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Automated operative workflow analysis of endoscopic pituitary surgery using machine learning: development and preclinical evaluation (IDEAL stage 0)

2021; American Association of Neurological Surgeons; Volume: 137; Issue: 1 Linguagem: Inglês

10.3171/2021.6.jns21923

1933-0693

Danyal Z. Khan, Imanol Luengo, Santiago Barbarisi, Carole Addis, Lucy Culshaw, Neil Dorward, Pinja Haikka, Abhiney Jain, Karen Kerr, Chan Hee Koh, Hugo Layard Horsfall, William Muirhead, Paolo Palmisciano, Baptiste Vasey, Danail Stoyanov, Hani J. Marcus,

Artificial Intelligence in Healthcare and Education

Surgical workflow analysis involves systematically breaking down operations into key phases and steps. Automatic analysis of this workflow has potential uses for surgical training, preoperative planning, and outcome prediction. Recent advances in machine learning (ML) and computer vision have allowed accurate automated workflow analysis of operative videos. In this Idea, Development, Exploration, Assessment, Long-term study (IDEAL) stage 0 study, the authors sought to use Touch Surgery for the development and validation of an ML-powered analysis of phases and steps in the endoscopic transsphenoidal approach (eTSA) for pituitary adenoma resection, a first for neurosurgery.