Mo1679 REAL-TIME ARTIFICIAL INTELLIGENCE “FULL COLONOSCOPY WORKFLOW” FOR AUTOMATIC DETECTION FOLLOWED BY OPTICAL BIOPSY OF COLORECTAL POLYPS

Byrne, Michael F.; Soudan, Florian; Henkel, Milagros; Oertel, Clemens; Chapados, Nicolas; Echagüe, Francisco J.; Ghalehjegh, Sina Hamidi; Guizard, Nicolas; Giguère, Sébastien; MacPhail, Margaret E.; Sullivan, Andrew W.; Chandelier, Florent; Rex, Douglas K.

doi:10.1016/j.gie.2018.04.2037

Cited by 3 publications

(2 citation statements)

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“…For the remaining 106 polyps histologically predicted with high confidence, the AI model demonstrated an accuracy of 94%, sensitivity of 98%, specificity of 83%, NPV of 97%, and positive predictive value (PPV) of 90%. In a significant study, Byrne et al [ 51 ] developed a new platform using three distinct AI CADe and CADx algorithms to provide endoscopists with a full workflow from detection to classification: An NBI light detector, a polyp detector, and an optical biopsy. The NBI light detector runs throughout the colonoscopy procedure to ensure the detection of all colorectal polyps with white light imaging, and the optical biopsy provides an accurate polyp classification using NBI light.…”

Section: Development Of Computer-assisted Diagnostic Systemsmentioning

confidence: 99%

Artificial intelligence-assisted colonoscopy: A review of current state of practice and research

Taghiakbari¹,

Mori²,

Renteln³

2021

WJG

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Colonoscopy is an effective screening procedure in colorectal cancer prevention programs; however, colonoscopy practice can vary in terms of lesion detection, classification, and removal. Artificial intelligence (AI)-assisted decision support systems for endoscopy is an area of rapid research and development. The systems promise improved detection, classification, screening, and surveillance for colorectal polyps and cancer. Several recently developed applications for AI-assisted colonoscopy have shown promising results for the detection and classification of colorectal polyps and adenomas. However, their value for real-time application in clinical practice has yet to be determined owing to limitations in the design, validation, and testing of AI models under real-life clinical conditions. Despite these current limitations, ambitious attempts to expand the technology further by developing more complex systems capable of assisting and supporting the endoscopist throughout the entire colonoscopy examination, including polypectomy procedures, are at the concept stage. However, further work is required to address the barriers and challenges of AI integration into broader colonoscopy practice, to navigate the approval process from regulatory organizations and societies, and to support physicians and patients on their journey to accepting the technology by providing strong evidence of its accuracy and safety. This article takes a closer look at the current state of AI integration into the field of colonoscopy and offers suggestions for future research.

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Section: Development Of Computer-assisted Diagnostic Systemsmentioning

confidence: 99%

Artificial intelligence-assisted colonoscopy: A review of current state of practice and research

Taghiakbari¹,

Mori²,

Renteln³

2021

WJG

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“…Deep Learning has also achieved great success in medical imaging on standard computer vision tasks, such as classification [36], detection [37], and segmentation [38]. However, only recently has Deep Learning been applied to problems in sensing and image reconstruction.…”

Section: Deep Learningmentioning

confidence: 99%

A Deep Learning Framework for Single-Sided Sound Speed Inversion in Medical Ultrasound

Feigin

Freedman

Anthony³

2020

IEEE Trans. Biomed. Eng.

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Objective: Ultrasound elastography is gaining traction as an accessible and useful diagnostic tool for such things as cancer detection and differentiation and thyroid disease diagnostics. Unfortunately, state of the art shear wave imaging techniques, essential to promote this goal, are limited to highend ultrasound hardware due to high power requirements; are extremely sensitive to patient and sonographer motion, and generally, suffer from low frame rates.Motivated by research and theory showing that longitudinal wave sound speed carries similar diagnostic abilities to shear wave imaging, we present an alternative approach using single sided pressure-wave sound speed measurements from channel data.Methods: In this paper, we present a single-sided sound speed inversion solution using a fully convolutional deep neural network. We use simulations for training, allowing the generation of limitless ground truth data.Results: We show that it is possible to invert for longitudinal sound speed in soft tissue at high frame rates. We validate the method on simulated data. We present highly encouraging results on limited real data.Conclusion: Sound speed inversion on channel data has significant potential, made possible in real time with deep learning technologies.Significance: Specialized shear wave ultrasound systems remain inaccessible in many locations. longitudinal sound speed and deep learning technologies enable an alternative approach to diagnosis based on tissue elasticity. High frame rates are possible.Index Terms-deep learning, inverse problems, ultrasound, sound speed inversion

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Detection of elusive polyps using a large-scale artificial intelligence system (with videos)

Livovsky

Veikherman

Golany

et al. 2021

Gastrointestinal Endoscopy

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Mo1679 REAL-TIME ARTIFICIAL INTELLIGENCE “FULL COLONOSCOPY WORKFLOW” FOR AUTOMATIC DETECTION FOLLOWED BY OPTICAL BIOPSY OF COLORECTAL POLYPS

Cited by 3 publications

References 0 publications

Artificial intelligence-assisted colonoscopy: A review of current state of practice and research

Artificial intelligence-assisted colonoscopy: A review of current state of practice and research

A Deep Learning Framework for Single-Sided Sound Speed Inversion in Medical Ultrasound

Detection of elusive polyps using a large-scale artificial intelligence system (with videos)

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