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Hyperspectral Imaging in Brain Tumor Surgery-Evidence of Machine Learning-Based Performance.

Sami Puustinen1, Hana Vrzáková2, Joni Hyttinen3

  • 1University of Eastern Finland, Faculty of Health Sciences, School of Medicine, Kuopio, Finland; Kuopio University Hospital, Eastern Finland Microsurgery Center, Kuopio, Finland.

World Neurosurgery
|April 8, 2023
PubMed
Summary
This summary is machine-generated.

Hyperspectral imaging (HSI) shows promise for surgical diagnostics. Establishing standardized HSI protocols and public datasets is crucial for its widespread clinical adoption in neurosurgery.

Keywords:
BiophotonicsHyperspectral imagingMachine learningMicrosurgeryNeurosurgeryTissue classification

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Area of Science:

  • Neurosurgery
  • Medical Imaging
  • Machine Learning

Background:

  • Hyperspectral imaging (HSI) offers potential for improved surgical tissue detection and diagnostics.
  • Current neurosurgical HSI lacks validated machine learning models, public datasets, and standardized imaging conventions.
  • Evidence-based paradigms for intraoperative neurosurgical HSI guidance are not yet established.

Approach:

  • A detailed clinical paradigm for establishing microneurosurgical HSI guidance was presented.
  • A systematic literature review summarized current neurosurgical HSI indications and performance, focusing on machine learning methods.
  • The study outlines a framework for systematic intraoperative HSI data collection.

Key Points:

  • Published neurosurgical HSI studies primarily consist of case series or reports for glioma tissue classification.
  • Deep learning achieved 80% accuracy in multitissue classification using HSI during glioma surgery.
  • The developed HSI system enabled intraoperative data acquisition and visualization with minimal surgical disruption.

Conclusions:

  • Neurosurgical HSI demonstrates unique capabilities compared to existing imaging techniques, though publications are limited.
  • Multidisciplinary collaboration is essential to develop standardized HSI protocols and assess clinical impact.
  • The proposed HSI paradigm supports the development of standards, medical device regulations, and value-based imaging systems.