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TRV-2026-1008Certified recordPeer-reviewed

Dual-Approach AI for Pediatric Supracondylar Fractures: Multiclass Radiograph Classification with Explainable AI and Diagnostic Meta-analysis of AI-Based Computational Approaches

Rationale and objectives Pediatric supracondylar fractures (SCFs) are the most common elbow injury in children, yet radiographic diagnosis remains challenging due to complex developmental anatomy, with initially missed fracture rates of 17-77%. Prior artificial intelligence (AI) studies have been limited to binary classification frameworks without Gartland subtype differentiation, and no diagnostic test accuracy meta-analysis specific to supracondylar fractures exists. This study aimed to develop the first multi…

Health · The Trace — both readings · certified 2026-09-07 · v1 · article view · machine-readable

Current reading — gain

YOLOv11 Nano achieved multiclass detection and Gartland I-III classification of pediatric supracondylar fractures with ~91-93% accuracy across validation strategies, improving further with bone segmentation.

Current reading — problem

Model generalizability for non-displaced Type I fractures is limited by small sample size, and pooled evidence remains preliminary with substantial heterogeneity across studies.

What this doesn’t fix

Generalizability is constrained by very small Type I sample and limited evidence base with substantial heterogeneity; authors note prospective multicenter validation is required before clinical deployment.

Evidence

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Truvace Impact Record TRV-2026-1008, v1: “Dual-Approach AI for Pediatric Supracondylar Fractures: Multiclass Radiograph Classification with Explainable AI and Diagnostic Meta-analysis of AI-Based Computational Approaches.” Truvace, 2026-09-07. /record/TRV-2026-1008 (accessed at citation time). sha256 ee48b3dc5a8638a9…

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