SynthesisPloS one2021
Comparison of diagnostic performance between convolutional neural networks and human endoscopists for diagnosis of colorectal polyp: A systematic review and meta-analysis.
Synthesis in PloS one, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers, 4 of them syntheses that pooled it.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
18 citing papers in PubMed, 4 syntheses or guidelines pooled it, 40 citations in OpenAlex.
- Diagnostic performance of AI-assisted endoscopy diagnosis of digestive system tumors: an umbrella review.Frontiers in oncology · 2025Pooled it
- A Systematic Review and Meta-analysis of Convolutional Neural Network in the Diagnosis of Colorectal Polyps and Cancer.The Turkish journal of gastroenterology : the official journal of Turkish Society of Gastroenterology · 2023Pooled it
- Effect of artificial intelligence-aided colonoscopy for adenoma and polyp detection: a meta-analysis of randomized clinical trials.International journal of colorectal disease · 2022Pooled it
- Ultrasound-based deep learning using the VGGNet model for the differentiation of benign and malignant thyroid nodules: A meta-analysis.Frontiers in oncology · 2022Pooled it
- [Role of Artificial Intelligence in Improving Quality of Colonoscopy].The Korean journal of gastroenterology = Taehan Sohwagi Hakhoe chi · 2025Review
- Effectiveness of data-augmentation on deep learning in evaluating rapid on-site cytopathology at endoscopic ultrasound-guided fine needle aspiration.Scientific reports · 2024Article
- A balance of clinical assessment and use of diagnostic imaging: A CT colonography comparative case report.Radiology case reports · 2024Article
- Optical diagnosis in still images of colorectal polyps: comparison between expert endoscopists and PolyDeep, a Computer-Aided Diagnosis system.Frontiers in oncology · 2024Article
- The Role of Artificial Intelligence in Colorectal Cancer Screening: Lesion Detection and Lesion Characterization.Cancers · 2023Review
- Clinical Validation Benchmark Dataset and Expert Performance Baseline for Colorectal Polyp Localization Methods.Journal of imaging · 2023Article
- Artificial Intelligence and Cancer Control: Toward Prioritizing Justice, Equity, Diversity, and Inclusion (JEDI) in Emerging Decision Support Technologies.Current oncology reports · 2023Review
- A Review of the Technology, Training, and Assessment Methods for the First Real-Time AI-Enhanced Medical Device for Endoscopy.Bioengineering (Basel, Switzerland) · 2023Review
- Mucosal imaging in colon polyps: New advances and what the future may hold.World journal of gastroenterology · 2022Review
- Experimental evidence of effective human-AI collaboration in medical decision-making.Scientific reports · 2022Article
- Development and Validation of an Artificial Intelligence Model for Small Bowel Capsule Endoscopy Video Review.JAMA network open · 2022Article
- Artificial Intelligence in Digestive Endoscopy-Where Are We and Where Are We Going?Diagnostics (Basel, Switzerland) · 2022Review
- Development of artificial intelligence technology in diagnosis, treatment, and prognosis of colorectal cancer.World journal of gastrointestinal oncology · 2022Review
- Artificial intelligence-assisted detection and classification of colorectal polyps under colonoscopy: a systematic review and meta-analysis.Annals of translational medicine · 2021Article
Corrections and comments
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Authors and funding
8 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Prospective randomized trials and observational studies have revealed that early detection, classification, and removal of neoplastic colorectal polyp (CP) significantly improve the prevention of colorectal cancer (CRC). The current effectiveness of the diagnostic performance of colonoscopy remains unsatisfactory with unstable accuracy. The convolutional neural networks (CNN) system based on artificial intelligence (AI) technology has demonstrated its potential to help endoscopists in increasing diagnostic accuracy. Nonetheless, several limitations of the CNN system and controversies exist on whether it provides a better diagnostic performance compared to human endoscopists. Therefore, this study sought to address this issue. Online databases (PubMed, Web of Science, Cochrane Library, and EMBASE) were used to search for studies conducted up to April 2020. Besides, the quality assessment of diagnostic accuracy scale-2 (QUADAS-2) was used to evaluate the quality of the enrolled studies. Moreover, publication bias was determined using the Deeks' funnel plot. In total, 13 studies were enrolled for this meta-analysis (ranged between 2016 and 2020). Consequently, the CNN system had a satisfactory diagnostic performance in the field of CP detection (sensitivity: 0.848 [95% CI: 0.692-0.932]; specificity: 0.965 [95% CI: 0.946-0.977]; and AUC: 0.98 [95% CI: 0.96-0.99]) and CP classification (sensitivity: 0.943 [95% CI: 0.927-0.955]; specificity: 0.894 [95% CI: 0.631-0.977]; and AUC: 0.95 [95% CI: 0.93-0.97]). In comparison with human endoscopists, the CNN system was comparable to the expert but significantly better than the non-expert in the field of CP classification (CNN vs. expert: RDOR: 1.03, P = 0.9654; non-expert vs. expert: RDOR: 0.29, P = 0.0559; non-expert vs. CNN: 0.18, P = 0.0342). Therefore, the CNN system exhibited a satisfactory diagnostic performance for CP and could be used as a potential clinical diagnostic tool during colonoscopy.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.