Optical classification of neoplastic colorectal polyps – a computer-assisted approach (the COACH study)

Renner, J; Phlipsen, Henrik; Haller, Bernhard; Navarro-Avila, F; Saint-Hill-Febles, Y; Mateus, Diana; Ponchon, Thierry; Poszler, A; Abdelhafez, Mohamed; Schmid, Roland M.; Delius, Stefan von; Klare, Peter

doi:10.1080/00365521.2018.1501092

Cited by 39 publications

(38 citation statements)

References 15 publications

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“…20 Renner et al developed a CAD system to distinguish neoplastic vs. non-neoplastic polyps using unmagnified endoscopic images. 21 Optical pathology was compared with histopathological diagnoses. The optical pathology CAD system achieved an accuracy of 78%, sensitivity of 92.3%, and NPV of 88.2% compared with histopathological diagnoses.…”

Section: Colon Polyp Optical Pathologymentioning

confidence: 99%

“…The optical pathology CAD system achieved an accuracy of 78%, sensitivity of 92.3%, and NPV of 88.2% compared with histopathological diagnoses. 21 Zachariah et al recently released the findings of a colon polyp AI with high optical pathology predictions. 22 Proposed in 2011 by the American Society of Gastrointestinal Endoscopy' s "Preservation and Incorporation of Valuable Endoscopic Innovations", the goal is to achieve an accuracy of >90% and an NPV of >90% for optical pathology compared with traditional histopathology.…”

Section: Colon Polyp Optical Pathologymentioning

confidence: 99%

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Artificial Intelligence in Gastrointestinal Endoscopy

et al. 2020

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Artificial intelligence (AI) is rapidly integrating into modern technology and clinical practice. Although in its nascency, AI has become a hot topic of investigation for applications in clinical practice. Multiple fields of medicine have embraced the possibility of a future with AI assisting in diagnosis and pathology applications. In the field of gastroenterology, AI has been studied as a tool to assist in risk stratification, diagnosis, and pathologic identification. Specifically, AI has become of great interest in endoscopy as a technology with substantial potential to revolutionize the practice of a modern gastroenterologist. From cancer screening to automated report generation, AI has touched upon all aspects of modern endoscopy. Here, we review landmark AI developments in endoscopy. Starting with broad definitions to develop understanding, we will summarize the current state of AI research and its potential applications. With innovation developing rapidly, this article touches upon the remarkable advances in AI-assisted endoscopy since its initial evaluation at the turn of the millennium, and the potential impact these AI models may have on the modern clinical practice. As with any discussion of new technology, its limitations must also be understood to apply clinical AI tools successfully. clin endosc 2020;53:132-141

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Section: Colon Polyp Optical Pathologymentioning

confidence: 99%

Section: Colon Polyp Optical Pathologymentioning

confidence: 99%

Artificial Intelligence in Gastrointestinal Endoscopy

et al. 2020

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“…The CAOB approach’s accuracy, sensitivity, and negative predictive value were 78.0, 92.3, and 88.2%, respectively. However, the accuracy obtained by two expert endoscopists was 84.0% ( p = 0.307) and 77.0% ( p = 1.000) and thus did not differ significantly from COAB predictions [ 63 ]. The studies mentioned above indicate that computer-assisted NBI image analysis may play a role in polyp characterization during colonoscopy.…”

Section: Polyp Characterizationmentioning

confidence: 99%

Scope of Artificial Intelligence in Screening and Diagnosis of Colorectal Cancer

Goyal

Mann

Gandhi

et al. 2020

JCM

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Globally, colorectal cancer is the third most diagnosed malignancy. It causes significant mortality and morbidity, which can be reduced by early diagnosis with an effective screening test. Integrating artificial intelligence (AI) and computer-aided detection (CAD) with screening methods has shown promising colorectal cancer screening results. AI could provide a “second look” for endoscopists to decrease the rate of missed polyps during a colonoscopy. It can also improve detection and characterization of polyps by integration with colonoscopy and various advanced endoscopic modalities such as magnifying narrow-band imaging, endocytoscopy, confocal endomicroscopy, laser-induced fluorescence spectroscopy, and magnifying chromoendoscopy. This descriptive review discusses various AI and CAD applications in colorectal cancer screening, polyp detection, and characterization.

