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Updated: May 10, 2026
A Method to Estimate Cadaveric Femur Cortical Strains During Fracture Testing Using Digital Image Correlation
Published on: September 14, 2017
Cross-validation of an artificial intelligence tool for fracture classification and localization on conventional
Huibert C Ruitenbeek1, Sahil Sahil2, Aradhana Kumar2
1Department of Radiology and Nuclear Medicine, Erasmus MC, University Medical Center Rotterdam, Rotterdam, The Netherlands.
Objectives:
The aim of this study is to validate the effectiveness of an AI tool trained on Indian data in a Dutch medical center and to assess its ability to classify and localize fractures.
Methods:
Conventional radiographs acquired between January 2019 and November 2022 were analyzed using a multitask deep neural network. The tool, trained on Indian data, identified and localized fractures in 17 body parts. The reference standard was based on radiology reports resulting from routine clinical workflow and confirmed by an experienced musculoskeletal radiologist. The analysis included both patient-wise and fracture-wise evaluations, employing binary and Intersection over Union (IoU) metrics to assess fracture detection and localization accuracy.
Results:
In total, 14,311 radiographs (median age, 48 years (range 18-98), 7265 male) were analyzed and categorized by body parts; clavicle, shoulder, humerus, elbow, forearm, wrist, hand and finger, pelvis, hip, femur, knee, lower leg, ankle, foot and toe. 4156/14,311 (29%) had fractures. The AI tool demonstrated overall patient-wise sensitivity, specificity, and AUC of 87.1% (95% CI: 86.1-88.1%), 87.1% (95% CI: 86.4-87.7%), and 0.92 (95% CI: 0.91-0.93), respectively. Fracture detection rate was 60% overall, ranging from 7% for rib fractures to 90% for clavicle fractures.
Conclusions:
This study validates a fracture detection AI tool on a Western-European dataset, originally trained on Indian data. While classification performance is robust on real clinical data, fracture-wise analysis reveals variability in localization accuracy, underscoring the need for refinement in fracture localization.
Critical Relevance Statement:
AI may provide help by enabling optimal use of limited resources or personnel. This study evaluates an AI tool designed to aid in detecting fractures, possibly reducing reading time or optimization of radiology workflow by prioritizing fracture-positive cases.
Key Points:
Cross-validation on a consecutive Dutch cohort confirms this AI tool's clinical robustness. The tool detected fractures with 87% sensitivity, 87% specificity, and 0.92 AUC. AI localizes 60% of fractures, the highest for clavicle (90%) and lowest for ribs (7%).
