5 Innovative Applications of ML in Surgery

5 Applications of Machine Learning in Surgery

Preoperative planning

• Anatomical classification of medical images or volumes of organs or lesions.

• Detection and spatial localization of regions of interest

• Segmentation of organs from CT scans and MRI

• Segmentation and localization of surgical instruments

• Registration: the spatial alignment between two medical images, volumes, or modalities.

• Complications prediction

Intraoperative guidance

• 3D shape instantiation

• Endoscopic navigation

• Tissue feature tracking

Surgical Robotics

• Perception: tracking instruments by detecting them, and optimizing the interaction between surgical tools and the environment

• Autonomous navigation

• Camera Guidance

• Human-robot interaction: tracking 2D/3D eye-gaze points of surgeons (touchless manipulation) to assist surgical instrument control and navigation

• Autonomous manipulations: autonomous suturing

Evaluation of Surgical Skills

The evaluation of surgical skills has traditionally been a subjective practice, often conducted by other trained surgeons. As robotic technology becomes more commonly used in surgeries, researchers are exploring automated methods of measuring surgical techniques.

A study presented at the 2016 World Congress on Engineering and Computer Science discussed using machine learning to evaluate surgeon performance in robot-assisted minimally invasive surgery.

The research team evaluated data collected from suturing performance and classified surgeons into two categories: novice and expert. The machine learning algorithm was developed to measure the following six features:

• Completion time

• Path length

• Depth perception

• Speed

• Smoothness

• Curvature

AR/VR Headsets and platforms

Platforms using augmented reality and/ or virtual reality headsets in surgery, use ML to analyze data and optimize user experience.