Artificial intelligence in luminal endoscopy

Abstract

Artificial intelligence is a strong focus of interest for global health development. Diagnostic endoscopy is an attractive substrate for artificial intelligence with a real potential to improve patient care through standardisation of endoscopic diagnosis and to serve as an adjunct to enhanced imaging diagnosis. The possibility to amass large data to refine algorithms makes adoption of artificial intelligence into global practice a potential reality. Initial studies in luminal endoscopy involve machine learning and are retrospective. Improvement in diagnostic performance is appreciable through the adoption of deep learning. Research foci in the upper gastrointestinal tract include the diagnosis of neoplasia, including Barrett’s, squamous cell and gastric where prospective and real-time artificial intelligence studies have been completed demonstrating a benefit of artificial intelligence–augmented endoscopy. Deep learning applied to small bowel capsule endoscopy also appears to enhance pathology detection and reduce capsule reading time. Prospective evaluation including the first randomised trial has been performed in the colon, demonstrating improved polyp and adenoma detection rates; however, these appear to be relevant to small polyps. There are potential additional roles of artificial intelligence relevant to improving the quality of endoscopic examinations, training and triaging of referrals. Further large-scale, multicentre and cross-platform validation studies are required for the robust incorporation of artificial intelligence–augmented diagnostic luminal endoscopy into our routine clinical practice.

Ultrasound Image Features under Decomposition Algorithm to Analyze the Nursing Intervention on Patients with Colon Polyps Undergoing Endoscopic Resection

“…Comprehensive and effective nursing intervention before and after surgery can effectively reduce the postoperative complications and help patients recover quickly [ 5 ]. With the development of endoscopes and auxiliary equipment, colonoscopy has made great progress in the diagnosis and treatment of colorectal diseases [ 6 ]. Colonoscopy can not only make correct judgments on various colorectal diseases but also becomes more and more important in terms of treatment [ 7 ].…”

Estimating the environmental impact of endoscopic activity at a tertiary center: a pilot study

“…The introduction of artificial intelligence (AI) and big data to clinical practice is expected to streamline the workflow in endoscopy units and the management of patient data [20,21]. Moreover, the rapid evolution of AI algorithms providing lesion detection (Computer-aided detection - CADe) and diagnosis (computer-aided diagnosis – CADx) is expected to mitigate the need for histological analysis in several situations [22]. The implementation of measures in gastroenterology units should be articulated with a broader policy for the healthcare system.…”

Untitled

“… 118 The rationalisation of endoscopy itself must go hand-in-hand with biopsy protocols and departments should agree protocols to minimise unnecessary use of histopathology. The advent of artificial intelligence in endoscopic diagnosis and characterisation may also help in reducing the need for histopathology in several settings, 119 but further work is required before the impact of such a pathway could be confirmed.…”