Ultrasonography（US）is the first-line imaging modality recommended by domestic and international guidelines for liver tumor screening，owing to its non-invasive nature，real-time dynamic imaging capability，cost-effectiveness，and operational convenience. In recent years，the integration of artificial intelligence（AI）and medical imaging has emerged as a major research focus. By leveraging large-scale data training，AI models can automatically recognize and analyze input imaging data and generate predictive outcomes. Notably，AI-based ultrasound imaging technology has achieved breakthrough advancements in the diagnosis and treatment of liver tumors. These innovations significantly improve diagnostic accuracy，optimize treatment strategies，predict disease progression and prognosis，and monitor therapeutic response. This article provides a comprehensive review of the latest applications and research progress of AI in ultrasound-based diagnosis and treatment of liver tumors.

Ultrasonography（US）is the first-line imaging modality recommended by domestic and international guidelines for liver tumor screening，owing to its non-invasive nature，real-time dynamic imaging capability，cost-effectiveness，and operational convenience. In recent years，the integration of artificial intelligence（AI）and medical imaging has emerged as a major research focus. By leveraging large-scale data training，AI models can automatically recognize and analyze input imaging data and generate predictive outcomes. Notably，AI-based ultrasound imaging technology has achieved breakthrough advancements in the diagnosis and treatment of liver tumors. These innovations significantly improve diagnostic accuracy，optimize treatment strategies，predict disease progression and prognosis，and monitor therapeutic response. This article provides a comprehensive review of the latest applications and research progress of AI in ultrasound-based diagnosis and treatment of liver tumors.

Objective:To compare and validate the diagnostic performance of the Ovarian-Adnexal Reporting and Data System (O-RADS ) and the ADNEX model in the diagnosis of malignant ovarian-adnexal lesions.Methods:A total of 275 patients who underwent surgery for ovarian-adnexal lesions at Tianjin Medical University Cancer Institute and Hospital from December 2020 to December 2022 were retrospectively collected. The clinical, pathological aud ultrasound dates of the patients were collected.Statistical methods, including chi-square tests and ROC curve analysis, were employed to assess the diagnostic performance of O-RADS and the ADNEX model for ovarian-adnexal lesions.Results:Among the 275 patients included in this study, 127 (46.2%) had benign lesions, and 148 (53.8%) had malignant lesions.Based on the O-RADS classification, 46 cases (16.7%) were O-RADS 2, 50 cases (18.2%) were O-RADS 3, 66 cases (24.0%) were O-RADS 4, and 113 cases (41.1%) were O-RADS 5. The malignancy rates for O-RADS 2, O-RADS 3, O-RADS 4, and O-RADS 5 were 0%, 0.08%, 56.06%, and 94.7%, respectively. ROC curve analysis for malignant ovarian-adnexal lesions yielded an area under ROC curve of 0.93(95% CI=0.90-0.96) for O-RADS and 0.94(95% CI=0.91-0.97) for the ADNEX model. Using O-RADS ≥4 and ADNEX model ≥10% as cutoff values, there was no significant difference in sensitivity between the two methods( P=0.740), but O-RADS exhibited higher specificity compared to the ADNEX model (72.4% vs 56.7%, P=0.044). Conclusions:When O-RADS ≥4 and the ADNEX model ≥10% are used as cutoff values, both methods demonstrate excellent diagnostic performance for malignant ovarian-adnexal lesions, with O-RADS exhibiting higher specificity.