Artificial Intelligence-Assisted Colonoscopy for Detection of Colon Polyps: a Prospective, Randomized Cohort Study

Luo, Yi; Zhang, Yi; Liu, Ming; Lai, Yihong; Li, Panpan; Wang, Zhen; Xing, Tongyin; Huang, Ying; Li, Yue; Li, Aiming; Wang, Yadong; Luo, Xin; Liu, Side; Han, Zelong

doi:10.1007/s11605-020-04802-4

Cited by 65 publications

(40 citation statements)

References 15 publications

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“…Finally, AI algorithms have been previously applied to enhance the detection of colonic polyps[ 87 ] and distinguish among subtypes of neoplastic colorectal lesions[ 88 ] in the general population. Although patients with IBD who have extensive colitis have a significantly greater risk of colorectal cancer compared to the general population [ 89 , 90 ], there have been limited studies applying AI technologies to improve colorectal cancer surveillance or develop prediction risk models in patients with IBD.…”

Section: Resultsmentioning

confidence: 99%

Artificial intelligence applications in inflammatory bowel disease: Emerging technologies and future directions

Gubatan¹,

Levitte²,

Patel³

et al. 2021

WJG

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Inflammatory bowel disease (IBD) is a complex and multifaceted disorder of the gastrointestinal tract that is increasing in incidence worldwide and associated with significant morbidity. The rapid accumulation of large datasets from electronic health records, high-definition multi-omics (including genomics, proteomics, transcriptomics, and metagenomics), and imaging modalities (endoscopy and endomicroscopy) have provided powerful tools to unravel novel mechanistic insights and help address unmet clinical needs in IBD. Although the application of artificial intelligence (AI) methods has facilitated the analysis, integration, and interpretation of large datasets in IBD, significant heterogeneity in AI methods, datasets, and clinical outcomes and the need for unbiased prospective validations studies are current barriers to incorporation of AI into clinical practice. The purpose of this review is to summarize the most recent advances in the application of AI and machine learning technologies in the diagnosis and risk prediction, assessment of disease severity, and prediction of clinical outcomes in patients with IBD.

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Section: Resultsmentioning

confidence: 99%

Artificial intelligence applications in inflammatory bowel disease: Emerging technologies and future directions

Gubatan¹,

Levitte²,

Patel³

et al. 2021

WJG

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“…Studies in this systematic review included preclinical studies for polyp detection (Table 1 [18][19][20][21][22][23][24][25][26][27][28][29][30][31][32][33][34][35]), preclinical studies for polyp characterization (Table 2 [11,13,[36][37][38][39][40][41][42][43][44][45][46][47][48][49][50][51][52][53][54][55]), and recent RCTs (Table 3 [56][57][58][59][60][61][62][63]). The studies were all published between 2003 and 2020.…”

Section: Search Results and Characteristicsmentioning

confidence: 99%

Diagnostic Accuracy of Artificial Intelligence and Computer-Aided Diagnosis for the Detection and Characterization of Colorectal Polyps: Systematic Review and Meta-analysis

et al. 2021

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Background Colonoscopy reduces the incidence of colorectal cancer (CRC) 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 artificial intelligence (AI) technologies to tackle the issues around missed polyps and as tools to increase the 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 (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines were followed. Studies reporting the use of computer-aided diagnosis for polyp detection or characterization during colonoscopy were included. Independent proportions and their differences were calculated and pooled through DerSimonian and Laird random-effects modeling. Results A total of 48 studies were included. The meta-analysis showed a significant increase in pooled polyp detection rate in patients with the use of AI for polyp detection during colonoscopy compared with patients who had standard colonoscopy (odds ratio [OR] 1.75, 95% CI 1.56-1.96; P<.001). 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<.001). 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 characterization 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. Trial Registration PROSPERO International Prospective Register of Systematic Reviews CRD42020169786; https://www.crd.york.ac.uk/prospero/display_record.php?ID=CRD42020169786

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“…A number of research studies have already suggested AI has the potential to transform the healthcare sector particularly in areas where image recognition can be applied [ 35 – 37 ]. In terms of colorectal diseases, AI has been applied to colonic polyps, adenomas, colorectal cancer, ulcerative colitis and intestinal motility disorders [ 38 – 41 ]. Owing to the rapid development of AI technology, AI is bound to continually play an important role in the field of colorectal diagnosis and treatment [ 42 ].…”

Section: Discussionmentioning

confidence: 99%

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

et al. 2021

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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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Artificial Intelligence-Assisted Colonoscopy for Detection of Colon Polyps: a Prospective, Randomized Cohort Study

Cited by 65 publications

References 15 publications

Artificial intelligence applications in inflammatory bowel disease: Emerging technologies and future directions

Artificial intelligence applications in inflammatory bowel disease: Emerging technologies and future directions

Diagnostic Accuracy of Artificial Intelligence and Computer-Aided Diagnosis for the Detection and Characterization of Colorectal Polyps: Systematic Review and Meta-analysis

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

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