AI: Current Realities, Future Possibilities

By VICTORIA STERN

In 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 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.

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 and 0.8 mm, as well as during hand and free flap during hand and free flap surgery (Nature Comm surgery (Nature Comm 2020;11[757]). 2020;11[757]).

White Light Imaging

Firefly imaging is not visible in white light imaging mode.

Standard Firefly Mode

In Standard mode, the image is displayed as a fluorescent green overlay on a black and white background view. The closer the endoscope is to the tissue, the stronger (more intense green) the signal appears.

Sensitive Firefly Mode

In Sensitive mode, the system attempts to automatically adjust the signal intensity and brightness to be consistent, whether the endoscope is moved closer or farther away from the tissue.

Note: Sensitive Firefly mode is only available with Endoscope Plus.

Scan to learn more about the evolution of surgery

Images above show porcine pelvic vasculature using the da Vinci Xi Endoscope Plus.

*Total da Vinci Practice refers to the transferable value of da Vinci surgery across procedures in surgeon’s minimally invasive surgery (MIS) practice. It is at the surgeon’s discretion to determine when a patient is a candidate for

MIS surgery and whether da Vinci surgery is an option.

Adjust Your Perspectice Using the da Vinci Firefly Imaging System

The da Vinci Xi® and da Vinci X™ surgical systems with integrated fluorescence imaging capability provide you with real-time endoscopic visible and near-infrared fluorescence imaging. This fluorescence imaging capability provides you with the opportunity for visual assessment of at least one of the major extra-hepatic bile ducts, as well as the cystic artery during cholecystectomy procedures performed using the da Vinci® system. It can also be used to assess vessels, blood flow, and related tissue perfusion during cases across your da Vinci Total Practice* when indicated.

Firefly Fluorescence Imaging

The da Vinci fluorescence imaging vision system (Firefly® fluorescence imaging) is intended to provide real-time endoscopic visible and near-infrared fluorescence imaging. The da Vinci fluorescence Imaging vision system enables surgeons to perform minimally invasive surgery using standard endoscopic visible light as well as visual assessment of vessels, blood flow, and related tissue perfusion, and at least one of the major extra-hepatic bile ducts (cystic duct, common bile duct and common hepatic duct), using near infrared imaging.

Fluorescence imaging of biliary ducts with the da Vinci fluorescence imaging vision system is intended for adjunctive use only, in conjunction with standard of care white light and when indicated, with intraoperative cholangiography. The device is not intended for standalone use for biliary duct visualization.

Intuitive’s ICG packs are available for sale in the U.S. ONLY. Intuitive’s ICG packs are cleared for commercial distribution in the U.S. for use in combination with the fluorescence-capable da Vinci HD vision system and Firefly integrated hardware. Intuitive-distributed ICG contains necessary directions for use of ICG with Firefly fluorescence imaging. Using generic ICG with Firefly fluorescence imaging is considered off-label and is not recommended. Anaphylactic deaths have been reported following ICG injection during cardiac catheterization. Total ICG dosage should not exceed 2 mg/kg per patient. Anaphylactic or urticarial reactions have been reported in patients with or without histories of allergy to iodides.

Important safety information

For Important Safety Information, indications for use, risks, full cautions and warnings, please refer to www.intuitive.com/safety.

Da Vinci Xi/X system precaution statement

The demonstration of safety and effectiveness for the specific procedure(s) discussed in this material was based on evaluation of the device as a surgical tool and did not include evaluation of outcomes related to the treatment of cancer (overall survival, disease-free survival, local recurrence) or treatment of the patient’s underlying disease/condition. Device usage in all surgical procedures should be guided by the clinical judgment of an adequately trained surgeon.

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