The application of artificial intelligence in improving colonoscopic adenoma detection rate: Where are we and where are we going

Gan, Peiling; Li, Peiling; Xia, Huifang; Zhou, Xian; Tang, Xiaowei

doi:10.1016/j.gastrohep.2022.03.009

Cited by 4 publications

(2 citation statements)

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“…Furthermore, classifiers such as the multilayer perceptron (MLP), random forest (RF), and decision tree (DT) can accurately predict the survival of patients with cancer [ 13 ]. More and more machine learning methods have been applied in cancer diagnosis and prognosis and have shown great potential [ 14 , 15 , 16 ].…”

Section: Introductionmentioning

confidence: 99%

A machine learning prediction model for cancer risk in patients with type 2 diabetes based on clinical tests

Qiu,

Chen,

Zhang

et al. 2024

THC

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BACKGROUND: The incidence of type 2 diabetes is rapidly increasing worldwide. Studies have shown that it is also associated with cancer-related morbidities. Early detection of cancer in patients with type 2 diabetes is crucial. OBJECTIVE: This study aimed to construct a model to predict cancer risk in patients with type 2 diabetes. METHODS: This study collected clinical data from a total of 5198 patients. A cancer risk prediction model was established by analyzing 261 items from routine laboratory tests. We screened 107 risk factors from 261 clinical tests based on the importance of the characteristic variables, significance of differences between groups (P< 0.05), and minimum description length algorithm. RESULTS: Compared with 16 machine learning classifiers, five classifiers based on the decision tree algorithm (CatBoost, light gradient boosting, random forest, XGBoost, and gradient boosting) had an area under the receiver operating characteristic curve (AUC) of > 0.80. The AUC for CatBoost was 0.852 (sensitivity: 79.6%; specificity: 83.2%). CONCLUSION: The constructed model can predict the risk of cancer in patients with type 2 diabetes based on tumor biomarkers and routine tests using machine learning algorithms. This is helpful for early cancer risk screening and prevention to improve patient outcomes.

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Section: Introductionmentioning

confidence: 99%

A machine learning prediction model for cancer risk in patients with type 2 diabetes based on clinical tests

Qiu,

Chen,

Zhang

et al. 2024

THC

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“…CRC has a high mortality rate globally [ 5 ], so prevention is essential. The lesions are usually missed because of the poor skills of the endoscopist and bowel movement status [ 6 ]; the lesions’ shape and anatomy also affect their diagnosis. Blind spots and lesions that are flat or depressed might be frequently overlooked.…”

Section: Introductionmentioning

confidence: 99%

Diagnostic accuracy of endocytoscopy via artificial intelligence in colorectal lesions: A systematic review and meta‑analysis

Zhang,

Yang,

Tao

et al. 2023

PLoS ONE

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Background Endocytoscopy (EC) is a nuclei and micro-vessels visualization in real-time and can facilitate "optical biopsy" and "virtual histology" of colorectal lesions. This study aimed to investigate the significance of employing artificial intelligence (AI) in the field of endoscopy, specifically in diagnosing colorectal lesions. The research was conducted under the supervision of experienced professionals and trainees. Methods EMBASE, PubMed, Cochrane Library, Web of Science, Chinese National Knowledge Infrastructure (CNKI) database, and other potential databases were surveyed for articles related to the EC with AI published before September 2023. RevMan (5.40), Stata (14.0), and R software (4.1.0) were used for statistical assessment. Studies that measured the accuracy of EC using AI for colorectal lesions were included. Two authors independently assessed the selected studies and their extracted data. This included information such as the country, literature, total study population, study design, characteristics of the fundamental study and control groups, sensitivity, number of samples, assay methodology, specificity, true positives or negatives, and false positives or negatives. The diagnostic accuracy of EC by AI was determined by a bivariate random-effects model, avoiding a high heterogeneity effect. The ANOVA model was employed to determine the more effective approach. Results A total of 223 studies were reviewed; 8 articles were selected that included 2984 patients (4241 lesions) for systematic review and meta-analysis. AI assessed 4069 lesions; experts diagnosed 3165 and 5014 by trainees. AI demonstrated high accuracy, sensitivity, and specificity levels in detecting colorectal lesions, with values of 0.93 (95% CI: 0.90, 0.95) and 0.94 (95% CI: 0.73, 0.99). Expert diagnosis was 0.90 (95% CI: 0.85, 0.94), 0.87 (95% CI: 0.78, 0.93), and trainee diagnosis was 0.74 (95% CI: 0.67, 0.79), 0.72 (95% CI: 0.62, 0.80). With the EC by AI, the AUC from SROC was 0.95 (95% CI: 0.93, 0.97), therefore classified as excellent category, expert showed 0.95 (95% CI: 0.93, 0.97), and the trainee had 0.79 (95% CI: 0.75, 0.82). The superior index from the ANOVA model was 4.00 (1.15,5.00), 2.00 (1.15,5.00), and 0.20 (0.20,0.20), respectively. The examiners conducted meta-regression and subgroup analyses to evaluate the presence of heterogeneity. The findings of these investigations suggest that the utilization of NBI technology was correlated with variability in sensitivity and specificity. There was a lack of solid evidence indicating the presence of publishing bias. Conclusions The present findings indicate that using AI in EC can potentially enhance the efficiency of diagnosing colorectal abnormalities. As a valuable instrument, it can enhance prognostic outcomes in ordinary EC procedures, exhibiting superior diagnostic accuracy compared to trainee-level endoscopists and demonstrating comparability to expert endoscopists. The research is subject to certain constraints, namely a limited number of clinical investigations and variations in the methodologies used for identification. Consequently, it is imperative to conduct comprehensive and extensive research to enhance the precision of diagnostic procedures.

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LiverAI: New tool in the landscape for liver health

Marti-Aguado,

Pazó,

Diaz-Gonzalez

et al. 2024

Gastroenterología y Hepatología (English Edition)

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The application of artificial intelligence in improving colonoscopic adenoma detection rate: Where are we and where are we going

Cited by 4 publications

References 56 publications

A machine learning prediction model for cancer risk in patients with type 2 diabetes based on clinical tests

A machine learning prediction model for cancer risk in patients with type 2 diabetes based on clinical tests

Diagnostic accuracy of endocytoscopy via artificial intelligence in colorectal lesions: A systematic review and meta‑analysis

LiverAI: New tool in the landscape for liver health

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