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.
Highlights
Based on 2018 reports, colorectal cancer (CRC) had approximately 1,800,000 new cases and 881,000 deaths, implying 1 in 10 cancer cases and deaths [1]
The inclusion criteria included (1) studies that included patients with CP; (2) colonoscopy was performed to detect or classify colorectal polyps; (3) convolutional neural networks (CNN) system was applied to improve the diagnostic performance of colonoscopy; (4) precise diagnostic data were presented in the article; (5) if the colorectal polyps were classified, the final pathology results were provided
By comparing them in pairs acording to relative diagnostic odds ratio (RDOR), we found the diagnostic performance of CNN is comparable to that of the expert, but significantly better than that of the non-expert. (Table 4)
Summary
Based on 2018 reports, colorectal cancer (CRC) had approximately 1,800,000 new cases and 881,000 deaths, implying 1 in 10 cancer cases and deaths [1]. 85% of CRCs developed from precancerous polyps through genetic and epigenetic mechanisms with a mean dwell time of at least 10 years [2, 3]. Early and precise detection of colorectal polyp (CP) has a great significance in the prevention of CRC. Colonoscopy is the most effective and essential method in the early diagnosis and prevention of CRC through detection and removal of the neoplastic lesion before its progression to invasive cancer [4]. The removal of colorectal polyps could significantly reduce the risk of CRC [6]. Achieving a better diagnostic accuracy of CP for their prevention and better treatment is critical
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