Sami Puustinen, Hana Vrzáková, Joni Hyttinen, Tuomas Rauramaa, Pauli Fält, Markku Hauta-Kasari, Roman Bednarik, Timo Koivisto, Susanna Rantala, Mikael von und zu Fraunberg, Juha E. Jääskeläinen, Antti-Pekka Elomaa, Hyperspectral Imaging in Brain Tumor Surgery—Evidence of Machine Learning-Based Performance, World Neurosurgery, Volume 175, 2023, Pages e614-e635, ISSN 1878-8750, https://doi.org/10.1016/j.wneu.2023.03.149
Hyperspectral imaging in brain tumor surgery : evidence of machine learning-based performance
|Author:||Puustinen, Sami1,2; Vrzáková, Hana2,3; Hyttinen, Joni3;|
1University of Eastern Finland, Faculty of Health Sciences, School of Medicine, Kuopio, Finland
2Kuopio University Hospital, Eastern Finland Microsurgery Center, Kuopio, Finland
3University of Eastern Finland, Faculty of Science and Forestry, School of Computing, Joensuu, Finland
4Kuopio University Hospital, Department of Clinical Pathology, Kuopio, Finland
5Kuopio University Hospital, Department of Neurosurgery, Kuopio, Finland
6Oulu University Hospital, Department of Neurosurgery, Oulu, Finland
7University of Oulu, Faculty of Medicine, Research Unit of Clinical Medicine, Oulu, Finland
|Online Access:||PDF Full Text (PDF, 3.4 MB)|
|Persistent link:|| http://urn.fi/urn:nbn:fi-fe20230919132119
|Publish Date:|| 2023-09-19
Background: Hyperspectral imaging (HSI) has the potential to enhance surgical tissue detection and diagnostics. Definite utilization of intraoperative HSI guidance demands validated machine learning and public datasets that currently do not exist. Moreover, current imaging conventions are dispersed, and evidence-based paradigms for neurosurgical HSI have not been declared.
Methods: We presented the rationale and a detailed clinical paradigm for establishing microneurosurgical HSI guidance. In addition, a systematic literature review was conducted to summarize the current indications and performance of neurosurgical HSI systems, with an emphasis on machine learning-based methods.
Results: The published data comprised a few case series or case reports aiming to classify tissues during glioma operations. For a multitissue classification problem, the highest overall accuracy of 80% was obtained using deep learning. Our HSI system was capable of intraoperative data acquisition and visualization with minimal disturbance to glioma surgery.
Conclusions: In a limited number of publications, neurosurgical HSI has demonstrated unique capabilities in contrast to the established imaging techniques. Multidisciplinary work is required to establish communicable HSI standards and clinical impact. Our HSI paradigm endorses systematic intraoperative HSI data collection, which aims to facilitate the related standards, medical device regulations, and value-based medical imaging systems.
|Pages:||e614 - e635|
|Type of Publication:||
A1 Journal article – refereed
|Field of Science:||
3126 Surgery, anesthesiology, intensive care, radiology
© 2023 The Author(s). Published by Elsevier Inc. This is an open access article
under the CC BY license (http://creativecommons.org/licenses/by/4.0/).