Artificial intelligence for polyp detection during colonoscopy: a systematic review and meta-analysis

Abstract Background Artificial intelligence (AI)-based polyp detection systems are used during colonoscopy with the aim of increasing lesion detection and improving colonoscopy quality. Patients and methods: We performed a systematic review and meta-analysis of prospective trials to determine the value of AI-based polyp detection systems for detection of polyps and colorectal cancer. We performed systematic searches in MEDLINE, EMBASE, and Cochrane CENTRAL. Independent reviewers screened studies and assessed eligibility, certainty of evidence, and risk of bias. We compared colonoscopy with and without AI by calculating relative and absolute risks and mean differences for detection of polyps, adenomas, and colorectal cancer. Results Five randomized trials were eligible for analysis. Colonoscopy with AI increased adenoma detection rates (ADRs) and polyp detection rates (PDRs) compared to colonoscopy without AI (values given with 95 %CI). ADR with AI was 29.6 % (22.2 % – 37.0 %) versus 19.3 % (12.7 % – 25.9 %) without AI; relative risk (RR] 1.52 (1.31 – 1.77), with high certainty. PDR was 45.4 % (41.1 % – 49.8 %) with AI versus 30.6 % (26.5 % – 34.6 %) without AI; RR 1.48 (1.37 – 1.60), with high certainty. There was no difference in detection of advanced adenomas (mean advanced adenomas per colonoscopy 0.03 for each group, high certainty). Mean adenomas detected per colonoscopy was higher for small adenomas (≤ 5 mm) for AI versus non-AI (mean difference 0.15 [0.12 – 0.18]), but not for larger adenomas (> 5 – ≤ 10 mm, mean difference 0.03 [0.01 – 0.05]; > 10 mm, mean difference 0.01 [0.00 – 0.02]; high certainty). Data on cancer are unavailable. Conclusions AI-based polyp detection systems during colonoscopy increase detection of small nonadvanced adenomas and polyps, but not of advanced adenomas.

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Impact of real-time use of artificial intelligence in improving adenoma detection during colonoscopy: A systematic review and meta-analysis

Endoscopy International Open ◽

10.1055/a-1341-0457 ◽

2021 ◽

Vol 09 (04) ◽

pp. E513-E521

Author(s):

Munish Ashat ◽

Jagpal Singh Klair ◽

Dhruv Singh ◽

Arvind Rangarajan Murali ◽

Rajesh Krishnamoorthi

Keyword(s):

Artificial Intelligence ◽

Systematic Review ◽

Meta Analysis ◽

Screening Colonoscopy ◽

Average Risk ◽

Polyp Detection ◽

Withdrawal Time ◽

Detection Rates ◽

Adenoma Detection ◽

The Impact

Abstract Background and study aims With the advent of deep neural networks (DNN) learning, the field of artificial intelligence (AI) is rapidly evolving. Recent randomized controlled trials (RCT) have investigated the influence of integrating AI in colonoscopy and its impact on adenoma detection rates (ADRs) and polyp detection rates (PDRs). We performed a systematic review and meta-analysis to reliably assess if the impact is statistically significant enough to warrant the adoption of AI -assisted colonoscopy (AIAC) in clinical practice. Methods We conducted a comprehensive search of multiple electronic databases and conference proceedings to identify RCTs that compared outcomes between AIAC and conventional colonoscopy (CC). The primary outcome was ADR. The secondary outcomes were PDR and total withdrawal time (WT). Results Six RCTs (comparing AIAC vs CC) with 5058 individuals undergoing average-risk screening colonoscopy were included in the meta-analysis. ADR was significantly higher with AIAC compared to CC (33.7 % versus 22.9 %; odds ratio (OR) 1.76, 95 % confidence interval (CI) 1.55–2.00; I2 = 28 %). Similarly, PDR was significantly higher with AIAC (45.6 % versus 30.6 %; OR 1.90, 95 %CI, 1.68–2.15, I2 = 0 %). The overall WT was higher for AIAC compared to CC (mean difference [MD] 0.46 (0.00–0.92) minutes, I2 = 94 %). Conclusions There is an increase in adenoma and polyp detection with the utilization of AIAC.

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New technologies improve adenoma detection rate, adenoma miss rate, and polyp detection rate: a systematic review and meta-analysis

Gastrointestinal Endoscopy ◽

10.1016/j.gie.2018.03.022 ◽

2018 ◽

Vol 88 (2) ◽

pp. 209-222.e11 ◽

Cited By ~ 20

Author(s):

Daniel Castaneda ◽

Violeta B. Popov ◽

Elijah Verheyen ◽

Praneet Wander ◽

Seth A. Gross

Keyword(s):

Systematic Review ◽

Detection Rate ◽

New Technologies ◽

Meta Analysis ◽

Adenoma Detection Rate ◽

Polyp Detection ◽

Polyp Detection Rate ◽

Adenoma Detection

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Artificial intelligence for pre-operative lymph node staging in colorectal cancer: a systematic review and meta-analysis

BMC Cancer ◽

10.1186/s12885-021-08773-w ◽

2021 ◽

Vol 21 (1) ◽

Author(s):

Sergei Bedrikovetski ◽

Nagendra N. Dudi-Venkata ◽

Hidde M. Kroon ◽

Warren Seow ◽

Ryash Vather ◽

...

