Position paper: a general framework for applying machine learning techniques in operating room

TL;DR

This paper proposes a comprehensive framework utilizing machine learning to enhance monitoring, error detection, and decision-making in operating rooms during laparoscopic surgeries.

Contribution

It introduces a layered architecture combining data preprocessing, pattern recognition, and expert systems for improved surgical environment awareness.

Findings

Framework effectively identifies human errors during surgery

Layered approach improves data processing and decision accuracy

Potential to automate monitoring in operating rooms

Abstract

In this position paper we describe a general framework for applying machine learning and pattern recognition techniques in healthcare. In particular, we are interested in providing an automated tool for monitoring and incrementing the level of awareness in the operating room and for identifying human errors which occur during the laparoscopy surgical operation. The framework that we present is divided in three different layers: each layer implements algorithms which have an increasing level of complexity and which perform functionality with an higher degree of abstraction. In the first layer, raw data collected from sensors in the operating room during surgical operation, they are pre-processed and aggregated. The results of this initial phase are transferred to a second layer, which implements pattern recognition techniques and extract relevant features from the data. Finally, in the last layer, expert systems are employed to take high level decisions, which represent the final output of the system.