1. Interstitial lung disease diagnosis and prognosis using an AI system integrating longitudinal data
- Author
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Mei, Xueyan, Liu, Zelong, Singh, Ayushi, Lange, Marcia, Boddu, Priyanka, Gong, Jingqi QX, Lee, Justine, DeMarco, Cody, Cao, Chendi, Platt, Samantha, Sivakumar, Ganesh, Gross, Benjamin, Huang, Mingqian, Masseaux, Joy, Dua, Sakshi, Bernheim, Adam, Chung, Michael, Deyer, Timothy, Jacobi, Adam, Padilla, Maria, Fayad, Zahi A, and Yang, Yang
- Subjects
Biomedical and Clinical Sciences ,Clinical Sciences ,Rare Diseases ,Biomedical Imaging ,Clinical Research ,Lung ,7.3 Management and decision making ,4.1 Discovery and preclinical testing of markers and technologies ,Management of diseases and conditions ,4.2 Evaluation of markers and technologies ,Detection ,screening and diagnosis ,Respiratory ,Humans ,Lung Diseases ,Interstitial ,Disease Progression ,Thorax ,Tomography ,X-Ray Computed ,Retrospective Studies - Abstract
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.
- Published
- 2023