Su1741 COLORECTAL POLYP DIAGNOSIS WITH CONTEMPORARY ARTIFICIAL INTELLIGENCE

Kandel, Pujan; LaLonde, Rodney; Ciofoaia, Victor; Wallace, Michael B.; Bağcı, Ulaş

doi:10.1016/j.gie.2019.03.613

Cited by 8 publications

(5 citation statements)

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“…39 Further developments of CNNs include the 'deep capsule neural network' where the architecture of the CNN is further truncated whilst maintaining overall diagnostic accuracy including for the diagnosis of hyperplastic polyps, adenomas and serrated adenomas, traditionally difficult for CAD based algorithms to diagnose. 40…”

Section: Polyp Diagnosismentioning

confidence: 99%

“…WLE based models, although an attractive modality given its global use has unfortunately to date not performed as desired (accuracy approximately 70%) even using CNNs and a decent sample size . Further developments of CNNs include the ‘deep capsule neural network’ where the architecture of the CNN is further truncated whilst maintaining overall diagnostic accuracy including for the diagnosis of hyperplastic polyps, adenomas and serrated adenomas, traditionally difficult for CAD based algorithms to diagnose …”

Section: Artificial Intelligence/computer‐assisted Diagnosismentioning

confidence: 99%

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The future of endoscopy: Advances in endoscopic image innovations

Gulati

Patel

Emmanuel

et al. 2019

Digestive Endoscopy

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The latest state of the art technological innovations have led to a palpable progression in endoscopic imaging and may facilitate standardisation of practice. One of the most rapidly evolving modalities is artificial intelligence with recent studies providing real-time diagnoses and encouraging results in the first randomised trials to conventional endoscopic imaging. Advances in functional hypoxia imaging offer novel opportunities to be used to detect neoplasia and the assessment of colitis. Three-dimensional volumetric imaging provides spatial information and has shown promise in the increased detection of small polyps. Studies to date of self-propelling colonoscopes demonstrate an increased caecal intubation rate and possibly offer patients a more comfortable procedure. Further development in robotic technology has introduced ex vivo automated locomotor upper gastrointestinal and small bowel capsule devices. Eye-tracking has the potential to revolutionise endoscopic training through the identification of differences in experts and non-expert endoscopist as trainable parameters. In this review, we discuss the latest innovations of all these technologies and provide perspective into the exciting future of diagnostic luminal endoscopy.

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Section: Polyp Diagnosismentioning

confidence: 99%

Section: Artificial Intelligence/computer‐assisted Diagnosismentioning

confidence: 99%

The future of endoscopy: Advances in endoscopic image innovations

Gulati

Patel

Emmanuel

et al. 2019

Digestive Endoscopy

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“…More recent research uses deep learning models, which have shown significant improvements in classification accuracy. Most use transfer learning [ 15 ] with off-the-shelf models such as ResNet [ 16 ] and Inception [ 17 ]–[ 19 ]. Others have combined traditional methods with deep learning approaches, such as fusion of wavelets and convolutional neural network features [ 20 ].…”

Section: Background and Previous Workmentioning

confidence: 99%

Improving Colonoscopy Lesion Classification Using Semi-Supervised Deep Learning

et al. 2021

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While data-driven approaches excel at many image analysis tasks, the performance of these approaches is often limited by a shortage of annotated data available for training. Recent work in semi-supervised learning has shown that meaningful representations of images can be obtained from training with large quantities of unlabeled data, and that these representations can improve the performance of supervised tasks. Here, we demonstrate that an unsupervised jigsaw learning task, in combination with supervised training, results in up to a 9.8% improvement in correctly classifying lesions in colonoscopy images when compared to a fully-supervised baseline. We additionally benchmark improvements in domain adaptation and out-of-distribution detection, and demonstrate that semi-supervised learning outperforms supervised learning in both cases. In colonoscopy applications, these metrics are important given the skill required for endoscopic assessment of lesions, the wide variety of endoscopy systems in use, and the homogeneity that is typical of labeled datasets.

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“…62 Further refinement of DL with the 'deep capsule neural network' demonstrates the ability to diagnose and differentiate hyperplastic polyps, adenomas and serrated adenomas, traditionally difficult for existing AI models. 63 NBI. NBI-driven (Olympus, Tokyo, Japan) AI is the most extensively studied modality to date.…”

Section: Polyp Detectionmentioning

confidence: 99%

Artificial intelligence in luminal endoscopy

Gulati

Emmanuel

Patel

et al. 2020

Clin Med Insights Gastroenterol

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Artificial intelligence is a strong focus of interest for global health development. Diagnostic endoscopy is an attractive substrate for artificial intelligence with a real potential to improve patient care through standardisation of endoscopic diagnosis and to serve as an adjunct to enhanced imaging diagnosis. The possibility to amass large data to refine algorithms makes adoption of artificial intelligence into global practice a potential reality. Initial studies in luminal endoscopy involve machine learning and are retrospective. Improvement in diagnostic performance is appreciable through the adoption of deep learning. Research foci in the upper gastrointestinal tract include the diagnosis of neoplasia, including Barrett’s, squamous cell and gastric where prospective and real-time artificial intelligence studies have been completed demonstrating a benefit of artificial intelligence–augmented endoscopy. Deep learning applied to small bowel capsule endoscopy also appears to enhance pathology detection and reduce capsule reading time. Prospective evaluation including the first randomised trial has been performed in the colon, demonstrating improved polyp and adenoma detection rates; however, these appear to be relevant to small polyps. There are potential additional roles of artificial intelligence relevant to improving the quality of endoscopic examinations, training and triaging of referrals. Further large-scale, multicentre and cross-platform validation studies are required for the robust incorporation of artificial intelligence–augmented diagnostic luminal endoscopy into our routine clinical practice.

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Su1741 COLORECTAL POLYP DIAGNOSIS WITH CONTEMPORARY ARTIFICIAL INTELLIGENCE

Cited by 8 publications

References 0 publications

The future of endoscopy: Advances in endoscopic image innovations

The future of endoscopy: Advances in endoscopic image innovations

Improving Colonoscopy Lesion Classification Using Semi-Supervised Deep Learning

Artificial intelligence in luminal endoscopy

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