Towards a robust and compact deep learning system for primary detection of early Barrett’s neoplasia: Initial image‐based results of training on a multi‐center retrospectively collected data set

Fockens, Kiki; Jukema, Jelmer; Boers, Tim; Jong, Martijn R.; Putten, Joost van der; Pouw, Roos E.; Weusten, Bas L.; Herrero, Lorenza Alvarez; Houben, Martin H.; Nagengast, Wouter B.; Westerhof, Jessie; Alkhalaf, A.; Mallant, Rosalie; Ragunath, Krish; Seewald, Stefan; Peter, Elbe; Barret, Maximilien; Fernández-Sordo, Jacobo Ortiz; Pech, Oliver; Beyna, Tors﻿ten; Sommen, Fons van der; With, P.H. de; Groof, Albert J. de; Bergman, Jacques J.G.H.M.

doi:10.1002/ueg2.12363

Cited by 16 publications

(6 citation statements)

References 27 publications

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“…Former AI trials on BE have usually compared the standalone performance of AI with the performance of endoscopists [28,29]. However, the stand-alone performance is only a small fraction of the equation because endoscopists may or may not follow the suggestions of AI.…”

Section: Discussionmentioning

confidence: 99%

“…However, the stand-alone performance is only a small fraction of the equation because endoscopists may or may not follow the suggestions of AI. Fockens et al compared AI performance with that of endoscopists and described, depending on the test set, a sensitivity of between 88% and 100% during an image-based study [28]. Abdelrahim et al demonstrated a sensitivity of more than 90% during a video-based study with 75 videos [29].…”

Section: Discussionmentioning

confidence: 99%

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Influence of artificial intelligence on the diagnostic performance of endoscopists in the assessment of Barrett’s esophagus: a tandem randomized and video trial

Meinikheim,

Mendel,

Palm

et al. 2024

Endoscopy

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Background and study aims To evaluate the effect of an AI-based clinical decision support system (AI) on the performance and diagnostic confidence of endoscopists during the assessment of Barrett's esophagus (BE). Patients and Methods Ninety-six standardized endoscopy videos were assessed by 22 endoscopists from 12 different centers with varying degrees of BE experience. The assessment was randomized into two video sets: Group A (review first without AI and second with AI) and group B (review first with AI and second without AI). Endoscopists were required to evaluate each video for the presence of Barrett's esophagus-related neoplasia (BERN) and then decide on a spot for a targeted biopsy. After the second assessment, they were allowed to change their clinical decision and confidence level. Results AI had a standalone sensitivity, specificity, and accuracy of 92.2%, 68.9%, and 81.6%, respectively. Without AI, BE experts had an overall sensitivity, specificity, and accuracy of 83.3%, 58.1 and 71.5%, respectively. With AI, BE nonexperts showed a significant improvement in sensitivity and specificity when videos were assessed a second time with AI (sensitivity 69.7% (95% CI, 65.2% - 74.2%) to 78.0% (95% CI, 74.0% - 82.0%); specificity 67.3% (95% CI, 62.5% - 72.2%) to 72.7% (95 CI, 68.2% - 77.3%). In addition, the diagnostic confidence of BE nonexperts improved significantly with AI. Conclusion BE nonexperts benefitted significantly from the additional AI. BE experts and nonexperts remained below the standalone performance of AI, suggesting that there may be other factors influencing endoscopists to follow or discard AI advice.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Influence of artificial intelligence on the diagnostic performance of endoscopists in the assessment of Barrett’s esophagus: a tandem randomized and video trial

Meinikheim,

Mendel,

Palm

et al. 2024

Endoscopy

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“…The advancement of artificial intelligence (AI) in diagnosing and managing Barrett's esophagus (BE) demonstrates considerable potential. Multiple methodologies, including computer-aided detection (CADe) systems, natural language processing (NLP), deep learning algorithms, and real-time and video Analysis, have shown impressive strides in enhancing diagnostic accuracy and efficiency [6,11,13,15]. This review aims to critically evaluate the empirical evidence supporting the use of these AI-based diagnostic systems in BE.…”

Section: Discussionmentioning

confidence: 99%

Artificial Intelligence in the Detection of Barrett's Esophagus: A Systematic Review

