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Diagnostics
Special Issues
Fifth Anniversary of "Machine Learning and Artificial Intelligence in Diagnostics"...
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Fifth Anniversary of "Machine Learning and Artificial Intelligence in Diagnostics" Section

A special issue of Diagnostics (ISSN 2075-4418). This special issue belongs to the section "Machine Learning and Artificial Intelligence in Diagnostics".

Deadline for manuscript submissions: 10 May 2024 | Viewed by 851

Special Issue Editors

Prof. Dr. Dania Cioni

E-Mail Website
Guest Editor
Academic Radiology Unit, Department of Surgical, Medical and Molecular Pathology and Critical Area, University of Pisa, 56126 Pisa, Italy
Interests: imaging of liver (diffuse, benign and malignant lesions); interventional oncology; treatment response criteria; application of artificial intelligence
Special Issues, Collections and Topics in MDPI journals
Dr. Salvatore Claudio Fanni

E-Mail Website
Guest Editor
Academic Radiology, Department of Translational Research, University of Pisa, 56126 Pisa, Italy
Interests: artificial intelligence; deep learning; radiomics; computed tomography; ultrasound; oncologic imaging; natural language processing; texture analysis

Special Issue Information

Dear Colleagues,

The field of diagnostics has undergone a significant transformation in recent years, with machine learning and artificial intelligence technologies playing an increasingly vital role. These cutting-edge techniques have opened a plethora of possibilities in detecting, diagnosing, and predicting various medical conditions with unprecedented accuracy and efficiency.

Machine learning algorithms, integrated with radiomics, have achieved remarkable advancements in disease diagnosis, treatment response assessment and patient prognosis prediction. Deep learning, a subset of machine learning, has proven to be particularly effective in image recognition tasks, such as identifying malignant tumors, and segmentation. In addition to image analysis, natural language processing applications have also gained traction in the field of diagnostics, by enabling the extraction of valuable information from medical reports and other textual data.

The primary goal of this Special Issue, dedicated to the fifth anniversary of the "Machine Learning and Artificial Intelligence in Diagnostics" section, is elucidate the latest research, developments, and breakthroughs that leverage machine learning and artificial intelligence in the field of diagnostics.

The submission of regular articles and review papers on the above-mentioned topics is most welcome.

Prof. Dr. Dania Cioni
Dr. Salvatore Claudio Fanni
Guest Editors

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.

Keywords

  • artificial intelligence
  • machine learning
  • radiomics
  • deep learning
  • natural language processing
  • large language models
  • texture analysis

Published Papers (1 paper)

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Research

13 pages, 2543 KiB  
Article
Quantitative CT Texture Analysis of COVID-19 Hospitalized Patients during 3–24-Month Follow-Up and Correlation with Functional Parameters
by Salvatore Claudio Fanni, Federica Volpi, Leonardo Colligiani, Davide Chimera, Michele Tonerini, Francesco Pistelli, Roberta Pancani, Chiara Airoldi, Brian J. Bartholmai, Dania Cioni, Laura Carrozzi, Emanuele Neri, Annalisa De Liperi and Chiara Romei
Diagnostics 2024, 14(5), 550; https://doi.org/10.3390/diagnostics14050550 - 05 Mar 2024
Viewed by 602
Abstract
Background: To quantitatively evaluate CT lung abnormalities in COVID-19 survivors from the acute phase to 24-month follow-up. Quantitative CT features as predictors of abnormalities’ persistence were investigated. Methods: Patients who survived COVID-19 were retrospectively enrolled and underwent a chest CT at baseline (T0) [...] Read more.
Background: To quantitatively evaluate CT lung abnormalities in COVID-19 survivors from the acute phase to 24-month follow-up. Quantitative CT features as predictors of abnormalities’ persistence were investigated. Methods: Patients who survived COVID-19 were retrospectively enrolled and underwent a chest CT at baseline (T0) and 3 months (T3) after discharge, with pulmonary function tests (PFTs). Patients with residual CT abnormalities repeated the CT at 12 (T12) and 24 (T24) months after discharge. A machine-learning-based software, CALIPER, calculated the CT percentage of the whole lung of normal parenchyma, ground glass (GG), reticulation (Ret), and vascular-related structures (VRSs). Differences (Δ) were calculated between time points. Receiver operating characteristic (ROC) curve analyses were performed to test the baseline parameters as predictors of functional impairment at T3 and of the persistence of CT abnormalities at T12. Results: The cohort included 128 patients at T0, 133 at T3, 61 at T12, and 34 at T24. The GG medians were 8.44%, 0.14%, 0.13% and 0.12% at T0, T3, T12 and T24. The Ret medians were 2.79% at T0 and 0.14% at the following time points. All Δ significantly differed from 0, except between T12 and T24. The GG and VRSs at T0 achieved AUCs of 0.73 as predictors of functional impairment, and area under the curves (AUCs) of 0.71 and 0.72 for the persistence of CT abnormalities at T12. Conclusions: CALIPER accurately quantified the CT changes up to the 24-month follow-up. Resolution mostly occurred at T3, and Ret persisting at T12 was almost unchanged at T24. The baseline parameters were good predictors of functional impairment at T3 and of abnormalities’ persistence at T12. Full article
(This article belongs to the Special Issue Fifth Anniversary of "Machine Learning and Artificial Intelligence in Diagnostics" Section)
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