syngo.CT CaScoring
Siemens Medical Solutions USA, Inc. • Radiology • 12/17/2019
Device Purpose
syngo.CT CaScoring is an image analysis software package designed to assist physicians by quantifying coronary artery calcification from CT datasets through interactive marking and automated algorithms calculating Agatston, mass, and volume scores. The system aims to enhance the evaluation and documentation of calcified coronary lesions, supporting clinical assessment with functionalities like precomputed scores and Rapid Results Technology.
Summary
The syngo.CT CaScoring device is a post-processing image analysis software package intended to support physicians in evaluating and documenting calcified coronary lesions from non-contrasted cardiac CT images. Users can load standard or low-dose spiral or sequential CT datasets, interactively mark calcified lesions, and assign them to specific coronary arteries including the right coronary artery (RCA), left main (LM), left anterior descending (LAD), and left circumflex (CX). The software calculates the Agatston equivalent score, mass score, and volume score for each marked lesion and provides total scores for each artery and across all coronary arteries. Additionally, it allows for the creation of paper reports that consolidate calcium scoring data, user-documented images, and cited literature. The updated software version, SOMARIS/8 VB40, introduces an automated calcium scoring algorithm that precomputes the calcium score and supports Rapid Results Technology. This automated algorithm functions by assigning a probability to a potential calcification based on its location within the heart, density, shape, and similar properties; if this probability exceeds a predefined threshold, the candidate is identified as a calcification. The system can send DICOM data to any generic DICOM viewer. Performance testing, including software verification and validation, confirmed that the device functions as intended, with bench tests for the automated calcium scoring demonstrating adequate overall accuracy in predicting calcium risk categories. The device adheres to relevant safety and performance standards, including DICOM and ISO guidelines for medical device software and risk management.
AI Regulatory Concerns
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Keywords
Device Metadata
Relevant Clinical Questions
- Q1. What is the quantitative performance (e.g., sensitivity, specificity, inter-observer variability) of the automated calcium scoring algorithm compared to manual expert annotation, particularly across diverse patient populations or varying CT image qualities?; How does the 'Rapid Results Technology' specifically integrate into existing clinical workflows, and what is its measured impact on the time from image acquisition to report generation?; What are the specific image acquisition parameters or patient characteristics (e.g., motion artifacts, obesity, low-dose protocols) that might affect the accuracy of the syngo.CT CaScoring calculations, and how does the software address these potential limitations?; Can the syngo.CT CaScoring software differentiate between calcifications in coronary arteries versus adjacent structures (e.g., valvular or aortic calcifications), and what mechanisms are in place to minimize misclassification?; Beyond the Agatston, mass, and volume scores, does the software provide any additional quantitative or qualitative data on lesion morphology or distribution that could be clinically relevant?
Risk Classification
ESL AnalysisFDA Class II via 510(k) pathway; IEC 62304 Class B (software safety classification); Authorized before §524B enforcement
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Powered by SBOMator™Based on AI-generated device summaries, not the full FDA submission. Items not detected here may still be present in the complete submission package.
Supply Chain BOM Evidence
Based on the CycloneDX / OWASP ECMA-424 xBOM ecosystem. Detection is from the AI-generated summary.
Analysis:
• Analysis is based on the AI-generated device summary, not the full FDA submission package. Click "Analyze Full FDA PDF" below to scan the actual submission document.
• Evidence of SBOM, ML-BOM, SaaSBOM detected in AI-generated device summary.
• This device was authorized before §524B took effect. Cybersecurity evidence requirements were less prescriptive at the time.
• Postmarket cybersecurity monitoring plan not clearly documented in AI-generated device summary.
• Not detected in AI-generated device summary: HBOM, CBOM, VDR/VEX, OBOM. These may be present in the full FDA submission.
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