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Objective: To develop a fully automated AI system to quantitatively assess the disease severity and disease progression of COVID-19 using thick-section chest CT images. Methods: In this retrospective study, an AI system was developed to automatically segment and quantify the COVID-19-infected lung regions on thick-section chest CT images. Five hundred thirty-one CT scans from 204 COVID-19 patients were collected from one appointed COVID-19 hospital. The automatically segmented lung abnormalities were compared with manual segmentation of two experienced radiologists using the Dice coefficient on a randomly selected subset (30 CT scans). Two imaging biomarkers were automatically computed, i.e., the portion of infection (POI) and the average infection HU (iHU), to assess disease severity and disease progression. The assessments were compared with patient status of diagnosis reports and key phrases extracted from radiology reports using the area under the receiver operating characteristic curve (AUC) and Cohen's kappa, respectively. Results: The dice coefficient between the segmentation of the AI system and two experienced radiologists for the COVID-19-infected lung abnormalities was 0.74 ± 0.28 and 0.76 ± 0.29, respectively, which were close to the inter-observer agreement (0.79 ± 0.25). The computed two imaging biomarkers can distinguish between the severe and non-severe stages with an AUC of 0.97 (p value < 0.001). Very good agreement (κ = 0.8220) between the AI system and the radiologists was achieved on evaluating the changes in infection volumes. Conclusions: A deep learning–based AI system built on the thick-section CT imaging can accurately quantify the COVID-19-associated lung abnormalities and assess the disease severity and its progressions. Key Points: • A deep learning–based AI system was able to accurately segment the infected lung regions by COVID-19 using the thick-section CT scans (Dice coefficient ≥ 0.74). • The computed imaging biomarkers were able to distinguish between the non-severe and severe COVID-19 stages (area under the receiver operating characteristic curve 0.97). • The infection volume changes computed by the AI system were able to assess the COVID-19 progression (Cohen's kappa 0.8220). [ABSTRACT FROM AUTHOR]

Aims: Paroxysmal atrial fibrillation (PAF) commonly recurs after radiofrequency catheter ablation (RFCA). This study aimed to assess left atrial appendage (LAA) volume and function by transesophageal echocardiography (TEE) and to explore its value in predicting PAF recurrence after RFCA. Methods: Eighty patients with PAF were recruited. The left atrial (LA) and LAA volume and function were measured by transthoracic echocardiography (TTE) and TEE before ablation. Patients were followed up for 12 months after RFCA, and recurrence was recorded. Odds ratios of candidate risk indicators were determined by logistic regression analysis. Prediction model was constructed using logistic regression with backward selection. Receiver operating characteristic (ROC) curve with area under curve (AUC) was performed to evaluate the prediction efficiency. Results: Twenty‐four (30%) PAF patients had recurrence (R group), and 56 (70%) patients had no recurrence (NR group). Compared to NR group, LA dimension (LAD), LA volume index (LAVI), LAA maximum volume (LAAVmax), and LAA minimum volume (LAAVmin) were significantly higher in R group, while LAA peak emptying flow velocity (LAAeV), LAA peak filling flow velocity (LAAfV), and LAA ejection fraction (LAAEF) significantly declined in R group. According to multivariate analysis and backward selection, LAVI, LAAEF, and LAAeV were significant risk factors for PAF recurrence. The LAVI + LAAEF + LAAeV joint model could effectively predict PAF recurrence with AUC of 0.893 (95% confidence interval = 0.816, 0.970), sensitivity of 0.75, and specificity of 0.929. Conclusions: This study demonstrated that LAVI, LAAEF, and LAAeV were significant predictors of PAF recurrence after RFCA. [ABSTRACT FROM AUTHOR]