Artificial intelligence in upper GI endoscopy ‐ current status, challenges and future promise

Yu, Honggang; Singh, Rajvinder; Shin, Seon Ho; Ho, Khek Yu

doi:10.1111/jgh.15354

Cited by 17 publications

(9 citation statements)

References 36 publications

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“…With further advances in technology, we do expect to see significant improvements in the performance of AI surpassing that of the endoscopist, and with the support of AI, it would also help different endoscopists achieve more consistent results. [17][18][19] This study also highlights the potential benefits of AI in augmenting endoscopy. In our study, the AI-based system took almost 20 times faster (677.14 vs 42.02 s) to evaluate and classify the 300 images as compared with the average time taken by the endoscopists.…”

Section: Discussionmentioning

confidence: 66%

Comparing artificial intelligence to humans for endoscopic diagnosis of gastric neoplasia: An external validation study

Quek

Lee

Zhu

et al. 2023

J of Gastro and Hepatol

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ObjectivesArtificial intelligence (AI) uses deep learning functionalities that may enhance the detection of early gastric cancer during endoscopy. An AI‐based endoscopic system for upper endoscopy was recently developed in Japan. We aim to validate this AI‐based system in a Singaporean cohort.MethodsThere were 300 de‐identified still images prepared from endoscopy video files obtained from subjects that underwent gastroscopy in National University Hospital (NUH). Five specialists and 6 non‐specialists (trainees) from NUH were assigned to read and categorize the images into “neoplastic” or “non‐neoplastic.” Results were then compared with the readings performed by the endoscopic AI system.ResultsThe mean accuracy, sensitivity, and specificity for the 11 endoscopists were 0.847, 0.525, and 0.872, respectively. These values for the AI‐based system were 0.777, 0.591, and 0.791, respectively. While AI in general did not perform better than endoscopists on the whole, in the subgroup of high‐grade dysplastic lesions, only 29.1% were picked up by the endoscopist rating, but 80% were classified as neoplastic by AI (P = 0.0011). The average diagnostic time was also faster in AI compared with endoscopists (677.1 s vs 42.02 s (P < 0.001).ConclusionWe demonstrated that an AI system developed in another health system was comparable in diagnostic accuracy in the evaluation of static images. AI systems are faster and not fatigable and may have a role in augmenting human diagnosis during endoscopy. With more advances in AI and larger studies to support its efficacy it would likely play a larger role in screening endoscopy in future.

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Section: Discussionmentioning

confidence: 66%

Comparing artificial intelligence to humans for endoscopic diagnosis of gastric neoplasia: An external validation study

Quek

Lee

Zhu

et al. 2023

J of Gastro and Hepatol

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“…Many studies have reported the development of AI models to detect gastric cancer, including our previous study, which showed favorable results [ 2 , 3 , 8 , 9 , 10 , 11 , 12 ]. All studies have used endoscopic static images to train AI models, whereas few have used endoscopic videos to test the AI model.…”

Section: Discussionmentioning

confidence: 99%

An Optimal Artificial Intelligence System for Real-Time Endoscopic Prediction of Invasion Depth in Early Gastric Cancer

Kim

Oh²,

Han

et al. 2022

Cancers

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We previously constructed a VGG-16 based artificial intelligence (AI) model (image classifier [IC]) to predict the invasion depth in early gastric cancer (EGC) using endoscopic static images. However, images cannot capture the spatio-temporal information available during real-time endoscopy—the AI trained on static images could not estimate invasion depth accurately and reliably. Thus, we constructed a video classifier [VC] using videos for real-time depth prediction in EGC. We built a VC by attaching sequential layers to the last convolutional layer of IC v2, using video clips. We computed the standard deviation (SD) of output probabilities for a video clip and the sensitivities in the manner of frame units to observe consistency. The sensitivity, specificity, and accuracy of IC v2 for static images were 82.5%, 82.9%, and 82.7%, respectively. However, for video clips, the sensitivity, specificity, and accuracy of IC v2 were 33.6%, 85.5%, and 56.6%, respectively. The VC performed better analysis of the videos, with a sensitivity of 82.3%, a specificity of 85.8%, and an accuracy of 83.7%. Furthermore, the mean SD was lower for the VC than IC v2 (0.096 vs. 0.289). The AI model developed utilizing videos can predict invasion depth in EGC more precisely and consistently than image-trained models, and is more appropriate for real-world situations.

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“…Several studies trained CNNs to aid early detection in GEA, recently summarized in a meta-analysis that found superiority of applying DL for detection of Barrett’s esophagus [ 132 , 133 , 134 , 135 , 136 , 137 ]. Currently, clinical trials are already investigating its sensitivity and specificity, if applied in a clinical setting, with several studies showing DL models to identify early GEA [ 138 , 139 ].…”

Section: Machine Learning—basic Concepts Specific Applications and Future Directions In Geamentioning

confidence: 99%

Machine Learning for Future Subtyping of the Tumor Microenvironment of Gastro-Esophageal Adenocarcinomas

Klein

Duda

2021

Cancers

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Tumor progression involves an intricate interplay between malignant cells and their surrounding tumor microenvironment (TME) at specific sites. The TME is dynamic and is composed of stromal, parenchymal, and immune cells, which mediate cancer progression and therapy resistance. Evidence from preclinical and clinical studies revealed that TME targeting and reprogramming can be a promising approach to achieve anti-tumor effects in several cancers, including in GEA. Thus, it is of great interest to use modern technology to understand the relevant components of programming the TME. Here, we discuss the approach of machine learning, which recently gained increasing interest recently because of its ability to measure tumor parameters at the cellular level, reveal global features of relevance, and generate prognostic models. In this review, we discuss the relevant stromal composition of the TME in GEAs and discuss how they could be integrated. We also review the current progress in the application of machine learning in different medical disciplines that are relevant for the management and study of GEA.

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Artificial intelligence in upper GI endoscopy ‐ current status, challenges and future promise

Cited by 17 publications

References 36 publications

Comparing artificial intelligence to humans for endoscopic diagnosis of gastric neoplasia: An external validation study

Comparing artificial intelligence to humans for endoscopic diagnosis of gastric neoplasia: An external validation study

An Optimal Artificial Intelligence System for Real-Time Endoscopic Prediction of Invasion Depth in Early Gastric Cancer

Machine Learning for Future Subtyping of the Tumor Microenvironment of Gastro-Esophageal Adenocarcinomas

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