[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"detail-sidebar-cat-0-en-105":3,"doc-seo-417167-105":59,"doc-detail-417167-en":131},{"code":4,"msg":5,"data":6},0,"success",[7,13,18,23,28,33,38,43,48,51,55],{"id":8,"doc_module":4,"doc_module_name":9,"category_name":10,"show_sort_weight":11,"slug":12},1,"Document","Story & Novel",90,"story-novel",{"id":14,"doc_module":4,"doc_module_name":9,"category_name":15,"show_sort_weight":16,"slug":17},2,"Literature",80,"literature",{"id":19,"doc_module":4,"doc_module_name":9,"category_name":20,"show_sort_weight":21,"slug":22},4,"Exam",70,"exam",{"id":24,"doc_module":4,"doc_module_name":9,"category_name":25,"show_sort_weight":26,"slug":27},5,"Comic",60,"comic",{"id":29,"doc_module":4,"doc_module_name":9,"category_name":30,"show_sort_weight":31,"slug":32},6,"Technology",50,"technology",{"id":34,"doc_module":4,"doc_module_name":9,"category_name":35,"show_sort_weight":36,"slug":37},7,"Healthcare",40,"healthcare",{"id":39,"doc_module":4,"doc_module_name":9,"category_name":40,"show_sort_weight":41,"slug":42},8,"Research & Report",30,"research-report",{"id":44,"doc_module":4,"doc_module_name":9,"category_name":45,"show_sort_weight":46,"slug":47},9,"Religion & Spirituality",20,"religion-spirituality",{"id":46,"doc_module":4,"doc_module_name":9,"category_name":49,"show_sort_weight":46,"slug":50},"World Cup","world-cup",{"id":52,"doc_module":4,"doc_module_name":9,"category_name":53,"show_sort_weight":52,"slug":54},10,"Lifestyle","lifestyle",{"id":56,"doc_module":4,"doc_module_name":9,"category_name":57,"show_sort_weight":24,"slug":58},19,"General","general",{"code":4,"msg":60,"data":61},"ok",{"site_id":62,"language":63,"slug":64,"title":65,"keywords":66,"description":67,"schema_data":68,"social_meta":124,"head_meta":126,"extra_data":128,"updated_unix":130},105,"en","yolov11-model-for-pcos-diagnosis-clinical-insights","YOLOv11 Model for PCOS Diagnosis - Clinical Insights","","Letter to the editor providing clinical and methodological commentary on the YOLOv11 model for automated recognition of ovarian ultrasound images in polycystic ovary syndrome (PCOS). It highlights strengths such as the use of Rotterdam criteria, prospective multi-center design, and SSIM/MI-based image analysis, but critiques high patient exclusion due to image quality, limited reproducibility details, and restricted East Asian adolescent inclusion. It proposes future multimodal, Rotterdam-style decision support and adaptive learning to improve generalizability.",{"@graph":69,"@context":123},[70,84,106],{"@type":71,"itemListElement":72},"BreadcrumbList",[73,77,79,82],{"item":74,"name":75,"@type":76,"position":8},"https://docshare.wps.com","Home","ListItem",{"item":78,"name":9,"@type":76,"position":14},"https://docshare.wps.com/document/",{"item":80,"name":35,"@type":76,"position":81},"https://docshare.wps.com/document/healthcare/",3,{"item":83,"name":65,"@type":76,"position":19},"https://docshare.wps.com/document/yolov11-model-for-pcos-diagnosis-clinical-insights/417167/",{"url":83,"name":65,"@type":85,"image":86,"author":91,"headline":65,"publisher":94,"fileFormat":97,"inLanguage":63,"description":67,"dateModified":98,"datePublished":99,"encodingFormat":97,"isAccessibleForFree":100,"interactionStatistic":101},"DigitalDocument",{"url":87,"@type":88,"width":89,"height":90},"https://docshare.wps.com/thumbnails/yolov11-model-for-pcos-diagnosis-clinical-insights/417167.png","ImageObject",300,407,{"name":92,"@type":93},"nayy☆","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-11-05","2026-09-28",true,{"@type":102,"interactionType":103,"userInteractionCount":105},"InteractionCounter",{"@type":104},"ViewAction",12,{"@type":107,"mainEntity":108},"FAQPage",[109,115,119],{"name":110,"@type":111,"acceptedAnswer":112},"What strengths does the letter identify in Zhao et al.’s YOLOv11 study?","Question",{"text":113,"@type":114},"It commends the prospective multi-center approach, the use of Rotterdam criteria as the diagnostic gold standard, randomized stratification across centers, and the detailed structural independence analysis using SSIM and MI indices.","Answer",{"name":116,"@type":111,"acceptedAnswer":117},"Which issues are raised about moving the model into clinical practice?",{"text":118,"@type":114},"The letter points out a 46.7% exclusion rate driven by image-quality requirements, which may widen the gap between experimental conditions and real-world workflows. It also notes missing technical details needed for replication.",{"name":120,"@type":111,"acceptedAnswer":121},"What future directions does the letter recommend?",{"text":122,"@type":114},"It suggests developing a multimodal AI agent aligned with Rotterdam criteria, combining YOLOv11 with LLMs to integrate ultrasound, anthropometric, hormonal, clinical, and anamnestic data. It also recommends continuous adaptive learning to sustain performance and improve generalizability, including for adolescent patients.","https://schema.org",{"og:url":83,"og:type":125,"og:title":65,"og:site_name":95,"og:description":67},"article",{"robots":127,"canonical":83},"index,follow",{"doc_id":129,"site_id":62},417167,1790978892,{"code":4,"msg":5,"data":132},{"doc_id":129,"user_id":133,"nickname":92,"user_avatar":134,"doc_module":4,"category_id":34,"category_name":35,"doc_title":65,"doc_description":67,"doc_content":135,"file_id":136,"file_url":137,"file_type":138,"file_size":139,"view_count":105,"is_deleted":4,"is_public":8,"is_downloadable":8,"audit_status":8,"page_count":14,"language":140,"language_code":63,"site_id":62,"html_lang":63,"table_of_contents":141,"faqs":142,"seo_title":143,"seo_description":67,"update_tm":144,"read_time":24,"preview_page_count":4},962090893153,"https://ap-avatar.wpscdn.com/davatar_9964176cb1d06d4a9deccf72a44ae3dc","Letter to the Editor  52  \nBalkan Med J 2026;43:52-3  \nContent of this journal is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.  \nYOLOv11 Model for PCOS Diagnosis: A Significant Contribution with  \nClinical Insights  \n Gianluca Mondillo  \nDepartment of Woman, Child and of General and Specialized Surgery, Università degli Studi della Campania “Luigi Vanvitelli”, Naples, Italy  \nI read with great interest the work of Zhao et al.1 on the YOLOv11 model for the automatic recognition of ovarian ultrasound images in polycystic ovary syndrome (PCOS) . The authors made a noteworthy contribution by conducting the first large-scale prospective study in women from East Asia, demonstrating excellent performance with an average training accuracy of 95.7% and validation accuracy of 97.6- 97.8%, coupled with a remarkable processing speed nearly 50 times faster than that of clinicians.  \nThe methodological rigor of the study deserves particular recognition. The adoption of the Rotterdam criteria as the diagnostic gold standard, the randomized stratification of patients across centers, and, most importantly, the detailed analysis of image structural independence using SSIM and MI indices are quality elements that set this work apart within the literature on artificial intelligence applied to PCOS. This aspect is especially relevant given that, according to a recent systematic review, only 10 of the 31 available studies (32%) on artifical intelligence (AI)/ML in PCOS diagnosis employed standardized diagnostic criteria (NIH, Rotterdam, or Revised International)2, underscoring the robustness of the methodological framework established by the authors. However, some methodological aspects raise important concerns regarding the transition to clinical application. The exclusion rate of 46.7% of initially screened patients, though justified by the need for adequate image quality, creates a significant gap between experimental conditions and realworld clinical practice. Paradoxically, the very cases that are most challenging to diagnose ultrasonographically, precisely those where AI assistance would be most valuable, were excluded from the study. Comparable studies have addressed these limitations by employing advanced preprocessing techniques to manage suboptimal images3, suggesting that future developments could focus on strategies to broaden the applicability of the system.  \nFrom a technical perspective, several fundamental details essential for reproducibility are missing. The authors do not specify which variant of YOLOv11 was used among the five available, a detail far  \nfrom trivial given the different trade-offs between computational speed and diagnostic accuracy. Similarly, the absence of information on batch size, number of epochs, early stopping criteria, and hardware configuration restricts the possibility of replicating the reported results.  \nThe geographic restriction of the study to East Asian populations, while understandable, raises concerns about the generalizability of the findings. The authors themselves note that 52% of Chinese PCOS patients present O + PCOM or H + PCOM subtypes, compared to only 30.2% in Western populations1, indicating potential phenotypic differences that could affect model performance across diverse cohorts. In addition, the systematic exclusion of adolescent patients represents a notable limitation. Given that early diagnosis is critical for effective management of PCOS and its long-term complications, extending the model to younger populations could substantially enhance its clinical utility and the overall impact.  \nIn light of the Rotterdam criteria, which integrate ultrasound, laboratory, and clinical data, a logical next step would be the development of a multimodal AI agent4, a true “Rotterdam agent.” Such a system could combine the YOLOv11 model with LLMs trained to process anthropometric, hormonal, clinical, and anamnestic information. This archit","cbCaig7kAul7htmx","https://ap.wps.com/l/cbCaig7kAul7htmx","pdf",73552,"English","# Correspondence and overall assessment\n## Study strengths\n## Methodological and clinical transition concerns\n## Reproducibility limitations\n## Generalizability concerns\n## Proposed next steps: multimodal Rotterdam agent\n## Conclusion and contribution","[{\"question\":\"What strengths does the letter identify in Zhao et al.’s YOLOv11 study?\",\"answer\":\"It commends the prospective multi-center approach, the use of Rotterdam criteria as the diagnostic gold standard, randomized stratification across centers, and the detailed structural independence analysis using SSIM and MI indices.\"},{\"question\":\"Which issues are raised about moving the model into clinical practice?\",\"answer\":\"The letter points out a 46.7% exclusion rate driven by image-quality requirements, which may widen the gap between experimental conditions and real-world workflows. It also notes missing technical details needed for replication.\"},{\"question\":\"What future directions does the letter recommend?\",\"answer\":\"It suggests developing a multimodal AI agent aligned with Rotterdam criteria, combining YOLOv11 with LLMs to integrate ultrasound, anthropometric, hormonal, clinical, and anamnestic data. It also recommends continuous adaptive learning to sustain performance and improve generalizability, including for adolescent patients.\"}]","YOLOv11 Model for PCOS Diagnosis - Clinical Insights | PDF",1790608307]