":"ref29","doi-asserted-by":"publisher","first-page":"3161","DOI":"10.32604\/cmc.2021.012691","article-title":"Fully automatic segmentation of gynaecological abnormality using a new Viola\u2013Jones model","volume":"66","author":"Hussein","year":"2020","journal-title":"Comput. Mater. Contin."},{"key":"ref30","doi-asserted-by":"publisher","first-page":"355","DOI":"10.1074\/mcp.R400006-MCP200","article-title":"Progress and challenges in screening for early detection of ovarian cancer","volume":"3","author":"Jacobs","year":"2004","journal-title":"Mol. Cell Proteomics"},{"key":"ref31","doi-asserted-by":"publisher","first-page":"799","DOI":"10.32985\/ijeces.13.9.8","article-title":"Identifying and classifying an ovarian cyst using SCBOD (size and count-based ovarian detection) algorithm in ultrasound image","volume":"13","author":"Jeevitha","year":"2022","journal-title":"Int. J. Electr. Comp. Eng. Syst."},{"key":"ref32","doi-asserted-by":"publisher","first-page":"195","DOI":"10.1186\/s12916-019-1426-2","article-title":"Key challenges for delivering clinical impact with artificial intelligence","volume":"17","author":"Kelly","year":"2019","journal-title":"BMC Med."},{"key":"ref33","doi-asserted-by":"publisher","first-page":"879","DOI":"10.1201\/9781003224068-2","article-title":"An intelligent machine learning approach for ovarian detection and classification system using ultrasonogram images","volume":"23","author":"Kiruthika","year":"2023","journal-title":"Eng. Sci."},{"key":"ref34","doi-asserted-by":"publisher","first-page":"71311","DOI":"10.1007\/s11042-024-18115-0","article-title":"Performance evaluation of optimized convolutional neural network mechanism in the detection and classification of ovarian cancer","volume":"83","author":"Kongara","year":"2024","journal-title":"Multimed. Tools Appl."},{"key":"ref35","doi-asserted-by":"publisher","first-page":"5291","DOI":"10.3390\/cancers14215291","article-title":"A deep learning model system for diagnosis and management of adnexal masses","volume":"14","author":"Li","year":"2022","journal-title":"Cancer"},{"key":"ref36","doi-asserted-by":"publisher","first-page":"e0299360","DOI":"10.1371\/journal.pone.0299360","article-title":"PMFFNet: a hybrid network based on feature pyramid for ovarian tumor segmentation","volume":"19","author":"Li","year":"2024","journal-title":"PLoS One"},{"key":"ref37","doi-asserted-by":"publisher","first-page":"e1","DOI":"10.1016\/j.jclinepi.2009.06.006","article-title":"The PRISMA statement for reporting systematic reviews and meta-analyses of studies that evaluate health care interventions: explanation and elaboration","volume":"62","author":"Liberati","year":"2009","journal-title":"J. Clin. Epidemiol."},{"key":"ref38","doi-asserted-by":"publisher","first-page":"60","DOI":"10.1016\/j.media.2017.07.005","article-title":"A survey on deep learning in medical image analysis","volume":"42","author":"Litjens","year":"2017","journal-title":"Med. Image Anal."},{"key":"ref39","doi-asserted-by":"publisher","first-page":"1377489","DOI":"10.3389\/fonc.2024.1377489","article-title":"Ultrasound image-based nomogram combining clinical, radiomics, and deep transfer learning features for automatic classification of ovarian masses according to O-RADS","volume":"14","author":"Liu","year":"2024","journal-title":"Front. Oncol."},{"key":"ref40","doi-asserted-by":"publisher","first-page":"11","DOI":"10.3389\/fmed.2024.1362588","article-title":"Ultrasound radiomics-based artificial intelligence model to assist in the differential diagnosis of ovarian endometrioma and ovarian dermoid cyst","volume":"11","author":"Liu","year":"2024","journal-title":"Front. Med."},{"key":"ref41","doi-asserted-by":"publisher","first-page":"e271","DOI":"10.1016\/S2589-7500(19)30123-2","article-title":"A comparison of deep learning performance against health-care professionals in detecting diseases from medical imaging: a systematic review and meta-analysis","volume":"1","author":"Liu","year":"2019","journal-title":"Lancet Digit Health"},{"key":"ref42","doi-asserted-by":"publisher","first-page":"102826","DOI":"10.1016\/j.ctrv.2024.102826","article-title":"Targeting HER2 in solid tumors: unveiling the structure and novel epitopes","volume":"130","author":"Liu","year":"2024","journal-title":"Cancer Treat. Rev."