Interstitial Lung Disease

Interstitial lung diseases (ILDs) are a group of disorders characterized by lung tissue scarring, posing significant diagnostic and prognostic challenges due to varied presentations and overlapping imaging features. Current research focuses on developing advanced computational methods, including deep convolutional neural networks and multimodal models like CLIP, to improve ILD classification and prediction of disease progression using quantitative CT scan analysis. These machine learning approaches aim to enhance diagnostic accuracy and personalize patient management by leveraging both image texture analysis and textual information from radiology reports. Improved diagnostic tools hold significant potential to improve patient outcomes and streamline clinical workflows.