scholarly journals The diagnostic accuracy of artificial intelligence and computer-aided diagnosis for the detection and characterisation of colorectal polyps: A systematic review and meta-analysis. (Preprint)

2021 ◽  
Author(s):  
Scarlet Nazarian ◽  
Ben Glover ◽  
Hutan Ashrafian ◽  
Ara Darzi ◽  
Julian Teare
Keyword(s):  
Diagnostic Accuracy ◽  
Meta Analysis ◽  
Computer Aided Diagnosis ◽  
Colorectal Polyps ◽  
Cochrane Library ◽  
Polyp Detection ◽  
Adenoma Detection ◽  
Computer Aided ◽  
The Cochrane Library ◽  
Aided Diagnosis

BACKGROUND Colonoscopy reduces the incidence of colorectal cancer by allowing detection and resection of neoplastic polyps. Evidence shows that many small polyps are missed on a single colonoscopy. There has been a successful adoption of AI technologies to tackle the issues around missed polyps and as a tool to increase adenoma detection rate (ADR). OBJECTIVE The aim of this review was to examine the diagnostic accuracy of AI-based technologies in assessing colorectal polyps. METHODS A comprehensive literature search was undertaken using the databases of EMBASE, Medline and the Cochrane Library. PRISMA guidelines were followed. Studies reporting use of computer-aided diagnosis for polyp detection or characterisation during colonoscopy were included. Independent proportion and their differences were calculated and pooled through DerSimonian and Laird random-effects modelling. RESULTS A total of 48 studies were included. The meta-analysis showed a significant increase in pooled PDR in patients with the use of AI for polyp detection during colonoscopy compared with patients who had standard colonoscopy (OR 1.75; 95% CI 1.56-1.96; p= 0.0005). When comparing patients undergoing colonoscopy with the use of AI to those without, there was also a significant increase in ADR (OR 1.53; 95% CI 1.32-1.77; p= 0005). CONCLUSIONS With the aid of machine learning, there is potential to improve ADR and consequently reduce the incidence of CRC. The current generation of AI-based systems demonstrate impressive accuracy for the detection and characterisation of colorectal polyps. However, this is an evolving field and before its adoption into a clinical setting, AI systems must prove worthy to patients and clinicians. CLINICALTRIAL Prospero registration - CRD42020169786

Optical diagnosis of colorectal polyp images using a newly developed computer-aided diagnosis system (CADx) compared to intuitive optical diagnosis

Endoscopy ◽  
2020 ◽  
Author(s):  
Quirine E.W. van der Zander ◽  
Ramon Michel Schreuder ◽  
Roger Fonollà ◽  
Thom Scheeve ◽  
Fons van der Sommen ◽  
...  
Keyword(s):  
Diagnostic Accuracy ◽  
Multimodal Imaging ◽  
Narrow Band Imaging ◽  
Computer Aided Diagnosis ◽  
Colorectal Polyps ◽  
Optical Diagnosis ◽  
High Definition ◽  
Diagnosis System ◽  
Computer Aided ◽  
Aided Diagnosis

Background: Optical diagnosis of colorectal polyps (CRPs) remains challenging. Imaging enhancement techniques such as narrow band imaging and blue light imaging (BLI) can improve optical diagnosis. We developed and prospectively validated a computer-aided diagnosis system (CADx) using high definition white light (HDWL) and BLI images, and compared it with the optical diagnosis of expert and novice endoscopists. Methods: The CADx characterized CRPs by exploiting artificial neural networks. Six experts and thirteen novices optically diagnosed 60 CRPs based on intuition. After a washout period of four weeks, the same set of CRPs was permuted and optically diagnosed using BASIC (BLI Adenoma Serrated International Classification). Results: The CADx had a diagnostic accuracy of 88.3% using HDWL images and 86.7% using BLI images. The overall diagnostic accuracy, combining HDWL and BLI (multimodal imaging), was 95.0% and significantly higher compared to experts (81.7%, p=0.031) and novices (66.5%, p<0.001). Sensitivity (95.6% vs. 61.1% and 55.4%) was also higher for CADx, while specificity was higher for experts compared to CADx and novices (94.1% vs 93.3% and 92.1%). For endoscopists, diagnostic accuracy did not increase using BASIC, neither for experts (Intuition 79.5% vs BASIC 81.7%, p=0.140) nor for novices (Intuition 66.7% vs BASIC 66.5%, p=0.953). Conclusion: The CADx had a significantly higher diagnostic accuracy than experts and novices for the optical diagnosis of CRPs. Multimodal imaging, incorporating both HDWL and BLI, improved the diagnostic accuracy of the CADx. BASIC did not increase the diagnostic accuracy of endoscopists compared to intuitive optical diagnosis.

