Orthopedic Prosthesis Recommendation via Machine Learning

The patent replaces deterministic rule-based mechanical decision-making with a machine learning model that processes medical image data. The ML model analyzes patient-specific anatomical features and predicts optimal prosthesis selections, substituting rigid algorithmic rules with adaptive data-driven predictions that improve accuracy while managing complexity through automated processing.

The system transforms the approach from fixed rule parameters to dynamic parameters derived from medical image data. By extracting anatomical measurements and features from patient scans and feeding them into the ML model, the system adapts its recommendations based on actual patient geometry rather than predetermined rules, improving precision without proportionally increasing complexity.

The system performs preliminary processing of medical image data to extract relevant anatomical features and parameters before the actual prosthesis selection. By pre-processing and pre-extracting key measurements from patient scans, the ML model receives refined input that reduces computation time during the critical decision-making phase, maintaining high reliability while minimizing time loss.

The patent extracts only the essential anatomical features and parameters from comprehensive medical image data that are most relevant to prosthesis selection. Rather than analyzing entire datasets, the system identifies and processes only the critical measurements needed for accurate predictions, reducing computational burden and processing time while maintaining high reliability through targeted feature extraction.