An End-to-End Learning-Based Control Signal Prediction for Autonomous Robotic Colonoscopy

We introduce a novel 3 degrees-of-freedom based robotic colonoscopy system that performs the necessary movements for colonoscopy while working within the movement range of a flexible colonoscope (FC). In addition, we have developed deep learning models to generate motor control signals directly from input images without the need for motor control signal labels. The first presented model comprises a deep learning algorithm for predicting steering points and an image-based visual servo control (IBVS) algorithm for generating the motor control signal. The experiments showed that the proposed model's cecal intubation time (CIT) and rate (CIR) are comparable to those of human operators, despite requiring a shorter training time. Furthermore, we propose a model that replaces the IBVS algorithm with a deep learning algorithm that does not rely on rotation angles. The second model showed similar CIT (165s) and CIR (92%) compared to the first model. Finally, the last model, which solely comprises a single deep learning algorithm, demonstrates a reduction in CIT (127s) and an increase in CIR (96%), resulting in reduced physical demand for operators, improved safety, and shorter patient recovery time.