Search Results
Found 1 results
510(k) Data Aggregation
(347 days)
Aorta-CAD
Aorta-CAD is a computer-assisted detection (CADe) software device that analyzes chest radiograph studies for suspicious regions of interest (ROIs). The device uses a deep learning algorithm to identify ROIs and produces boxes around the ROls. The boxes are labeled with one of the following radiographic findings: Aortic calcification or Dilated aorta.
Aorta-CAD is intended for use as a concurrent reading aid for physicians looking for ROIs with radiographic findings suggestive of Aortic Atherosclerosis or Aortic Ectasia. It does not replace the role of the physician or of other diagnosic testing in the standard of care. Aorta-CAD is indicated for adults only.
Aorta-CAD is computer-assisted detection (CADe) software designed for physicians to increase the accurate detection of findings on chest radiographs that are suggestive of chronic conditions in the aorta. The ROIs are labeled with one of the following radiographic findings: Aortic calcification or Dilated aorta. Aorta-CAD is intended for use as a concurrent reading aid for physicians looking for suspicious ROIs with radiographic findings suggestive of Aortic Atherosclerosis or Aortic Ectasia. Aorta-CAD's output is available for physicians as a concurrent reading aid and does not replace the role of the physician or of other diagnostic testing in the standard of care for the distinct conditions. Aorta-CAD uses modern deep learning and computer vision techniques to analyze chest radiographs.
For each image within a study, Aorta-CAD generates a DICOM Presentation State file (output overlay). If any ROI is detected by Aorta-CAD in the study, the output overlay for each image includes which radiographic finding(s) were identified and what chronic condition in the aorta is suggested by these findings, such as "Aortic calcification suggestive of Aortic Atherosclerosis." In addition, if ROI(s) are detected in an image, bounding boxes surrounding each detected ROI are included in the output overlay for that image and are labeled with the radiographic findings, such as "Aortic calcification". If no ROI is detected by Aorta-CAD in the study, the output overlay for each image will include the text "No Aorta-CAD ROI(s)" and no bounding boxes will be included. Regardless of whether an ROI is detected, the overlay includes text identifying the X-ray study as analyzed by Aorta-CAD and a customer configurable message containing a link to our instructions for users to access labeling documents. The Aorta-CAD overlay can be toggled on or off by the physician within their Picture Archiving and Communication System (PACS) viewer, allowing for concurrent review of the X-ray study.
Here's a breakdown of the acceptance criteria and the study proving the device meets them, based on the provided text:
1. Table of Acceptance Criteria and Reported Device Performance
The document doesn't explicitly list "acceptance criteria" in a separate section with pass/fail thresholds. Instead, it describes "performance assessment" and "clinical study" results which implicitly serve as the demonstration that the device performs acceptably and is substantially equivalent. The key performance metrics are presented from the standalone and clinical studies.
Metric (Implicit Acceptance Criteria) | Reported Device Performance (Standalone Study) | Reported Device Performance (Clinical Study - Aided vs. Unaided) |
---|---|---|
Overall Standalone Performance | ||
Sensitivity | 0.910 (95% CI: 0.896, 0.922) | Not directly comparable (MRMC study focuses on reader improvement, 0.910 refers to algorithm only) |
Specificity | 0.896 (95% CI: 0.889, 0.902) | Not directly comparable (MRMC study focuses on reader improvement, 0.896 refers to algorithm only) |
AUC (Overall) | 0.974 (95% Bootstrap CI: 0.971, 0.977) | Not directly comparable (MRMC study focuses on reader improvement, 0.974 refers to algorithm only) |
Category-Specific Standalone Performance | ||
Aortic calcification suggestive of Aortic Atherosclerosis (AUC) | 0.972 (95% Bootstrap CI: 0.967, 0.976) | Reader AUC estimates significantly improved (p |
Ask a specific question about this device
Page 1 of 1