Artificial intelligence in colonoscopy: A review on the current status

Larsen, Solveig Linnea Veen; Mori, Yuichi

doi:10.1002/deo2.109

Cited by 10 publications

(4 citation statements)

References 36 publications

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“…CADe involves real-time image analysis to detect polyps, with the aim of increasing the ADR and decreasing the AMR [ 7 , 25 ]. Studies have shown the ADR to be inversely related to the risk of interval colorectal cancer and mortality [ 23 , 26 ], and a US study of more than 300,000 colonoscopies reported a 3% decrease in the risk of interval colorectal cancer for every 1% increase in the ADR [ 27 ]. CADe systems have enabled the real-time AI-aided detection of adenomas with higher sensitivity than the average endoscopist [ 28 ].…”

Section: Ai In Colonoscopymentioning

confidence: 99%

“…Studies have reported a minimum number of 100 to 200 colonoscopy procedures to reach technical competence [ 66 – 68 ], with an average training period of 4 years in the United Kingdom [ 69 ]. Thus, there is bound to be great variation amongst endoscopists in terms of skill [ 26 ]. A US study reported differential ADR rates of 7.4% to 52.5% between endoscopists [ 27 ].…”

Section: Future Directions For Ai In Colonoscopymentioning

confidence: 99%

“…AI riod of 4 years in the United Kingdom [69]. Thus, there is bound to be great variation amongst endoscopists in terms of skill [26].…”

Section: Annals Of Coloproctologymentioning

confidence: 99%

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The imitation game: a review of the use of artificial intelligence in colonoscopy, and endoscopists’ perceptions thereof

Tham¹,

Koh²,

Ladlad³

et al. 2023

Ann Coloproctol

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The development of deep learning systems in artificial intelligence (AI) has enabled advances in endoscopy, and AI-aided colonoscopy has recently been ushered into clinical practice as a clinical decision-support tool. This has enabled real-time AI-aided detection of polyps with a higher sensitivity than the average endoscopist, and evidence to support its use has been promising thus far. This review article provides a summary of currently published data relating to AI-aided colonoscopy, discusses current clinical applications, and introduces ongoing research directions. We also explore endoscopists’ perceptions and attitudes toward the use of this technology, and discuss factors influencing its uptake in clinical practice.

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Section: Ai In Colonoscopymentioning

confidence: 99%

Section: Future Directions For Ai In Colonoscopymentioning

confidence: 99%

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The imitation game: a review of the use of artificial intelligence in colonoscopy, and endoscopists’ perceptions thereof

Tham¹,

Koh²,

Ladlad³

et al. 2023

Ann Coloproctol

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“…Images collected during colonoscopy, a common colorectal cancer screening method, can be used to train artificial intelligence (AI), machine learning (ML), and deep learning (DL) systems for computer-aided diagnosis (CADx) [ 10 ]. By analysing and interpreting the colonoscopy images, these diagnostic tools may facilitate a more precise diagnosis of colorectal cancer.…”

Section: Introductionmentioning

confidence: 99%

EnsemDeepCADx: Empowering Colorectal Cancer Diagnosis with Mixed-Dataset Features and Ensemble Fusion CNNs on Evidence-Based CKHK-22 Dataset

Raju

Venkatesh

2023

Bioengineering

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Colorectal cancer is associated with a high mortality rate and significant patient risk. Images obtained during a colonoscopy are used to make a diagnosis, highlighting the importance of timely diagnosis and treatment. Using techniques of deep learning could enhance the diagnostic accuracy of existing systems. Using the most advanced deep learning techniques, a brand-new EnsemDeepCADx system for accurate colorectal cancer diagnosis has been developed. The optimal accuracy is achieved by combining Convolutional Neural Networks (CNNs) with transfer learning via bidirectional long short-term memory (BILSTM) and support vector machines (SVM). Four pre-trained CNN models comprise the ADaDR-22, ADaR-22, and DaRD-22 ensemble CNNs: AlexNet, DarkNet-19, DenseNet-201, and ResNet-50. In each of its stages, the CADx system is thoroughly evaluated. From the CKHK-22 mixed dataset, colour, greyscale, and local binary pattern (LBP) image datasets and features are utilised. In the second stage, the returned features are compared to a new feature fusion dataset using three distinct CNN ensembles. Next, they incorporate ensemble CNNs with SVM-based transfer learning by comparing raw features to feature fusion datasets. In the final stage of transfer learning, BILSTM and SVM are combined with a CNN ensemble. The testing accuracy for the ensemble fusion CNN DarD-22 using BILSTM and SVM on the original, grey, LBP, and feature fusion datasets was optimal (95.96%, 88.79%, 73.54%, and 97.89%). Comparing the outputs of all four feature datasets with those of the three ensemble CNNs at each stage enables the EnsemDeepCADx system to attain its highest level of accuracy.

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