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.

Keywords: computerised image analysis; endoscopy; gastrointesinal endoscopy.

Conflict of interest statement

Competing interests: None declared.

© Author(s) (or their employer(s)) 2020. Re-use permitted under CC BY. Published by BMJ.

Figures

Figure 1

Graphical display of overfitting of training data. In this figure, the leftmost panel displays data points of two classes, in which the class is indicated by the colour. The centre panel shows the same data including the prediction of a model trained on that data as the background colour. Overfitting is clearly visible as the model isolates points of the red class, rather than capturing the class as a whole. The rightmost panel shows the prediction of a different model as background colour. Although this model makes mistakes (red points can be seen on a blue background and vice versa), this model demonstrates better generalisation, as it captures the class distributions rather than individual points.

Figure 2

Visualisation of training, validation and test set and overfitting, and their appropriate use. The training dataset is used to train the model, followed by validation. In case of unsatisfactory performance, the model is changed, retrained and again validated. In case of satisfactory performance, the model is then tested on a separate test set to evaluate model performance.

Figure 3

Graphical display of fourfold cross-validation.

Figure 4

Exemplary case of subtle Barrett’s neoplasia, delineated by three experts (yellow, blue and green). Parts of the lesion (‘the sweet spot’) are recognised by all experts (black), yet other parts are only recognised by one or two experts. Reprinted from Bergman J, de Groof AJ, Pech O, et al. An interactive web-based educational tool improves detection and delineation of Barrett's esophagus-related neoplasia. Gastroenterology 2019;156:1299-1308, with permission from Elsevier.