Artificial Intelligence for Detecting Acute Fractures in Patients Admitted to an Emergency Department: Real-Life Performance of Three Commercial Algorithms.

RATIONALE AND OBJECTIVES: 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. MATERIALS AND METHODS: 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). RESULTS: 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). CONCLUSION: 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.

Application of artificial intelligence to imaging interpretations in the musculoskeletal area: Where are we? Where are we going?

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.

Diagnostic performance of sacroiliac and spinal MRI for the diagnosis of non-radiographic axial spondyloarthritis in patients with inflammatory back pain.

OBJECTIVE: The lack of specificity of the ASAS MRI criteria for non-radiographic axial spondylarthritis (NR-axSpA) justifies the evaluation of the discriminatory capacity of other MRI abnormalities in the sacroiliac joints and dorsolumbar spine. METHODS: In patients hospitalized for inflammatory lumbar back pain, the diagnostic performance (sensitivity, specificity, positive likelihood ratio (PLR)) of MRI abnormalities was calculated using the rheumatologist expert opinion as a reference: (i) sacroiliac joints: Bone marrow edema (BME) (number and location), extended edema>1cm (deep lesion), fatty metaplasia (number), erosion (number and location), backfill. (ii) Dorsolumbar spine: BME (number and location), fatty metaplasia (number), posterior segment involvement. RESULTS: In this prospective cohort, 40 NR-axSpA cases and 79 other diagnoses were included. The presence of at least 3 inflammatory signals in the sacroiliac joints (PLR: 25.67 [95% CI: 3.48-48.9]), the presence of at least one sacroiliac erosion (PLR: 12.80 [3.04-54]), the combination of an inflammatory signal and sacroiliac erosion (PLR: 11.85 [2.79-50]), the combination of deep lesion and fatty metaplasia (PLR: 15.80 [2.05-121.9]) or erosion (PLR: 11.86 [1.47-95.01]) had the best diagnostic performance. The combination of spinal and sacroiliac MRI criteria significantly increased diagnostic performance for the diagnosis of NR-axSpA. CONCLUSION: When NR-axSpA is suspected, in addition to the presence and number of inflammatory lesions, MRI interpretation should include the location and the extent of the sacroiliac lesions, the presence of erosion or fatty metaplasia, and anterior involvement of the lumbar spine.