K-numberK241543
Device nameDrAid™ for Liver Segmentation
ApplicantVinbrain Joint Stock Company
Product codeQIH
Device classClass II
Decision dateDec 6, 2024
DecisionSubstantially Equivalent
Regulation892.2050
AI Summary extracted from FDA summary PDF · never regenerated
Intended use

DrAid™ for Liver Segmentation is a web-based software application for visualization, evaluation, and reporting of liver and physician-identified lesions using multiphase CT images. It is designed for use by trained healthcare professionals (physicians and technicians) in hospital settings to assess liver volume, lesion volume, and maximum lesion diameter for evaluation and treatment planning. The software does not generate diagnoses; clinical interpretation remains the responsibility of qualified healthcare professionals.

Technological characteristics

The subject device provides semi-automated liver segmentation using an AI algorithm with editable tools, whereas the predicate device (IQQA-LIVER) provides manual segmentation. Both are software applications compatible with DICOM image data and designed for multiphase liver image analysis in healthcare environments. The subject device operates as a web-based platform on standard hospital workstations, while differences in deployment platforms, operating systems, and some features exist between the two devices.

Test standards cited

Not stated in this summary.

Substantial equivalence argument

Both devices share the same intended use (liver and lesion segmentation for trained professionals), are compatible with DICOM multiphase imaging, provide essential image viewing and reporting tools, and are intended for hospital healthcare environments. Although the subject device uses AI-assisted segmentation while the predicate uses manual segmentation, the applicant argues these differing techniques do not raise different safety or effectiveness questions because advanced algorithms enhance efficiency and accuracy without compromising clinical performance. The device demonstrated robust performance (Dice score 0.9649, volume error 2.73%) across diverse patient pathologies and multiple CT scanner manufacturers, supporting equivalence in performance outcomes regardless of the segmentation method used.

Extracted by AI from the official FDA summary PDF →
Source

View the full FDA submission: accessdata.fda.gov

Researching this as a predicate?
Want a transparent AI-ranking score, AI-discovered related predicates, ongoing safety and warning-letter monitoring, full predicate chain lineage, and a drafted SE rationale — all saved to your own project? That's what an account adds.
Start free trial →

Everything you need for a 510(k) submission. Nothing you don't.

14-day free trial. No setup. Cancel anytime.

Start free trial →
Building an AI or ML-enabled device? Predicate search, PCCP tracking, and AI-specific FDA intelligence — built exclusively for AI/ML devices. Try AIFDA Intel →