Real-time assessment of video images for esophageal squamous cell carcinoma invasion depth using artificial intelligence

Shimamoto, Yusaku; Ishihara, Ryu; Kato, Yusuke; Shoji, Ayaka; Inoue, Takahiro; Matsueda, Katsunori; Miyake, Muneaki; Waki, Kotaro; Kono, Mitsuhiro; Fukuda, Hiromu; Matsuura, Noriko; Nagaike, Koji; Aoi, Kenji; Yamamoto, Katsumi; Inoue, Takuya; Nakahara, Masanori; Nishihara, Akihiro; Tada, Takeshi

doi:10.1007/s00535-020-01716-5

Cited by 37 publications

(43 citation statements)

References 24 publications

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“…The accuracy, sensitivity, and specificity with ME were 89%, 71%, and 95% for the AI system and 84%, 42%, and 97% for expert endoscopists. Although the performance of AI was not perfect, it appeared to outperform endoscopists 32 …”

Section: The Use Of Artificial Intelligence In the Esophagusmentioning

confidence: 92%

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

Singh

Shin

et al. 2021

J of Gastro and Hepatol

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White‐light endoscopy with biopsy is the current gold standard modality for detecting and diagnosing upper gastrointestinal (GI) pathology. However, missed lesions remain a challenge. To overcome interobserver variability and learning curve issues, artificial intelligence (AI) has recently been introduced to assist endoscopists in the detection and diagnosis of upper GI neoplasia. In contrast to AI in colonoscopy, current AI studies for upper GI endoscopy are smaller pilot studies. Researchers currently lack large volume, well‐annotated, high‐quality datasets in gastric cancer, dysplasia in Barrett's esophagus and early esophageal squamous cell cancer. This review will look at the latest studies of AI in upper GI endoscopy, discuss some of the challenges facing researchers, and predict what the future may hold in this rapidly changing field.

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Section: The Use Of Artificial Intelligence In the Esophagusmentioning

confidence: 92%

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

Singh

Shin

et al. 2021

J of Gastro and Hepatol

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“…The accuracy was found to be 91.0%, with a performance similar to 16 experienced endoscopists. Shimamoto et al 41 . developed an AI system to distinguish EP‐SM1 from SM2‐3 in superficial ESCC using 102 video images consisting of two types: non‐ME with WLI and ME with NBI/blue‐laser imaging.…”

Section: Ai For Diagnosis Of Esophageal Squamous Cell Carcinomamentioning

confidence: 99%

Implementation of artificial intelligence in upper gastrointestinal endoscopy

Nagao

Tani

Shibata³

et al. 2022

DEN Open

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The application of artificial intelligence (AI) using deep learning has significantly expanded in the field of esophagogastric endoscopy. Recent studies have shown promising results in detecting and differentiating early gastric cancer using AI tools built using white light, magnified, or image‐enhanced endoscopic images. Some studies have reported the use of AI tools to predict the depth of early gastric cancer based on endoscopic images. Similarly, studies based on using AI for detecting early esophageal cancer have also been reported, with an accuracy comparable to that of endoscopy specialists. Moreover, an AI system, developed to diagnose pharyngeal cancer, has shown promising performance with high sensitivity. These reports suggest that, if introduced for regular use in clinical settings, AI systems can significantly reduce the burden on physicians. This review summarizes the current status of AI applications in the upper gastrointestinal tract and presents directions for clinical practice implementation and future research.

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“…The model achieved a higher sensitivity for SCC detection and higher accuracy for SCC characterization from normal tissue than endoscopic experts. Two studies by Nakagawa et al [ 31 ] and Shimamoto et al [ 32 ] aimed at developing models that predicted esophageal malignancy depth utilizing a DL model based on a CNN with a belief-propagation decoder using independent validation datasets. These models achieved an accuracy of 89.2% and 91% in predicting invasion depth, with sensitivities of 70.8% and 90.1%, and specificities of 94.4% and 95.8%, respectively.…”

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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Real-time assessment of video images for esophageal squamous cell carcinoma invasion depth using artificial intelligence

Cited by 37 publications

References 24 publications

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

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

Implementation of artificial intelligence in upper gastrointestinal endoscopy

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

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