卷积神经网络是一类深度神经网络，用于不同的临床目的，包括提高结直肠病变的检出率。本系统评价和荟萃分析旨在评估基于卷积神经网络模型在结直肠息肉和结直肠癌的检测或分类中的性能。在MEDLINE、SCOPUS、Web of Science和其他相关数据库中进行了系统搜索。计算了卷积神经网络模型在结直肠息肉和结直肠癌检测的最佳和最差准确性情景下的性能指标。使用Stata和R软件进行荟萃分析。从3368个检索记录中，包括了24项主要研究。在最差和最佳情景下，卷积神经网络模型在预测结直肠息肉中的敏感性和特异性分别在84.7%至91.6%和86.0%至93.8%之间变化。在预测结直肠癌中，这些值分别在93.2%至94.1%和94.6%至97.7%之间变化。在这些情景中，结直肠息肉的阳性和阴性似然比分别在6.2至14.5和0.09至0.17之间变化，在预测结直肠癌中分别为17.1至41.2和0.07至0.06。卷积神经网络模型在最差和最佳情景下预测结直肠息肉的诊断比值比和准确度分别在36%至162%和80.5%至88.6%之间变化。在最差和最佳情景下，这些值在预测结直肠癌中分别为239.63%至677.47%和88.2%至96.4%。在最差和最佳情景下，结直肠息肉的受试者工作特征曲线下面积分别在0.92和0.97之间变化，结直肠息肉预测中分别为0.98和0.99。基于卷积神经网络的模型显示出在检测结直肠息肉和结直肠癌中具有可接受的准确性。

Convolutional neural networks are a class of deep neural networks used for different clinical purposes, including improving the detection rate of colorectal lesions. This systematic review and meta-analysis aimed to assess the performance of convolutional neural network-based models in the detection or classification of colorectal polyps and colorectal cancer. A systematic search was performed in MEDLINE, SCOPUS, Web of Science, and other related databases. The performance measures of the convolutional neural network models in the detection of colorectal polyps and colorectal cancer were calculated in the 2 scenarios of the best and worst accuracy. Stata and R software were used for conducting the meta-analysis. From 3368 searched records, 24 primary studies were included. The sensitivity and specificity of convolutional neural network models in predicting colorectal polyps in worst and best scenarios ranged from 84.7% to 91.6% and from 86.0% to 93.8%, respectively. These values in predicting colorectal cancer varied between 93.2% and 94.1% and between 94.6% and 97.7%. The positive and negative likelihood ratios varied between 6.2 and 14.5 and 0.09 and 0.17 in these scenarios, respectively, in predicting colorectal polyps, and 17.1-41.2 and 0.07-0.06 in predicting colorectal polyps. The diagnostic odds ratio and accuracy measures of convolutional neural network models in predicting colorectal polyps in worst and best scenarios ranged between 36% and 162% and between 80.5% and 88.6%, respectively. These values in predicting colorectal cancer in the worst and the best scenarios varied between 239.63% and 677.47% and between 88.2% and 96.4%. The area under the receiver operating characteristic varied between 0.92 and 0.97 in the worst and the best scenarios in colorectal polyps, respectively, and between 0.98 and 0.99 in colorectal polyps prediction. Convolutional neural network-based models showed an acceptable accuracy in detecting colorectal polyps and colorectal cancer.