In musculoskeletal medicine, AI promises to play a key role in the coming years and decades. Not only will AI assist in interpreting radiological studies such as X-rays and MRIs, it will also assist in remotely monitoring and assisting patients with their therapies. There are already highly-valued companies that use AI to monitor and provide feedback to patients with musculoskeletal issues doing home physical therapy (e.g., Sword, Hinge, etc)
Due to the large number of X-rays that have to be read by radiologists, patients often have to wait hours in the ER before they can be seen, evaluated, and receive treatment. Fracture interpretation errors represent 24% of harmful diagnostic errors in the ER. Inconsistencies in diagnosis of fractures are most common between 5pm and 3am due in part to fatigue.
One of the first musculoskeletal AI systems to be approved by the FDA is Imagen’s Osteodetect. Osteodetect is computer-aided detection and diagnosis software designed to detect wrist fractures in adult patients. The OsteoDetect software is a computer-aided detection and diagnostic software that uses an artificial intelligence algorithm to analyze two-dimensional X-ray images for signs of distal radius fracture, a common type of wrist fracture. The software marks the location of the fracture on the image to aid the provider in detection and diagnosis. It is an adjunct tool and is not intended to replace a clinician’s review of the radiograph or his or her clinical judgment.
Studies demonstrated that the readers’ performance in detecting wrist fractures was improved using the software, including increased sensitivity, specificity, positive and negative predictive values, when aided by OsteoDetect, as compared with their unaided performance according to standard clinical practice.
Gleamer AI demonstrated that their algorithm can quickly detect and flag X-rays with positive fractures helping hospitals reduce missed fractures by 29%. In this retrospective diagnostic study, a dataset of 480 X-rays was read by 24 readers who assessed the X-rays with and without AI. To simulate real-life scenario, the study included readers from many disciplines including radiologists, orthopedic surgeons, emergency physicians, physician assistants, rheumatologists, and family physicians, all of whom read X-rays real clinical practice to diagnose fractures in their patients.
Each reader’s diagnostic accuracy of fractures, with and without AI assistance, were compared against the gold standard. They also assessed the diagnostic performance of AI alone against the gold standard. The AI model was trained on a large x-ray data set from multiple hospitals to identify limb, pelvis, torso, lumbar spine, and rib cage fractures. The AI increased sensitivity by 16% for exams with 1 fracture and by 30% for exams with more than 1 fracture. The AI algorithm can quickly detect and flag X-rays with positive fractures so that radiologists can prioritize reading them.