This study aimed to develop and evaluate an automated classification model for diagnosing chronic hepatitis B (CHB) complicated with non-alcoholic fatty liver disease (NAFLD) using deep learning techniques applied to two-dimensional liver imaging. We retrospectively analyzed data from 2803 patients diagnosed with CHB and NAFLD via two-dimensional ultrasound and FibroScan at our hospital between June 2019 and December 2022. These patients contributed a total of 20,540 two-dimensional liver images. An additional 150 patients with CHB complicated with NAFLD, who had liver biopsy results, were selected (with a total of 922 liver 2D ultrasound images) for validation of the deep learning model. The diagnostic performance was assessed using sensitivity, specificity, and accuracy metrics for independent diagnoses made by clinicians or with AI assistance. Diagnostic performance was assessed under blinded conditions by physicians of varying expertise, primary, intermediate, and senior clinicians, either independently or assisted by the AI model. We then conducted a statistical analysis of the results, focusing on sensitivity, specificity, and accuracy metrics. The sensitivity, specificity, and accuracy for independent diagnoses made by primary, intermediate, and senior physicians were 24.3%, 29.1%, and 33.8% for sensitivity; 100% for specificity; and 25.3%, 30.0%, and 34.7% for accuracy, respectively. The AI model alone achieved 68.9% sensitivity, 100% specificity, and 69.3% accuracy. When diagnoses were AI-assisted, the sensitivity increased significantly to 73.7% for primary physicians, 73.7% for intermediate physicians, and 75.7% for senior physicians, while specificity remained at 100%, and accuracy was 74.0%, 74.0%, and 76.0%, respectively. The area under the receiver operating characteristic curve (AUC) for AI-independent diagnosis (0.845) was significantly higher than that for primary, intermediate, and senior physicians (0.622, 0.645, and 0.669, respectively; $P<0.001$ ). Similarly, AI-assisted diagnosis AUC values (0.868, 0.868, and 0.878, respectively) surpassed those for independent diagnosis by physicians at each level of expertise ( $P<0.001$ ). In conclusion, the deep learning model based on two-dimensional liver imaging demonstrated feasibility for assisting in the identification of liver fibrosis in patients with CHB and NAFLD. Our results suggest that integrating AI models into the diagnostic process can significantly improve the accuracy of diagnosing liver conditions in this patient population.

Keywords: Artificial intelligence; Chronic hepatitis B; Liver fibrosis; Non-invasive diagnosis; Nonalcoholic fatty liver disease.

© 2025. The Author(s) under exclusive licence to Society for Imaging Informatics in Medicine.

本研究旨在利用深度学习技术开发和评估一种自动化分类模型，用于通过二维肝脏成像诊断慢性乙型肝炎（CHB）合并非酒精性脂肪肝病（NAFLD）。我们回顾性分析了2019年6月至2022年12月期间在我院接受二维超声和FibroScan检查并被确诊为CHB及NAFLD的2803名患者的资料，这些患者总共贡献了20,540张二维肝脏图像。另外选取了150例患有CHB合并NAFLD且有肝活检结果的患者（共922张肝脏2D超声图像）用于验证深度学习模型的有效性。通过敏感性、特异性和准确性的指标来评估独立诊断和人工智能辅助下医生做出诊断的表现，并在盲法条件下由初级、中级及高级医师分别进行评价，或者由AI模型提供帮助的情况下进行评价。我们随后对结果进行了统计分析，重点关注了敏感性、特异性以及准确性等关键指标。

对于单独由初级、中级和高级医师独立作出的诊断，其敏感性分别为24.3%、29.1%和33.8%，特异性均为100%，准确率则分别为25.3%、30.0%和34.7%。仅依赖AI模型的情况下，敏感性和准确性分别达到了68.9%、100%以及69.3%。当诊断过程得到人工智能辅助时，初级医师的敏感性显著提高到73.7%，中级医师为73.7%，高级医师为75.7%，特异性仍保持在100%，准确率则分别为74.0%、74.0%和76.0%。独立诊断与AI辅助诊断的受试者操作特征曲线（ROC）下的面积比较表明，人工智能辅助诊断的AUC值明显高于各层级医师独立作出诊断的情况。

本研究结果表明，基于二维肝脏成像开发的深度学习模型在帮助识别CHB和NAFLD患者中的肝纤维化方面具有可行性。我们的研究表明，在这一特定患者群体中将AI模型整合到诊断过程中可以显著提高对肝脏疾病的诊断准确性。

关键词：人工智能；慢性乙型肝炎；肝纤维化；非侵入性诊断；非酒精性脂肪肝病。