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Review
. 2025 Apr 7;15(7):943.
doi: 10.3390/diagnostics15070943.

Computer-Aided Evaluation of Interstitial Lung Diseases

Davide Colombi  1 , Maurizio Marvisi  2 , Sara Ramponi  2 , Laura Balzarini  2 , Chiara Mancini  2 , Gianluca Milanese  3 , Mario Silva  3 , Nicola Sverzellati  3 , Mario Uccelli  1 , Francesco Ferrozzi  1
Affiliations
Review

Computer-Aided Evaluation of Interstitial Lung Diseases

Davide Colombi et al. Diagnostics (Basel). .

Abstract

The approach for the diagnosis and treatment of interstitial lung diseases (ILDs) has changed in recent years, mainly for the identification of new entities, such as interstitial lung abnormalities (ILAs) and progressive pulmonary fibrosis (PPF). Clinicians and radiologists are facing new challenges for the screening, diagnosis, prognosis, and follow-up of ILDs. The detection and classification of ILAs or the identification of fibrosis progression at high-resolution computed tomography (HRCT) is difficult, with high inter-reader variability, particularly for non-expert radiologists. In the last few years, various software has been developed for ILD evaluation at HRCT, with excellent results, equal to or more reliable than humans. AI tools can classify ILDs, quantify the extent, analyze the features hidden from the human eye, predict prognosis, and evaluate the progression of the disease. More advanced tools can incorporate clinical and radiological data to obtain personalized prognosis, with the potential ability to steer treatment decisions. To step forward and implement in daily practice such tools, more collaboration is required to collect more homogeneous clinical and radiological data; furthermore, more robust, prospective trials, with the new AI-derived biomarkers compared with each other, are needed to demonstrate the real reliability of the computer-aided evaluation of ILDs.

Keywords: AI (artificial intelligence); hierarchical learning; interstitial lung disease.

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Conflict of interest statement

The authors declare no conflict of interest.

Figures

Figure 1
Figure 1
An example of quantitative computed tomography (QCT) applied in interstitial lung disease; in idiopathic pulmonary fibrosis (IPF) the increase over time (17 months) in density (HU) of the 80th percentile of the lung density histogram (d) as compared to baseline (b), reflected reticulation progression as depicted visually in high-resolution computed tomography (HRCT) images at follow-up (c) in comparison to baseline HRCT (a) (adapted by Colombi et al.) [13].
Figure 2
Figure 2
Hierarchy of the artificial intelligence tool in medical images (adapted by Najjar et al.) [16].
Figure 3
Figure 3
Computer vision tasks in deep learning (adapted by Najjar et al.) [16,17].
Figure 4
Figure 4
Data augmentation based on affine transformations or generative adversarial networks (GANs) (adapted by Cheng et al.) [17].
Figure 5
Figure 5
Convolutional neural network diagram with input data that finally reach an output (in the case presented, recognition of lesions and organs) after convolutional and pooling serial layers (adapted by Cheng et al.) [17].
Figure 6
Figure 6
Intersection over union (IOU) and DICE are methods that evaluate the degree of overlap in segmentation of an object as compared to human segmentation (adapted by Cheng et al.) [17].
Figure 7
Figure 7
The illustration shows the AI model developed by Mei et al. that included high-resolution computed tomography (HRCT) and clinical data at different time-points to predict survival probability at three years (adapted by Mei et al.) [38].
See this image and copyright information in PMC

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