Over the past few years, developments in artificial intelligence (AI), especially in radiomics and deep learning, have enabled the extraction of pathophysiology-related information from varied medical imaging and are progressively transforming medical practice. AI applications are extending into domains previously thought to be accessible only to human experts. Recent research has demonstrated that ultrasound-derived radiomics and deep learning represent an enticing opportunity to benefit preoperative evaluation and prognostic monitoring of diffuse and focal liver disease. This review summarizes the application of radiomics and deep learning in ultrasound liver imaging, including identifying focal liver lesions and staging of liver fibrosis, as well as the evaluation of pathobiological properties of malignant tumors and the assessment of recurrence and prognosis. Besides, we identify important hurdles that must be overcome while also discussing the challenges and opportunities of radiomics and deep learning in clinical applications.

Background and Aims. Recurrence of gastroesophageal varices (GEVs) after sclerotherapy is a public health problem. However, mass screening of recurrence of GEVs through gastroscopy is a high‐cost procedure. We aim to evaluate the changes in liver stiffness (LS) over time after endoscopic injection sclerotherapy (EIS) and determine its value in predicting the recurrence of GEVs. Methods. One hundred and thirty‐five patients with GEVs who underwent EIS treatment were included in this study. The patients were divided into two groups, namely, the nonrecurrence and recurrence groups, based on endoscopic findings at 6 months after discharge. LS measurements were obtained on five occasions. Repeated measure analysis of variance was employed to assess LS differences at different time points and compare them between the two groups. Results. The LS values during the 6‐month postdischarge period were consistently higher than the baseline value (measured on the day of hospitalization). The recurrence group demonstrated sustained elevated LS levels throughout the 6‐month follow‐up period, while the nonrecurrence group showed a gradual decline in LS. The difference in LS trend between the two groups was statistically significant (P = 0.04). The area under the curve (AUC) values for LS differences were 0.806, with a corresponding 95% confidence interval (CI) of 0.640‐0.918 and a cut‐off value of 0.556, indicating their potential utility in predicting GEV recurrence. Conclusions. Longitudinal assessment of LS values in post‐EIS patients can provide valuable information for predicting the recurrence of GEVs.

Background: It is difficult to make a definite diagnosis of borderline epithelial ovarian tumors before surgery. In order to avoid incorrectly classifying tumors as benign, a differential diagnosis model was developed to distinguish between benign and borderline epithelial tumors utilizing multimodal information. Method: A multicenter study was conducted. A retrospective analysis of the transvaginal ultrasonography and clinical data of patients who underwent surgery and received pathological diagnoses of borderline and benign epithelial ovarian tumors was conducted. Both Univariate and multivariate logistic regression analyses were used to develop a diagnostic model for borderline epithelial tumors. The efficacy and feasibility of this model were assessed through examination of training, internal validation, and external test sets. Results: There was a significant difference in D-dimer levels between borderline and benign epithelial tumors. Abnormal CA125, D-dimer, maximum mass diameter > 10 cm, regular and irregular solid portions, and blood flow in the mass were independent risk factors for borderline epithelial ovarian tumors. The diagnostic model was evaluated by the Hosmer–Lemeshow test and demonstrated strong fitting capabilities. ROC curve analysis of the training set, verification set, and external test set confirmed the model’s predictive ability. Conclusions: These independent risk factors may be combined to assess the risk of borderline epithelial ovarian tumors. Our findings will assist novice gynecologic sonographers in distinguishing between benign and borderline epithelial tumors.