Artificial intelligence for real-time detection of early esophageal cancer: another set of eyes to better visualize

Thakkar, Shyam; Kochhar, Gursimran

doi:10.1016/j.gie.2019.09.036

Cited by 15 publications

(10 citation statements)

References 9 publications

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“…When they analyzed 33 original videos of full-range normal esophagus, they acquired a SPE of 99.95% and 90.9% for per-frame and per-case analysis, respectively. The ability of this model to process each frame with a maximum time of 0.04 s and latency less than 0.1 s set a good example for future model optimization for real-time applications[ 80 ].…”

Section: Morphology-based Cadmentioning

confidence: 99%

Artificial intelligence-assisted esophageal cancer management: Now and future

Zhang

Guo

Yuan

et al. 2020

WJG

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Esophageal cancer poses diagnostic, therapeutic and economic burdens in high-risk regions. Artificial intelligence (AI) has been developed for diagnosis and outcome prediction using various features, including clinicopathologic, radiologic, and genetic variables, which can achieve inspiring results. One of the most recent tasks of AI is to use state-of-the-art deep learning technique to detect both early esophageal squamous cell carcinoma and esophageal adenocarcinoma in Barrett’s esophagus. In this review, we aim to provide a comprehensive overview of the ways in which AI may help physicians diagnose advanced cancer and make clinical decisions based on predicted outcomes, and combine the endoscopic images to detect precancerous lesions or early cancer. Pertinent studies conducted in recent two years have surged in numbers, with large datasets and external validation from multi-centers, and have partly achieved intriguing results of expert’s performance of AI in real time. Improved pre-trained computer-aided diagnosis algorithms in the future studies with larger training and external validation datasets, aiming at real-time video processing, are imperative to produce a diagnostic efficacy similar to or even superior to experienced endoscopists. Meanwhile, supervised randomized controlled trials in real clinical practice are highly essential for a solid conclusion, which meets patient-centered satisfaction. Notably, ethical and legal issues regarding the black-box nature of computer algorithms should be addressed, for both clinicians and regulators.

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Section: Morphology-based Cadmentioning

confidence: 99%

Artificial intelligence-assisted esophageal cancer management: Now and future

Zhang

Guo

Yuan

et al. 2020

WJG

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“…Moreover, attempts should be guided to exploit the use of videos rather than images to minimize the processing time and keep DL algorithms working at almost real-time level. Therefore, as suggested by Mori et al [ 73 ] and Thakkar et al [ 74 ], the AI systems may be treated as an extra pair of eyes to prevent the absence of subtle lesions.…”

Section: Challenges and Recommendationsmentioning

confidence: 99%

Deep learning for diagnosis of precancerous lesions in upper gastrointestinal endoscopy: A review

Yan

Wong

Qin

2021

WJG

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Upper gastrointestinal (GI) cancers are the leading cause of cancer-related deaths worldwide. Early identification of precancerous lesions has been shown to minimize the incidence of GI cancers and substantiate the vital role of screening endoscopy. However, unlike GI cancers, precancerous lesions in the upper GI tract can be subtle and difficult to detect. Artificial intelligence techniques, especially deep learning algorithms with convolutional neural networks, might help endoscopists identify the precancerous lesions and reduce interobserver variability. In this review, a systematic literature search was undertaken of the Web of Science, PubMed, Cochrane Library and Embase, with an emphasis on the deep learning-based diagnosis of precancerous lesions in the upper GI tract. The status of deep learning algorithms in upper GI precancerous lesions has been systematically summarized. The challenges and recommendations targeting this field are comprehensively analyzed for future research.

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“…Several studies evaluated the use of CAD to better discriminate neoplastic from nonneoplastic lesions of the esophagus by using ECS [ 94 ], including the model developed by Kodashima et al [ 95 ], which enabled microscopic visualization of the mucosa, and the one developed by Shin et al [ 96 ], which obtained a sensitivity and specificity of 87% and 97%, respectively. This model was subsequently improved by Quang et al [ 97 ] by incorporating full automation with real-time analysis (tablet-interfaced high-resolution endomicroscopy).…”

Section: Principal Applications Of Ai For Assessment Of Precanceromentioning

confidence: 99%

The Impact of Artificial Intelligence in the Endoscopic Assessment of Premalignant and Malignant Esophageal Lesions: Present and Future

Lazăr

Avram

et al. 2020

Medicina

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In the gastroenterology field, the impact of artificial intelligence was investigated for the purposes of diagnostics, risk stratification of patients, improvement in quality of endoscopic procedures and early detection of neoplastic diseases, implementation of the best treatment strategy, and optimization of patient prognosis. Computer-assisted diagnostic systems to evaluate upper endoscopy images have recently emerged as a supporting tool in endoscopy due to the risks of misdiagnosis related to standard endoscopy and different expertise levels of endoscopists, time-consuming procedures, lack of availability of advanced procedures, increasing workloads, and development of endoscopic mass screening programs. Recent research has tended toward computerized, automatic, and real-time detection of lesions, which are approaches that offer utility in daily practice. Despite promising results, certain studies might overexaggerate the diagnostic accuracy of artificial systems, and several limitations remain to be overcome in the future. Therefore, additional multicenter randomized trials and the development of existent database platforms are needed to certify clinical implementation. This paper presents an overview of the literature and the current knowledge of the usefulness of different types of machine learning systems in the assessment of premalignant and malignant esophageal lesions via conventional and advanced endoscopic procedures. This study makes a presentation of the artificial intelligence terminology and refers also to the most prominent recent research on computer-assisted diagnosis of neoplasia on Barrett’s esophagus and early esophageal squamous cell carcinoma, and prediction of invasion depth in esophageal neoplasms. Furthermore, this review highlights the main directions of future doctor–computer collaborations in which machines are expected to improve the quality of medical action and routine clinical workflow, thus reducing the burden on physicians.

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Artificial intelligence for real-time detection of early esophageal cancer: another set of eyes to better visualize

Cited by 15 publications

References 9 publications

Artificial intelligence-assisted esophageal cancer management: Now and future

Artificial intelligence-assisted esophageal cancer management: Now and future

Deep learning for diagnosis of precancerous lesions in upper gastrointestinal endoscopy: A review

The Impact of Artificial Intelligence in the Endoscopic Assessment of Premalignant and Malignant Esophageal Lesions: Present and Future

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