Evidence map›Paper›PMID 40304484›Full record

ArticleNeurosurgery2026

Automated Operative Phase and Step Recognition in Vestibular Schwannoma Surgery: Development and Preclinical Evaluation of a Deep Learning Neural Network (IDEAL Stage 0).

Simon C Williams, Dorothée Duvaux, Adrito Das, Siddharth Sinha, Hugo Layard Horsfall, Jonathan P Funnell, John G Hanrahan, Danyal Z Khan, William Muirhead, Neil Kitchen and 5 more

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Article in Neurosurgery, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

  1. Article
  2. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

15 authors.

Simon C WilliamsVictor Horsley Department of Neurosurgery, National Hospital for Neurology and Neurosurgery, London , UK.ORCID 0000-0003-1770-1797
Dorothée DuvauxUCL Hawkes Institute, University College London, London , UK.
Adrito DasUCL Hawkes Institute, University College London, London , UK.
Siddharth SinhaVictor Horsley Department of Neurosurgery, National Hospital for Neurology and Neurosurgery, London , UK.
Hugo Layard HorsfallVictor Horsley Department of Neurosurgery, National Hospital for Neurology and Neurosurgery, London , UK.
Jonathan P FunnellVictor Horsley Department of Neurosurgery, National Hospital for Neurology and Neurosurgery, London , UK.
John G HanrahanVictor Horsley Department of Neurosurgery, National Hospital for Neurology and Neurosurgery, London , UK.
Danyal Z KhanVictor Horsley Department of Neurosurgery, National Hospital for Neurology and Neurosurgery, London , UK.
William MuirheadVictor Horsley Department of Neurosurgery, National Hospital for Neurology and Neurosurgery, London , UK.
Neil KitchenVictor Horsley Department of Neurosurgery, National Hospital for Neurology and Neurosurgery, London , UK.
Francisco VasconcelosUCL Hawkes Institute, University College London, London , UK.
Sophia BanoUCL Hawkes Institute, University College London, London , UK.
Danail StoyanovVictor Horsley Department of Neurosurgery, National Hospital for Neurology and Neurosurgery, London , UK.
Patrick GroverVictor Horsley Department of Neurosurgery, National Hospital for Neurology and Neurosurgery, London , UK.
Hani J MarcusVictor Horsley Department of Neurosurgery, National Hospital for Neurology and Neurosurgery, London , UK.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

BACKGROUND AND

objectivesMachine learning (ML) in surgical video analysis offers promising prospects for training and decision support in surgery. The past decade has seen key advances in ML-based operative workflow analysis, though existing applications mostly feature shorter surgeries (<2 hours) with limited scene changes. The aim of this study was to develop and evaluate a ML model capable of automated operative workflow recognition for retrosigmoid vestibular schwannoma (VS) resection. In doing so, this project furthers previous research by applying workflow prediction platforms to lengthy (median >5 hours duration), data-heavy surgeries, using VS resection as an exemplar.

methodsA video dataset of 21 microscopic retrosigmoid VS resections was collected at a single institution over 3 years and underwent workflow annotation according to a previously agreed expert consensus (Approach, Excision, and Closure phases; and Debulking or Dissection steps within the Excision phase). Annotations were used to train a ML model consisting of a convolutional neural network and a recurrent neural network. 5-fold cross-validation was used, and performance metrics (accuracy, precision, recall, F1 score) were assessed for phase and step prediction.

resultsMedian operative video time was 5 hours 18 minutes (IQR 3 hours 21 minutes-6 hours 1 minute). The "Tumor Excision" phase accounted for the majority of each case (median 4 hours 23 minutes), whereas "Approach and Exposure" (28 minutes) and "Closure" (17 minutes) comprised shorter phases. The ML model accurately predicted operative phases (accuracy 81%, weighted F1 0.83) and dichotomized steps (accuracy 86%, weighted F1 0.86).

conclusionThis study demonstrates that our ML model can accurately predict the surgical phases and intraphase steps in retrosigmoid VS resection. This demonstrates the successful application of ML in operative workflow recognition on low-volume, lengthy, data-heavy surgical videos. Despite this, there remains room for improvement in individual step classification. Future applications of ML in low-volume high-complexity operations should prioritize collaborative video sharing to overcome barriers to clinical translation.

Indexed as

Deep LearningNeural Networks, ComputerNeuroma, AcousticNeurosurgical ProceduresHumansVideo RecordingWorkflowAcoustic neuromaArtificial intelligenceComputer visionMachine learningNeural networksOperative workflowRetrosigmoidSurgical workflowVestibular schwannoma

Identifiers

PMID40304484
PMCPMC12962350

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.