Year: 2024
Type: article
Abstract: : Modern operating rooms (ORs) are equipped with numerous devices that generate large amounts of data, creating opportunities and challenges for medical teams. Current systems rely on the expertise of medical staff to process and interpret these data streams in real time. The OR of the future requires intelligent systems capable of interpreting and analysing multi-source data to provide context-aware support. This project aims to develop approaches for surgical workflow recognition, a curial component in such systems, targeting three key tasks. Machine learning and deep learning approaches were employed to tackle surgical smoke detection, surgical tool classification, and surgical phase recognition. Achieved results demonstrate high performance of the developed models and the potential of integrating them into real practice.
Authors: Abdulbaki Alshirbaji, T., Jalal, N.A., Arabian, H., Ding, N., Moeller, K.
Institutions: Furtwangen University, Leipzig University
Language: English
Subfield: Surgery
Field: Medicine
Domain: Health Sciences
Open Access status: green
OpenAlex ID: W6893169232
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