Stage (stratigraphy)

Transsphenoidal surgery

Database

Convolutional neural network

Artificial intelligence

Craniopharyngioma

Pituitary adenoma

Neurosurgery

Biology

Workflow

Computer science

Adenoma

Internal medicine

Surgery

Paleontology

Medicine

Journal of neurosurgery

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.

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