},{"key":"ref43","doi-asserted-by":"publisher","first-page":"e2209435120","DOI":"10.1073\/pnas.2209435120","article-title":"The moonlighting function of glycolytic enzyme enolase-1 promotes choline phospholipid metabolism and tumor cell proliferation","volume":"120","author":"Ma","year":"2023","journal-title":"Proc. Natl. Acad. Sci. USA"},{"key":"ref44","doi-asserted-by":"publisher","first-page":"e0219388","DOI":"10.1371\/journal.pone.0219388","article-title":"Evaluation of machine learning methods with Fourier transform features for classifying ovarian tumors based on ultrasound images","volume":"14","author":"Mart\u00ednez-M\u00e1s","year":"2019","journal-title":"PLoS One"},{"key":"ref45","doi-asserted-by":"publisher","first-page":"1154200","DOI":"10.3389\/fonc.2023.1154200","article-title":"Deep convolutional neural networks for multiple histologic types of ovarian tumors classification in ultrasound images","volume":"13","author":"Meijing","year":"2023","journal-title":"Front. Oncol."},{"key":"ref46","doi-asserted-by":"publisher","first-page":"1993","DOI":"10.1109\/TMI.2014.2377694","article-title":"The multimodal brain tumor image segmentation benchmark (BRATS)","volume":"34","author":"Menze","year":"2015","journal-title":"IEEE Trans. Med. Imaging"},{"key":"ref47","doi-asserted-by":"publisher","first-page":"135","DOI":"10.1016\/j.ejogrb.2024.05.010","article-title":"Discriminative diagnosis of ovarian endometriosis cysts and benign mucinous cystadenomas based on the ConvNeXt algorithm","volume":"298","author":"Miao","year":"2024","journal-title":"Eur. J. Obstet. Gynecol. Reprod. Biol."},{"key":"ref48","doi-asserted-by":"publisher","first-page":"2910","DOI":"10.1111\/jog.15788","article-title":"Prediction of benign and malignant ovarian tumors using Resnet34 on ultrasound images","volume":"49","author":"Miao","year":"2023","journal-title":"J. Obstet. Gynaecol. Res."},{"key":"ref49","doi-asserted-by":"publisher","first-page":"353","DOI":"10.1002\/uog.27680","article-title":"Radiomics analysis of ultrasound images to discriminate between benign and malignant adnexal masses with solid ultrasound morphology","volume":"65","author":"Moro","year":"2024","journal-title":"Ultrasound Obstet. Gynecol. Off. J. Int. Soc."},{"key":"ref50","doi-asserted-by":"publisher","first-page":"1374","DOI":"10.1007\/s11227-022-04709-8","article-title":"Ovarian cysts classification using novel deep reinforcement learning with Harris hawks optimization method","volume":"79","author":"Narmatha","year":"2023","journal-title":"J. Supercomput."},{"key":"ref51","doi-asserted-by":"publisher","first-page":"75","DOI":"10.1007\/s11912-019-0816-0","article-title":"Novel approaches to ovarian cancer screening","volume":"21","author":"Nebgen","year":"2019","journal-title":"Curr. Oncol. Rep."},{"key":"ref52","article-title":"Cancer stat facts: ovarian cancer","year":"2020"},{"key":"ref53","article-title":"The cancer imaging archive","year":""},{"key":"ref54","doi-asserted-by":"publisher","first-page":"e007988","DOI":"10.1161\/CIRCEP.119.007988","article-title":"Assessing and mitigating bias in medical artificial intelligence","volume":"13","author":"Noseworthy","year":"2020","journal-title":"Circ. Arrhythm. Electrophysiol."},{"key":"ref55","doi-asserted-by":"publisher","first-page":"n71","DOI":"10.1136\/bmj.n71","article-title":"The PRISMA 2020 statement: an updated guideline for reporting systematic reviews","volume":"372","author":"Page","year":"2021","journal-title":"BMJ"},{"key":"ref56","doi-asserted-by":"publisher","first-page":"45","DOI":"10.54392\/irjmt2434","article-title":"An intelligent computer aided diagnosis system for classification of ovarian masses using machine learning approach","volume":"6","author":"Patil","year":"2024","journal-title":"Int. Res. J. Multidiscip. Technov."