Case Study | Best Practice AI

Imagen Tech''s Osteodetect platform which uses deep learning to detect wrist fractures has acquired FDA approval

OsteoDetect has earned FDA approval to be classified as computer-aided detection and diagnostic software. It uses deep learning to analyse 2-dimensional X-ray images to detect distal radius fracture (a common wrist fracture) in adult patients. The software marks the fracture location on the X-ray to aid radiologists in diagnosis.

Context

"Currently, injuries to the hand and wrist account for approximately 20% of visits to the emergency department. By detecting a potentially occult distal radial fracture earlier, pain, post traumatic arthritis, and possible disability are alleviated"

The Project

"OsteoDetect analyzes wrist radiographs using machine learning techniques to identify and highlight regions of distal radius fracture during the review of posterior-anterior (front and back) and medial-lateral (sides) X-ray images of adult wrists. OsteoDetect is intended to be used by clinicians in various settings, including primary care, emergency medicine, urgent care and specialty care, such as orthopedics. It is an adjunct tool and is not intended to replace a clinician’s review of the radiograph or his or her clinical judgment."

Data

X-Rays

Results

Imagen''s study demonstrated a device-aided sensitivity of 80.3% and specificity of 91.4% compared to sensitivity and specificity of 74.7% and 88.9% when done manually without software aid

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