Theresia Putri Sinaga
Department of Public Health, CMHC Research Center, Palembang, Indonesia

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Development and Validation of an Explainable Machine-Learning Model to Predict Cumulative Live Birth in PCOS-Associated Infertility Anies Fatmawati; Hesti Putri; Theresia Putri Sinaga
Sriwijaya Journal of Obstetrics and Gynecology Vol. 3 No. 2 (2025): Sriwijaya Journal of Obstetrics & Gynecology
Publisher : Phlox Institute: Indonesian Medical Research Organization

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59345/sjog.v3i2.282

Abstract

Introduction: Polycystic ovary syndrome (PCOS) is the leading cause of anovulatory infertility, yet predicting the cumulative live birth rate (CLBR) after in vitro fertilization (IVF) is difficult because of marked clinical heterogeneity. Conventional linear models discriminate modestly and most machine-learning (ML) tools remain uninterpretable. We aimed to develop and internally validate an explainable ML model for CLBR in women with PCOS. Methods: In a multicenter retrospective cohort at two tertiary reproductive-medicine centers in Indonesia (January 2018–December 2023), 1,245 women with PCOS (Rotterdam criteria) undergoing a first IVF/intracytoplasmic sperm injection cycle were analysed. Five algorithms (logistic regression, support vector machine, random forest, gradient boosting and eXtreme Gradient Boosting [XGBoost]) were trained (70%) and internally validated (30%) with 5-fold cross-validation. Discrimination used the area under the ROC curve (AUC) with DeLong confidence intervals (CI); SHapley Additive exPlanations (SHAP) quantified feature importance. Multivariable logistic regression, calibration and number-needed-to-treat (NNT) were also derived. Results: Overall CLBR was 54.6% (95% CI 51.8–57.4%). XGBoost performed best (AUC 0.82, 95% CI 0.79–0.85; accuracy 76.4%; sensitivity 78.2%; specificity 74.1%) and significantly exceeded logistic regression (AUC 0.72; ΔAUC 0.10, p<0.001). SHAP ranked top-quality embryo number, maternal age, anti-Müllerian hormone and oocyte yield as dominant predictors. Each additional top-quality embryo raised CLBR odds (adjusted OR 1.85, 95% CI 1.66–2.06, p<0.001); ≥3 versus <3 embryos yielded an NNT of 3.3. Conclusion: An explainable XGBoost model accurately predicts CLBR in PCOS-associated infertility and can support individualised counselling and cycle-management decisions. Prospective external validation is warranted before clinical deployment.
Deep learning-assisted digital VIA via telemedicine and HPV self-sampling for CIN2+ detection in remote archipelago women: a prospective diagnostic accuracy study Theresia Putri Sinaga; Nur Diana; Firman Hadi; Gayatri Putri
Sriwijaya Journal of Obstetrics and Gynecology Vol. 4 No. 1 (2026): Sriwijaya Journal of Obstetrics & Gynecology
Publisher : Phlox Institute: Indonesian Medical Research Organization

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59345/sjog.v4i1.302

Abstract

Background: Cervical cancer disproportionately burdens women in low- and middle-income countries, where archipelagic geography and colposcopist shortages obstruct screening. Human papillomavirus (HPV) self-sampling is highly sensitive but poorly specific, generating colposcopy referrals that exceed remote-area capacity. Objective: To evaluate whether deep-learning (DL)-assisted digital visual inspection with acetic acid (VIA), delivered by telemedicine, can triage HPV-positive women and detect high-grade cervical intraepithelial neoplasia (CIN2+). Methods: In a prospective, double-blind, STARD-compliant diagnostic accuracy study, 642 women aged 30–50 years at an urban tertiary referral hospital (Center A) and five remote community health centers (Region B) in an Indonesian archipelago province underwent HPV-DNA self-sampling and smartphone-captured digital VIA analyzed by a MobileNetV2 convolutional neural network. All participants received colposcopy-directed biopsy as the reference standard, eliminating verification bias. Metrics used Wilson 95% confidence intervals (CI); multivariable logistic regression and decision-analytic triage metrics were derived. Results: CIN2+ prevalence was 13.1% (95% CI 10.7–15.9). DL-assisted VIA achieved sensitivity 91.7% (95% CI 83.8–95.9), specificity 88.4% (85.4–90.8), and AUC 0.93, exceeding human-read VIA (sensitivity 67.9%). HPV self-sampling was most sensitive (95.2%) but least specific (81.5%). A sequential HPV→DL-VIA pathway raised specificity to 95.7% and positive predictive value to 75.5%, reducing colposcopy referrals by 46.4% and unnecessary referrals by 76.7% (number-needed-to-screen 7.6). HPV positivity dominated the multivariable model (adjusted OR 91.5, 95% CI 32.7–256.2, p<0.001; Nagelkerke R² 0.50). Conclusion: Telemedicine-delivered, DL-assisted VIA is an accurate triage for HPV-positive women that conserves scarce colposcopy capacity while preserving CIN2+ detection. This decentralized two-step pathway is a scalable strategy for advancing cervical-cancer elimination in geographically isolated populations.