TEC HN O LO GY
AI: Current Realities, Future Possibilities By VICTORIA STERN
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n 2012, a patient arrived at Stanford University Medical Center with a large wound on his foot. The man had been walking on it for a month before coming in for care. Elsie Gyang Ross, MD, an assistant professor of surgery and medicine at Stanford University School of Medicine, in California, quickly recognized the patient had a circulatory condition known as peripheral artery disease (PAD), in which vessels narrow and restrict or block blood flow to the limbs. The condition affects an estimated 8 million to 12 million Americans but often goes undiagnosed. If left untreated, PAD can increase the risk for stroke and myocardial infarction or, in this case, lead to a wound that doesn’t heal. Dr. Ross’ patient had wet gangrene of the foot. Despite an antibiotic regimen, debridement and revascularization, he ultimately required a below-knee amputation. “But a lot can be done to treat patients before we need to consider surgery, such as amputation or lower extremity angiography,” Dr. Ross told OR Management News. In particular, Dr. Ross said, what if clinicians could use artificial intelligence to diagnose patients sooner or predict who would develop PAD so patients can begin treatment earlier and avoid surgery? In 2014, Dr. Ross, along with her colleagues at the Stanford Center for Biomedical Informatics Research, began evaluating machine learning algorithms to identify patients at risk for PAD. Machine learning algorithms—mathematical tools that help make sense of data—learn from experience. In other words, when more data are fed into the algorithm, the diagnosis or prediction becomes more accurate. A 2016 analysis described the process of constructing machine learning models using data from 1,755 patients who had undergone elective coronary angiography but whose PAD status was unknown. Dr. Ross and her team found that the machine learning algorithms could recognize PAD and predict mortality better than standard
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AI in the Surgery World The presenters discussed a range of other promising AI tools that support surgical decision making and automation of care. Here are a few highlights. Diagnosis Radiology: Aidoc developed a suite of AI-based software that can flag abnormalities on CT scans, including pulmonary embolism, intracranial hemorrhage and large-vessel occlusion. Pathology: PathAI is developing machine learning models to help pathologists diagnose a range of conditions from liver and breast biopsies, and predict disease progression as well as the best treatment options.
Decision Making Surgical site infection prediction: DASH Analytics developed a machine learning platform to predict a patient’s risk for SSIs. A threeyear study found that the algorithm reduced SSIs by almost 75% in a subset of general and colorectal surgery patients at University of Iowa Hospitals and Clinic.
Automation Robot-assisted surgery: The medical device company Microsure designed MUSA, the first surgical robot for open microsurgery, which received certification for clinical use in Europe in 2019. The robot stabilizes a surgeon’s movements to enable more precision during lymphatic surgery to connect vessels with diameters between 0.3 and 0.8 mm, as well as during hand and free flap surgery (Nature Comm 2020;11[757]).
OR Management News • Volume 16 • September 2021
linear regression models (J Vasc Surg 2016;64[5]:1515-1522.e3). A 2019 study that included 7,686 patients with PAD and incorporated almost 1,000 variables from electronic health records reported that the machine learning models could accurately forecast which PAD patients would develop major cardiac and cerebrovascular events (Circ Cardiovasc Qual Outcomes 2019;12[3]:e004741). The goal of these models and others, Dr. Ross explained, is not for machine learning or its operationalized counterpart, AI, to take over care or make decisions for surgeons. “AI is there to make us better,” she said. “It should run quietly in the background, catching our blind spots and helping us make more informed decisions about patient care.” Developing and validating AI and machine learning models take time and an enormous amount of data before they can be integrated into clinical practice. However, interest in AI technology has ballooned in the past few years, with approvals by the FDA increasing from just two at the end of 2017 to more than 80 by September 2020. At the 2020 American College of Surgeons Clinical Congress, Dr. Ross, along with Genevieve B. Melton-Meaux, MD, PhD, FACS, and Rachael A. Callcut, MD, FACS, explored the latest advances in surgical AI, highlighting tools that can improve diagnosis, decision making and outcomes in surgery (abstract PS424). In addition to diagnosing and prioritizing more urgent cases, AI-based tools can help predict outcomes or improve surgeons’ performance in the OR. During her talk, Dr. Ross discussed an imaging platform that guides surgeons during endovascular aneurysm repair. The intraoperative image-based, 3D fusion CT automates conversion of preoperative scans into 3D models and, in the OR, overlays these images onto the patient’s live fluoroscopy to highlight the renal arteries for position of the guidewire. continued on page 8
