United Orthopedic Knee Patient Specific Instrumentation

The United Orthopedic Knee Patient Specific Instrumentation system aims to enhance total knee arthroplasty by providing pre-operative surgical planning and customized surgical guides derived from patient-specific imaging. Its methodology integrates deep learning algorithms for segmenting CT and X-ray images and an optimization algorithm for determining implant size and position, facilitating the creation of precise, single-use instrumentation.

The United Orthopedic Knee Patient Specific Instrumentation is an orthopedic instrument system designed to assist surgeons in the positioning of compatible total knee arthroplasty (TKA) systems. It comprises surgical planning software (Intelligent Surgery Knee CT Segmentation Engine/Knee X-ray Segmentation Engine, and Implant Recognition Engine), patient-specific surgical guides, and anatomical models. The system is intended to preoperatively plan the surgical placement of United Orthopedics Knee implants using patient radiological images (CT or X-ray) and 3D bone reconstructions, and is indicated for patients without severe bone deformities (e.g., HKA greater than 15° or deformities due to prior fracture) for use with the U2 Total Knee System. The instruments, including guides and models, are single-use only. The system's methodology relies on deep learning algorithms within its segmentation engines to process DICOM medical images, extracting femur and tibia data to generate 3D models for treatment planning and surgical guide design. The Implant Recognition Engine uses an optimization algorithm to assist in selecting implant size and position by identifying anatomical landmarks and articular surface topographies, guiding the design of patient-specific surgical instruments. This process is managed by Enhatch technicians who interface with the software to produce outputs for surgeons. Non-clinical performance testing, including segmentation accuracy, model verification, software system testing, guide wear, and full system verification using cadaver specimens, demonstrated satisfactory performance and accuracy in line with its intended use.

AI Regulatory Concerns

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• Q1. How does the accuracy of the deep learning segmentation engine compare between CT and X-ray modalities, particularly in patients with borderline bone deformities close to the exclusion criteria (HKA ~15°)?; What is the typical turnaround time from image acquisition to the availability of the surgical plan and patient-specific guides, and how might this impact urgent or time-sensitive cases?; Given that Enhatch technicians interface with the software, what quality control measures are in place to ensure consistency and accuracy in the preoperative plans provided to surgeons?; How does the optimization algorithm for implant sizing and positioning account for soft tissue considerations or patient-specific ligamentous balancing, if at all?; What were the quantitative accuracy results (e.g., mean error in degrees and millimeters for resection angles and levels) from the cadaveric system verification and validation tests compared to the preoperative plan?