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Background: Accurate interpretation of coronary computed tomography angiography (CCTA) is a labor-intensive and expertise-driven endeavor, as inexperienced readers may inadvertently overestimate stenosis severity. Recent artificial intelligence (AI) advances in medical imaging present compelling prospects for auxiliary diagnostic tools in CCTA. This study aimed to externally validate an AI-assisted analysis system capable of rapidly evaluating stenosis severity, exploring its potential integration into routine clinical workflows. Methods: This multicenter study consisted of an internal and external cohort of patients who underwent CCTA scans between April 2017 and February 2023. CCTA scans were evaluated using Coronary Artery Disease Reporting and Data System (CAD-RADS) scores to determine stenosis severity, while ground-truth stents were manually annotated by expert readers. The InferRead CT Heart (version 1.6; Infervision Medical Technology Co., Ltd., Beijing, China), which incorporates AI-assisted coronary artery stenosis quantification and automatic stent segmentation, was employed for CCTA scan analysis. AI-based stenosis assessment performance was determined using sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV), while the AI-based stent segmentation overlap was assessed using the Dice similarity coefficient (DSC). Results: For ≥50% stenosis diagnoses, the AI system attained per-patient sensitivity, specificity, PPV, and NPV surpassing 90.0% for the internal dataset; for the external dataset, the per-patient values were 88.0% [95% confidence interval (CI): 81.0-94.4%], 94.5% (95% CI: 90.7-97.6%), 90.0% (95% CI: 83.3-95.6%), and 93.4% (95% CI: 89.2-96.8%), respectively. For ≥70% stenosis diagnoses, the per-patient values on the internal dataset were 94.2% (95% CI: 89.2-98.1%), 95.8% (95% CI: 94.1-97.4%), 80.8% (95% CI: 73.5-87.7%), and 98.9% (95% CI: 97.9-99.6%), respectively; for the external dataset, the per-patient values were 91.9% (95% CI: 82.6-100.0%), 97.3% (95% CI: 94.9-99.1%), 85.0% (95% CI: 72.5-94.6%), and 98.6% (95% CI: 96.8-100.0%), respectively. Regarding CAD-RADS categorization, the Cohen kappa was 0.75 and 0.81 for the internal per-patient and per-vessel basis, respectively, and 0.72 and 0.76 for the external per-patient and per-vessel basis, respectively. The DSC for stent segmentation was 0.96±0.06. Conclusions: The AI-assisted analysis system for CCTA interpretation exhibited exceptional proficiency in stenosis quantification and stent segmentation, indicating that AI holds considerable potential in advancing CCTA postprocessing techniques.
RESUMO
OBJECTIVE: Daweizi (DWZ) is a famous indigenous pig breed in China and characterized by tender meat and high fat percentage. However, the expression profiles and functions of transcripts in DWZ pigs is still in infancy. The object of this study was to depict the transcript profiles in DWZ pigs and screen the potential pathway influence adipogenesis and fat deposition. METHODS: Histological analysis of backfat tissue was firstly performed between DWZ and lean-type Yorkshire pigs, and then RNA sequencing technology was utilized to explore miRNAs, lncRNAs and mRNAs profiles in backfat tissue. 18 differentially expressed (DE) transcripts were randomly selected for quantitative real-time polymerase chain reaction (QPCR) to validate the reliability of the sequencing results. Finally, gene ontology (GO) and Kyoto encyclopedia of genes and genomes (KEGG) enrichment analysis were conducted to investigate the potential pathways influence adipocyte differentiation, adipogenesis and lipid metabolism, and a schematic model was further proposed. RESULTS: A total of 1,625 differentially expressed transcripts were identified in DWZ pigs, including 27 upregulated and 45 downregulated miRNAs, 64 upregulated and 119 downregulated lncRNA, 814 upregulated and 556 downregulated mRNAs. QPCR analysis exhibited strong consistency with the sequencing data. GO and KEGG analysis elucidated that the differentially expressed transcripts were mainly associated with cell growth and death, signal transduction, peroxisome proliferator-activated receptors (PPAR), AMP-activated protein kinase (AMPK), PI3K-Akt, adipocytokine and foxo signaling pathways, all of which are strongly involved in cell development, lipid metabolism and adipogenesis. Further analysis indicated that the BGIR9823_87926/miR-194a-5p/AQP7 network may be effective in the process of adipocyte differentiation or adipogenesis. CONCLUSION: Our study provides comprehensive insights into the regulatory network of backfat deposition and lipid metabolism in pigs from the point of view of miRNAs, lncRNAs and mRNAs.
