Garuda - Garba Rujukan Digital

Diagnostic Accuracy of a Federated Learning Algorithm for Proliferative Diabetic Retinopathy Detection: A Multicenter Indonesian Study Rachmat Hidayat; Dedi Sucipto; Alexander Mulya; Ifah Shandy
Sriwijaya Journal of Ophthalmology Vol. 8 No. 2 (2025): Sriwijaya Journal of Ophthalmology
Publisher : Department of Opthalmology, Faculty of Medicine, Universitas Sriwijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37275/sjo.v8i2.136

Introduction: Proliferative diabetic retinopathy (PDR) remains a leading cause of preventable blindness in resource-limited settings. Federated learning (FL) enables collaborative artificial intelligence (AI) model training without sharing patient data. This study evaluated the diagnostic accuracy of an FL-based algorithm for PDR detection across three Indonesian ophthalmology centers. Methods: This multicenter, prospective, diagnostic accuracy study enrolled 512 eyes from 289 patients with type 2 diabetes at three private hospital ophthalmology clinics in Palembang, Jakarta, and Surabaya, Indonesia (January 2023–December 2024). All eyes underwent standardized fundus photography, spectral-domain optical coherence tomography, and comprehensive ophthalmic examination. The FL-based deep learning algorithm was evaluated against two independent retinal specialists using the International Clinical Diabetic Retinopathy classification. Primary outcomes were sensitivity, specificity, and area under the receiver operating characteristic curve (AUC). Results: The FL-based AI achieved a sensitivity of 94.6% (95% CI 81.8–99.3), specificity of 91.2% (95% CI 88.2–93.6), and AUC of 0.962 (95% CI 0.943–0.981) for PDR detection. Agreement with retinal specialists was substantial (κ = 0.87). Performance was consistent across centers (AUC 0.955–0.968; p = 0.841). Media opacity was the strongest predictor of misclassification (OR 3.42; 95% CI 1.87–6.25; p < 0.001). Conclusion: The FL-based AI demonstrated high diagnostic accuracy for PDR detection comparable to retinal specialists across multiple Indonesian centers. This privacy-preserving approach may facilitate scalable diabetic retinopathy screening in resource-limited ophthalmology settings.

Empowering Educators, Supporting Students: A Quasi-Experimental Evaluation of a Train-the-Trainer Model for School Mental Health in Indonesia Ahmad Badruddin; Omar Alieva; Ifah Shandy; Henny Kesuma; Benyamin Wongso; Winata Putri; Habiburrahman Said
Indonesian Community Empowerment Journal Vol. 5 No. 2 (2025): Indonesian Community Empowerment Journal
Publisher : HM Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37275/icejournal.v5i2.49

Adolescent mental health is a pressing concern in urban Indonesian schools, where a significant gap exists between student needs and the availability of professional support. This study evaluated the efficacy of a culturally-adapted, school-based "Train-the-Trainer" (TtT) community service model designed to build sustainable mental health support capacity by empowering teachers. A quasi-experimental study with a matched control group was conducted in 20 public high schools in South Sumatera, Indonesia. Ten schools (n=150 teachers, n=1500 students) received the TtT intervention, where core teachers were trained to cascade mental health literacy and foundational support skills to their peers. Ten matched schools (n=145 teachers, n=1450 students) served as a control group. Data on teacher self-efficacy, student-reported support awareness, and school mental health policies were collected at baseline, 6-months, and 12-months. A linear mixed-effects model revealed a significant time-by-group interaction, with teachers in the intervention group reporting substantially higher confidence in supporting students at 12 months (M=4.15, 95% CI [4.01, 4.29]) compared to the control group (M=2.51, 95% CI [2.37, 2.65]), a large effect (d=2.41). Intervention students were significantly more likely to know how to access support (78% vs. 27%; OR=9.82, 95% CI [8.11, 11.89], p < 0.001). Intervention schools demonstrated a massive increase in formalized mental health protocols compared to control schools (IRR=7.94, p < 0.001). In conclusion, the TtT model is a highly effective and scalable strategy for building a foundational mental health support system within existing school structures in resource-constrained settings. By investing in local educators, this model fosters a sustainable, multi-tiered support ecosystem, offering a viable pathway for national policy and practice in Indonesia.

