Deep Learning and Gastric Cancer: Systematic Review of AI-Assisted Endoscopy.
Eyal KlangAli SouroshGirish N NadkarniDennis McGonagleAdi LahatPublished in: Diagnostics (Basel, Switzerland) (2023)
The promise of artificial intelligence in improving and standardizing gastric neoplasia detection, diagnosis, and segmentation is significant. This review is limited by predominantly single-center studies and undisclosed datasets used in AI training, impacting generalizability and demographic representation. Further, retrospective algorithm training may not reflect actual clinical performance, and a lack of model details hinders replication efforts. More research is needed to substantiate these findings, including larger-scale multi-center studies, prospective clinical trials, and comprehensive technical reporting of DL algorithms and datasets, particularly regarding the heterogeneity in DL algorithms and study designs.
Keyphrases
- artificial intelligence
- deep learning
- big data
- machine learning
- systematic review
- convolutional neural network
- clinical trial
- case control
- rna seq
- virtual reality
- meta analyses
- single cell
- high grade
- emergency department
- adverse drug
- randomized controlled trial
- quality improvement
- label free
- loop mediated isothermal amplification
- finite element analysis