ArticleFrontiers in endocrinology2021
Deep Learning Algorithm for Automated Detection of Polycystic Ovary Syndrome Using Scleral Images.
Article in Frontiers in endocrinology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers, 4 of them syntheses that pooled it.
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Who cites it
16 citing papers in PubMed, 4 syntheses or guidelines pooled it.
- Machine learning and deep learning for diagnosis of Polyendocrine Metabolic Ovarian Syndrome: systematic review and meta-analysis.Frontiers in endocrinology · 2026Pooled it
- Artificial intelligence in polycystic ovary syndrome: a systematic review of diagnostic and predictive applications.BMC medical informatics and decision making · 2025Pooled it
- Artificial intelligence in polycystic ovarian syndrome management: past, present, and future.La Radiologia medica · 2025Pooled it
- Nutritional management recommendation systems in polycystic ovary syndrome: a systematic review.BMC women's health · 2024Pooled it
- AutoPCOS: a stepwise multimodal intelligent framework for polycystic ovary syndrome risk stratification and diagnostic support.Frontiers in endocrinology · 2026Article
- Non-Invasive and AI-Enabled Diagnostic Approaches for Polycystic Ovary Syndrome: A PRISMA-Guided Narrative Review.International journal of women's health · 2026Review
- A deep-learning system for the assessment of coronary heart disease risk via scleral photographs.Scientific reports · 2025Article
- Feature fusion context attention gate UNet for detection of polycystic ovary syndrome.Scientific reports · 2025Article
- A Deep Learning-Based Automatic Recognition Model for Polycystic Ovary Ultrasound ImagesBalkan medical journal · 2025Article
- Harnessing Microbiome, Bacterial Extracellular Vesicle, and Artificial Intelligence for Polycystic Ovary Syndrome Diagnosis and Management.Biomolecules · 2025Review
- Intelligent detection for Polycystic Ovary Syndrome (PCOS): Taxonomy, datasets and detection tools.Computational and structural biotechnology journal · 2025Review
- Blockchain and explainable-AI integrated system for Polycystic Ovary Syndrome (PCOS) detection.PeerJ. Computer science · 2025Article
- F-Net: Follicles Net an efficient tool for the diagnosis of polycystic ovarian syndrome using deep learning techniques.PloS one · 2024Article
- Diagnosing Systemic Disorders with AI Algorithms Based on Ocular Images.Healthcare (Basel, Switzerland) · 2023Review
- Deep Learning Algorithms for Screening and Diagnosis of Systemic Diseases Based on Ophthalmic Manifestations: A Systematic Review.Diagnostics (Basel, Switzerland) · 2023Review
- Multi-omics and machine learning for the prevention and management of female reproductive health.Frontiers in endocrinology · 2023Review
Corrections and comments
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Authors and funding
15 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The high prevalence of polycystic ovary syndrome (PCOS) among reproductive-aged women has attracted more and more attention. As a common disorder that is likely to threaten women's health physically and mentally, the detection of PCOS is a growing public health concern worldwide. In this paper, we proposed an automated deep learning algorithm for the auxiliary detection of PCOS, which explores the potential of scleral changes in PCOS detection. The algorithm was applied to the dataset that contains the full-eye images of 721 Chinese women, among which 388 are PCOS patients. Inputs of the proposed algorithm are scleral images segmented from full-eye images using an improved U-Net, and then a Resnet model was applied to extract deep features from scleral images. Finally, a multi-instance model was developed to achieve classification. Various performance indices such as AUC, classification accuracy, precision, recall, precision, and F1-score were adopted to assess the performance of our algorithm. Results show that our method achieves an average AUC of 0.979 and a classification accuracy of 0.929, which indicates the great potential of deep learning in the detection of PCOS.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.