Machine learning in GI endoscopy: practical guidance in how to interpret a novel field

Abstract

There has been a vast increase in GI literature focused on the use of machine learning in endoscopy. The relative novelty of this field poses a challenge for reviewers and readers of GI journals. To appreciate scientific quality and novelty of machine learning studies, understanding of the technical basis and commonly used techniques is required. Clinicians often lack this technical background, while machine learning experts may be unfamiliar with clinical relevance and implications for daily practice. Therefore, there is an increasing need for a multidisciplinary, international evaluation on how to perform high-quality machine learning research in endoscopy. This review aims to provide guidance for readers and reviewers of peer-reviewed GI journals to allow critical appraisal of the most relevant quality requirements of machine learning studies. The paper provides an overview of common trends and their potential pitfalls and proposes comprehensive quality requirements in six overarching themes: terminology, data, algorithm description, experimental setup, interpretation of results and machine learning in clinical practice.

A risk-stratified approach to colorectal cancer prevention and diagnosis

“…The complexity and size of datasets generated by ‘–omic’ analyses will be challenging for the incorporation of these laboratory biomarker outputs into clinically useful risk models. However, the rapid pace of progress in the fields of artificial intelligence and machine learning holds great promise for the future use of complex data alongside clinical factors in risk models 59 . Such tools could potentially enable multiple complex factors such as ‘–omics’ to be more rapidly assessed and clinically applied.…”

Role of artificial intelligence in the diagnosis of oesophageal neoplasia: 2020 an endoscopic odyssey

“…Machine learning is the use of mathematical models to capture structure in data[ 7 ]. The algorithms improve automatically through experience and do not need to be explicitly programmed[ 8 ].…”

“…Overfitting means that the model will perform well on the training data but not on the unseen testing data. The test set is used to evaluate the performance of the predictive final model[ 7 ] (Figure 2 ).…”

Artificial intelligence-aided colonoscopy: Recent developments and future perspectives

“…While mucosal exposure depends on the endoscopist’s examination technique and the quality of bowel preparation, failure to recognise a polyp when visible on the endoscopy screen can be addressed and improved by the application of artificial intelligence (AI), or “deep learning” systems[ 8 , 9 ]. Contrary to human-programmed computer systems, “deep learning” systems autonomously learn to distinguish the characteristics within the images provided using multiple levels of processing[ 10 ]. In this way, AI systems can recognize discriminatory characteristics between images that differ from those commonly used and elaborated by the human brain.…”

“…Consequently, AI systems can flag the suspect area during the endoscopic examination. These systems have shown a high accuracy when retrospectively applied to still images or stored videos, and more recently have been tested in trials during endoscopic examinations[ 10 ]. The other domain in which AI is believed to have a considerable impact on everyday clinical practice is lesion characterization and aid in “optical diagnosis”.…”