BrightHeart View Classifier

The BrightHeart View Classifier is a software-only medical device designed to enhance fetal 2D ultrasound examinations by employing machine learning to automatically detect standard cardiac views. This AI-driven image analysis tool serves as an adjunct for improved efficiency and consistency in fetal heart scanning during the second or third trimesters.

The BrightHeart View Classifier is a software-only device designed to analyze fetal 2D ultrasound images and video clips. It uses machine learning to automatically detect standard views during fetal heart scanning, serving as an adjunct to the acquisition and interpretation of fetal anatomic ultrasound examinations performed with transabdominal probes during the second or third trimesters. This device has been determined to be substantially equivalent to a legally marketed product, permitting its market introduction subject to general regulatory controls, including annual registration, device listing, and good manufacturing practices. The determination of substantial equivalence for this device also encompassed the review and acceptance of its Predetermined Change Control Plan (PCCP). This plan allows for future modifications to the device's underlying neural network without necessitating new premarket submissions, provided these changes align with the established plan and do not significantly affect safety or effectiveness. However, any major changes or deviations from the PCCP would require a new premarket submission. The device is further subject to ongoing requirements such as Quality System regulations, medical device reporting, and compliance with the Unique Device Identification (UDI) system, all aimed at ensuring continued safety and effectiveness.

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 performance metrics (e.g., sensitivity, specificity, accuracy) were used to establish the device's substantial equivalence, and how do they compare to human expert performance?; How does the machine learning algorithm specifically identify and differentiate between various standard fetal heart views, and what are its limitations regarding atypical presentations or complex anomalies?; Beyond standard view detection, does the BrightHeart View Classifier provide any quantitative measurements or qualitative assessments that could aid in the interpretation of fetal cardiac health?; What are the long-term data collection and update mechanisms under the Predetermined Change Control Plan, and how will the device's performance be monitored post-market introduction, especially with neural network modifications?; What training or integration support is required for sonographers and clinicians to effectively incorporate this software-only device into their existing fetal ultrasound workflows?