brAIn™ Shoulder Positioning

The Avatar Medical brAIn™ Shoulder Positioning device is a Class II cloud-based medical image management system designed to assist in preoperative surgical planning for primary total shoulder replacement. It employs machine learning for automatic anatomical segmentation from CT-scan DICOM series, providing an interactive 3D viewer and multimodal planning summary, with nonclinical tests affirming its safety and effectiveness compared to a predicate device.

Avatar Medical's brAIn™ Shoulder Positioning device has been reviewed and determined to be substantially equivalent to a legally marketed predicate device. This determination permits the marketing of the device, which is a Class II Medical Image Management and Processing System. The product, dated February 14, 2025, is specifically intended to serve as an information tool for assisting in the preoperative surgical planning and visualization of a primary total shoulder replacement. The brAIn™ Shoulder Positioning software operates as a cloud-based application that processes patient CT-scan DICOM series. It features automatic segmentation of scapula and humerus anatomy using machine learning, an interactive 3D viewer for soft tissue visualization and implant positioning, and generates a multimodal planning summary. The device is subject to various regulatory requirements, including those for annual registration, device listing, good manufacturing practices, labeling, medical device reporting, and the Unique Device Identification System. Nonclinical tests, including software verification and validation, segmentation performance, shoulder side detection, measurement accuracy, and landmark performance, have confirmed that the device meets its specifications and is as safe and effective as its predicate.

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 clinical outcomes or improvements are anticipated in surgical accuracy or patient recovery due to the use of this preoperative planning tool?; How does the machine learning model in brAIn™ handle anatomical variations or pathologies that might deviate significantly from typical shoulder anatomy?; What is the level of user-friendliness and integration capability of this cloud-based system with existing hospital IT infrastructure and PACS systems?; Are there plans for expanding the brAIn™ platform's capabilities beyond primary total shoulder replacement to other orthopedic procedures or anatomical regions?; What are the long-term data security and privacy protocols for patient CT-scan data processed and stored on the cloud-based application?