OTS Hip

The OTS Hip system provides a comprehensive solution for orthopedic surgeons, enabling precise preoperative planning and intraoperative navigation for Total Hip Arthroplasty using CT-based 3D anatomical models. It leverages machine learning algorithms for automated image processing and an optical tracking system with passive markers for real-time surgical guidance and accurate prosthesis placement.

The OTS Hip system is designed to support surgeons in preoperative planning and provide intraoperative guidance during orthopedic hip joint replacement surgery. It comprises software and hardware components, including the OTS Hip Plan (OHP) for 3D preoperative planning based on CT medical imaging data, and the OTS Hip Guide (OHG) for real-time intraoperative navigation. The hardware suite consists of a camera, computer stand, medical panel PC, footswitch, passive markers called Tracers, adapters for surgical instruments, and compatible tools. The system operates by linking a freehand probe, tracked by a passive marker sensor system, to a virtual computer image space generated from the patient's preoperative CT data. This enables surgeons to accurately navigate compatible prostheses to the preoperatively planned position, specifically for Total Hip Arthroplasty (THA) performed via a posterior approach. Key technological aspects of the OTS Hip include the use of fixed/static machine learning (ML) algorithms for automated anatomical landmark detection and segmentation, which aids in generating 3D models from CT scans. The system features updated hardware components, such as a new camera and disposable Tracers that replace previous passive marker designs. Performance testing of the OTS Hip system demonstrated acceptable system-level accuracy concerning implant inclination, anteversion, and position error. Leg Length Inequality (LLI) measurements were found to be within clinically acceptable values. The ML algorithms underwent validation using a diverse dataset representing US, Japanese, and European populations, including cases with dysplastic hips, and showed acceptable performance, although two complex segmentation cases did not meet the predefined acceptance criteria. The system also met standards for electrical safety (IEC 60601-1, IEC 60601-1-2) and electromagnetic compatibility (EMC), and its design verification included accuracy testing as per ASTM F2554-18.

AI Regulatory Concerns

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Jev reads the full FDA letter and returns calibrated concern probabilities per regulatory category. These are AI estimates, not regulatory advice.

• Q1. What specific fixed/static machine learning algorithms are employed for segmentation and landmark identification, and what were the characteristics of the two clinically complex cases where segmentation validation did not pass acceptance criteria?; How does the system's accuracy in leg length inequality (LLI) measurement compare to traditional methods, and what is the clinical significance of the achieved LLI values in post-operative patient outcomes?; What is the learning curve for orthopedic surgeons to become proficient with the OTS Hip system, particularly regarding the transition from preoperative planning (OHP) to intraoperative guidance (OHG)?; Could the performance of the machine learning algorithms be affected by CT scanner variations or image quality issues not extensively covered in the test dataset (which included multiple CT manufacturers), and are there ongoing efforts to expand the training data diversity further?; Given the system is indicated for Total Hip Arthroplasty using a posterior approach, what are the specific technical limitations or considerations preventing its application to other surgical approaches for hip arthroplasty, and are there plans for future expansion?