Automated AI fracture detection in initial presentation pediatric wrist X-rays: effects and benefits of adding follow-up examinations

Abstract

BACKGROUND: Artificial Intelligence (AI) in radiology has shown promise in detecting fractures on initial X-rays. However, the role of follow-up examinations in enhancing AI performance remains unexplored. This study evaluates the impact of including follow-up X-rays on the performance of neural networks in detecting pediatric wrist fractures. METHODS: Using the publicly available GRAZPEDWRI-DX dataset of 20,327 pediatric wrist X-rays, we created four training datasets: initial X-rays alone and combinations with follow-up X-rays (with and without casts). Two neural networks, EfficientNet (image classification) and YOLOv8 (object detection), were trained and evaluated using precision, recall, F1 score, and AP metrics. The dataset was divided into training, validation, and test sets, with 500 initial X-rays separated and reserved for testing. RESULTS: EfficientNet models showed no statistically significant improvements in classification performance with the inclusion of follow-up X-rays. In contrast, YOLOv8 demonstrated improved object detection metrics, particularly AP50 (p = 0.003) and F1 score (p = 0.009), when follow-up X-rays were included. The improvement was most evident when both cast and non-cast follow-ups were incorporated. CONCLUSION: Adding follow-up X-rays did not enhance classification performance but improved fracture localization in object detection tasks. These findings suggest that including follow-up data shows no relevant improvement in the detection rate of fractures but can enhance AI applications for pediatric wrist fracture detection, particularly for object detection models.

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