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Edwin G. Fischer, Antonio V. Lorenzo, William J. Landis, Keasley Welch, Stephen K. Ofori-Kwakye, Brent Dorval, Karen J. Hodgens, and Clare S. Kerr

✓ The authors report a study of the cerebral vasculature of premature rabbits pertaining to the germinal matrix (GM). A pigmented silicone material (Microfil) was injected into the carotid artery of anesthetized rabbits. Methyl methacrylate vascular casts of a similar group of premature rabbits were examined by scanning electron microscopy. The GM is supplied by arteries from both the basal and convexity surfaces of the brain. Vessels could be identified as arteries or veins by their typical patterns of branching and by the characteristic impressions made on the methyl methacrylate casts by endothelial nuclei. Specific evidence of structural weaknesses in the vasculature, which could be a site of predilection for GM bleeding, was not observed. The similarities in basal ganglia vasculature between premature rabbits and humans justifies using the rabbit model to study vascular aspects of the GM and intraventricular hemorrhage.

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

OBJECTIVE

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.

METHODS

The surgical phases and steps of 50 anonymized eTSA operative videos were labeled by expert surgeons. Forty videos were used to train a combined convolutional and recurrent neural network model by Touch Surgery. Ten videos were used for model evaluation (accuracy, F1 score), comparing the phase and step recognition of surgeons to the automatic detection of the ML model.

RESULTS

The longest phase was the sellar phase (median 28 minutes), followed by the nasal phase (median 22 minutes) and the closure phase (median 14 minutes). The longest steps were step 5 (tumor identification and excision, median 17 minutes); step 3 (posterior septectomy and removal of sphenoid septations, median 14 minutes); and step 4 (anterior sellar wall removal, median 10 minutes). There were substantial variations within the recorded procedures in terms of video appearances, step duration, and step order, with only 50% of videos containing all 7 steps performed sequentially in numerical order. Despite this, the model was able to output accurate recognition of surgical phases (91% accuracy, 90% F1 score) and steps (76% accuracy, 75% F1 score).

CONCLUSIONS

In this IDEAL stage 0 study, ML techniques have been developed to automatically analyze operative videos of eTSA pituitary surgery. This technology has previously been shown to be acceptable to neurosurgical teams and patients. ML-based surgical workflow analysis has numerous potential uses—such as education (e.g., automatic indexing of contemporary operative videos for teaching), improved operative efficiency (e.g., orchestrating the entire surgical team to a common workflow), and improved patient outcomes (e.g., comparison of surgical techniques or early detection of adverse events). Future directions include the real-time integration of Touch Surgery into the live operative environment as an IDEAL stage 1 (first-in-human) study, and further development of underpinning ML models using larger data sets.