256 – Artificial Intelligence for Real-Time Multiple Polyp Detection with Identification, Tracking, and Optical Biopsy During Colonoscopy

Guizard, Nicolas; Ghalehjegh, Sina Hamidi; Henkel, Milagros; Ding, Liqiang; Shahidi, Neal; Jonathan, Garcia R.; Lahr, Rachel E.; Chandelier, Florent; Rex, Doug; Byrne, Michael F.

doi:10.1016/s0016-5085(19)36900-8

Cited by 15 publications

(4 citation statements)

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“…However, the design of such a system, particularly with seamless transition from detection to characterization of the same lesion, may be challenging. To date, no prospective study has been published that evaluates a system combining both CADe and CADx into one workflow, although demonstrations have been published as a video case report and abstract 27 28 . Future research should specifically address workflow challenges, such as the ability to reliably detect and characterize the same unique polyp when switching from white light to virtual chromoendoscopy, dealing with instances when multiple polyps are in view, and preferably avoiding the need for manual selection of a region of interest.…”

Section: Discussionmentioning

confidence: 99%

Establishing key research questions for the implementation of artificial intelligence in colonoscopy: a modified Delphi method

et al. 2020

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Background and Aims Artificial intelligence (AI) research in colonoscopy is progressing rapidly but widespread clinical implementation is not yet a reality. We aimed to identify the top implementation research priorities. Methods An established modified Delphi approach for research priority setting was used. Fifteen international experts, including endoscopists and translational computer scientists/engineers from 9 countries participated in an online survey over 9 months. Questions related to AI implementation in colonoscopy were generated as a long-list in the first round, and then scored in two subsequent rounds to identify the top 10 research questions. Results The top 10 ranked questions were categorised into 5 themes. Theme 1: Clinical trial design/end points (4 questions), related to optimum trial designs for polyp detection and characterisation, determining the optimal end-points for evaluation of AI and demonstrating impact on interval cancer rates. Theme 2: Technological Developments (3 questions), including improving detection of more challenging and advanced lesions, reduction of false positive rates and minimising latency. Theme 3: Clinical adoption/Integration (1 question) concerning effective combination of detection and characterisation into one workflow. Theme 4: Data access/annotation (1 question) concerning more efficient or automated data annotation methods to reduce the burden on human experts. Theme 5: Regulatory Approval (1 question) related to making regulatory approval processes more efficient. Conclusions This is the first reported international research priority setting exercise for AI in colonoscopy. The study findings should be used as a framework to guide future research with key stakeholders to accelerate the clinical implementation of AI in endoscopy.

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Section: Discussionmentioning

confidence: 99%

Establishing key research questions for the implementation of artificial intelligence in colonoscopy: a modified Delphi method

et al. 2020

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“…Mori and colleagues 17 designed a novel CAD that included two algorithm, a deep learning–based CAD for polyp detection with WLI, and an algorithm for optical biopsy by endocytoscopic images. Guizard and colleagues 46 developed a full work flow system using both WL and NBI, which was also able to tag polyps with unique identifiers that could be tracked throughout the procedure. Ozawa and colleagues designed a CNN-based CAD for both WLI and NBI, using a single-shot MultiBox detector that could detect and characterize a target object simultaneously.…”

Section: Full Workflow Systems (Cade + Cadx)mentioning

confidence: 99%

Artificial intelligence for identification and characterization of colonic polyps

Parsa

Byrne

2021

Clin Med Insights Gastroenterol

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Colonoscopy remains the gold standard exam for colorectal cancer screening due to its ability to detect and resect pre-cancerous lesions in the colon. However, its performance is greatly operator dependent. Studies have shown that up to one-quarter of colorectal polyps can be missed on a single colonoscopy, leading to high rates of interval colorectal cancer. In addition, the American Society for Gastrointestinal Endoscopy has proposed the “resect-and-discard” and “diagnose-and-leave” strategies for diminutive colorectal polyps to reduce the costs of unnecessary polyp resection and pathology evaluation. However, the performance of optical biopsy has been suboptimal in community practice. With recent improvements in machine-learning techniques, artificial intelligence–assisted computer-aided detection and diagnosis have been increasingly utilized by endoscopists. The application of computer-aided design on real-time colonoscopy has been shown to increase the adenoma detection rate while decreasing the withdrawal time and improve endoscopists’ optical biopsy accuracy, while reducing the time to make the diagnosis. These are promising steps toward standardization and improvement of colonoscopy quality, and implementation of “resect-and-discard” and “diagnose-and-leave” strategies. Yet, issues such as real-world applications and regulatory approval need to be addressed before artificial intelligence models can be successfully implemented in clinical practice. In this review, we summarize the recent literature on the application of artificial intelligence for detection and characterization of colorectal polyps and review the limitation of existing artificial intelligence technologies and future directions for this field.

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“…This would necessitate simultaneous detection and characterization of polyps during the procedure. A recent and impressive example of this has been simultaneous detection, tracking and characterization of polyps during standard white light colonoscopy [54] (Fig. 5).…”

Section: Simultaneous Polyp Detection and Characterizationmentioning

confidence: 99%

Artificial Intelligence for Diagnosing G.I. Tract Lesions

Chahal

Shahidi

Byrne

2021

Gastrointestinal and Pancreatico-Biliary Diseases: Advanced Diagnostic and Therapeutic Endoscopy

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Artificial intelligence (AI) has emerged as a powerful tool to aid endoscopists in the diagnosis of benign and malignant gastrointestinal (GI) lesions. Herein, we will discuss how AI is being used to detect and characterize gastrointestinal lesions and assess malignant lesion depth of invasion, as well as its application in capsule endoscopy and inflammatory bowel disease. We will briefly touch on the challenges of incorporating AI into clinical practice, including workflow integration, data storage, data privacy, and technology regulation.

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256 – Artificial Intelligence for Real-Time Multiple Polyp Detection with Identification, Tracking, and Optical Biopsy During Colonoscopy

Cited by 15 publications

References 0 publications

Establishing key research questions for the implementation of artificial intelligence in colonoscopy: a modified Delphi method

Establishing key research questions for the implementation of artificial intelligence in colonoscopy: a modified Delphi method

Artificial intelligence for identification and characterization of colonic polyps

Artificial Intelligence for Diagnosing G.I. Tract Lesions

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