Non-endoscopic Applications of Machine Learning in Gastric Cancer: A Systematic Review

ConclusionThe systematic review suggests that there are numerous examples of non-endoscopic applications of ML that are relevant to gastric cancer. These studies have utilized various specimens, even non-conventional ones, thus showing great promise for the development of more non-invasive techniques. However, most of these studies are still in the early stages and will take more time before they can be clinically deployed. Moving forward, researchers in this field of study are encouraged to improve data curation and annotation, improve model interpretability, and compare model performance with the currently accepted standard in the clinical practice.
Source: Journal of Gastrointestinal Cancer - Category: Cancer & Oncology Source Type: research