New artificial intelligence system: first validation study versus experienced endoscopists for colorectal polyp detection

Hassan, Cesare; Wallace, Michael B.; Sharma, Prateek; Maselli, Roberta; Craviotto, Vincenzo; Spadaccini, Marco; Repici, Alessandro

doi:10.1136/gutjnl-2019-319914

Cited by 133 publications

(98 citation statements)

References 7 publications

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“…Artificial intelligence, also known as machine learning, is a nonlinear mathematical modeling technology that is used extensively in modern day living, such as email communications, social media, web searching, stores and services, banking and finance, aviation and prediction of machinery failure, maps and directions, criminology, and war. 15,31 Recently, artificial intelligence is being increasingly used in clinical medicine including gastroenterology 32,33 endoscopy, 34 and hepatology, 15 radiology, 35 pathology, 36 dentistry, 37 oncology, 38 cardiology, 39 dermatology, 40 neurosurgery, 41 gynecology, 42 and in medical research, particularly big data analysis. Whereas convolutional neural network is the usual network used for image analysis, 43 feed-forward multilayer perceptron networks are the modeling technique for clinical prediction and have been used in the current study as well.…”

Section: Discussionmentioning

confidence: 99%

Prediction of outcome of treatment of acute severe ulcerative colitis using principal component analysis and artificial intelligence

et al. 2020

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Background and Aim: About 15% patients with acute severe ulcerative colitis (UC) fail to respond to medical treatment and may require colectomy. An early prediction of response may help the treating team and the patients and their family to prepare for alternative treatment options. Methods: Data of 263 patients (mean age 37.0 AE 14.0-years, 176, 77% male) with acute severe UC admitted during a 12-year period were used to study predictors of response using univariate analysis, multivariate linear principal component analysis (PCA), and nonlinear artificial neural network (ANN). Results: Of 263 patients, 231 (87.8%) responded to the initial medical treatment that included oral prednisolone (n = 14, 5.3%), intravenous (IV) hydrocortisone (n = 238, 90.5%), IV cyclosporine (n = 9, 3.4%), and inflixmab (n = 2, 0.7%), and 28 (10.6%) did not respond and the remaining 4 (1.5%) died, all of whom did were also nonresponders. Nonresponding patients had to stay longer in the hospital and died more often. On univariate analysis, the presence of complications, the need for use of cyclosporin, lower Hb, platelets, albumin, serum potassium, and higher C-reactive protein were predictors of nonresponse. Hb and albumin were strong predictive factors on both PCA and ANN. Though the nonlinear modeling using ANN had a good predictive accuracy for the response, its accuracy for predicting nonresponse was lower. Conclusion: It is possible to predict the response to medical treatment in patients with UC using linear and nonlinear modeling technique. Serum albumin and Hb are strong predictive factors.

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Section: Discussionmentioning

confidence: 99%

Prediction of outcome of treatment of acute severe ulcerative colitis using principal component analysis and artificial intelligence

et al. 2020

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“…In a validation study, GI Genius had an overall sensitivity per lesion of 99.7% and detected polyps faster than endoscopists in 82% of cases. 47 In a recent randomized controlled trial, Repici et al 48 demonstrated a 14% increase in adenoma detection rates using this CAD system.…”

Section: Ai-assisted Endoscopymentioning

confidence: 93%

History of artificial intelligence in medicine

Kaul

Enslin

Gross

2020

Gastrointestinal Endoscopy

455

231

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“…CADe systems are fed with data derived from video recordings of colonoscopies that are post-hoc revised by experts. Among them, a Convolutional Neural Network (GI-genius, Medtronic) for instance was trained and validated (99.7% sensitivity, 0.9% of activation noise due to false-positive frames)[ 23 ] using a video library of 2684 Lesions that were confirmed at histology, in a high-quality clinical trial[ 24 ]. This CAD system collects input from the digital frames of the standard endoscopic processor and outputs the coordinates of a signal box only when instance of the target lesions are recognized in the image.…”

Section: How Could Ai Help In Polyp Detectionmentioning

confidence: 99%

Use of artificial intelligence in improving adenoma detection rate during colonoscopy: Might both endoscopists and pathologists be further helped

Sinagra¹,

Badalamenti²,

Maida³

et al. 2020

WJG

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Colonoscopy remains the standard strategy for screening for colorectal cancer around the world due to its efficacy in both detecting adenomatous or pre-cancerous lesions and the capacity to remove them intra-procedurally. Computer-aided detection and diagnosis (CAD), thanks to the brand new developed innovations of artificial intelligence, and especially deep-learning techniques, leads to a promising solution to human biases in performance by guarantying decision support during colonoscopy. The application of CAD on real-time colonoscopy helps increasing the adenoma detection rate, and therefore contributes to reduce the incidence of interval cancers improving the effectiveness of colonoscopy screening on critical outcome such as colorectal cancer related mortality. Furthermore, a significant reduction in costs is also expected. In addition, the assistance of the machine will lead to a reduction of the examination time and therefore an optimization of the endoscopic schedule. The aim of this opinion review is to analyze the clinical applications of CAD and artificial intelligence in colonoscopy, as it is reported in literature, addressing evidence, limitations, and future prospects.

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New artificial intelligence system: first validation study versus experienced endoscopists for colorectal polyp detection

Cited by 133 publications

References 7 publications

Prediction of outcome of treatment of acute severe ulcerative colitis using principal component analysis and artificial intelligence

Prediction of outcome of treatment of acute severe ulcerative colitis using principal component analysis and artificial intelligence

History of artificial intelligence in medicine

Use of artificial intelligence in improving adenoma detection rate during colonoscopy: Might both endoscopists and pathologists be further helped

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