OTU-07 Near focus narrow and imaging driven artificial intelligence for the diagnosis of gastro-oesophageal reflux disease

Gulati, Shraddha; Bernth, Julius E.; Liao, Junkai; Poliyivets, Dmytro; Chatu, Sukhdev; Emmanuel, Andrew; Haji, Amyn; Liu, Hongbin; Hayee, Bu’Hussain

doi:10.1136/gutjnl-2019-bsgabstracts.7

Cited by 3 publications

(11 citation statements)

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“…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).…”

Section: Resultsmentioning

confidence: 99%

“…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 ,…”

Section: Resultsmentioning

confidence: 99%

“…52 The effectiveness of a real-time endoscopic GERD diagnosis and AI algorithm for prediction of EoE diagnosis were also shown. 53,55 A CAD tool demonstrated to recognise stationary manometry motor patterns with accuracy, 54 but the application of novel CAD tools to high-resolution manometry re-…”

Section: Discussionmentioning

confidence: 99%

“…In this setting, AI models autonomously extracted and analysed pH‐impedance tracings and also individuated a novel pH‐impedance metric that segregated responders to GERD treatment from non‐responders 52 . The effectiveness of a real‐time endoscopic GERD diagnosis and AI algorithm for prediction of EoE diagnosis were also shown 53,55 . A CAD tool demonstrated to recognise stationary manometry motor patterns with accuracy, 54 but the application of novel CAD tools to high‐resolution manometry recordings is yet to be evaluated.…”

Section: Discussionmentioning

confidence: 99%

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

Visaggi

Barberio

Gregori

et al. 2022

Aliment Pharmacol Ther

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Summary Background Artificial intelligence (AI) has recently been applied to endoscopy and questionnaires for the evaluation of oesophageal diseases (ODs). Aim We performed a systematic review with meta‐analysis to evaluate the performance of AI in the diagnosis of malignant and benign OD. Methods We searched MEDLINE, EMBASE, EMBASE Classic and the Cochrane Library. A bivariate random‐effect model was used to calculate pooled diagnostic efficacy of AI models and endoscopists. The reference tests were histology for neoplasms and the clinical and instrumental diagnosis for gastro‐oesophageal reflux disease (GERD). The pooled area under the summary receiver operating characteristic (AUROC), sensitivity, specificity, positive and negative likelihood ratio (PLR and NLR) and diagnostic odds ratio (DOR) were estimated. Results For the diagnosis of Barrett's neoplasia, AI had AUROC of 0.90, sensitivity 0.89, specificity 0.86, PLR 6.50, NLR 0.13 and DOR 50.53. AI models’ performance was comparable with that of endoscopists (P = 0.35). For the diagnosis of oesophageal squamous cell carcinoma, the AUROC, sensitivity, specificity, PLR, NLR and DOR were 0.97, 0.95, 0.92, 12.65, 0.05 and DOR 258.36, respectively. In this task, AI performed better than endoscopists although without statistically significant differences. In the detection of abnormal intrapapillary capillary loops, the performance of AI was: AUROC 0.98, sensitivity 0.94, specificity 0.94, PLR 14.75, NLR 0.07 and DOR 225.83. For the diagnosis of GERD based on questionnaires, the AUROC, sensitivity, specificity, PLR, NLR and DOR were 0.99, 0.97, 0.97, 38.26, 0.03 and 1159.6, respectively. Conclusions AI demonstrated high performance in the clinical and endoscopic diagnosis of OD.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

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

Visaggi

Barberio

Gregori

et al. 2022

Aliment Pharmacol Ther

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“…Changes in intrapapillary capillary loops and incremental increase as identified by NBI using deep CNN’s have been proposed as a disease marker for GERD. Near focus-NBI containing AI model has been described as a novel method for the diagnosis of GERD[ 16 ].…”

Section: Ai and Endoscopymentioning

confidence: 99%

Evolving role of artificial intelligence in gastrointestinal endoscopy

Parasher¹,

Wong²,

Rawat³

2020

WJG

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Artificial intelligence (AI) is a combination of different technologies that enable machines to sense, comprehend, and learn with human-like levels of intelligence. AI technology will eventually enhance human capability, provide machines genuine autonomy, and reduce errors, and increase productivity and efficiency. AI seems promising, and the field is full of invention, novel applications; however, the limitation of machine learning suggests a cautious optimism as the right strategy. AI is also becoming incorporated into medicine to improve patient care by speeding up processes and achieving greater accuracy for optimal patient care. AI using deep learning technology has been used to identify, differentiate catalog images in several medical fields including gastrointestinal endoscopy. The gastrointestinal endoscopy field involves endoscopic diagnoses and prognostication of various digestive diseases using image analysis with the help of various gastrointestinal endoscopic device systems. AI-based endoscopic systems can reliably detect and provide crucial information on gastrointestinal pathology based on their training and validation. These systems can make gastroenterology practice easier, faster, more reliable, and reduce inter-observer variability in the coming years. However, the thought that these systems will replace human decision making replace gastrointestinal endoscopists does not seem plausible in the near future. In this review, we discuss AI and associated various technological terminologies, evolving role in gastrointestinal endoscopy, and future possibilities.

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Artificial Intelligence in the Diagnosis of Upper Gastrointestinal Diseases

Visaggi

Bortoli

Barberio

et al. 2021

Journal of Clinical Gastroenterology

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Artificial intelligence (AI) has enormous potential to support clinical routine workflows and therefore is gaining increasing popularity among medical professionals. In the field of gastroenterology, investigations on AI and computer-aided diagnosis (CAD) systems have mainly focused on the lower gastrointestinal (GI) tract. However, numerous CAD tools have been tested also in upper GI disorders showing encouraging results. The main application of AI in the upper GI tract is endoscopy; however, the need to analyze increasing loads of numerical and categorical data in short times has pushed researchers to investigate applications of AI systems in other upper GI settings, including gastroesophageal reflux disease, eosinophilic esophagitis, and motility disorders. AI and CAD systems will be increasingly incorporated into daily clinical practice in the coming years, thus at least basic notions will be soon required among physicians. For noninsiders, the working principles and potential of AI may be as fascinating as obscure. Accordingly, we reviewed systematic reviews, meta-analyses, randomized controlled trials, and original research articles regarding the performance of AI in the diagnosis of both malignant and benign esophageal and gastric diseases, also discussing essential characteristics of AI.

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OTU-07 Near focus narrow and imaging driven artificial intelligence for the diagnosis of gastro-oesophageal reflux disease

Cited by 3 publications

References 0 publications

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

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

Evolving role of artificial intelligence in gastrointestinal endoscopy

Artificial Intelligence in the Diagnosis of Upper Gastrointestinal Diseases

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