Department of General Surgery, Changzhou Wujin People's Hospital Affiliated to Jiangsu University, The Wujin Clinical College of Xuzhou Medical University, Changzhou, Jiangsu Province, China.
Department of Endoscopy, Jiangsu Provincial Hospital of Traditional Chinese Medicine, Nanjing, Jiangsu, China.
PLoS One. 2021 Feb 16;16(2):e0246892. doi: 10.1371/journal.pone.0246892. eCollection 2021.
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.
前瞻性随机试验和观察性研究表明，早期检测、分类和切除肿瘤性结直肠息肉（CP）可显著提高结直肠癌（CRC）的预防效果。目前，结肠镜检查的诊断性能仍然不尽如人意，准确性不稳定。基于人工智能（AI）技术的卷积神经网络（CNN）系统已显示出帮助内镜医生提高诊断准确性的潜力。然而，CNN 系统存在一些局限性，并且存在关于它是否比人类内镜医生提供更好的诊断性能的争议。因此，本研究旨在解决这一问题。在线数据库（PubMed、Web of Science、Cochrane Library 和 EMBASE）用于搜索截至 2020 年 4 月进行的研究。此外，还使用诊断准确性量表 2（QUADAS-2）进行质量评估，以评估纳入研究的质量。此外，还使用 Deeks 漏斗图确定发表偏倚。共有 13 项研究被纳入本荟萃分析（范围在 2016 年至 2020 年之间）。因此，CNN 系统在 CP 检测领域具有令人满意的诊断性能（敏感性：0.848 [95%CI：0.692-0.932]；特异性：0.965 [95%CI：0.946-0.977]；AUC：0.98 [95%CI：0.96-0.99]）和 CP 分类（敏感性：0.943 [95%CI：0.927-0.955]；特异性：0.894 [95%CI：0.631-0.977]；AUC：0.95 [95%CI：0.93-0.97]）。与人类内镜医生相比，CNN 系统在 CP 分类领域与专家相当，但明显优于非专家（CNN 与专家：RDOR：1.03，P = 0.9654；非专家与专家：RDOR：0.29，P = 0.0559；非专家与 CNN：0.18，P = 0.0342）。因此，CNN 系统对 CP 具有令人满意的诊断性能，可作为结肠镜检查中潜在的临床诊断工具。
