Assessment of a combined musculoskeletal and chest deep learning-based detection solution in an emergency setting.

Triage and diagnostic deep learning-based support solutions have started to take hold in everyday emergency radiology practice with the hope of alleviating workflows. Although previous works had proven that artificial intelligence (AI) may increase radiologist and/or emergency physician reading performances, they were restricted to finding, bodypart and/or age subgroups, without evaluating a routine emergency workflow composed of chest and musculoskeletal adult and pediatric cases. We aimed at evaluating a multiple musculoskeletal and chest radiographic findings deep learning-based commercial solution on an adult and pediatric emergency workflow, focusing on discrepancies between emergency and radiology physicians. This retrospective, monocentric and observational study included 1772 patients who underwent an emergency radiograph between July and October 2020, excluding spine, skull and plain abdomen procedures. Emergency and radiology reports, obtained without AI as part of the clinical workflow, were collected and discordant cases were reviewed to obtain the radiology reference standard. Case-level AI outputs and emergency reports were compared to the reference standard. DeLong and Wald tests were used to compare ROC-AUC and Sensitivity/Specificity, respectively. Results showed an overall AI ROC-AUC of 0.954 with no difference across age or body part subgroups. Real-life emergency physicians' sensitivity was 93.7 %, not significantly different to the AI model ( This study highlighted that multiple findings AI solution for emergency radiographs is efficient and complementary to emergency physicians, and could help reduce misdiagnosis in the absence of immediate radiological expertize.

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© 2023 The Authors.

Déclaration de conflit d'intérêts

This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors. A.P. is the cofounder, and chairman of Milvue. M.C. is employee at Arterys Inc. C.P. and M.M. has no conflict of interest to declare. Valenciennes Hospital radiology department received nonfinancial support by Milvue and Arterys Inc. which provided the AI model and the cloud-based infrastructure to run it free of charge, respectively.The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: Conflicts of interest are as follows: A.P. is the cofounder, and chairman of Milvue. M.C. is employee at Arterys Inc. C.P. and M.M. has no conflict of interest to declare.