Artificial intelligence–based deep learning model for evaluating procedural consistency in microvascular anastomosis
The LSTM-based model objectively assessed microanastomosis performance, capturing consistency and efficiency. Economy and flow of motion metrics were further validated. Future studies will extend the model's application to more surgeons and refine interpretation of the performance metrics.
Authors
- Marco Santello (ORCID: https://orcid.org/0000-0001-8879-7912)
- Kıvanç Yangı (ORCID: https://orcid.org/0000-0003-0500-218X)
- Mark C. Preul (ORCID: https://orcid.org/0000-0002-3477-4931)
- Jiuxu Chen
- Michael T. Lawton (ORCID: https://orcid.org/0000-0002-8020-6388)
- Jonathan A. Tangsrivimol (ORCID: https://orcid.org/0000-0003-0142-3569)
- Thomas J. On (ORCID: https://orcid.org/0009-0005-7449-4204)
- Yuan Xu (ORCID: https://orcid.org/0009-0004-2067-9847)
- Rokuya Tanikawa
- Baoxin Li (ORCID: https://orcid.org/0009-0004-4553-0627)
Institutions
- St. Joseph's Hospital and Medical Center (US)
- Keiyukai Sapporo Hospital (JP)
- Toujinkai Hospital (JP)
- Arizona State University (US)
Publication Details
- Journal
- Journal of neurosurgery
- Published
- 2025-09-27
- DOI
- https://doi.org/10.3171/2025.6.jns25128
- Citations
- 1
- Primary Topic
- Radiomics and Machine Learning in Medical Imaging
- Type
- article
- Field-Weighted Citation Impact
- 1.36