Laparoscopic AI

AI in the OR

Copying music, making bad art and training the next generation of surgeons, just some of the important applications of artificial intelligence (AI). 

Regular readers will know that the topic of AI in healthcare is becoming well-trodden ground in our coverage of developments in the field of medical technology. Here we will focus on the application of AI to laparoscopic surgery.

Laparoscopic surgery 

Laparoscopic surgery, also known as keyhole surgery, is performed by making one or more small cuts through which a thin, flexible tube containing a camera (a laparoscope) and the necessary surgical instruments can be passed. 

The benefit of laparoscopic surgery is the significantly smaller scars left behind after recovery when compared to open surgery. 

The drawback of laparoscopic surgery, at least for the surgeon, is the reduced visibility of the surgical site. A lot of development has been directed towards improving the capabilities of the cameras contained in laparoscopes. However, even with improved imaging capabilities, these procedures remain challenging and require the surgeon to undergo a significant amount of training in order that they can perform the procedures effectively and safely.

An existing method of training laparoscopic skills is the use of so-called “box trainers” which are opaque boxes with holes, with similar sizes to the cuts in a real surgical scenario, and a practise surgical target inside. Whilst providing a safe means to practise the basics of laparoscopic surgery, these box trainers are often relatively rudimentary and cannot provide a full recreation of a real surgical scenario. 


Surgical training is the first stage at which AI can be introduced in order to improve patient outcomes in laparoscopic surgery.

Researchers at the National Robotarium of Heriot-Watt University, in collaboration with the Dundee Institute for Healthcare Simulation of the University of Dundee, are developing an AI-empowered system to help trainee surgeons practise their laparoscopic skills. 

The system, called AILap, uses AI to monitor human movements in real-time, and combines machine learning and machine vision technologies with a physical box trainer to provide trainee surgeons with real-time feedback that improves their keyhole surgery techniques and skills.

The precise functions of the AILap system have not been published; however, by way of example, the system may capture a video of a trainee using the box trainer to complete a given training exercise. The video may then be reviewed in real-time by the AI in order to assess the movements of the trainee during the exercise. The trainee’s movements could be compared to an expert performing the same exercise and, based on this comparison, the system could provide feedback to the trainee for improving their technique. 

The aim of AILap is to support surgical trainees by increasing their access to training through self-directed exercises and with the benefit of immediate feedback powered by AI. The system may also be used by clinical academics, who are responsible for the training of surgeons, to restructure their training programmes to teach more trainees with greater effectiveness with the support of AI feedback.


AI can also be used to support surgeons beyond training, during real surgery. 

An exciting new development in this area is being led by EnAcuity, a new spin-out company from Imperial College London and UCL. 

EnAcuity is aiming to develop AI-powered hyperspectral imaging software to provide real-time image enhancement of the images captured by a laparoscope. The power behind this development is the ability to apply it to existing surgical hardware, whilst still providing an enhanced set of images for the surgeon to monitor the progress of an ongoing operation by.

Image enhancement in this context could, for example, include the recognition and highlighting of an organ in an image captured by the laparoscope. The system may even be able to recognise the current position of the laparoscope within the body in order to provide guidance to the surgeon as to where they need to move the laparoscope next. 

IP trends

Both the fields of minimally invasive surgical instruments and computer-aided surgery have been a source of a considerable number of patent filings over the last 20 years. Whilst there has been a steady linear increase in the number of filings relating to surgical instruments, the number of filings related to computer-aided surgery has increased in a nearly exponential manner over this time frame. Whilst it is true that computer-aided surgery is applicable to a wider range of surgical techniques than just laparoscopic surgery, it is clearly an area of increasing innovation that is critical to the future of minimally invasive surgery.