Abstract
Endoscopic assessment of ulcerative colitis (UC) can be performed by Mayo Endoscopic Score (MES), or the Ulcerative Colitis Endoscopic Index of Severity (UCEIS). In this meta-analysis, we assessed the pooled diagnostic accuracy parameters of deep machine learning, by means of convolutional neural network (CNN) algorithms, in predicting UC severity on endoscopic images.
Databases including Medline, Scopus, and Embase, were searched in June 2022. Outcomes of interest were the pooled accuracy, sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV). Standard meta-analysis methods were employed using the random-effects model, and heterogeneity was assessed using the I2statistics.
Twelve studies were included in the final analysis. The pooled diagnostic parameters of CNN-based machine learning algorithms in endoscopic severity assessment of UC were: Accuracy 91.5% (95% confidence interval (CI) [88.3-93.8],I2=84%), sensitivity 82.8% ([78.3-86.5],I2=89%), specificity 92.4% ([89.4-94.6],I2=84%), PPV 86.6% ([82.3-90],I2=89%) and NPV 88.6% ([85.7-91],I2=78%). Subgroup analysis revealed significantly better sensitivity and PPV with UCEIS scoring system as compared to MES (93.6% [87.5-96.8],I2=77% vs 82% [75.6-87],I2=89%, p=0.003; and 93.6% [88.7-96.4],I2=68% vs 83.6% [76.8-88.8],I2=77%; p=0.007, respectively).
CNN-based machine learning algorithms demonstrated excellent pooled diagnostic accuracy parameters in the endoscopic severity assessment of UC. Utilizing UCEIS scores in CNN training might offer better results than MES. Further studies are warranted to establish these findings in real-life.