Endoscopic training programs face limited access to affordable, high-fidelity simulators. Existing solutions are often expensive, lack realistic haptic feedback, or require clinical environments, patients, or cameras. Many low-cost models fail to provide performance feedback or diverse case scenarios, restricting scalable, remote, and objective skills training, especially in speech-language pathology and underfunded healthcare education programs.
The ADEPT Simulator addresses these gaps through a low-cost, AI-enhanced, camera-free endoscopic training platform. By combining 3D-printed anatomical models, magnetic tracking, and synchronized real-patient videos, the system delivers realistic, hands-on practice with objective feedback. Its portable, scalable design enables widespread adoption for distance learning, skills assessment, and consistent training outcomes across institutions.
This figure is the Illustration of the ADEPT system showing a 3D-printed head with embedded magnetic tracking sensors, microcontroller interface, and a connected AI simulation platform. The trainer allows realistic, camera-free endoscopic practice using magnetic tracking and real patient video integration.