ATS 2024 Final Program

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232

TUESDAY • MAY 21

911 Lung Texture Analysis Predicts Clinical Progression in a Cohort of Patients With Dermatomyositis and Scleroderma-related Interstitial Lung Disease 912 e-Lung CT Biomarkers Can Stratify Patients at Risk of IPF Progression at 52 Weeks; Post-hoc Analysis From a Randomised Control Trial 913 A Machine Learning Approach to Predict Lymphangioleiomyomatosis Lung Disease Progression 914 Evaluating the Performance of an Ensemble Neural Network Pipeline in the Classification of Chest CT Scans as Control, Diffuse Cystic Lung Disease and Emphysema 915 Quantitative Assessment of CT Lung Abnormalities in Cystic Pulmonary Langerhans Cell Histiocytosis Using Parametric Response Mapping 916 In Silico Modeling of Airflow and Dosimetry in Sarcoidosis Airway Disease 917 The Utility of Machine Learning for Predicting Donor Discard in Lung Transplantation 918 Automated Measurements of Lung Sizes Using Deep Learning and Computer Vision 919 The Role of Imaging Biomarkers in Predicting Outcomes for Patients With Non-IPF Fibrotic ILD 920 Machine Learning Classifier Predicts Mortality in Interstitial Lung Disease: A Validation Study 921 Novel Machine Learning Algorithm Predicts All-cause Mortality in the National Lung Screening Trial 922 Deep Learning-based Segmentation of CT Scans Predicts Disease Progression and Mortality in IPF 923 A Novel Radiomics Tool for Early Detection of Idiopathic Pulmonary Fibrosis 924 Validation of Automated Segmentation of Pulmonary Cysts in Lymphangioleiomyomatosis

CLINICAL • TRANSLATIONAL POSTER DISCUSSION SESSION

C23 MACHINE LEARNED: AI-DRIVEN SOLUTIONS IN ILD AND LUNG TRANSPLANT 9:15 a.m. - 11:15 a.m.

San Diego Convention Center Room 33A-C (Upper Level)

Poster Viewing

9:15-10:00

Discussion 10:00-11:15 901 Survival Machine Learning Approach to Evaluate Proteomic Biomarkers of Idiopathic Pulmonary Fibrosis: A Window to Precision Medicine 902 Evaluation of Quantitative Imaging in a Phase 2a Study for the Treatment of Idiopathic Pulmonary Fibrosis With Bexotegrast (INTEGRIS-IPF) 903 Utilising 3 Deep Learning Models for Outcome Prediction in Patients With Idiopathic Pulmonary Fibrosis 904 Artificial Intelligence-based Decision Support for HRCT Stratification in Fibrotic Lung Disease: An International Study of 195 Observers From 43 Countries 905 Association of Quantitative Lung Fibrosis (QLF) Score With the Severity and Progression of Progressive Pulmonary Fibrosis (PPF) 906 Assessment of In Vivo Pulmonary Microvasculature in Interstitial Lung Disease Using Deep Learning-based Optical Coherence Tomography 907 Beyond Quantitative Interstitial Lung Diseases on High-resolution CT: Association of Single Timepoint Prediction (STP) Positive Score With Progression-free Survival 908 IS-IPF: Deep Learning Idiopathic Pulmonary Fibrosis (IPF) Classification and Its Association With Progression-free Survival 909 Using Latent Class Analysis to Predict Outcomes and Inform Disease Classification in Fibrotic Interstitial Lung Disease 910 Multi-modal Machine Learning Classifier for Idiopathic Pulmonary Fibrosis Predicts Mortality in Interstitial Lung Diseases

ATS 2024 Conference Program • San Diego, CA