K-numberK250290
Device nameSurgiTwin
ApplicantTwinsight
Product codeLLZ
Device classClass II
Decision dateAug 29, 2025
DecisionSubstantially Equivalent
Regulation892.2050
AI Summary extracted from FDA summary PDF · never regenerated
Intended use

SurgiTwin is a web-based software platform that assists healthcare professionals in pre-operative planning for total knee replacement surgery. It uses algorithms to create 2D segmented images, 3D models, and measurements from patient imaging studies, allowing surgeons to manually adjust plans and verify accuracy. The system generates a PDF report as output but does not provide diagnosis or surgical recommendations.

Technological characteristics

SurgiTwin is cloud-based medical imaging software with automatic bone segmentation using machine learning models, interactive 3D model positioning and dimensioning, digital overlap of prosthetic materials, and automatic anatomical landmark placement (validated by the user). Unlike the predicate PeekMed web, SurgiTwin does not support manual landmark modification and is limited to knee procedures rather than multiple anatomical regions.

Test standards cited

Not stated in this summary. The document references FDA guidance documents on device software, cybersecurity, human factors, quantitative imaging, and off-the-shelf software, but does not cite specific consensus standards such as ISO, IEC, or ASTM.

Substantial equivalence argument

SurgiTwin is substantially equivalent because it shares the same product code (LLZ, QIH), regulatory classification (21 CFR 892.2050), intended use workflow, and technological approach as the predicate PeekMed web. The knee anatomical region and total knee arthroplasty procedure covered by SurgiTwin are a subset of the predicate's broader capabilities. Although SurgiTwin uses automatic rather than manual landmark placement, performance testing confirmed the automatic feature is more accurate, and user approval remains required before proceeding. Nonclinical testing demonstrated acceptable performance against predefined clinical acceptance criteria for all functions including segmentation, measurement accuracy, and implant placement algorithms.

Extracted by AI from the official FDA summary PDF →
Source

View the full FDA submission: accessdata.fda.gov

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