Evidence map›Paper›PMID 42038983›Full record

ArticleInternational journal of women's health2026

Age-Stratified Modeling of Clinical Heterogeneity in Polycystic Ovary Morphology Using Ultrasound-Based Machine Learning.

Chenke Kuang, Qiao Wei, Zichao Liu, Yanfen Zhang, Zhuomin Zhong, Nian Hu, Chun Xiao, Liang Bao, Yaocheng Xiao, Meng Du and 1 more

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Article in International journal of women's health, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

What it found

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2 · The registry

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3 · Its place in the literature

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4 · The record

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5 · Who and what money

Authors and funding

11 authors.

Chenke Kuang *Department of Ultrasound, Department of Medical Imaging, The Affiliated Changsha Central Hospital, Hengyang Medical School, University of South China, Changsha, Hunan, People's Republic of China.
Qiao Wei *The Key Laboratory of Medical Imaging Precision Theranostics and Radiation Protection, College of Hunan Province, The Affiliated Changsha Central Hospital, Hengyang Medical School, University of South China, Changsha, Hunan, People's Republic of China.
Zichao Liu *The Key Laboratory of Medical Imaging Precision Theranostics and Radiation Protection, College of Hunan Province, The Affiliated Changsha Central Hospital, Hengyang Medical School, University of South China, Changsha, Hunan, People's Republic of China.
Yanfen ZhangDepartment of Ultrasound, Department of Medical Imaging, The Affiliated Changsha Central Hospital, Hengyang Medical School, University of South China, Changsha, Hunan, People's Republic of China.
Zhuomin ZhongDepartment of Ultrasound, Department of Medical Imaging, The Affiliated Changsha Central Hospital, Hengyang Medical School, University of South China, Changsha, Hunan, People's Republic of China.
Nian HuDepartment of Ultrasound, Department of Medical Imaging, The Affiliated Changsha Central Hospital, Hengyang Medical School, University of South China, Changsha, Hunan, People's Republic of China.
Chun XiaoDepartment of Ultrasound, Department of Medical Imaging, The Affiliated Changsha Central Hospital, Hengyang Medical School, University of South China, Changsha, Hunan, People's Republic of China.
Liang BaoDepartment of Ultrasound, Department of Medical Imaging, The Affiliated Changsha Central Hospital, Hengyang Medical School, University of South China, Changsha, Hunan, People's Republic of China.
Yaocheng XiaoDepartment of Ultrasound, Department of Medical Imaging, The Affiliated Changsha Central Hospital, Hengyang Medical School, University of South China, Changsha, Hunan, People's Republic of China.
Meng DuDepartment of Ultrasound, Department of Medical Imaging, The Affiliated Changsha Central Hospital, Hengyang Medical School, University of South China, Changsha, Hunan, People's Republic of China.
Zhiyi ChenDepartment of Ultrasound, Department of Medical Imaging, The Affiliated Changsha Central Hospital, Hengyang Medical School, University of South China, Changsha, Hunan, People's Republic of China.ORCID 0000-0002-5973-4522

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Purpose: This study aims to employ machine learning for automated age stratification in patients with polycystic ovary morphology (PCOM) and to clarify the age-specific contributions of key clinical characteristics, thereby improving diagnostic accuracy. Patients and Methods: A total of 192 ovaries with corresponding clinical and ultrasound data were analyzed. Automated age stratification was performed using the K-means unsupervised clustering model along with the elbow method. XGBoost, Random Forest (RF), Support Vector Machine (SVM), and Artificial Neural Network (ANN) algorithms were applied to compare full and selected feature sets. Model performance was evaluated using five-fold cross-validation with metrics including accuracy, sensitivity, F1-score, and area under the curve (AUC), validating the stratification robustness and feature selection strategy. Results: Automated age stratification categorized PCOM patients into three distinct age groups. A stable positive correlation was observed between ovarian volume and follicle count across all strata; however, key diagnostic drivers varied significantly by age. Beyond ovarian volume, follicle count, and BMI, the 18-22 years group was primarily influenced by menstrual cycle phase. The 23-29 years group was characterized by the number of pregnancies and the ovarian stromal artery blood flow RI. In the 30-40 years group, the ovarian stromal artery blood flow S/D ratio and the number of live births showed increasing importance. Conclusion: This study highlights the differential role of age-sensitive indicators in ultrasound-based diagnosis of PCOM and provides a framework for more personalized diagnostic assessment.

Indexed as

age stratificationclinical heterogeneitymachine learningpolycystic ovary morphologyultrasound

Identifiers

PMID42038983
PMCPMC13110032

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