Artificial Intelligence Versus Stepwise Regression for Stature Estimation from Tibial Dimensions: A Forensic Osteometric Study in South Sumatran Malay Adults Sari Sulistyoningsih; Abu Bakar; Eduardo Michael Perez; Ifah Shandy
Sriwijaya Journal of Forensic and Medicolegal Vol. 3 No. 2 (2025): Sriwijaya Journal of Forensic and Medicolegal
Publisher : Phlox Institute: Indonesian Medical Research Organization

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59345/sjfm.v3i2.248

Introduction: Stature estimation from skeletal elements constitutes a foundational component of forensic biological profiling, critically supporting disaster victim identification in disaster-prone nations such as Indonesia. Traditional Stepwise Multiple Linear Regression (SMLR), while widely employed, is constrained by linearity assumptions that inadequately model the complex, multidimensional osteometric biology of population-specific cohorts. Methods: This cross-sectional study enrolled 450 healthy adult South Sumatran Malay participants (225 males, 225 females), aged 20–50 years, from Palembang and surrounding regencies. Five percutaneous tibial measurements were acquired under standardized protocols by a single trained anthropologist. A 70:30 stratified train-test split yielded 315 training and 135 test observations. Predictive performance of SMLR was rigorously compared against an optimized three-hidden-layer Multilayer Perceptron Artificial Neural Network (MLP-ANN). Result: Significant sexual dimorphism was demonstrated across all variables (independent samples t-test, p < 0.001). Percutaneous Tibial Length (PTL) was the strongest individual stature predictor (males: r = 0.812; females: r = 0.795). The best SMLR pooled model (PTL + PDB + DDB) achieved R-squared = 0.742 and RMSE = ±4.82 cm. The MLP-ANN substantially outperformed SMLR across all subgroups, achieving a pooled R-squared of 0.914 and RMSE of ±2.78 cm-representing a 23.2% improvement in R-squared and a 42.3% reduction in prediction error. Conclusion: These population-specific AI-driven standards offer forensic practitioners in the Indonesian medicolegal context a markedly more reliable tool for biological profiling of incomplete human remains.

Diagnostic Accuracy of a Federated Learning Algorithm for Proliferative Diabetic Retinopathy Detection: A Multicenter Indonesian Study Rachmat Hidayat; Dedi Sucipto; Alexander Mulya; Ifah Shandy
Sriwijaya Journal of Ophthalmology Vol. 8 No. 2 (2025): Sriwijaya Journal of Ophthalmology
Publisher : Department of Ophthalmology, Faculty of Medicine, Universitas Sriwijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37275/sjo.v8i2.136

Introduction: Proliferative diabetic retinopathy (PDR) remains a leading cause of preventable blindness in resource-limited settings. Federated learning (FL) enables collaborative artificial intelligence (AI) model training without sharing patient data. This study evaluated the diagnostic accuracy of an FL-based algorithm for PDR detection across three Indonesian ophthalmology centers. Methods: This multicenter, prospective, diagnostic accuracy study enrolled 512 eyes from 289 patients with type 2 diabetes at three private hospital ophthalmology clinics in Palembang, Jakarta, and Surabaya, Indonesia (January 2023–December 2024). All eyes underwent standardized fundus photography, spectral-domain optical coherence tomography, and comprehensive ophthalmic examination. The FL-based deep learning algorithm was evaluated against two independent retinal specialists using the International Clinical Diabetic Retinopathy classification. Primary outcomes were sensitivity, specificity, and area under the receiver operating characteristic curve (AUC). Results: The FL-based AI achieved a sensitivity of 94.6% (95% CI 81.8–99.3), specificity of 91.2% (95% CI 88.2–93.6), and AUC of 0.962 (95% CI 0.943–0.981) for PDR detection. Agreement with retinal specialists was substantial (κ = 0.87). Performance was consistent across centers (AUC 0.955–0.968; p = 0.841). Media opacity was the strongest predictor of misclassification (OR 3.42; 95% CI 1.87–6.25; p < 0.001). Conclusion: The FL-based AI demonstrated high diagnostic accuracy for PDR detection comparable to retinal specialists across multiple Indonesian centers. This privacy-preserving approach may facilitate scalable diabetic retinopathy screening in resource-limited ophthalmology settings.

Title

Found 4 Documents
Search

Abstract

Abstract

Abstract

Abstract

Title Search

Found 4 Documents Search

Abstract

Abstract

Abstract

Abstract

Title

Found 4 Documents
Search