ABSTRACT
Background@#To propose a deep learning architecture for automatically detecting the complex structure of the aortic annulus plane using cardiac computed tomography (CT) for transcatheter aortic valve replacement (TAVR). @*Methods@#This study retrospectively reviewed consecutive patients who underwent TAVR between January 2017 and July 2020 at a tertiary medical center. Annulus Detection Permuted AdaIN network (ADPANet) based on a three-dimensional (3D) U-net architecture was developed to detect and localize the aortic annulus plane using cardiac CT. Patients (N = 72) who underwent TAVR between January 2017 and July 2020 at a tertiary medical center were enrolled. Ground truth using a limited dataset was delineated manually by three cardiac radiologists. Training, tuning, and testing sets (70:10:20) were used to build the deep learning model. The performance of ADPANet for detecting the aortic annulus plane was analyzed using the root mean square error (RMSE) and dice similarity coefficient (DSC). @*Results@#In this study, the total dataset consisted of 72 selected scans from patients who underwent TAVR. The RMSE and DSC values for the aortic annulus plane using ADPANet were 55.078 ± 35.794 and 0.496 ± 0.217, respectively. @*Conclusion@#Our deep learning framework was feasible to detect the 3D complex structure of the aortic annulus plane using cardiac CT for TAVR. The performance of our algorithms was higher than other convolutional neural networks.
ABSTRACT
Hypersensitivity pneumonitis (HP) is an interstitial lung disease (ILD) characterized by an inhaled inciting antigen that leads to the inflammation of the lung parenchyma and small airway with immunologic reactions. Over the last decades, the most effective therapeutic option for HP has been limited to antigen avoidance. The differential diagnosis of HP from other ILDs is the beginning of treatment as well as diagnosis. However, the presence of several overlapping clinical and radiologic features makes differentiating HP from other ILDs particularly challenging. In 2020, a multidisciplinary committee of experts from the American Thoracic Society, Japanese Respiratory Society, and Asociación Latinoamericana del Tórax suggested a new clinical practice guideline classifying HP into nonfibrotic and fibrotic phenotypes on the basis of chest high-resolution CT (HRCT) findings. Therefore, we introduced a new diagnostic algorithm based on chest HRCT in the clinical practice guideline for the diagnosis of HP.
ABSTRACT
Hypersensitivity pneumonitis (HP) is an interstitial lung disease (ILD) characterized by an inhaled inciting antigen that leads to the inflammation of the lung parenchyma and small airway with immunologic reactions. Over the last decades, the most effective therapeutic option for HP has been limited to antigen avoidance. The differential diagnosis of HP from other ILDs is the beginning of treatment as well as diagnosis. However, the presence of several overlapping clinical and radiologic features makes differentiating HP from other ILDs particularly challenging. In 2020, a multidisciplinary committee of experts from the American Thoracic Society, Japanese Respiratory Society, and Asociación Latinoamericana del Tórax suggested a new clinical practice guideline classifying HP into nonfibrotic and fibrotic phenotypes on the basis of chest high-resolution CT (HRCT) findings. Therefore, we introduced a new diagnostic algorithm based on chest HRCT in the clinical practice guideline for the diagnosis of HP.