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Interpreting radiographs in emergency settings is stressful and a burden for radiologists. The main objective was to assess the performance of three commercially available artificial intelligence (AI) algorithms for detecting acute peripheral fractures on radiographs in daily emergency practice. Radiographs were collected from consecutive patients admitted for skeletal trauma at our emergency department over a period of 2 months. Three AI algorithms—SmartUrgence, Rayvolve, and BoneView—were used to analyze 13 body regions. Four musculoskeletal radiologists determined the ground truth from radiographs. The diagnostic performance of the three AI algorithms was calculated at the level of the radiography set. Accuracies, sensitivities, and specificities for each algorithm and two-by-two comparisons between algorithms were obtained. Analyses were performed for the whole population and for subgroups of interest (sex, age, body region). A total of 1210 patients were included (mean age 41.3 ± 18.5 years; 742 [61.3%] men), corresponding to 1500 radiography sets. The fracture prevalence among the radiography sets was 23.7% (356/1500). Accuracy was 90.1%, 71.0%, and 88.8% for SmartUrgence, Rayvolve, and BoneView, respectively; sensitivity 90.2%, 92.6%, and 91.3%, with specificity 92.5%, 70.4%, and 90.5%. Accuracy and specificity were significantly higher for SmartUrgence and BoneView than Rayvolve for the whole population (P <.0001) and for subgroups. The three algorithms did not differ in sensitivity (P =.27). For SmartUrgence, subgroups did not significantly differ in accuracy, specificity, or sensitivity. For Rayvolve, accuracy and specificity were significantly higher with age 27-36 than ≥53 years (P =.0029 and P =.0019). Specificity was higher for the subgroup knee than foot (P =.0149). For BoneView, accuracy was significantly higher for the subgroups knee than foot (P =.0006) and knee than wrist/hand (P =.0228). Specificity was significantly higher for the subgroups knee than foot (P =.0003) and ankle than foot (P =.0195). The performance of AI detection of acute peripheral fractures in daily radiological practice in an emergency department was good to high and was related to the AI algorithm, patient age, and body region examined. [ABSTRACT FROM AUTHOR]

L'intelligence artificielle (IA) suscite un intérêt croissant auprès des chercheurs, cliniciens et radiologues. Le deep learning (apprentissage profond) est une forme d'IA dérivée du machine learning (apprentissage automatique) qui permet de générer des algorithmes capables d'identifier de façon autonome les caractéristiques d'imagerie les plus optimales pour résoudre une problématique donnée. Les réseaux de neurones convolutifs (CNN) constituent l'architecture la plus courante pour le traitement d'images médicales par deep learning. Les applications du deep learning en imagerie ostéoarticulaire sont la détection d'anomalies à la radiographie ou à l'imagerie en coupe transversale (scanner, IRM), comme les fractures, les déchirures méniscales, les ruptures du ligament croisé antérieur, les lésions dégénératives du rachis ou les métastases osseuses, la classification des images comme la sténose lombaire, la dégénérescence discale ou l'évaluation de l'âge osseux mais aussi la segmentation comme par exemple celle du cartilage. Le développement de logiciels a déjà modifié la pratique courante de l'imagerie orthopédique grâce à la détection automatique des fractures sur les radiographies. L'amélioration des protocoles d'acquisition et de la qualité des images scanner à faible dose, la réduction du temps d'acquisition en IRM ou l'amélioration de la résolution des images obtenues par résonance magnétique sont désormais possibles par la technique du deep learning. Le deep learning est une approche automatisée qui permet de se décharger des processus manuels fastidieux et d'améliorer les performances des praticiens. Cet article propose un état des lieux de l'IA appliquée à l'imagerie ostéo-articulaire. The interest of researchers, clinicians and radiologists, in artificial intelligence (AI) continues to grow. Deep learning is a subset of machine learning, in which the computer algorithm itself can determine the optimal imaging features to answer a clinical question. Convolutional neural networks are the most common architecture for performing deep learning on medical images. The various musculoskeletal applications of deep learning are the detection of abnormalities on X-rays or cross-sectional images (CT, MRI), for example the detection of fractures, meniscal tears, anterior cruciate ligament tears, degenerative lesions of the spine, bone metastases, classification of e.g. dural sac stenosis, degeneration of intervertebral discs, assessment of skeletal age, and segmentation, for example of cartilage. Software developments are already impacting the daily practice of orthopedic imaging by automatically detecting fractures on radiographs. Improving image acquisition protocols, improving the quality of low-dose CT images, reducing acquisition times in MRI, or improving MR image resolution is possible through deep learning. Deep learning offers an automated way to offload time-consuming manual processes and improve practitioner performance. This article reviews the current state of AI in musculoskeletal imaging. [ABSTRACT FROM AUTHOR]

The interest of researchers, clinicians and radiologists, in artificial intelligence (AI) continues to grow. Deep learning is a subset of machine learning, in which the computer algorithm itself can determine the optimal imaging features to answer a clinical question. Convolutional neural networks are the most common architecture for performing deep learning on medical images. The various musculoskeletal applications of deep learning are the detection of abnormalities on X-rays or cross-sectional images (CT, MRI), for example the detection of fractures, meniscal tears, anterior cruciate ligament tears, degenerative lesions of the spine, bone metastases, classification of e.g., dural sac stenosis, degeneration of intervertebral discs, assessment of skeletal age, and segmentation, for example of cartilage. Software developments are already impacting the daily practice of orthopedic imaging by automatically detecting fractures on radiographs. Improving image acquisition protocols, improving the quality of low-dose CT images, reducing acquisition times in MRI, or improving MR image resolution is possible through deep learning. Deep learning offers an automated way to offload time-consuming manual processes and improve practitioner performance. This article reviews the current state of AI in musculoskeletal imaging. [ABSTRACT FROM AUTHOR]