(210 days)
SurgiTwin is a web-based platform designed to help healthcare professionals carry out pre-operative planning for knee reconstruction procedures, based on their patients' imported imaging studies. Experience in usage and a clinical assessment is necessary for the proper use of the system in the revision and approval of the output of the planning.
The system works with a database of digital representations related to surgical materials supplied by their manufacturers. SurgiTwin generates a PDF report as an output. End users of the generated SurgiTwin reports are trained healthcare professionals. SurgiTwin does not provide a diagnosis or surgical recommendation.
SurgiTwin is a semi-automated Software as a Medical Device (SaMD) that assists health care professionals in the pre-operative planning of total knee replacement surgery. Using a series of algorithms, the software creates 2D segmented images, a 3D model, and relevant measurements derived from the patient's pre-dimensioned medical images. The software interface allows the user to adjust the plan manually to verify the accuracy of the model and achieve the desired clinical targets. SurgiTwin generates a PDF report as an output. SurgiTwin does not provide a diagnosis or surgical recommendation.
The intended patient population is patients over 22 undergoing total knee replacement surgery without any existing material in the operated lower limb.
Here's a breakdown of the acceptance criteria and the study that proves the device meets them, based on the provided FDA 510(k) clearance letter for SurgiTwin:
1. Acceptance Criteria and Reported Device Performance
The provided document specifically details acceptance criteria for the segmentation ML model. Other functions (automatic landmark function, metric generation, implant placement, osteophyte removal) are mentioned as having "predefined clinical acceptance criteria" and "all acceptance criteria were met," but the specific numeric criteria are not listed.
Table of Acceptance Criteria (for the Segmentation ML Model) and Reported Device Performance:
| Metric | Acceptance Criteria | Reported Device Performance |
|---|---|---|
| Mean DSC (Dice Similarity Coefficient) | > 0.95 | Met (> 0.95, implied by "met the acceptance criteria") |
| Mean voxel based AHD (Average Hausdorff Distance) | 0.9 | Met (> 0.9, implied by "met the acceptance criteria") |
| 95th percentile of the boundary based HD 95 (Hausdorff Distance 95th percentile) |
§ 892.2050 Medical image management and processing system.
(a)
Identification. A medical image management and processing system is a device that provides one or more capabilities relating to the review and digital processing of medical images for the purposes of interpretation by a trained practitioner of disease detection, diagnosis, or patient management. The software components may provide advanced or complex image processing functions for image manipulation, enhancement, or quantification that are intended for use in the interpretation and analysis of medical images. Advanced image manipulation functions may include image segmentation, multimodality image registration, or 3D visualization. Complex quantitative functions may include semi-automated measurements or time-series measurements.(b)
Classification. Class II (special controls; voluntary standards—Digital Imaging and Communications in Medicine (DICOM) Std., Joint Photographic Experts Group (JPEG) Std., Society of Motion Picture and Television Engineers (SMPTE) Test Pattern).