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“…Since the 21st century, there have been signi cant advancements in arti cial intelligence (AI) technology, resulting in applications of AI in several aspects of medicine, particularly in aiding diagnosis. Speci cally, in gastroenterology, AI-assisted systems have been studied in various diseases including for the endoscopic detection and classi cation of colorectal cancer (13,14) , assessment of liver brosis and steatosis, and prediction of risk and disease outcomes of diseases using multiple clinical parameters. Recently, the application of AI to facilitate the diagnosis of cirrhosis and NAFLD/NASH has been gaining popularity and shown tremendous potential.…”

Section: Introductionmentioning

confidence: 99%

Application of Artificial Intelligence in Chronic Liver Diseases: A Systematic Review and Meta-analysis

Decharatanachart

Chaiteerakij

Tiyarattanachai

et al. 2020

Preprint

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Background: The gold standard for the diagnosis of liver fibrosis and nonalcoholic fatty liver disease (NAFLD) is liver biopsy. Various noninvasive modalities, e.g., ultrasonography, elastography and clinical predictive scores, have been used as alternatives to liver biopsy, with limited performance. Recently, artificial intelligence (AI) models have been developed and integrated into noninvasive diagnostic tools to improve their performance. Methods: We systematically searched for studies on AI-assisted diagnosis of liver fibrosis and NAFLD on MEDLINE, Scopus, Web of Science and Google Scholar. The pooled sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV) and diagnostic odds ratio (DOR) with their 95% confidence intervals (95%CIs) were calculated using a random effects model. A summary receiver operating characteristic curve and the area under the curve was generated to determine the diagnostic accuracy of the AI-assisted system. Subgroup analyses by diagnostic modalities were performed.Results: We included 17 studies reporting the performances of AI-assisted ultrasonography, elastrography, computed tomography (CT), magnetic resonance imaging (MRI) and clinical parameters for the diagnosis of liver fibrosis and steatosis. For the diagnosis of liver fibrosis, the pooled sensitivity, specificity, PPV, NPV and DOR were 0.78 (0.70 – 0.84), 0.88 (0.79 – 0.94), 0.73 (0.58 – 0.84), 0.91 (0.88 – 0.94) and 29.57 (10.65 – 82.10), respectively, for cirrhosis; 0.86 (0.80 – 0.91), 0.87 (0.79 – 0.92), 0.85 (0.74 – 0.91), 0.88 (0.82 – 0.92) and 37.95 (15.52 – 92.77), respectively; for advanced fibrosis; and 0.86 (0.78 – 0.92), 0.81 (0.77 – 0.84), 0.88 (0.80 – 0.93), 0.77 (0.58 – 0.89) and 26.79 (14.47 – 49.62), respectively, for significant fibrosis. Subgroup analyses showed significant differences in performance for the diagnosis of fibrosis among different modalities. The pooled sensitivity, specificity, PPV, NPV and DOR were 0.97 (0.76 – 1.00), 0.91 (0.78 – 0.97), 0.95 (0.87 – 0.98), 0.93 (0.80 – 0.98) and 191.52 (38.82 – 944.81), respectively, for the diagnosis of liver steatosis. Conclusions: AI-assisted systems have promising potential for the diagnosis of liver fibrosis and NAFLD. Validations of their performances are warranted before implementing these AI-assisted systems in clinical practice.Trial registration: The protocol was registered with PROSPERO (CRD42020183295).

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Optical classification of neoplastic colorectal polyps – a computer-assisted approach (the COACH study)

Cited by 39 publications

References 15 publications

Artificial Intelligence in Gastrointestinal Endoscopy

Artificial Intelligence in Gastrointestinal Endoscopy

Scope of Artificial Intelligence in Screening and Diagnosis of Colorectal Cancer

Application of Artificial Intelligence in Chronic Liver Diseases: A Systematic Review and Meta-analysis

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