Position statement on priorities for artificial intelligence in GI endoscopy: a report by the ASGE Task Force

Abstract

Artificial intelligence (AI) in GI endoscopy holds tremendous promise to augment clinical performance, establish better treatment plans, and improve patient outcomes. Although there are promising initial applications and preliminary clinical data for AI in gastroenterology, the field is still in a very early phase, with limited clinical use. The American Society for Gastrointestinal Endoscopy has convened an AI Task Force to develop guidance around clinical implementation, testing/validating algorithms, and building pathways for successful implementation of AI in GI endoscopy. This White Paper focuses on 3 areas: (1) priority use cases for development of AI algorithms in GI, both for specific clinical scenarios and for streamlining clinical workflows, quality reporting, and practice management; (2) data science priorities, including development of image libraries, and standardization of methods for storing, sharing, and annotating endoscopic images/video; and (3) research priorities, focusing on the importance of high-quality, prospective trials measuring clinically meaningful patient outcomes. (Gastrointest Endosc 2020;92:951-9.) Artificial intelligence (AI) is poised to play a crucial role throughout the practice of gastroenterology. Machine learning (ML) is a branch of AI focusing on computer algorithms that can learn from data and perform specific tasks and analyses, often without explicit human programming. Deep learning is an advanced subset of ML that relies on specific algorithms termed artificial neural networks. The enthusiasm for medical AI applications has centered around expectations that these technologies may assist physicians by assimilating, triaging, and interpreting clinical data (laboratory results, electrocardiographic tracings, CTs, etc) to support physician performance in a consistent manner in all phases of clinical care and automate certain burdensome tasks, such as clinical documentation and billing, to allow physicians to refocus on direct patient care and other meaningful professional activities.

Addressing false-positive findings with artificial intelligence for polyp detection

“…Academic endoscopy societies are increasingly interested in AI guidance. The European Society of Gastrointestinal Endoscopy published its first guideline endorsing the use of AI during colonoscopy in 2019 [2], and the American Society for Gastrointestinal Endoscopy recently published a position statement to accelerate the implementation of AI in endoscopy practice [3]. However, as clinical implementation of AI polyp detection products has just started, it is uncertain how they will perform in real-world practice.…”

“…These decisions are obviously important; however, the assessment process of these authorities has not been standardized owing to the lack of a definition of performance measures, such as false positives and common test data. Some research groups have been addressing the latter issue by providing publicly accessible video databases [3,6,7]; however, the former issue, namely the false-positive definition, has not been addressed. Therefore, the authors' unique attempt is notable in that it potentially launches an active discussion of how to objectively define AI performance.…”

May the force be with you: will artificial intelligence take over traditional endoscopy?

“…Artificial intelligence (AI), first proposed in the 1950 s, uses computers to simulate certain thought processes and forms of intelligent behavior [2]. Machine learning is a branch of AI focusing on computer algorithms that can learn from data and perform specific tasks and analyses [3]. Deep learning is an advanced subset of machine learning that relies on specific algorithms termed artificial neural networks.…”

“…Deep learning is an advanced subset of machine learning that relies on specific algorithms termed artificial neural networks. AI technologies may assist physicians by assimilating and interpreting clinical data to support physician performance [3]. Most of these AI algorithms refer to the field of computer vision, using technologies that allow for recognition and interpretation of visual data (i. e. the live endoscopic image [4]), and fall into two main groupscomputer-aided detection (CADe) systems and computer-aided diagnosis (CADx) systems, both of which have already been extensively studied for polyp detection and characterization in colonoscopy [5 -7].…”

“…Further potential applications may be used to reduce physician burden, including automatization of documentation, repetitive and administrative tasks, as well as generation of reports and analytics. Moreover, computer-aided monitoring (CADm) systems have been designed to evaluate the examination procedure and improve the quality of endoscopy (e. g. completeness of mucosal visualization during screening colonoscopy [3]). AI may also be used to facilitate therapeutic procedures in endoscopy; future therapeutic AI algorithms could support physicians in determining the optimal treatment strategy as well as in performing therapeutic procedures (e. g. identification and delineation of tumor margins or labelling anatomical landmarks).…”