Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
Review
. 2023 Aug 1;58(8):602-609.
doi: 10.1097/RLI.0000000000000974. Epub 2023 Apr 11.

Artificial Intelligence and Interstitial Lung Disease: Diagnosis and Prognosis

Ethan Dack  1 , Andreas Christe  2 , Matthias Fontanellaz  1 , Lorenzo Brigato  1 , Johannes T Heverhagen  2 , Alan A Peters  2 , Adrian T Huber  2 , Hanno Hoppe, Stavroula Mougiakakou  1 , Lukas Ebner  2
Affiliations
Review

Artificial Intelligence and Interstitial Lung Disease: Diagnosis and Prognosis

Ethan Dack et al. Invest Radiol. .

Abstract

Interstitial lung disease (ILD) is now diagnosed by an ILD-board consisting of radiologists, pulmonologists, and pathologists. They discuss the combination of computed tomography (CT) images, pulmonary function tests, demographic information, and histology and then agree on one of the 200 ILD diagnoses. Recent approaches employ computer-aided diagnostic tools to improve detection of disease, monitoring, and accurate prognostication. Methods based on artificial intelligence (AI) may be used in computational medicine, especially in image-based specialties such as radiology. This review summarises and highlights the strengths and weaknesses of the latest and most significant published methods that could lead to a holistic system for ILD diagnosis. We explore current AI methods and the data use to predict the prognosis and progression of ILDs. It is then essential to highlight the data that holds the most information related to risk factors for progression, e.g., CT scans and pulmonary function tests. This review aims to identify potential gaps, highlight areas that require further research, and identify the methods that could be combined to yield more promising results in future studies.

PubMed Disclaimer

Conflict of interest statement

Conflicts of interest and sources of funding: none declared.

Figures

FIGURE 1
FIGURE 1
A typical workflow of a AI-based model for medical diagnosis. The first stage is to acquire data from different medical imaging modalities, patient demographic and clinical/laboratory data. This will require extensive processing to be ready for AI model development. The models developed needs to be evaluated against ground truths, and validated against human experts.
FIGURE 2
FIGURE 2
The analysis of ILDs can be separated into different stages. Firstly, segmentation of the various anatomical structures followed by detection, characterisation and quantification of pathological patterns using deep and radiomic features. The most informative features are identified and analysed with respect to clinical outcomes including diagnosis and disease progression.
FIGURE 3
FIGURE 3
Quantification of pathological interstitial lung tissue (middle column) and visualisation using radial histograms (right column). Each sector denotes a region of the lung and is split into 2 parts, one for the central (inner) and one for peripheral (outer). Solid lines denote the division of left and right lungs. Top case is a typical UIP with reticulation (orange) and honeycombing (red) and bottom a non-IPF with ground glass opacity (purple).
FIGURE 4
FIGURE 4
Quantifying pulmonary fibrosis I: comparing the original CT image (A-1 and B-1) with the automatically segmented honeycombing regions (A-2, A-3, B-2, B-3) to calculate the fibrosis percentage. A, Area of honeycombing is 5% to 25% of the entire lung (CT stage II). B, Area of honeycombing is <5% of the entire lung (CT stage I).
FIGURE 5
FIGURE 5
Quantifying pulmonary fibrosis II: Conversion of segmentation maps into 4 montages, then calculation of probability of UIP for each montage.
FIGURE 6
FIGURE 6
A summary of using proportional hazard modeling in prognosis of ILDs.
See this image and copyright information in PMC

References

    1. Mcculloch W, Pitts W. A logical calculus of ideas immanent in nervous activity. Bull Math Biophys. 1943;5:127–147. - PubMed
    1. Turning AM. Computing machinery and intelligence. Mind. 1950;LIX:433–460.
    1. McCarthy J Minsky ML Rochester N, et al. . A proposal for the Dartmouth summer research project on artificial intelligence. August 19, 1955. https://web.archive.org/web/20080930164306/http://www-formal.stanford.ed.... Accessed December 11, 2022.
    1. Samuel AL. Some studies in machine learning using the game of checkers. IBM J Res Dev. 1959;3:210–229.
    1. Chollet F. Deep Learning with Python. New York, NY: Manning Publications. 2017.