Detection of multiple lesions of gastrointestinal tract for endoscopy using artificial intelligence model: a pilot study

Systematic review with meta‐analysis: artificial intelligence in the diagnosis of oesophageal diseases

“…From these we identified 67 separate articles that appeared to be relevant to the study question. In total, 42 studies 17‐58 reported on the performance of AI in the diagnosis of various ODs and were included in the qualitative synthesis (Supplementary Table S1). Among the included studies, 19 17‐35 reported complete data for extraction and were included in the meta‐analysis: 9 on BN, 17‐25 5 on OSCC, 26‐30 2 on abnormal IPCLs 31,32 and 3 on GERD 33‐35 (Figure 2).…”

“…From these we identified 67 separate articles that appeared to be relevant to the study question. In total, 42 studies 17 , 18 , 19 , 20 , 21 , 22 , 23 , 24 , 25 , 26 , 27 , 28 , 29 , 30 , 31 , 32 , 33 , 34 , 35 , 36 , 37 , 38 , 39 , 40 , 41 , 42 , 43 , 44 , 45 , 46 , 47 , 48 , 49 , 50 , 51 , 52 , 53 , 54 , 55 , 56 , 57 , 58 reported on the performance of AI in the diagnosis of various ODs and were included in the qualitative synthesis (Supplementary Table S1 ). Among the included studies, 19 17 , 18 , 19 , 20 , 21 , 22 , 23 , 24 , 25 , 26 ,…”

“…Among the 42 studies included in the qualitative synthesis, 23 36 , 37 , 38 , 39 , 40 , 41 , 42 , 43 , 44 , 45 , 46 , 47 , 48 , 49 , 50 , 51 , 52 , 53 , 54 , 55 , 56 , 57 , 58 could not be included in the meta‐analysis for various reasons. They also included investigations on eosinophilic oesophagitis (n = 1), reflux monitoring (n = 1), optical endoscopic diagnosis of GERD (n = 1), motility assessment (n = 1), diagnosis of cytomegalovirus and herpes simplex virus oesophagitis (n = 1) and varices (n = 1).…”

“…They also included investigations on eosinophilic oesophagitis (n = 1), reflux monitoring (n = 1), optical endoscopic diagnosis of GERD (n = 1), motility assessment (n = 1), diagnosis of cytomegalovirus and herpes simplex virus oesophagitis (n = 1) and varices (n = 1). In particular, nine studies 38 , 39 , 41 , 42 , 43 , 44 , 48 , 49 , 50 did not report complete data for extraction, five studies 45 , 46 , 47 , 51 , 58 reported non‐poolable data, and nine 36 , 37 , 40 , 52 , 53 , 54 , 55 , 56 , 57 were the unique retrieved studies of their type. We included these studies in Supplementary Table S1 for completeness, but not in the final quantitative synthesis through meta‐analysis.…”

Automatic Classification of Hepatic Cystic Echinococcosis Using Ultrasound Images and Deep Learning

“…The subjectivity of diagnostic criteria to each sonographer may also result in a poor interobserver agreement. An alternative approach would be the use of the computer‐aided diagnosis (CAD) system, which has been applied in various diseases in the past few decades and achieved outstanding performance in most cases 16–20 . Recently, deep learning with convolutional neural networks (CNNs) has been gaining attention with respect to pattern recognition of images and as an artificial intelligence strategy used in CAD systems as it has distinct advantages over traditional machine learning methods in providing an end‐to‐end feature extraction and efficient classification framework to free users from the troublesome handcrafted feature extraction 21–24 .…”