A5039584115- Radiomics and Machine Learning in Medical Imaging
- COVID-19 diagnosis using AI
- Lung Cancer Diagnosis and Treatment
- COVID-19 Clinical Research Studies
Siemens Healthcare (Germany)
2020-2021
Purpose To present a method that automatically segments and quantifies abnormal CT patterns commonly in COVID-19, namely ground-glass opacities consolidations. Materials Methods In this retrospective study, the proposed takes as input noncontrast chest lesions, lungs, lobes three dimensions, based on dataset of 9749 volumes. The outputs two combined measures severity lung lobe involvement, quantifying both extent COVID-19 abnormalities presence high opacities, deep learning reinforcement...
Purpose: To leverage volumetric quantification of airspace disease (AD) derived from a superior modality (CT) serving as ground truth, projected onto digitally reconstructed radiographs (DRRs) to: 1) train convolutional neural network to quantify on paired CXRs; and 2) compare the DRR-trained CNN expert human readers in CXR evaluation patients with confirmed COVID-19. Materials Methods: We retrospectively selected cohort 86 COVID-19 (with positive RT-PCR), March-May 2020 at tertiary hospital...
Purpose: To present a method that automatically segments and quantifies abnormal CT patterns commonly in coronavirus disease 2019 (COVID-19), namely ground glass opacities consolidations. Materials Methods: In this retrospective study, the proposed takes as input non-contrasted chest lesions, lungs, lobes three dimensions, based on dataset of 9749 volumes. The outputs two combined measures severity lung lobe involvement, quantifying both extent COVID-19 abnormalities presence high opacities,...
Objectives: To investigate machine-learning classifiers and interpretable models using chest CT for detection of COVID-19 differentiation from other pneumonias, ILD normal CTs. Methods: Our retrospective multi-institutional study obtained 2096 CTs 16 institutions (including 1077 patients). Training/testing cohorts included 927/100 COVID-19, 388/33 ILD, 189/33 559/34 (no pathologies) A metric-based approach classification used features, relying on logistic regression random forests. deep...