K-numberK242647
Device nameMySegmenter (v2.0.0)
ApplicantMysegmenter Technologies, Inc.
Product codeLLZ
Device classClass II
Decision dateApr 16, 2025
DecisionSubstantially Equivalent
Regulation892.2050
AI Summary extracted from FDA summary PDF · never regenerated
Intended use

MySegmenter (v2.0.0) is a software tool that converts CT and MRI scans into 3D anatomical models for surgical planning and educational purposes. It is designed for use by surgeons and medical professionals under supervision, specifically for pre-operative planning in orthopedic and craniomaxillofacial cases, and must not be used in the operating room.

Technological characteristics

Both MySegmenter and the predicate device (Materialise Mimics Medical 21.0) are standalone software platforms that import DICOM images, perform manual and semi-automatic segmentation, provide 3D visualization and measurement tools, support anonymization and video recording, and export models in STL format. MySegmenter supports DICOM images only, while the predicate also supports BMP and JPEG formats. Both run on 64-bit Windows 10 or superior and require network connectivity with license-based activation.

Test standards cited

Not stated in this summary.

Substantial equivalence argument

MySegmenter demonstrates substantial equivalence through comprehensive accuracy studies comparing its outputs directly to the predicate device. CT and MRI accuracy testing across 30 datasets showed DICE and Jaccard similarity coefficients of 90–98% for volume and Hausdorff distance deviations below 0.6 mm (within the 1 mm acceptance criterion). 3D printing accuracy validation on 20 datasets confirmed models were consistently within 1 mm of predicate-generated models across critical clinical scenarios including fracture management and craniomaxillofacial applications. Both devices share identical regulatory classification, product code (LLZ), intended clinical uses, and core functionality, with testing confirming MySegmenter is as safe and effective as the predicate despite minor algorithmic differences.

Extracted by AI from the official FDA summary PDF →
Source

View the full FDA submission: accessdata.fda.gov

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