Viz HCM
Viz.ai, Inc. is listed for Viz HCM (DEN230003). The FDA list gives August 3, 2023 as the date of the final decision. The lead panel is Cardiovascular, and the database link on the FDA list is the De Novo pathway. openFDA describes product code QXO as “Cardiovascular Machine Learning-Based Notification Software”: Cardiovascular machine learning-based notification software employs machine learning techniques to suggest the likelihood of a cardiovascular disease or condition for further referral or diagnostic follow-up. The software identifies a single condition based on one or more non-invasive physiological inputs as part of routine medical care. It is intended as the basis for further testing and is not intended to provide diagnostic quality output. It is not intended to identify or detect arrhythmias. openFDA records this submission’s decision description as “Unknown.” This paragraph is assembled only from those public fields. It is not an FDA review, not a determination of safety or effectiveness, and not medical advice. Official record: https://www.accessdata.fda.gov/scripts/cdrh/cfdocs/cfpmn/denovo.cfm?id=DEN230003
- Date of final decision
- August 3, 2023
- Company on the FDA list
- Viz.ai, Inc.
- Lead panel
- Cardiovascular
- Primary product code
- QXO
- openFDA device name for the code
- Cardiovascular Machine Learning-Based Notification Software
- Device class
- 2
- Regulation number
- 870.2380
- openFDA decision description
- Unknown
- openFDA decision code
- DENG
- Statement or summary label
- Not returned
Product-code text from openFDA
Cardiovascular machine learning-based notification software employs machine learning techniques to suggest the likelihood of a cardiovascular disease or condition for further referral or diagnostic follow-up. The software identifies a single condition based on one or more non-invasive physiological inputs as part of routine medical care. It is intended as the basis for further testing and is not intended to provide diagnostic quality output. It is not intended to identify or detect arrhythmias.