Patel,

Arora,

Roknsharifi

et al. 2023

Cureus

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Barrett's esophagus (BE) remains a significant precursor to esophageal adenocarcinoma, requiring accurate and efficient diagnosis and management. The increasing application of machine learning (ML) technologies presents a transformative opportunity for diagnosing and treating BE. This systematic review evaluates the effectiveness and accuracy of machine learning technologies in BE diagnosis and management by conducting a comprehensive search across PubMed, Scopus, and Web of Science databases up to the year 2023. The studies were organized into five categories: computer-aided systems, natural language processing and text-based systems, deep learning on histology and biopsy images, real-time and video analysis, and miscellaneous studies. Results indicate high sensitivity and specificity across machine learning applications. Specifically, computer-aided systems showed sensitivities ranging from 84% to 100% and specificities from 64% to 90.7%. Natural language processing and text-based systems achieved an accuracy as high as 98.7%. Deep learning techniques applied to histology and biopsy images displayed sensitivities up to greater than 90% and a specificity of 100%. Furthermore, real-time and video analysis technologies demonstrated high performance with assessment speeds of up to 48 frames per second (fps) and a mean average precision of 75.3%. Overall, the reviewed literature underscores the growing capability and efficiency of machine learning technologies in diagnosing and managing Barrett's esophagus, often outperforming traditional diagnostic methods. These findings highlight the promising future role of machine learning in enhancing clinical practice and improving patient care for Barrett's esophagus.

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“…The application of AI in BE neoplastic detection and delineation shows performance similar to that of expert endoscopists, which is higher than that of non-expert endoscopists [ 12 , 22 ]. Fockens et al also proved that the CAD system with high sensitivity (depending on the datasets, 88% and 100%) has better performance compared to general endoscopists, but with slightly lower specificity (64–66%) [ 23 ].…”

Section: Barrett’s Esophagus and Esophageal Adenocarcinomamentioning

confidence: 99%

“…A summary of the results of selected studies in the application of AI for the detection of neoplastic BE is shown in Table 1 [ 9 , 10 , 12 , 21 , 23 , 24 , 25 , 26 , 28 ].…”

Section: Barrett’s Esophagus and Esophageal Adenocarcinomamentioning

confidence: 99%

The Importance of Artificial Intelligence in Upper Gastrointestinal Endoscopy

Popovic,

Glisic,

Milosavljevic

et al. 2023

Diagnostics

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Recently, there has been a growing interest in the application of artificial intelligence (AI) in medicine, especially in specialties where visualization methods are applied. AI is defined as a computer’s ability to achieve human cognitive performance, which is accomplished through enabling computer “learning”. This can be conducted in two ways, as machine learning and deep learning. Deep learning is a complex learning system involving the application of artificial neural networks, whose algorithms imitate the human form of learning. Upper gastrointestinal endoscopy allows examination of the esophagus, stomach and duodenum. In addition to the quality of endoscopic equipment and patient preparation, the performance of upper endoscopy depends on the experience and knowledge of the endoscopist. The application of artificial intelligence in endoscopy refers to computer-aided detection and the more complex computer-aided diagnosis. The application of AI in upper endoscopy is aimed at improving the detection of premalignant and malignant lesions, with special attention on the early detection of dysplasia in Barrett’s esophagus, the early detection of esophageal and stomach cancer and the detection of H. pylori infection. Artificial intelligence reduces the workload of endoscopists, is not influenced by human factors and increases the diagnostic accuracy and quality of endoscopic methods.

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Towards a robust and compact deep learning system for primary detection of early Barrett’s neoplasia: Initial image‐based results of training on a multi‐center retrospectively collected data set

Cited by 16 publications

References 27 publications

Influence of artificial intelligence on the diagnostic performance of endoscopists in the assessment of Barrett’s esophagus: a tandem randomized and video trial

Influence of artificial intelligence on the diagnostic performance of endoscopists in the assessment of Barrett’s esophagus: a tandem randomized and video trial

Artificial Intelligence in the Detection of Barrett's Esophagus: A Systematic Review

The Importance of Artificial Intelligence in Upper Gastrointestinal Endoscopy

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