Ortho AI

Ortho AI is a Class II Software as a Medical Device (SaMD) that leverages AI/ML models to provide semi-automated, accurate measurements from preoperative radiographic images, assisting orthopaedic and neurosurgeons in planning total hip, total knee, and lumbar spine fusion surgeries. Its substantial equivalence determination was supported by rigorous software validation and verification, demonstrating its reliability and performance for clinical use.

The Ortho AI device, developed by Ortho AI LLC, is a Class II image-processing software designed to assist orthopaedic and neurosurgeons. Functioning as Software as a Medical Device (SaMD), it utilizes AI/ML models to provide semi-automated measurements from preoperative radiographic images, specifically for total hip, total knee, and lumbar spine fusion surgeries. This system allows for user modification of digital annotations, aiming to enhance the precision of surgical planning. The device has received a determination of substantial equivalence to a legally marketed device, facilitating its market entry under general and additional regulatory controls. This regulatory clearance was underpinned by comprehensive software validation and verification processes, which robustly demonstrated the device's accuracy in critical measurements, such as lengths and angles, and its consistent overall performance across various anatomical models, ensuring reliability for clinical application.

AI Regulatory Concerns

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Jev reads the full FDA letter and returns calibrated concern probabilities per regulatory category. These are AI estimates, not regulatory advice.

• Q1. What specific AI/ML algorithms are employed by Ortho AI, and how do they handle variability in image quality or patient anatomy?; How does the user modification feature impact measurement accuracy and what are the clinical implications of potential user errors or biases?; What comparative studies, if any, have been conducted to demonstrate Ortho AI's performance against manual measurement methods or other existing software solutions in a clinical setting?; Are there any documented limitations of the device regarding specific surgical scenarios, patient populations, or types of radiographic images?; What is the long-term plan for post-market surveillance and continuous improvement of the AI/ML models, especially concerning data drift or new anatomical variations?