Deteksi Fraktur pada Citra Rontgen Wrist Pediatrik Berbasis YOLOv8: Evaluasi Performa dan Analisis Keterbatasan Generalisasi Model

Timothy Fajar Sinaga; Hardy Gustino

doi:10.55606/termometer.v4i4.7128

Authors

Timothy Fajar Sinaga hardygustino885@gmail.com
Hardy Gustino Universitas Pembangunan Nasional "Veteran" Jawa Timur

DOI:

https://doi.org/10.55606/termometer.v4i4.7128

Keywords:

External Generalization, Fracture Detection, Pediatric Wrist, Radiograph Analysis, YOLOv8

Abstract

Pediatric wrist fractures are common in emergency care and require specialized expertise for accurate radiographic interpretation. This study developed and evaluated a fracture-only detector for pediatric wrist radiographs using YOLOv8 and assessed its external performance. The public GRAZPEDWRI-DX dataset, comprising 20,327 images from 6,091 patients, was used, with 18,090 fracture bounding boxes retained and non-fracture images included as background. Images were normalized, enhanced using contrast limited adaptive histogram equalization (CLAHE), and resized to 640×640 pixels. Data were split by patient identity into training, validation, and test sets at a 70/20/10 ratio to prevent patient overlap. A pretrained YOLOv8s model was trained with early stopping. On the held-out test set of 1,918 images from 610 patients, the model achieved a precision of 0.9236, recall of 0.8607, mAP50 of 0.9275, and mAP50-95 of 0.5374, with no evidence of validation overfitting. However, zero-shot evaluation on the FracAtlas dataset showed very low performance, including an mAP50 of 0.0343 overall and 0.0336 on the hand/wrist subset. CLAHE matching and lower IoU thresholds produced minimal improvement. These findings indicate strong internal performance but poor external generalization. The study is limited by a single run, single architecture, single-institution dataset, and absence of comparison models or clinical validation.

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