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Viz CTP

Viz.ai, Inc. • Radiology • 04/20/2018

510(k)IEC 62304 Class B
FDA Summary PDF

Device Purpose

Viz CTP is a specialized software designed to analyze CT perfusion scans of the brain by automatically processing DICOM data and calculating critical hemodynamic parameters like CBF, CBV, MTT, and TMax. Its methodology relies on sophisticated image analysis algorithms to generate quantitative parametric maps, which have demonstrated accuracy comparable to established methods using simulated datasets, facilitating informed clinical decisions by medical professionals.

Summary

Viz CTP is an image processing software package designed for trained medical professionals, including physicians and technicians, to analyze computed tomography (CT) perfusion scans of the brain. The software operates on standard computers or virtual platforms, acquiring data and images from DICOM-compliant devices. Its core function involves processing dynamic imaging datasets obtained via CT Perfusion protocols, automatically receiving, identifying, extracting, and analyzing these studies. The system includes modules for DICOM communication, study processing, image analysis algorithms, a DICOM storage database, and system health monitoring, enabling bi-directional data communication and continuous processing. The scientific basis of Viz CTP lies in its image analysis capabilities, which calculate key perfusion-related parameters such as Cerebral Blood Flow (CBF), Cerebral Blood Volume (CBV), Mean Transit Time (MTT), residue function time-to-peak (TMax), and Arterial Input Function (AIF). The results of this processing, including CT perfusion parameter maps, are exported in the standard DICOM format for viewing on existing radiological imaging systems, aiding in clinical decision-making. Performance testing, conducted using a simulated dataset with known ground truth values, demonstrated that Viz CTP accurately computes these perfusion parameters, achieving pre-established goals and performing comparably to other commercially available and academic CTP post-processing software.

AI Regulatory Concerns

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Keywords

CT PerfusionDICOMImage ProcessingCerebral Blood Flow (CBF)Cerebral Blood Volume (CBV)Mean Transit Time (MTT)TMax (Time to Maximum Residue)Arterial Input Function (AIF)Parametric MapsBrain Imaging

Device Metadata

Submission Number
K180161
Company
Viz.ai, Inc.
Panel
Radiology
Product Code
N/A
Data Type
Images
Clinical Function
Assessment
AI Function
Analysis
AI Subclass
quantification/feature localization

Relevant Clinical Questions

  • Q1. How does the Viz CTP software's algorithm for Arterial Input Function (AIF) detection and soft matter extraction compare in robustness and accuracy to other leading CTP analysis tools across diverse patient populations and scanner types?; What is the typical processing time for a standard CTP study, from data acquisition to the availability of parametric maps, and how does this impact workflow in acute stroke settings?; Are there specific CT scanner vendor protocols or acquisition parameters that optimize the performance and accuracy of Viz CTP's perfusion calculations?; Beyond the listed parameters (CBF, CBV, MTT, TMax), does Viz CTP offer any advanced quantitative metrics or a 'core/penumbra' estimation based on these values?; What were the specific 'ground truth' values and the range of clinically relevant perfusion parameters used in the simulated dataset for performance testing, and how did Viz CTP's correlation compare quantitatively to the other 7 software packages mentioned?

Risk Classification

ESL Analysis
FDA Device ClassII

FDA Class II via 510(k) pathway; IEC 62304 Class B (software safety classification); Authorized before §524B enforcement

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Cybersecurity Evidence

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.

Cyber Score50/100
SBOM Evidence✓ Detected in Summary
Cyber Risk Assessment⚠ Not mentioned in summary
Postmarket Plan✓ Detected in Summary
§524B ApplicableNo

Supply Chain BOM Evidence

Based on the CycloneDX / OWASP ECMA-424 xBOM ecosystem. Detection is from the AI-generated summary.

SBOMSoftware Bill of Materials(software package)
✓ Detected
ML-BOMMachine Learning Bill of Materials(algorithm, dataset, ground truth)
✓ Detected
SaaSBOMSoftware-as-a-Service Bill of Materials(platform)
✓ Detected
HBOMHardware Bill of Materials
- Not detected
CBOMCryptography Bill of Materials
- Not detected
VDR/VEXVulnerability Disclosure Report / Vulnerability Exploitability eXchange
- Not detected
OBOMOperations Bill of Materials
- Not detected

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.

• 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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Viz CTP - Viz.ai, Inc. | ESL FDA AI Device Intelligence