Deep learning in interstitial lung disease: classification and prognostic insights

This is the authors' abstract. We don't add key points for this paper.

Interstitial lung disease (ILD) comprises diverse parenchymal lung disorders, and are an important cause of morbidity and mortality among lung diseases. Disagreement is frequently observed among radiologic reads, pathologic interpretations, and multidisciplinary discussion consensus. Therefore, establishing a definitive diagnosis of ILD by using current techniques and criteria poses a considerable challenge. High-resolution computed tomography (HRCT) plays a crucial role in characterizing imaging patterns and predicting ILD prognosis. However, the substantial overlap in radiographic findings hinders accurate diagnosis of ILD in HRCT, even by experienced radiologists. Recently, deep learning (DL), a strategy that can automatically learn important characteristic features and patterns within CT images, has shown great potential in classifying and predicting ILD prognosis. This review summarizes current DL applications in ILD classification and prognosis evaluation; discusses challenges in clinical implementation; and presents insights for advancing this field. In conclusion, advanced DL can enhance diagnostic accuracy and enable more personalized treatment, thus providing new perspectives for managing ILD in the future.