Detecting Fifth Metatarsal Fractures on Radiographs through the Lens of Smartphones: A FIXUS AI Algorithm

Abstract

Abstract Background Fifth metatarsal (5MT) fractures are common but challenging to diagnose, particularly with limited expertise or subtle fractures. Deep learning shows promise but faces limitations due to image quality requirements. This study develops a deep learning model to detect 5MT fractures from smartphone-captured radiograph images, enhancing accessibility of diagnostic tools. Methods A retrospective study included patients aged >18 with 5MT fractures (n=1240) and controls (n=1224). Radiographs (AP, oblique, lateral) from Electronic Health Records (EHR) were obtained and photographed using a smartphone, creating a new dataset (SP). Models using ResNet 152V2 were trained on EHR, SP, and combined datasets, then evaluated on a separate smartphone test dataset (SP-test). Results On validation, the SP model achieved optimal performance (AUROC: 0.99). On the SP-test dataset, the EHR model’s performance decreased (AUROC: 0.83), whereas SP and combined models maintained high performance (AUROC: 0.99). Conclusions Smartphone-specific deep learning models effectively detect 5MT fractures, suggesting their practical utility in resource-limited settings.

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