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.

The authors have declared no competing interest.

This study did not receive any funding.

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Declarations of interest The authors declare no conflict of interest.

Declaration of generative AI and AI-assisted technologies in the writing process During the preparation of this work, the authors used ChatGPT in order to improve formatting and grammatical errors. After using this tool, the authors reviewed and edited the content as needed and take full responsibility for the content of the publication.

Funding This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.

Data availability statement De-identified data will be made available upon reasonable request to the editorial office, pending approval from the Institutional Review Board.

All data produced in the present study are available upon reasonable request to the authors and approval from the IRB.