Fr612 INFLUENCE OF COMPUTER AIDED DIAGNOSIS WITH AUGMENTED VISUALIZATION ON ENDOSCOPIC CLASSIFICATION OF COLORECTAL POLYPS

2021 ◽  
Vol 160 (6) ◽  
pp. S-376
Author(s):  
Eladio Rodriguez-Diaz ◽  
Gyorgy Baffy Wai-Kit Lo ◽  
Hiroshi Mashimo ◽  
Aparna Repaka ◽  
Alexander Goldowsky ◽  
...  
Keyword(s):  
Computer Aided Diagnosis ◽  
Colorectal Polyps ◽  
Computer Aided ◽  
Endoscopic Classification ◽  
Aided Diagnosis

Convolutional Neural Networks in Computer-Aided Diagnosis of Colorectal Polyps and Cancer: A Review

2021 ◽  
Author(s):  
Kamyab Keshtkar
Keyword(s):  
Colorectal Cancer ◽  
Neural Networks ◽  
Deep Learning ◽  
Computer Aided Diagnosis ◽  
Colorectal Polyps ◽  
Support Vector ◽  
Training Process ◽  
Computer Aided ◽  
Conventional Models ◽  
Aided Diagnosis

As a relatively high percentage of adenoma polyps are missed, a computer-aided diagnosis (CAD) tool based on deep learning can aid the endoscopist in diagnosing colorectal polyps or colorectal cancer in order to decrease polyps missing rate and prevent colorectal cancer mortality. Convolutional Neural Network (CNN) is a deep learning method and has achieved better results in detecting and segmenting specific objects in images in the last decade than conventional models such as regression, support vector machines or artificial neural networks. In recent years, based on the studies in medical imaging criteria, CNN models have acquired promising results in detecting masses and lesions in various body organs, including colorectal polyps. In this review, the structure and architecture of CNN models and how colonoscopy images are processed as input and converted to the output are explained in detail. In most primary studies conducted in the colorectal polyp detection and classification field, the CNN model has been regarded as a black box since the calculations performed at different layers in the model training process have not been clarified precisely. Furthermore, I discuss the differences between the CNN and conventional models, inspect how to train the CNN model for diagnosing colorectal polyps or cancer, and evaluate model performance after the training process.

Diagnostic Accuracy and Reading Time to Detect Intracranial Aneurysms on MR Angiography Using a Computer-Aided Diagnosis System

2008 ◽  
Vol 190 (2) ◽  
pp. 459-465 ◽  
Author(s):  
Shingo Kakeda ◽  
Yukunori Korogi ◽  
Hidetaka Arimura ◽  
Toshinori Hirai ◽  
Shigehiko Katsuragawa ◽  
...  
Keyword(s):  
Diagnostic Accuracy ◽  
Intracranial Aneurysms ◽  
Mr Angiography ◽  
Reading Time ◽  
Computer Aided Diagnosis ◽  
Diagnosis System ◽  
Computer Aided ◽  
Aided Diagnosis

Su1705 REAL-TIME COMPUTER-AIDED DIAGNOSIS OF DIMINUTIVE COLORECTAL POLYPS USING AN AUTOFLUORESCENCE IMAGING SYSTEM

2019 ◽  
Vol 89 (6) ◽  
pp. AB387
Author(s):  
Hideka Horiuchi ◽  
Naoto Tamai ◽  
Shunsuke Kamba ◽  
Hiroko Inomata ◽  
Tomohiko R. Ohya ◽  
...  
Keyword(s):  
Real Time ◽  
Imaging System ◽  
Computer Aided Diagnosis ◽  
Colorectal Polyps ◽  
Autofluorescence Imaging ◽  
Computer Aided ◽  
Aided Diagnosis
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