Deep learning in interstitial lung disease-how long until daily practice.

Area of Science:

• Pulmonology
• Radiology
• Artificial Intelligence in Medicine

Background:

• Interstitial lung diseases (ILDs) are a heterogeneous group of lung disorders characterized by inflammation and fibrosis of the lung interstitium.
• These conditions present significant morbidity and mortality, necessitating improved diagnostic strategies.
• High-resolution computed tomography (HRCT) is crucial for ILD diagnosis and management, particularly for fibrotic lung disease.

Purpose of the Study:

• To review computer-aided diagnosis (CADx) systems utilizing deep learning for improved ILD diagnosis.
• To highlight challenges and practical implementation of CADx systems in daily clinical practice.
• To emphasize the importance of early diagnosis of idiopathic pulmonary fibrosis (IPF).

Main Methods:

• Review of deep learning approaches, specifically convolutional neural networks (CNNs), applied to ILD diagnosis.
• Focus on pattern recognition capabilities of AI in identifying different ILD subtypes.
• Discussion of the role of HRCT in conjunction with AI for diagnostic accuracy.

Main Results:

• Deep learning algorithms demonstrate potential in recognizing patterns associated with various ILDs.
• AI-driven systems can enhance the diagnostic process, particularly for early detection of IPF.
• The integration of CNNs into clinical workflows shows promise for improving diagnostic efficiency.

Conclusions:

• Deep learning offers a powerful tool for the pattern recognition required in diagnosing ILDs.
• The development of accessible CNNs deployable on standard computer stations is crucial for widespread adoption, especially in non-academic centers.
• Future advancements in AI are expected to facilitate earlier and more cost-effective diagnosis of IPF, reducing healthcare burdens.