Design and Validation of a Smart 3D-Printed Cochlear Model with Real-Time Feedback for ENT Electrode Insertion Simulation

Cochlear implantation is a delicate surgical procedure requiring precise electrode insertion to minimize intracochlear trauma and optimize patient outcomes. Traditional training methods often lack objective performance assessments, limiting a trainee's ability to refine their technique effectively. This study presents a real-time feedback system designed to enhance surgical training by providing quantitative performance metrics, including insertion depth, backouts, kinks, and speed. The system enables immediate skill evaluation and adjustment, fostering a data-driven approach to surgical education. Post-training analysis demonstrated significant improvements, with insertion depth increasing by 11.8%, backouts decreasing by 60.3%, and kinks reducing by 54.9%, indicating enhanced accuracy and control. Although speed decreased by 36.4%, this trade-off allowed for more deliberate and precise electrode placement, minimizing errors. While these results validate the system's effectiveness, limitations include the model's current inability to fully replicate patient-specific anatomical variability. Future work will focus on integrating machine learning for adaptive feedback and expanding validation through multi-institutional trials. Beyond technical refinements, we aim to incorporate neurosensory monitoring to optimize cognitive-technical skill integration, ultimately creating a more holistic training platform. By bridging simulation-based training with real-world surgical performance, this system establishes a new standard in cochlear implantation education, with potential applications extending to other precision surgical procedures.