Video Analysis of Neurosurgical Technical Performance and Adverse Events - PROJECT SUMMARY
The proposed research and career development plan aim to provide the candidate with the knowledge,
experience, and resources necessary to become an independent neurosurgeon-scientist whose research
reduces stroke and neurologic disability after neurosurgery through the design and implementation of machine
learning (ML) and computer vision (CV) systems that provide surgeons with feedback to improve surgical
performance. After formal training, practicing neurosurgeons receive little feedback and instead learn by
experience accrued during procedures. However, most do not accrue sufficient case volume to achieve optimal
outcomes in every procedure. Ultimately, >16,000 patients are harmed by preventable neurosurgical errors
each year, resulting in stroke and neurologic disability in up to 70% and death in up to 16% of affected
patients. Unfortunately, the study of harmful adverse events during surgery is obstructed by a lack of datasets
containing surgical actions leading up to adverse events or outcomes. The candidate proposes to overcome
this limitation by using advanced CV and ML methods to analyze a previously unstudied, multimodal dataset
combining pituitary surgical video and clinical data. Pituitary surgery is performed >10,000 times annually in
the U.S., can be recorded for analysis, and its steps, errors, and adverse events were recently standardized in
an international consensus statement. Specifically, the candidate will test the central hypothesis that the
interaction of visible surgeon skill factors with visualized features, including patient anatomy and disease
pathology, produces identifiable step-specific surgical errors that result in postoperative stroke, neurologic
disability, and other adverse events. Specific Aims: 1) Use CV to identify step-specific errors (defined by tool
usage, step progression, and phase characteristics) preceding adverse events; 2) Train ML models to predict
upcoming adverse events from prior metrics of surgeon skill and current visual features of the surgical field.
Methods to identify high-risk neurosurgical actions, predict upcoming adverse events from a surgeon’s
movements, and retrospectively highlight critical timepoints and visual features associated with adverse events
are necessary to rationally design and implement interventions to reduce stroke, neurologic disability, and
other adverse events. The feasibility and success of this work will be facilitated by the candidate’s outstanding
mentoring team, including a surgeon-scientist with experience conducting CV-based surgical performance
assessments from procedural visual data and experts in ML using medical image and clinical data, biomedical
applications of deep learning in complex prediction models, and multi-institutional pituitary surgical research. In
the final year of the award, the candidate will apply for an R01 award to prospectively implement generalizable
predictive models (developed in Aim 2) using a larger dataset of videos from several surgical procedures and
to develop methods to collect and act upon data from operative video in real-time.