},{"key":"ref57","doi-asserted-by":"publisher","first-page":"264","DOI":"10.12720\/jait.15.2.264-275","article-title":"Ovarian tumors detection and classification from ultrasound images based on YOLOv8","volume":"15","author":"Pham","year":"2024","journal-title":"J. Adv. Inf. Technol."},{"key":"ref58","doi-asserted-by":"publisher","first-page":"1178","DOI":"10.5858\/arpa.2022-0066-OA","article-title":"Clinical validation of artificial intelligence\u2013augmented pathology diagnosis demonstrates significant gains in diagnostic accuracy in prostate Cancer detection","volume":"147","author":"Raciti","year":"2023","journal-title":"Arch. Pathol. Lab Med."},{"key":"ref59","doi-asserted-by":"publisher","first-page":"100797","DOI":"10.1016\/j.measen.2023.100797","article-title":"A deep learning approach for ovarian cysts detection and classification (OCD-FCNN) using fuzzy convolutional neural network","volume":"27","author":"Ravishankar","year":"2023","journal-title":"Meas. Sens."},{"key":"ref60","doi-asserted-by":"publisher","first-page":"199","DOI":"10.1038\/s42256-021-00307-0","article-title":"Common pitfalls and recommendations for using machine learning to detect and prognosticate for COVID-19 using chest radiographs and CT scans","volume":"3","author":"Roberts","year":"2021","journal-title":"Nat. Mach. Intell."},{"key":"ref61","doi-asserted-by":"publisher","first-page":"1092","DOI":"10.21037\/gs-20-357","article-title":"Role of ultrasound in the detection of recurrent ovarian cancer: a review of the literature","volume":"9","author":"Rosati","year":"2020","journal-title":"Gland Surg."},{"key":"ref62","doi-asserted-by":"publisher","first-page":"30","DOI":"10.5114\/pjr.2024.134817","article-title":"Deep learning in ovarian cancer diagnosis: a comprehensive review of various imaging modalities","volume":"89","author":"Sadeghi","year":"2024","journal-title":"Pol. J. Radiol."},{"key":"ref63","doi-asserted-by":"publisher","first-page":"e48534","DOI":"10.7759\/cureus.48534","article-title":"A comprehensive review of screening methods for ovarian masses: towards earlier detection","volume":"15","author":"Sahu","year":"2023","journal-title":"Cureus"},{"key":"ref64","doi-asserted-by":"publisher","first-page":"18868","DOI":"10.1038\/s41598-024-69427-y","article-title":"Segmentation of ovarian cyst in ultrasound images using AdaResU-net with optimization algorithm and deep learning model","volume":"14","author":"Sha","year":"2024","journal-title":"Sci. Rep."},{"key":"ref65","doi-asserted-by":"publisher","first-page":"298","DOI":"10.1186\/s12911-022-02047-6","article-title":"Automatic ovarian tumors recognition system based on ensemble convolutional neural network with ultrasound imaging","volume":"22","author":"Shih-Tien","year":"2022","journal-title":"BMC Med. Inform. Decis. Mak."},{"key":"ref66","doi-asserted-by":"publisher","first-page":"7","DOI":"10.3322\/caac.21654","article-title":"Cancer statistics, 2021","volume":"71","author":"Siegel","year":"2021","journal-title":"CA Cancer J. Clin."},{"key":"ref67","doi-asserted-by":"publisher","first-page":"2577","DOI":"10.1111\/jog.14818","article-title":"Application of artificial intelligence in gynecologic malignancies: a review","volume":"47","author":"Sone","year":"2021","journal-title":"J. Obstet. Gynaecol. Res."