FDA approves AI algorithm to help detect wrist fractures

Software from Imagen Technologies analyzes X-ray images using machine learning techniques.

The Food and Drug Administration has cleared new computer-aided detection and diagnosis software that uses an artificial intelligence algorithm to analyze X-ray images to detect wrist fractures in adult patients.

The OsteoDetect software from Imagen Technologies, which was reviewed through the De Novo premarket regulatory pathway for low to moderate risk devices, analyzes wrist radiographs using machine learning techniques to identify and highlight regions of distal radius fracture—a common type of wrist fracture—to aid detection and diagnosis.

“Artificial intelligence algorithms have tremendous potential to help healthcare providers diagnose and treat medical conditions,” said Robert Ochs, acting deputy director for radiological health, Office of In Vitro Diagnostics and Radiological Health in the FDA’s Center for Devices and Radiological Health. “This software can help providers detect wrist fractures more quickly and aid in the diagnosis of fractures.”

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According to the FDA, the software is “intended to be used by clinicians in various settings, including primary care, emergency medicine, urgent care and specialty care, such as orthopedics.” However, the regulatory agency also adds that “it is an adjunct tool and is not intended to replace a clinician’s review of the radiograph or his or her clinical judgment.”

Imagen submitted a retrospective study of 1,000 radiograph images that assessed the independent performance of the image analysis algorithm for detecting wrist fractures and the accuracy of the fracture localization of OsteoDetect against the performance of three board-certified orthopedic hand surgeons. In addition, the company submitted a retrospective study of 24 providers who reviewed 200 patient cases.

“Both 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,” stated the FDA announcement.

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