Recent advances in machine learning for precision diagnosis and treatment of esophageal disorders

Figure 2 Integrated data-drive modeling pipeline for esophageal disorders leveraging deep learning and machine learning for improved diagnosis and prognosis. CT: Computed tomography; NLP: Natural language processing; VCF: Variant call format; VGG: Visual geometry group network; YOLO: You only look once; UNet: U-shaped convolutional network; ResNet: Residual neural network; kNN: K-nearest neighbors; LR: Logistic regression; SVM: Support vector machine; RF: Random forest; DT: Decision tree; GERD: Gastroesophageal reflux disease; BE: Barrett’s esophagus; EC: Esophageal carcinoma; EV: Esophageal varices.