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Related Experiment Video

Updated: Jun 19, 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

Predicting future surgical steps during MCA aneurysm clipping using a multimodal transformer.

Thomas J On1, Jonathan A Tangsrivimol1,2, Jiuxu Chen3

  • 1The Loyal and Edith Davis Neurosurgical Research Laboratory, Department of Neurosurgery, Barrow Neurological Institute, St. Joseph's Hospital and Medical Center, Phoenix, Arizona.

Frontiers in Surgery
|June 18, 2026
PubMed
Summary

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Predicting surgical steps during middle cerebral artery aneurysm clipping is feasible using AI. A multimodal approach combining video and step labels showed the strongest predictive performance for neurosurgery.

Area of Science:

  • Neurosurgery
  • Artificial Intelligence
  • Surgical Analytics

Background:

  • Intracranial aneurysm clipping is a complex neurosurgical procedure requiring dynamic decision-making.
  • While AI aids surgical phase recognition, predicting future surgical steps in neurosurgery is underexplored.

Purpose of the Study:

  • To evaluate the feasibility of fixed-horizon surgical step prediction for middle cerebral artery (MCA) aneurysm clipping using recorded microscope videos.
  • To assess the performance of different AI input configurations for this prediction task.

Main Methods:

  • Retrospective analysis of 25 MCA aneurysm clipping surgeries.
  • Annotation of surgeries into 12 standardized operative steps.
  • Evaluation of a transformer-based prediction framework using video features, step labels, or combined inputs to predict subsequent steps.
Keywords:
anticipating surgical stepsartificial intelligencemiddle cerebral artery aneurysmneurosurgical educationpredicting surgical stepssurgical videovisual-annotation model

Related Experiment Videos

Last Updated: Jun 19, 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

Main Results:

  • The multimodal model (video + step labels) achieved the highest accuracy (0.683) and F1 score (0.673).
  • Video-only and annotation-only models showed lower performance.
  • The multimodal model demonstrated superior sequence-level alignment.

Conclusions:

  • Fixed-horizon surgical step prediction is feasible for MCA aneurysm clipping under controlled conditions.
  • Multimodal AI modeling offers the most promising approach for predictive surgical analytics.
  • Further validation is needed in fully automated systems.