Robust Path Planning via Learning from Demonstrations for Robotic Catheters in Deformable Environments.

Objective: Navigation through tortuous and deformable vessels using catheters with limited steering capability underscores the need for reliable path planning. State-of-the-art path planners do not fully account for the deformable nature of the environment.

Methods: This work proposes a robust path planner via a learning from demonstrations method, named Curriculum Generative Adversarial Imitation Learning (C-GAIL).

Autonomous robotic surgery makes light work of anastomosis.

An autonomous robotic laparoscopic surgical technique is capable of tracking tissue motion and offers consistency in suturing for the anastomosis of the small bowel.

Inverse Reinforcement Learning Intra-Operative Path Planning for Steerable Needle.

Objective: This paper presentsa safe and effective keyhole neurosurgery intra-operative planning framework for flexible neurosurgical robots. The framework is intended to support neurosurgeons during the intra-operative procedure to react to a dynamic environment.

Methods: The proposed system integrates inverse reinforcement learning path planning algorithm combined with 1) a pre-operative path planning framework for fast and intuitive user interaction, 2) a realistic, time-bounded simulator based on Position-based Dynamics (PBD) simulation that mocks brain deformations due to catheter insertion and 3) a simulated robotic system.

A laboratory study of the accuracy of three electronic apex locators: influence of embedding media and radiographic assessment of the electronic apical limit.

Aim: To evaluate several factors that might interfere with the use of electronic root canal length measurement devices (ERCLMDs) in a laboratory setting, including two different embedding media (alginate and electroconductive gel), three different types of devices and the radiographic view on the assessment of the electronic readings.

Methodology: Thirty single-rooted extracted human mandibular premolars were selected. After access and canal pre-flaring, a size 10 K-file was inserted in the canal up to the major apical foramen under magnification (×10), and this length was recorded as the actual length (AL) of the canal.

Artificial intelligence for brain diseases: A systematic review.

Artificial intelligence (AI) is a major branch of computer science that is fruitfully used for analyzing complex medical data and extracting meaningful relationships in datasets, for several clinical aims. Specifically, in the brain care domain, several innovative approaches have achieved remarkable results and open new perspectives in terms of diagnosis, planning, and outcome prediction. In this work, we present an overview of different artificial intelligent techniques used in the brain care domain, along with a review of important clinical applications.