A Systematic Review and Meta-analysis of Convolutional Neural Network in the Diagnosis of Colorectal Polyps and Cancer

Abstract

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.

Figure 1.

PRISMA flowchart showing the stages from search to selection processes and including primary studies in this systematic review.

Figure 2.

The accuracy measures of CNN models in CRP prediction in the pessimistic (A) and optimistic (B) scenarios. CNN, convolutional neural networks; CRP, colorectal polyps.

Figure 3.

The accuracy measures of CNN models in CRC prediction in the pessimistic (A) and optimistic (B) scenarios. CNN, convolutional neural networks; CRC, colorectal cancer.

Figure 4.

Meta-regression of the effects of number of CNN layers and image size (the number of pixels in the horizontal or vertical axes) on the accuracy of CRP (A and C, respectively) and CRC (B and D, respectively).

Supplementary Figure 1.

The area under the ROC curve in CRP prediction in the optimistic (a) and pessimistic (b) scenarios. CRP, colorectal polyps.

Supplementary Figure 2.

Deek’s funnel plot on the logit of accuracy measure in CRP outcome in the pessimistic (a) and optimistic (b) scenarios and CRC outcome in the pessimistic (c) and optimistic (d) scenarios.