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Diagnostics
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Artificial Intelligence in Lung Diseases: 3rd Edition
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Artificial Intelligence in Lung Diseases: 3rd Edition

A special issue of Diagnostics (ISSN 2075-4418). This special issue belongs to the section "Medical Imaging and Theranostics".

Deadline for manuscript submissions: closed (31 January 2025) | Viewed by 722

Special Issue Editor

Prof. Dr. Cesar A. Moran

E-Mail Website
Guest Editor
Department of Pathology, University of Texas MD Anderson Cancer Center, Houston, TX, USA
Interests: thoracic pathology (lung, pleura, and mediastinum)
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Precision medicine, more specifically artificial intelligence (AI) in the diagnosis and treatment of pulmonary diseases, has evolved in various important ways. The developments in thoracic imaging, thoracic pathology, and thoracic oncology are meaningful components that play a role in bringing those modalities to the bedside. Digital radiology and digital pathology are becoming portable specialties that, combined with advanced oncological algorithms, can be used for the betterment of patients afflicted by different thoracic diseases. Such technological advancements should not be limited to neoplastic diseases, but should also include other non-neoplastic processes in which such technology is applicable. In our current practice, the team approach to disease seems to have a better impact on the clinical outcomes of patients; therefore, it is highly important that these technologies, as they advance, become part of the armamentaria of tools that all clinicians need to be familiar with. By having this team approach, the different aspects of our individual specialties will also possibly expand.

Diagnostics is dedicating this Special Issue to the role of AI in pulmonary diseases, with a specific focus on the technological advancements in this area. The goal is to bring such advancements to all individuals involved in the care of patients afflicted by a range of pulmonary diseases. It is our hope that you will contribute to this Special Issue, whether in regards to pathology, radiology, oncology, or pulmonary medicine.

Prof. Dr. Cesar A. Moran
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Diagnostics is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

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Related Special Issue

Published Papers (1 paper)

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Research

16 pages, 2180 KiB  
Article
Machine Learning Based Multi-Parameter Modeling for Prediction of Post-Inflammatory Lung Changes
by Gerlig Widmann, Anna Katharina Luger, Thomas Sonnweber, Christoph Schwabl, Katharina Cima, Anna Katharina Gerstner, Alex Pizzini, Sabina Sahanic, Anna Boehm, Maxmilian Coen, Ewald Wöll, Günter Weiss, Rudolf Kirchmair, Leonhard Gruber, Gudrun M. Feuchtner, Ivan Tancevski, Judith Löffler-Ragg and Piotr Tymoszuk
Diagnostics 2025, 15(6), 783; https://doi.org/10.3390/diagnostics15060783 - 20 Mar 2025
Viewed by 280
Abstract
Objectives: Prediction of lung function deficits following pulmonary infection is challenging and suffers from inaccuracy. We sought to develop machine-learning models for prediction of post-inflammatory lung changes based on COVID-19 recovery data. Methods: In the prospective CovILD study (n = [...] Read more.
Objectives: Prediction of lung function deficits following pulmonary infection is challenging and suffers from inaccuracy. We sought to develop machine-learning models for prediction of post-inflammatory lung changes based on COVID-19 recovery data. Methods: In the prospective CovILD study (n = 420 longitudinal observations from n = 140 COVID-19 survivors), data on lung function testing (LFT), chest CT including severity scoring by a human radiologist and density measurement by artificial intelligence, demography, and persistent symptoms were collected. This information was used to develop models of numeric readouts and abnormalities of LFT with four machine learning algorithms (Random Forest, gradient boosted machines, neural network, and support vector machines). Results: Reduced DLCO (diffusion capacity for carbon monoxide <80% of reference) was found in 94 (22%) observations. Those observations were modeled with a cross-validated accuracy of 82–85%, AUC of 0.87–0.9, and Cohen’s κ of 0.45–0.5. No reliable models could be established for FEV1 or FVC. For DLCO as a continuous variable, three machine learning algorithms yielded meaningful models with cross-validated mean absolute errors of 11.6–12.5% and R2 of 0.26–0.34. CT-derived features such as opacity, high opacity, and CT severity score were among the most influential predictors of DLCO impairment. Conclusions: Multi-parameter machine learning trained with demographic, clinical, and artificial intelligence chest CT data reliably and reproducibly predicts LFT deficits and outperforms single markers of lung pathology and human radiologist’s assessment. It may improve diagnostic and foster personalized treatment. Full article
(This article belongs to the Special Issue Artificial Intelligence in Lung Diseases: 3rd Edition)
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