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Added Value of an Artificial Intelligence Solution for Fracture Detection in the Radiologist's Daily Trauma Emergencies Workflow by Lisa Canoni-Meynet; Pierre Verdot; Alexis Danner; Paul Calame; Sébastien Aubry is a Medicine article available to read on EtoBox.

What is Added Value of an Artificial Intelligence Solution for Fracture Detection in the Radiologist's Daily Trauma Emergencies Workflow about?

## Purpose: The main objective of this study was to compare radiologists' performance without and with artificial intelligence (ai) assistance for the detection of bone fractures from trauma emergencies. ## Materials and methods: Five hundred consecutive patients (232 women, 268 men) with a mean age of 37 ± 28 (sd) years (age range: 0.25-99 years) were retrospectively included. three radiologists independently interpreted radiographs without then with ai assistance after a 1-month minimum washout period. the ground truth was determined by consensus reading between musculoskeletal radiologists and ai results. patient-wise sensitivity, specificity, positive predictive value (ppv) and negative predictive value (npv) for fracture detection and reading time were compared between unassisted and ai-assisted readings of radiologists. their performances were also assessed by receiver operating characteristic (roc) curves. ## Results: Ai improved the patient-wise sensitivity of radiologists for fracture detection by 20% (95% confidence interval [ci]: 14-26), p< 0.001) and their specificity by 0.6% (95% ci: -0.9-1.5; p = 0.47). it increased the ppv by 2.9% (95% ci: 0.4-5.4; p = 0.08) and the

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Author

Lisa Canoni-Meynet; Pierre Verdot; Alexis Danner; Paul Calame; Sébastien Aubry

Publisher

Elsevier BV

Published

2022

Language

EN

Field

Medicine (Health Sciences)