AutoContour (Model RADAC V4)

The AutoContour (Model RADAC V4) is a Class II medical image management and processing system engineered to automate the contouring process for radiation therapy treatment planning. This device leverages machine learning to accurately delineate various organs and volumes of interest on both CT and MR images, with its performance rigorously validated through comprehensive non-clinical testing, including Dice Similarity Coefficient analysis, ensuring clinical utility.

Radformation, Inc.'s device, AutoContour (Model RADAC V4), a medical image management and processing system classified as Class II, has been determined to be substantially equivalent to a legally marketed predicate device. This software is designed to assist radiation treatment planners in automatically contouring and reviewing structures within medical images, specifically for radiation therapy treatment planning.The updated AutoContour (Model RADAC V4) expands upon its predecessor by incorporating additional organs and volumes of interest for machine-learning based contouring on both CT and MR image types. Comprehensive non-clinical verification and validation testing, including Dice Similarity Coefficient (DSC) analysis and qualitative clinical appropriateness grading by experts, confirmed that all new models met predefined performance criteria. The testing demonstrated the device's accuracy and clinical utility, supporting the conclusion that it is as safe and effective as the predicate device.

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 organs and volumes of interest have been added to AutoContour V4, and how does their performance compare across different anatomical sites?; Beyond Dice Similarity Coefficient (DSC) analysis, what other quantitative metrics were used to assess contouring accuracy, particularly for complex or critical structures?; How does the machine learning model adapt to variations in image quality, patient anatomy, or imaging protocols, and what are its limitations in these scenarios?; What is the long-term impact of using AutoContour V4 on clinical workflow efficiency and inter-observer variability in radiation treatment planning?; Are there plans to integrate AutoContour with adaptive radiation therapy workflows or expand its capabilities to other imaging modalities or treatment areas?