Tissue handling training system.

Tissue handling, both in open and MIS settings, is a valuable skill for surgeons in all specialties, as it allows them to perform safe procedures while minimizing the risk of damaging tissue in the operating area. However, for many surgical trainees this skill is difficult to master due to the lack of immediate feedback on the performance of the procedure in this regard.

The objective of this project is to develop a low-cost force sensing system, compatible with currently available laparoscopic surgical simulators, to carry out an objective evaluation of learned surgical skills and improve the tissue handling skills of novice surgeons.

The system will allow self-assessment through an application based on artificial intelligence, which will analyze the performance of the trainee using the force data collected during the procedure. The app will rate various aspects of tissue handling and assign an overall score to compare with certified surgeons.

The tissue hanlding training module consists of a force sensing system that connects to a PC, which runs custom software for immediate data evaluation. The force sensing system consists of two 3D printed bases joined by a set of springs, the lower base houses the logic board that collects the sensor information and sends it to the PC, while the upper base contains a three-axis digital accelerometer along with a stainless steel reference mass. A synthetic tissue pad is placed on the upper base to practice various suture knotting techniques, the accelerometer detects the movement of the tissue caused by the trainee, and the electronic board uses the accelerometer data to calculate the dynamic force exerted on the tissue during the procedure. The data processed by the electronics is sent to a PC, which runs the device software. The purpose of the software is to perform the evaluation of the procedure given the captured data. Once the procedure is finished, the software creates an evaluation report in PDF format that is automatically sent to the student's email and indicates a skill level estimated by an artificial neural network.

With the force sensing system, we hope to establish a different way of teaching how to safely perform a suture in both conventional surgery and minimally invasive surgery, with various knotting techniques, such as the instrument-tie knot tying technique, hand-tie knot tying techniques, the intracorporeal knot tying technique, and the extracorporeal knot tying technique. In addition, the surgeon will obtain immediate feedback on the force applied to the tissue, which will strengthen his daily surgical practice and improve his skills in the handling of organs and tissues.

This project will benefit medical students and resident surgeons of low-income institutions by allowing them access to an efficient, low-cost and easily portable training system that will allow the possibility of practicing at home and will record the progress of each trainee by saving their evaluation reports of each training session. This way the apprentice will be able to visualize his errors and correct them, carrying out training sessions until he has reached the desired skill level to classify as technically competent and to be able to safely perform a procedure on a patient.

We expect that the use of the tissue handling training module will improve the learning curve of suturing skills, effectively decreasing the time needed to prepare an aspiring surgeon to be able to perform a surgical procedure on a patient, we expect the skills acquired while training with the module to directly impact the safety of the procedure, minimizing the risk of complications during surgery caused by an improper tissue handling by part of the attending surgeon, which will allow patients to have a quick recovery minimizing the time needed to stay at the hospital.

PhD Arturo Minor Martínez (Team lead).

PhD Fernando Pérez Escamirosa (Technical expert).

MsC Salvador Montoya Alvarez (Technical Expert).

Dr. Ricardo Manuel Ordorica Flores (Educator/Clinician).

Dr. Luis Padilla Sánchez (Clinician).

Mechanical Engineer

Software Engineer.

Biomedical Engineer

Data Scientist.