Papers
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2
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About
Owais Sarwar is a researcher at the intersection of surgical data science, medical robotics, and pulmonary interventions. His primary contributions lie in statistically modeling surgical processes to quantify performance and workflow, particularly in bronchoscopy. In his highly cited work, "Statistical surgical process modeling of performance and workflow in bronchoscopy," Sarwar introduces a rigorous framework for comparing complex clinical approaches—such as fluoroscopy-guided radial EBUS and CBCT-guided robot-assisted bronchoscopy—used in transbronchial biopsy for lung cancer diagnosis. By applying statistical models to surgical motion and decision-making data, he provides objective metrics to evaluate efficiency, skill, and procedural outcomes. This work not only advances the field of computer-assisted intervention but also offers a pathway toward standardized training and quality assurance in minimally invasive thoracic procedures. With his research gaining traction in both clinical and engineering communities, Sarwar is helping to bridge the gap between data-driven analysis and real-world surgical practice, paving the way for safer, more effective diagnostic interventions in pulmonary medicine.
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Top Papers
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