Interstitial lung disease diagnosis and prognosis using an AI system integrating longitudinal data

Xueyan Mei, Zelong Liu, Ayushi Singh, Marcia Lange, Priyanka Boddu, Jingqi Q.X. Gong, Justine Lee, Cody DeMarco, Chendi Cao, Samantha Platt, Ganesh Sivakumar, Benjamin Gross, Mingqian Huang, Joy Masseaux, Sakshi Dua, Adam Bernheim, Michael Chung, Timothy Deyer, Adam Jacobi, Maria PadillaZahi A. Fayad, Yang Yang

Research output: Contribution to journalArticlepeer-review

1 Scopus citations


For accurate diagnosis of interstitial lung disease (ILD), a consensus of radiologic, pathological, and clinical findings is vital. Management of ILD also requires thorough follow-up with computed tomography (CT) studies and lung function tests to assess disease progression, severity, and response to treatment. However, accurate classification of ILD subtypes can be challenging, especially for those not accustomed to reading chest CTs regularly. Dynamic models to predict patient survival rates based on longitudinal data are challenging to create due to disease complexity, variation, and irregular visit intervals. Here, we utilize RadImageNet pretrained models to diagnose five types of ILD with multimodal data and a transformer model to determine a patient’s 3-year survival rate. When clinical history and associated CT scans are available, the proposed deep learning system can help clinicians diagnose and classify ILD patients and, importantly, dynamically predict disease progression and prognosis.

Original languageEnglish
Article number2272
JournalNature Communications
Issue number1
StatePublished - Dec 2023


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