AI-Guided Surgery Using Touch Sensors and Machine Learning

This patent describes a system that uses touch sensors on surgical tools and artificial intelligence to give real-time guidance to surgeons or robots during an operation.

What it covers

This system provides computer-assisted navigation during surgery by using a 'sensor circuit' to gather 'intra-operative tactile sensing data'. This data indicates a 'sensed characteristic' of a surgical tool as it touches a patient's anatomy within the surgical site. A 'processing circuitry' then feeds this touch data into a 'machine learning model' to generate 'intra-operative navigated guidance data'. This guidance can be shown on a 'display device' for a surgeon or used to control the movement of a 'surgical robot'. For example, if a surgeon is removing a tumor, the tool's sensors could detect the difference in texture between healthy and diseased tissue, and the AI would then highlight the tumor's exact boundary on a screen or guide a robot to cut precisely.

What it doesn't cover

• —Does not cover surgical navigation systems that rely solely on visual data (like cameras or X-rays) without incorporating tactile feedback.
• —Does not cover systems that use tactile sensing but process the data with traditional, rule-based algorithms rather than a 'machine learning model'.
• —Does not cover systems where tactile data is used for post-operative analysis or training, rather than 'intra-operative navigated guidance'.
• —Does not cover systems that provide guidance without a 'display device' or direct control of a 'surgical robot'.
• —Does not cover systems where the tactile sensing data is not specifically about a 'tool contacting a location on patient anatomy'.

The clever bit

The clever part is using a 'machine learning model' to interpret subtle 'intra-operative tactile sensing data' from a surgical tool in real-time. This allows the system to understand complex tissue interactions and provide precise 'navigated guidance' that goes beyond simple force feedback.

Why it matters

This technology aims to enhance surgical precision by restoring a sense of touch that can be lost in minimally invasive or robotic procedures. It could lead to safer operations, fewer errors, and faster patient recovery by giving surgeons or robots better real-time information about tissue interaction. By integrating AI, the system can interpret complex tactile information, potentially enabling more delicate and accurate surgical maneuvers.

Real-world examples

• 1.Robotic surgery systems like da Vinci
• 2.Computer-assisted orthopedic surgery tools
• 3.Minimally invasive surgery platforms