Artificial intelligence in gastrointestinal endoscopy

Pannala, Rahul; Krishnan, Kumar; Melson, Joshua; Parsi, Mansour A.; Schulman, Allison R.; Sullivan, Shelby; Trikudanathan, Guru; Trindade, Arvind J.; Watson, Rabindra R.; Maple, John T.; Lichtenstein, David R.

doi:10.1016/j.vgie.2020.08.013

Cited by 53 publications

(39 citation statements)

References 100 publications

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“…, chromoendoscopy with lugol or NBI) have relatively low specificity[ 18 ]. As complex novel methods that rely heavily on endoscopic imaging such as endocytoscopy or volumetric laser endomicroscopy have been progressively implemented in clinical practice, the interpretation of large volumes of images has been noted to be a challenging and time-consuming issue[ 19 ]. Therefore, AI-assisted image interpretation has also found a use in identifying abnormalities in the image inputs[ 17 ].…”

Section: Upper Gastrointestinal Tractmentioning

confidence: 99%

Artificial intelligence in gastroenterology: A state-of-the-art review

Kröner¹,

Engels²,

Glicksberg³

et al. 2021

WJG

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The development of artificial intelligence (AI) has increased dramatically in the last 20 years, with clinical applications progressively being explored for most of the medical specialties. The field of gastroenterology and hepatology, substantially reliant on vast amounts of imaging studies, is not an exception. The clinical applications of AI systems in this field include the identification of premalignant or malignant lesions ( e.g. , identification of dysplasia or esophageal adenocarcinoma in Barrett’s esophagus, pancreatic malignancies), detection of lesions ( e.g. , polyp identification and classification, small-bowel bleeding lesion on capsule endoscopy, pancreatic cystic lesions), development of objective scoring systems for risk stratification, predicting disease prognosis or treatment response [ e.g. , determining survival in patients post-resection of hepatocellular carcinoma), determining which patients with inflammatory bowel disease (IBD) will benefit from biologic therapy], or evaluation of metrics such as bowel preparation score or quality of endoscopic examination. The objective of this comprehensive review is to analyze the available AI-related studies pertaining to the entirety of the gastrointestinal tract, including the upper, middle and lower tracts; IBD; the hepatobiliary system; and the pancreas, discussing the findings and clinical applications, as well as outlining the current limitations and future directions in this field.

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Section: Upper Gastrointestinal Tractmentioning

confidence: 99%

Artificial intelligence in gastroenterology: A state-of-the-art review

Kröner¹,

Engels²,

Glicksberg³

et al. 2021

WJG

View full text Add to dashboard Cite

show abstract

“…Its incidence has been falling steadily, mostly due to screening using colonoscopy and treatment of polyps that can effectively prevent the development of colon cancer [ 71 , 72 ]. The use of AI is increasingly studied to improve polyp and cancer detection in the colon [ 73 ].…”

Section: Colorectal Cancermentioning

confidence: 99%

Scope of Artificial Intelligence in Gastrointestinal Oncology

Goyal

Sherazi

Mann

et al. 2021

Cancers

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Gastrointestinal cancers are among the leading causes of death worldwide, with over 2.8 million deaths annually. Over the last few decades, advancements in artificial intelligence technologies have led to their application in medicine. The use of artificial intelligence in endoscopic procedures is a significant breakthrough in modern medicine. Currently, the diagnosis of various gastrointestinal cancer relies on the manual interpretation of radiographic images by radiologists and various endoscopic images by endoscopists. This can lead to diagnostic variabilities as it requires concentration and clinical experience in the field. Artificial intelligence using machine or deep learning algorithms can provide automatic and accurate image analysis and thus assist in diagnosis. In the field of gastroenterology, the application of artificial intelligence can be vast from diagnosis, predicting tumor histology, polyp characterization, metastatic potential, prognosis, and treatment response. It can also provide accurate prediction models to determine the need for intervention with computer-aided diagnosis. The number of research studies on artificial intelligence in gastrointestinal cancer has been increasing rapidly over the last decade due to immense interest in the field. This review aims to review the impact, limitations, and future potentials of artificial intelligence in screening, diagnosis, tumor staging, treatment modalities, and prediction models for the prognosis of various gastrointestinal cancers.

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“…The diagnostic performance of AI is used in endoscopic images to detect pre-cancerous and cancer lesions. The application of AI in H. pylori infection is to decrease interobserver disagreement and time consumption (Pannala et al, 2020). The development of AI potentially detects H. pylori infection by integrating data into endoscopic images.…”

Section: Artificial Intelligence For Predicting H Pylori Infection In Endoscopic Imagesmentioning

confidence: 99%

Endoscopic Diagnosis for H. pylori Infection: White Light Imaging (WLI) vs. Image-Enhanced Endoscopy (IEE)

Chatrangsun

Vilaichone

2021

Asian Pac J Cancer Prev

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infection. However, each technique has some limitations. For example, patients who use proton pump inhibitor PPI), antibiotics, anti-platelet, anti-coagulant, or direct oral anti-coagulant (DOAC) medications. Real-time endoscopy along with conventional white light imaging WLI) and image-enhanced endoscopic (IEE) techniques, such as narrow-band imaging (NBI), linked color imaging (LCI) and blue laser imaging (BLI), appear to have important roles in clinical practice to identify H. pylori-infected status (Malfertheiner et al., 2007). This article aimed to review the endoscopic diagnostic options and findings for H. pylori infection. Current diagnostic tests for H. pylori infectionMany diagnostic tests are available, including invasive

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Artificial intelligence in gastrointestinal endoscopy

Cited by 53 publications

References 100 publications

Artificial intelligence in gastroenterology: A state-of-the-art review

Artificial intelligence in gastroenterology: A state-of-the-art review

Scope of Artificial Intelligence in Gastrointestinal Oncology

Endoscopic Diagnosis for H. pylori Infection: White Light Imaging (WLI) vs. Image-Enhanced Endoscopy (IEE)

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