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• W4381885508 abstract "Purpose This systematic review aimed to (1) determine the model performance of artificial intelligence (AI) in detecting rotator cuff pathology using different imaging modalities and (2) to compare capability with physicians in clinical scenarios. Methods The review followed the PRISMA guidelines and was registered on PROSPERO. The criteria were as follows: (1) studies on the application of AI in detecting rotator cuff pathology using medical images, and (2) studies on smart devices for assisting in diagnosis were excluded. The following data were extracted and recorded: statistical characteristics, input features, AI algorithms used, sample sizes of training and testing sets, and model performance. The data extracted from the included studies were narratively reviewed. Results A total of 14 articles, comprising 23,119 patients, met the inclusion and exclusion criteria. The pooled mean age of the patients was 56.7 years, and the female rate was 56.1%. The area under the curve (AUC) of the algorithmic model to detect rotator cuff pathology from ultrasound images, MRI images, and radiographic series ranged from 0.789 to 0.950, 0.844 to 0.943, and 0.820 to 0.830, respectively. Notably, 1 of the studies reported that AI models based on ultrasound images demonstrated a diagnostic performance similar to that of radiologists. Another comparative study demonstrated that AI models utilizing MRI images exhibited greater accuracy and specificity compared to orthopedic surgeons in the diagnosis of rotator cuff pathology, albeit not in sensitivity. Conclusion The detection of rotator cuff pathology has been significantly aided by the exceptional performance of AI models. In particular, these models are equally adept as musculoskeletal radiologists in utilizing ultrasound to diagnose rotator cuff pathology. Furthermore, AI models exhibit statistically superior levels of accuracy and specificity when utilizing MRI to diagnose rotator cuff pathology, albeit with no marked difference in sensitivity, in comparison to orthopaedic surgeons." @default.
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• W4381885508 date "2023-06-01" @default.
• W4381885508 modified "2023-10-16" @default.
• W4381885508 title "Artificial Intelligence Aids Detection of Rotator Cuff Pathology: A Systematic Review" @default.
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• W4381885508 doi "https://doi.org/10.1016/j.arthro.2023.06.018" @default.
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