Background: Transthoracic echocardiography (TTE) requires time-intensive integration of quantitative measure‑ ments and qualitative visual assessment. Fully automated artificial intelligence (AI)-based analysis may reduce total analysis time while preserving accuracy, but systematic real-world validation remains limited. Methods: This prospective, single-center pilot study enrolled 40 TTE examinations. Identical deidentified DICOM datasets were independently provided to a trained cardiac sonographer and a fully automated AI system comprising quantitative and qualitative visual interpretation modules. All outputs were compared with a cardiologist-adjudicated reference standard. Primary endpoints were total analysis time and noninferiority of AI-derived left ventricular ejection fraction (LVEF) versus the reference standard, with a prespecified margin of 3 percentage points (one-sided α = 0.025). Results: Median analysis time was 94 s (interquartile range [IQR], 82–106 s) for the AI workflow versus 490 s (IQR, 438–626 s) for the human workflow (P < 0.001). AI-derived LVEF met the noninferiority criterion (mean difference, 0.00 percentage points; upper one-sided 95% confidence bound, 1.41 percentage points; P < 0.001), with an intraclass correlation coefficient (ICC) of 0.902 (95% confidence interval, 0.822–0.947). ICCs for secondary quantitative indi‑ ces ranged from 0.625 to 0.989. For aortic regurgitation severity grading, AI’s overall accuracy was 75.0% (quadratic weighted κ = 0.762), compared with 82.5% for human interpretation (κ = 0.812, McNemar P = 0.579). Conclusions Fully automated AI-assisted TTE analysis substantially reduced total analysis time while maintaining noninferior LVEF accuracy and acceptable performance across secondary quantitative and qualitative indices. These findings support the use of AI as a practical workflow accelerator in routine echocardiography.

Experimental autoimmune encephalomyelitis (EAE), an animal model of human multiple sclerosis (MS), reflects pathophysiologic steps in MS such as the influence of T cells and antibodies reactive to the myelin sheath, and the cytotoxic effect of cytokines. Galectin-9 (Gal-9) is a member of animal lectins that plays an essential role in various biological functions. The expression of Gal-9 is significantly enhanced in MS lesions; however, its role in autoimmune disease has not been fully elucidated. To identify the role of Gal-9 in EAE, we measured changes in mRNA and protein expression of Gal-9 as EAE progressed. Expression increased with disease progression, with a sharp rise occurring at its peak. Gal-9 immunoreactivity was mainly expressed in astrocytes and microglia of the central nervous system (CNS) and macrophages of spleen. Flow cytometric analysis revealed that Gal-9+CD11b+ cells were dramatically increased in the spleen at the peak of disease. Increased expression of tumor necrosis factor (TNF)-R1 and p-Jun N-terminal kinase (JNK) was observed in the CNS of EAE mice, suggesting that TNF-R1 and p-JNK might be key regulators contributing to the expression of Gal-9 during EAE. These results suggest that identification of the relationship between Gal-9 and EAE progression is critical for better understanding Gal-9 biology in autoimmune disease.
Animals ; Antibodies ; Astrocytes ; Autoimmune Diseases ; Biology ; Central Nervous System ; Cytokines ; Disease Progression ; Encephalomyelitis, Autoimmune, Experimental* ; Humans ; Lectins ; Macrophages ; Mice ; Microglia ; Models, Animal ; Multiple Sclerosis ; Myelin Sheath ; Phosphotransferases ; RNA, Messenger ; Spleen ; T-Lymphocytes ; Tumor Necrosis Factor-alpha