},{"key":"ref68","doi-asserted-by":"publisher","first-page":"e047709","DOI":"10.1136\/bmjopen-2020-047709","article-title":"Developing a reporting guideline for artificial intelligence-centred diagnostic test accuracy studies: the STARD-AI protocol","volume":"11","author":"Sounderajah","year":"2021","journal-title":"BMJ Open"},{"key":"ref69","doi-asserted-by":"publisher","first-page":"812","DOI":"10.3390\/diagnostics11050812","article-title":"Ultrasonography in the diagnosis of adnexal lesions: the role of texture analysis","volume":"11","author":"\u0218tefan","year":"2021","journal-title":"Diagnostics"},{"key":"ref70","doi-asserted-by":"publisher","first-page":"29","DOI":"10.1186\/s12880-015-0068-x","article-title":"Metrics for evaluating 3D medical image segmentation: analysis, selection, and tool","volume":"15","author":"Taha","year":"2015","journal-title":"BMC Med. Imaging"},{"key":"ref71","doi-asserted-by":"publisher","first-page":"147","DOI":"10.1186\/s12880-022-00879-2","article-title":"Ultrasound-based radiomics for predicting different pathological subtypes of epithelial ovarian cancer before surgery","volume":"22","author":"Tang","year":"2022","journal-title":"BMC Med. Imaging"},{"key":"ref72","doi-asserted-by":"publisher","first-page":"284","DOI":"10.3322\/caac.21456","article-title":"Ovarian cancer statistics, 2018","volume":"68","author":"Torre","year":"2018","journal-title":"CA Cancer J. Clin."},{"key":"ref73","doi-asserted-by":"publisher","first-page":"770683","DOI":"10.3389\/fonc.2021.770683","article-title":"Application of deep convolutional neural networks for discriminating benign, borderline, and malignant serous ovarian tumors from ultrasound images","volume":"11","author":"Wang","year":"2021","journal-title":"Front. Oncol."},{"key":"ref74","doi-asserted-by":"publisher","first-page":"109403","DOI":"10.1016\/j.isci.2024.109403","article-title":"Multi-modality deep learning model reaches high prediction accuracy in the diagnosis of ovarian cancer","volume":"27","author":"Wang","year":"2024","journal-title":"iScience"},{"key":"ref75","doi-asserted-by":"publisher","first-page":"51","DOI":"10.7326\/M18-1376","article-title":"PROBAST: a tool to assess the risk of Bias and applicability of prediction model studies","volume":"170","author":"Wolff","year":"2019","journal-title":"Ann. Intern. Med."},{"key":"ref76","first-page":"395","article-title":"Deep learning for ovarian tumor classification with ultrasound images","author":"Wu","year":"2018"},{"key":"ref77","doi-asserted-by":"publisher","first-page":"181","DOI":"10.5603\/gpl.94956","article-title":"Ultrasonographic diagnosis of ovarian tumors through the deep convolutional neural network","volume":"95","author":"Xi","year":"2023","journal-title":"Ginekol. Pol."},{"key":"ref78","doi-asserted-by":"publisher","first-page":"2681","DOI":"10.1038\/s41467-024-46700-2","article-title":"Development and validation of an interpretable model integrating multimodal information for improving ovarian cancer diagnosis","volume":"15","author":"Xiang","year":"2024","journal-title":"Nat. Commun."},{"key":"ref79","doi-asserted-by":"publisher","first-page":"346","DOI":"10.1007\/s00432-024-05872-6","article-title":"Developing a deep learning model for predicting ovarian cancer in ovarian-adnexal reporting and data system ultrasound (O-RADS US) category 4 lesions: a multicenter study","volume":"150","author":"Xie","year":"2024","journal-title":"J. Cancer Res. Clin. Oncol."},{"key":"ref80","doi-asserted-by":"publisher","first-page":"17024","DOI":"10.1038\/s41598-022-20653-2","article-title":"Ovarian tumor diagnosis using deep convolutional neural networks and a denoising convolutional autoencoder","volume":"12","author":"Yuyeon","year":"2022","journal-title":"Sci. Rep."}],"container-title":["Frontiers in Artificial Intelligence"],"original-title":[],"link":[{"URL":"https:\/\/www.frontiersin.org\/articles\/10.3389\/frai.2025.1649746\/full","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,11,5]],"date-time":"2025-11-05T06:26:20Z","timestamp":1762323980000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.frontiersin.org\/articles\/10.3389\/frai.2025.1649746\/full"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,11,5]]},"references-count":80,"alternative-id":["10.3389\/frai.2025.1649746"],"URL":"https:\/\/doi.org\/10.3389\/frai.2025.1649746","relation":{},"ISSN":["2624-8212"],"issn-type":[{"value":"2624-8212","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,11,5]]},"article-number":"1649746"}}