Simulation of manual and automatic navigation of magnetically controlled wireless capsule endoscopy examination of human gastric

The magnetically controlled capsule endoscopy (MCCE) is an emerging modality for assessing gastrointestinal disorders due to its advantages. However, current assignments of MCCE rely on manual controlling and gastric landmarks, which are prone to omissions. We improve the scanning protocol of the MCCE in human gastric using both manual and automatic controlling methods. We design a quantitative scanning coverage ratio to measure the process of MCCE scanning within the human gastric. The proposed scanning coverage ratio is capable of guiding the manual and automatic scanning process of human gastric. Moreover, we design a deep reinforcement learning (DRL) controller for automatically navigating the capsule. Our DRL controller achieves a higher coverage ratio compared to previous research.