Blood vessel detection, localization and estimation using a smart laparoscopic grasper: a Monte Carlo study.

For centuries, surgeons have relied on their sense of touch to identify vital structures such as blood vessels in traditional open surgery. Over the past two decades, surgeons have shifted to minimally invasive surgical (MIS) approaches, including laparoscopic surgery, which include benefits such as less scarring, less risk for infection, and quicker recovery times. In fact, some surgeries such as cholecystectomies have seen more than an 80% adoption of this technique because of those benefits. However, due to the fundamental challenges associated with using laparoscopic surgery, there has been a lower adoption in more complex specialties, such as colorectal and thoracic surgery, where the field of surgery has bleeding, fat, scar tissue, and adhesions. These problems are exacerbated by complicating factors such as inflammation, cancer, chronic disease, obesity, and re-operations. Importantly, surgeons will often convert from laparoscopy to open surgery if they can no longer proceed using the minimally invasive approach because of issues described with these complicating factors, thereby negating the benefits that the patient would have seen. When the surgeon does attempt these procedures with those issues, the surgery takes on average 30 min - 1 hour longer. A new method by which surgeons can visualize structures like blood vessels could reduce the conversion rates and operating time, thereby driving a greater adoption of laparoscopic surgery in these complex procedures. Here, we show that by adding near infrared (NIR) LEDs and a linear image sensor onto the opposing jaws of the laparoscopic graspers, blood vessels that are embedded within tissues can be detected and localized efficiently, even those not visible using current imaging techniques. We show the results of Monte Carlo simulations to support our claim, including that blood vessels ranging from 2 to 6 mm and buried under up to 1 cm of tissue can be detected. We also report developing a smart grasper handheld prototype to run