The impact of deep convolutional neural network‐based artificial intelligence on colonoscopy outcomes: A systematic review with meta‐analysis

Aziz, Muhammad; Fatima, Rawish; Dong, Charles; Lee‐Smith, Wade; Nawras, Ali

doi:10.1111/jgh.15070

Cited by 61 publications

(56 citation statements)

References 29 publications

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“…A recent meta-analysis focused on diagnostic performance of AI systems but not on direct comparison with HD colonoscopy for ADR [15] . Few other meta-analyses have recently been published highlighting similar findings as this study [ [20] , [21] , [22] ]. However, this study differs in the reporting of expanded pooled rates of colonoscopy parameters including the scope withdrawal time.…”

Section: Discussionsupporting

confidence: 88%

Real-time computer aided colonoscopy versus standard colonoscopy for improving adenoma detection rate: A meta-analysis of randomized-controlled trials

et al. 2020

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Background: Recent prospective randomized controlled trials have evaluated deep convolutional neural network (CNN) based computer aided detection (CADe) of lesions in real-time colonoscopy. We conducted this meta-analysis to compare the adenoma detection rate (ADR) of deep CNN based CADe assisted colonoscopy to standard colonoscopy (SC) from randomized controlled trials (RCTs). Methods: Multiple databases were searched (from inception to May 2020) and parallel RCTs that compared deep CNN based CADe assisted colonoscopy to SC were included for this analysis. Using Mantel-Haenzel (M-H) random effects model, pooled risk ratios (RR) and mean difference (MD) were calculated. In between study heterogeneity was assessed by I 2 % values. Outcomes assessed included other per patient adenoma parameters. Findings: Six RCTs were included in our final analysis that utilized deep CNN based CADe system in real-time colonoscopy. Total numbers of patients assessed were 4962 (2480 in CADe and 2482 in SC group). CADe based colonoscopy demonstrated statistically higher pooled ADR, RR=1.5 (95% CI 1.3À1.72), p<0.0001, I 2 =56%; and pooled PDR, RR=1.42 (95% CI 1.33À1.51), p<0.00001, I 2 =9%; when compared to SC. Per patient adenoma detection parameters were significantly better with CADe colonoscopy when compared to SC, with increased scope withdrawal time (mean difference = 0.38, 95% CI 0.05À0.72, p = 0.02). Interpretation: Based on our meta-analysis, deep CNN based CADe colonoscopy achieved significantly higher ADR metrics, albeit with increased scope withdrawal time when compared to SC.

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Section: Discussionsupporting

confidence: 88%

Real-time computer aided colonoscopy versus standard colonoscopy for improving adenoma detection rate: A meta-analysis of randomized-controlled trials

et al. 2020

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“…A recent study 12 found that AI methods working with 2 D colonoscopy images produced a high rate of false positives and produced miss rates comparable to experienced practitioners. The existence of false positives was also noted in 11 as an issue encountered with the AI method studied.…”

Section: Discussionmentioning

confidence: 92%

“…In the next stage of development, the system can employ AI and deep learning algorithms to further improve polyp detection. The availability of both 3 D and 2 D data as inputs to the AI, as opposed to only the traditional 2 D image data used in previously studied systems 10 11 , is expected to eliminate problems associated with the lack of strong features common in colonoscopy images, and thus improve the accuracy of operation. A recent study 12 found that AI methods working with 2 D colonoscopy images produced a high rate of false positives and produced miss rates comparable to experienced practitioners.…”