Keyword(s):

Artificial Intelligence ◽

Colorectal Cancer ◽

Systematic Review ◽

Deep Learning ◽

Lymph Node ◽

Lymph Node Metastasis ◽

Meta Analysis ◽

Cochrane Library ◽

Learning Models ◽

Node Metastasis

Abstract Background Artificial intelligence (AI) is increasingly being used in medical imaging analysis. We aimed to evaluate the diagnostic accuracy of AI models used for detection of lymph node metastasis on pre-operative staging imaging for colorectal cancer. Methods A systematic review was conducted according to PRISMA guidelines using a literature search of PubMed (MEDLINE), EMBASE, IEEE Xplore and the Cochrane Library for studies published from January 2010 to October 2020. Studies reporting on the accuracy of radiomics models and/or deep learning for the detection of lymph node metastasis in colorectal cancer by CT/MRI were included. Conference abstracts and studies reporting accuracy of image segmentation rather than nodal classification were excluded. The quality of the studies was assessed using a modified questionnaire of the QUADAS-2 criteria. Characteristics and diagnostic measures from each study were extracted. Pooling of area under the receiver operating characteristic curve (AUROC) was calculated in a meta-analysis. Results Seventeen eligible studies were identified for inclusion in the systematic review, of which 12 used radiomics models and five used deep learning models. High risk of bias was found in two studies and there was significant heterogeneity among radiomics papers (73.0%). In rectal cancer, there was a per-patient AUROC of 0.808 (0.739–0.876) and 0.917 (0.882–0.952) for radiomics and deep learning models, respectively. Both models performed better than the radiologists who had an AUROC of 0.688 (0.603 to 0.772). Similarly in colorectal cancer, radiomics models with a per-patient AUROC of 0.727 (0.633–0.821) outperformed the radiologist who had an AUROC of 0.676 (0.627–0.725). Conclusion AI models have the potential to predict lymph node metastasis more accurately in rectal and colorectal cancer, however, radiomics studies are heterogeneous and deep learning studies are scarce. Trial registration PROSPERO CRD42020218004.

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Accuracy of the Quantitative Fecal Immunochemical Test (FIT) for Colorectal Cancer and Advanced Adenoma Detection: An Updated Systematic Review and Meta-Analysis: Presidential Poster Award

The American Journal of Gastroenterology ◽

10.14309/00000434-201810001-00252 ◽

2018 ◽

Vol 113 (Supplement) ◽

pp. S145

Author(s):

Emma Levine ◽

Kevin Selby ◽

Cecilia Doan ◽

Anton Gies ◽

Hermann Brenner ◽

...

Keyword(s):

Colorectal Cancer ◽

Systematic Review ◽

Meta Analysis ◽

Fecal Immunochemical Test ◽

Advanced Adenoma ◽

Adenoma Detection ◽

Poster Award ◽

Immunochemical Test

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Comparative Effectiveness of Fecal Immunochemical Tests versus Flexible Sigmoidoscopy for Colorectal Cancer Screening: A Systematic Review and Meta-Analysis of Randomized Clinical Trials

Journal of Gastrointestinal and Liver Diseases ◽

10.15403/jgld-3432 ◽

2021 ◽

Author(s):

Hemant Mutneja ◽

Rohit Agrawal ◽

Abhishek Bhurwal ◽

Shilpa Arora ◽

Andrew Go ◽

...

Keyword(s):

Colorectal Cancer ◽

Systematic Review ◽

Clinical Trials ◽

Randomized Clinical Trials ◽

Flexible Sigmoidoscopy ◽

Colorectal Neoplasia ◽

Meta Analysis ◽

Detection Rates ◽

Crc Screening ◽

Advanced Colorectal Neoplasia

Background and Aims: Fecal immunochemical tests (FITs) and flexible sigmoidoscopies are commonly used modalities for colorectal cancer (CRC) screening. We performed a systematic review and meta-analysis to compare the effectiveness of FIT and sigmoidoscopy in CRC screening. Methods: PRISMA statement and Cochrane guidelines were followed for this review. Digital dissertation databases were searched from inception till December 1st 2020 and randomized clinical trials comparing the detection rates of CRC for FIT and sigmoidoscopy were included. Outcomes for analysis included participation rates and detection rates of CRC, advanced adenomas and advanced colorectal neoplasia for both screening modalities. Results: Five randomized clinical trials with a total of 261,755 patients were included for the analysis. The participation rate for FIT was significantly higher compared to flexible sigmoidoscopy (OR 2.11, 95% CI 1.29-3.44, p=0.003). In intention-to-screen analysis, the detection rate for advanced colorectal neoplasia was significantly lower with FIT (OR 0.62, 95% CI 0.45-0.84, p=0.002) as compared to flexible sigmoidoscopy but not statistically different for CRC (OR 1.15, 95% CI 0.65-2.02, p=0.63). Conclusion: Despite lower participation amongst patients, CRC screening with flexible sigmoidoscopy leads to higher detection of advanced colorectal neoplasia, when compared to a single round of fecal immunochemical testing.

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