syngo Dynamics (Version VA41D)

Siemens syngo Dynamics (Version VA41D) is a cardiology image management and processing system designed to aid disease diagnosis and patient management through advanced image review and quantitative analysis. Its core scientific methodology involves the application of a re-trained AI/ML-enabled algorithm to enhance the precision of left ventricular ejection fraction calculations from ultrasound images.

Siemens Healthcare GmbH's syngo Dynamics (Version VA41D) is a Class II multimodality, vendor-agnostic medical image management and processing system, specifically designed for cardiology. It facilitates the review, digital processing, and post-image manipulation and quantification of medical images, thereby assisting clinicians in disease detection, diagnosis, and patient management within a healthcare network, though it explicitly excludes digital mammography. This updated version maintains substantial equivalence to its predicate device, syngo Dynamics (Version VA40F), sharing identical core technological characteristics. Its primary enhancement lies in a re-trained AI/ML-enabled Auto EF algorithm, which leverages expanded data to improve the calculation of left ventricular ejection fraction from ultrasound images, with validated safety and effectiveness. Future algorithmic improvements are managed under a Predetermined Change Control Plan, streamlining modifications consistent with the plan without new submissions.

AI Regulatory Concerns

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• Q1. How does the re-trained AI/ML-enabled Auto EF algorithm specifically improve accuracy or efficiency compared to the previous version's algorithm in diverse patient populations?; What are the specific data sets used for re-training the Auto EF algorithm, and how do they ensure robust performance across various ultrasound machine types and image qualities?; Beyond LVEF, what other quantitative metrics or post-processing functions does syngo Dynamics (VA41D) offer, and what is the scientific basis for their clinical utility?; What are the limitations or potential biases of the AI/ML algorithm, particularly concerning image artifacts, specific cardiac pathologies, or non-standard views?; How will the Predetermined Change Control Plan ensure continued validation and transparency of future AI/ML algorithm improvements without requiring full re-submission processes?