Section: Discussionmentioning

confidence: 99%

Novel polyp detection technology for colonoscopy: 3D optical scanner

Refai¹,

Koudsi²,

Kudsi

2020

Endosc Int Open

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Background and study aims Fifty-eight percent of American adults aged 50 to 75 undergo colonoscopies. Multiple factors result in missed lesions, at a rate of approximately 20 %, potentially subjecting patients to colorectal cancer. We report on use of a miniaturized optical scanner and accompanying processing software capable of detecting, measuring, and locating polyps with sub-millimeter accuracy, all in real time. Materials and methods A prototype 3 D optical scanner was developed that fits within the dimensions of a standard endoscope. After calibration, the system was evaluated in an ex-vivo porcine colon model, using silicon-made polyps. Results The average distance between two adjacent points in the 3 D point cloud was 94 µm. The results demonstrate high-accuracy measurements and 3 D models while operating at short distances. The scanner detected 6 mm × 3 mm polyps in every trial and identified polyp location with 95-µm accuracy. Registration errors were less than 0.8 % between point clouds based on physical features. Conclusion We demonstrated that a novel 3 D optical scanning system improves the performance of colonoscopy procedures by using a combination of 3 D and 2 D optical scanning and fast, accurate software for extracting data and generating models. Further studies of the system are warranted.

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“…Current efforts are directed to increase the ADR for endoscopists by using numerous interventions (electronic chromoendoscopy, add-on devices, antispasmodic medications, multiple observers, water-aided methods, second forward exam or retroflexion in right colon, artificial intelligence) 7 8 9 10 11 12 13 . Two such add-on devices used in recent times are Endocuff (ECU) and Endocuff Vision (ECV) (Arc Medical Design, Leeds, UK) 14 .…”

Section: Introductionmentioning

confidence: 99%

Efficacy of Endocuff Vision compared to first-generation Endocuff in adenoma detection rate and polyp detection rate in high-definition colonoscopy: a systematic review and network meta-analysis

et al. 2021

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Background and study aims Recently, the newer Endocuff Vision (ECV) has been evaluated for improving colonoscopy outcome metrics such as adenoma detection rate (ADR) and polyp detection rate (PDR). Due to lack of direct comparative studies between ECV and original Endocuff (ECU), we performed a systematic review and network meta-analysis to evaluate these outcomes. Methods The following databases were searched: PubMed, Embase, Cochrane, and Web of Sciences to include randomized controlled trials (RCTs) comparing ECV or ECU colonoscopy to high-definition (HD) colonoscopy. Direct as well as network meta-analyses comparing ADR and PDR were performed using a random effects model. Relative-risk (RR) with 95 % confidence interval (CI) was calculated. Results A total of 12 RCTs with 8638 patients were included in the final analysis. On direct meta-analysis, ECV did not demonstrate statistically improved ADR compared to HD colonoscopy (RR: 1.12, 95 % CI 0.99–1.27). A clinically and statistically improved PDR was noted for ECV compared to HD (RR: 1.15, 95 % CI 1.03–1.28) and ECU compared to HD (RR: 1.26, 95 % CI 1.09–1.46) as well as improved ADR (RR: 1.22, 95 % CI 1.05–1.43) was observed for ECU colonoscopy when compared to HD colonoscopy. These results were also consistent on network meta-analysis. Lower overall complication rates (RR: 0.14, 95 % CI 0.02–0.84) and particularly lacerations/erosions (RR: 0.11, 95 % CI 0.02–0.70) were noted with ECV compared to ECU colonoscopy. Conclusions Although safe, the newer ECV did not significantly improve ADR compared to ECU and HD colonoscopy. Further device modification is needed to increase the overall ADR and PDR.

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The impact of deep convolutional neural network‐based artificial intelligence on colonoscopy outcomes: A systematic review with meta‐analysis

Cited by 61 publications

References 29 publications

Real-time computer aided colonoscopy versus standard colonoscopy for improving adenoma detection rate: A meta-analysis of randomized-controlled trials

Real-time computer aided colonoscopy versus standard colonoscopy for improving adenoma detection rate: A meta-analysis of randomized-controlled trials

Novel polyp detection technology for colonoscopy: 3D optical scanner

Efficacy of Endocuff Vision compared to first-generation Endocuff in adenoma detection rate and polyp detection rate in high-definition colonoscopy: a systematic review and network meta-analysis

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