Creating an AI-powered platform for neurosurgery alongside a usability examination: Progressing towards minimally invasive robotics

Abstract

Recent advancements in artificial intelligence have paved the way for promising applications in neurosurgery, aiming to improve patient outcomes while minimizing risks. This paper introduces a novel AI-driven system designed to assist neurosurgeons in accurately identifying and localizing brain tumors. Leveraging deep learning algorithms, the system was trained on a comprehensive dataset of brain MRI scans for segmentation and classification tasks. Evaluation of the system on an independent set of brain MRI scans revealed an average Dice similarity coefficient of 0.87, indicating high performance. Moreover, a user experience assessment conducted at the Department of Neurosurgery, University Hospital Ulm, demonstrated notable enhancements in accuracy, efficiency, and reduced cognitive load and stress levels among users. Notably, the system showcased adaptability across various surgical scenarios and provided personalized guidance to users. These findings underscore the potential of AI to augment the quality of neurosurgical interventions and ultimately enhance patient outcomes. Future endeavors will focus on integrating this system with robotic surgical tools to facilitate minimally invasive surgeries.