From pixels to prognosis: unlocking the potential of deep learning in fibrotic lung disease imaging analysis.
Br J Radiol
; 97(1161): 1517-1525, 2024 Sep 01.
Article
in En
| MEDLINE
| ID: mdl-38781513
ABSTRACT
The licensing of antifibrotic therapy for fibrotic lung diseases, including idiopathic pulmonary fibrosis (IPF), has created an urgent need for reliable biomarkers to predict disease progression and treatment response. Some patients experience stable disease trajectories, while others deteriorate rapidly, making treatment decisions challenging. High-resolution chest CT has become crucial for diagnosis, but visual assessments by radiologists suffer from low reproducibility and high interobserver variability. To address these issues, computer-based image analysis, called quantitative CT, has emerged. However, many quantitative CT methods rely on human input for training, therefore potentially incorporating human error into computer training. Rapid advances in artificial intelligence, specifically deep learning, aim to overcome this limitation by enabling autonomous quantitative analysis. While promising, deep learning also presents challenges including the need to minimize algorithm biases, ensuring explainability, and addressing accessibility and ethical concerns. This review explores the development and application of deep learning in improving the imaging process for fibrotic lung disease.
Key words
Full text:
1
Collection:
01-internacional
Database:
MEDLINE
Main subject:
Tomography, X-Ray Computed
/
Idiopathic Pulmonary Fibrosis
/
Deep Learning
Limits:
Humans
Language:
En
Journal:
Br J Radiol
Year:
2024
Document type:
Article