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Related Experiment Video

Updated: May 26, 2026

A Pipeline for 3D Multimodality Image Integration and Computer-assisted Planning in Epilepsy Surgery
09:41

A Pipeline for 3D Multimodality Image Integration and Computer-assisted Planning in Epilepsy Surgery

Published on: May 20, 2016

AENEAS Project: Live Image-Based Navigation and Roadmap Generation in Endoscopic Neurosurgery Using Machine Vision.

Victor E Staartjes1, Gary Sarwin2, Alessandro Carretta3

  • 1Machine Intelligence in Clinical Neuroscience (MICN) Laboratory, Department of Neurosurgery, University Hospital Zurich, Clinical Neuroscience Centre, University of Zurich, Zurich , Switzerland.

Operative Neurosurgery (Hagerstown, Md.)
|April 28, 2025
PubMed
Summary

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Endoscopic endonasal approach for recurrent craniopharyngioma in adults.

Neurosurgical focus·2026
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Recovery of daily life upper limb use during stroke rehabilitation: neuroanatomical correlates and associated variables.

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Combined surgery and proton radiotherapy in the management of craniopharyngiomas: an update with paradigmatic and challenging case scenarios.

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This study introduces an AI-powered machine vision system for endoscopic neurosurgery, creating a real-time anatomical roadmap. The AI accurately identifies structures and predicts upcoming ones, enhancing surgical navigation.

Area of Science:

  • Neurosurgery
  • Artificial Intelligence
  • Computer Vision
  • Medical Imaging

Background:

  • Cognitive processes can be replicated by artificial intelligence (AI) algorithms.
  • Human roadmap generation in endoscopic neurosurgery is complex.
  • A live image-based machine vision method was developed to replicate this process.

Purpose of the Study:

  • To replicate human roadmap generation for endoscopic neurosurgery using AI.
  • To develop a live image-based machine vision method for surgical navigation.
  • To enhance anatomical identification and prediction during procedures.

Main Methods:

  • Utilized YOLOv7 for object detection to identify anatomical structures.
  • Employed an autoencoder to encode detected structures into an anatomical roadmap.
Keywords:
Anatomical guidanceAnatomical recognitionArtificial intelligenceMachine learningMachine vision

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Last Updated: May 26, 2026

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  • Extrapolated future anatomical structures based on current detections for predictive navigation.

    • Main Results:

    • The AI model achieved an average precision of 53.4 on a test set of 20 videos.
    • The autoencoder demonstrated reliable detection of anatomical structures and their position within the roadmap.
    • Demonstrated mixed reality head-up display for real-time anatomical navigation.

    Conclusions:

    • The developed method reliably identifies key anatomical structures in endoscopic endonasal trans-sphenoidal surgery.
    • The AI encodes a surgical roadmap by identifying and extrapolating anatomical structures.
    • This technology may lead to real-time mixed reality navigation software for neurosurgeons.