Quantitative CT texture-based method to predict diagnosis and prognosis of fibrosing interstitial lung disease patterns

no code implementations • 20 Jun 2022 • Babak Haghighi, Warren B. Gefter, Lauren Pantalone, Despina Kontos, Eduardo Mortani Barbosa Jr

Purpose: To utilize high-resolution quantitative CT (QCT) imaging features for prediction of diagnosis and prognosis in fibrosing interstitial lung diseases (ILD).

Management Survival Analysis

Automated detection and quantification of COVID-19 airspace disease on chest radiographs: A novel approach achieving radiologist-level performance using a CNN trained on digital reconstructed radiographs (DRRs) from CT-based ground-truth

no code implementations • 13 Aug 2020 • Eduardo Mortani Barbosa Jr., Warren B. Gefter, Rochelle Yang, Florin C. Ghesu, Si-Qi Liu, Boris Mailhe, Awais Mansoor, Sasa Grbic, Sebastian Piat, Guillaume Chabin, Vishwanath R S., Abishek Balachandran, Sebastian Vogt, Valentin Ziebandt, Steffen Kappler, Dorin Comaniciu

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 a convolutional neural network to quantify airspace disease on paired CXRs; and 2) compare the DRR-trained CNN to expert human readers in the CXR evaluation of patients with confirmed COVID-19.