K-numberK243672
Device nameCardIQ Suite
ApplicantGE Medical Systems SCS
Product codeJAK
Device classClass II
Decision dateJun 18, 2025
DecisionSubstantially Equivalent
Regulation892.1750
AI Summary extracted from FDA summary PDF · never regenerated
Intended use

CardIQ Suite is a non-invasive software application that analyzes cardiac CT imaging data to evaluate cardiovascular anatomy and pathology. It provides automated and manual tools for calcium scoring, coronary artery assessment, chamber visualization, vessel measurement, and cardiac structure analysis to aid in diagnosis and treatment planning for coronary artery disease and related conditions.

Technological characteristics

CardIQ Suite uses identical image input requirements and fundamental scoring methods (Agatson and Volume scoring) as the predicate device. New features include: a deep learning algorithm for non-contrast heart segmentation, automated heart fat volume estimation using HU thresholds, enhanced coronary centerline tracking with lumen segmentation, improved coronary labeling algorithms, and new lumen diameter quantification with narrowing highlights. Most core algorithms remain unchanged from the predicate.

Test standards cited

The device complies with NEMA PS 3.1-3.20 (2022) Digital Imaging and Communications in Medicine (DICOM) standard. It was designed and manufactured under 21 CFR 820 (Quality System Regulations) and ISO 13485.

Substantial equivalence argument

CardIQ Suite is substantially equivalent because it employs the same fundamental scientific technology and performs the same intended function as the predicate CardIQ Suite (K233731). New features (heart fat estimation, enhanced coronary tracking, lumen quantification) already exist in reference devices CardIQ Xpress 2.0 and SmartScore 4.0, establishing predicate support. Engineering bench testing on 94-111 CT exams demonstrated that new and modified algorithms met acceptance criteria, with enhanced performance compared to predicates. No new hazards or safety/effectiveness questions were identified during design verification and risk management processes.

Extracted by AI from the official FDA summary PDF →
Source

View the full FDA submission: accessdata.fda.gov

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