| Title: |
Automated Operative Phase and Step Recognition in Vestibular Schwannoma Surgery: Development and Preclinical Evaluation of a Deep Learning Neural Network (IDEAL Stage 0) |
| Authors: |
Williams, Simon C; Duvaux, Dorothée; Das, Adrito; Sinha, Siddharth; Layard Horsfall, Hugo; Funnell, Jonathan P; Hanrahan, John G; Khan, Danyal Z; Muirhead, William; Kitchen, Neil; Vasconcelos, Francisco; Bano, Sophia; Stoyanov, Danail; Grover, Patrick; Marcus, Hani J |
| Source: |
Neurosurgery (2025) (In press). |
| Publisher Information: |
Ovid Technologies (Wolters Kluwer Health) |
| Publication Year: |
2025 |
| Collection: |
University College London: UCL Discovery |
| Subject Terms: |
Acoustic neuroma; Artificial intelligence; Computer vision; Machine learning; Neural networks; Operative workflow; Retrosigmoid; Surgical workflow; Vestibular schwannoma |
| Description: |
BACKGROUND AND OBJECTIVES: Machine 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 (5 hours duration), data-heavy surgeries, using VS resection as an exemplar. METHODS: A 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. RESULTS: Median 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). CONCLUSION: This 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 ... |
| Document Type: |
article in journal/newspaper |
| File Description: |
text |
| Language: |
English |
| Relation: |
https://discovery.ucl.ac.uk/id/eprint/10208130/1/automated_operative_phase_and_step_recognition_in.1600.pdf; https://discovery.ucl.ac.uk/id/eprint/10208130/ |
| Availability: |
https://discovery.ucl.ac.uk/id/eprint/10208130/1/automated_operative_phase_and_step_recognition_in.1600.pdf; https://discovery.ucl.ac.uk/id/eprint/10208130/ |
| Rights: |
open |
| Accession Number: |
edsbas.FC4E07BB |
| Database: |
BASE |