Special Issue "The Challenge of Advanced Medical Imaging Data Analysis in COVID-19"

A special issue of Tomography (ISSN 2379-139X). This special issue belongs to the section "Artificial Intelligence in Medical Imaging".

Deadline for manuscript submissions: 1 August 2022 | Viewed by 562

Special Issue Editors

Dr. Pietro Andrea Bonaffini
E-Mail Website
Guest Editor
Department of Radiology, Papa Giovanni XXIII Hospital, Bergamo; School of Medicine, University Milano Bicocca, Milan, Italy
Interests: abdominal radiology; hepato-biliary and pancreatic disease; gynecologic imaging; oncologic imaging; emergency radiology; radiomics
Dr. Clarissa Valle
E-Mail Website
Guest Editor
Department of Radiology, Papa Giovanni XXIII Hospital, Bergamo; School of Medicine, University Milano Bicocca, Milan, Italy
Interests: abdominal radiology; pediatric radiology; emergency radiology oncologic imaging

Special Issue Information

Dear Colleagues,

The COVID-19 pandemic has profoundly impacted healthcare systems around the world, leading to increased efforts in all steps for patient management. Facing this worldwide emergency, imaging experts have extensively put in play standard validated techniques (X-ray, CT) but also firmly invested in and tested new advanced tools. Namely, COVID-19 has challenged the most recent advances and improvements in Artificial Intelligence (AI) and quantitative and computational imaging. Even straightforward imaging techniques, such as lung ultrasound, have been employed from new perspectives. Data analysis and integration have been implemented over the last decade, allowing the assimilation of radiological, clinical, and laboratory information. COVID-19 has shown the usefulness of these tools in diagnosis, patient stratification, and prognostic evaluation. This Special Issue will focus on pertinent research papers, commentaries, and reviews informing readers about the role and challenges of the leading innovative imaging tools and new employment of strengthened techniques in assessing COVID-19 patients with lung disease. We welcome submissions describing computational and quantitative imaging, application of Artificial Intelligence, and machine learning in the diagnosis, follow-up, and prognosis of COVID-19 lung disease. Accepted modalities will include chest X-ray, CT, and lung ultrasound.

Dr. Pietro Andrea Bonaffini
Dr. Clarissa Valle
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. Tomography 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 1800 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

  • COVID-19
  • SARS-CoV-2
  • Artificial Intelligence
  • segmentation
  • computational imaging
  • CT
  • lung
  • ultrasound
  • imaging data analysis

Published Papers (1 paper)

Order results
Result details
Select all
Export citation of selected articles as:

Research

Article
Follow-Up CT Patterns of Residual Lung Abnormalities in Severe COVID-19 Pneumonia Survivors: A Multicenter Retrospective Study
Tomography 2022, 8(3), 1184-1195; https://doi.org/10.3390/tomography8030097 - 20 Apr 2022
Viewed by 282
Abstract
Prior studies variably reported residual chest CT abnormalities after COVID-19. This study evaluates the CT patterns of residual abnormalities in severe COVID-19 pneumonia survivors. All consecutive COVID-19 survivors who received a CT scan 5–7 months after severe pneumonia in two Italian hospitals (Reggio [...] Read more.
Prior studies variably reported residual chest CT abnormalities after COVID-19. This study evaluates the CT patterns of residual abnormalities in severe COVID-19 pneumonia survivors. All consecutive COVID-19 survivors who received a CT scan 5–7 months after severe pneumonia in two Italian hospitals (Reggio Emilia and Parma) were enrolled. Individual CT findings were retrospectively collected and follow-up CT scans were categorized as: resolution, residual non-fibrotic abnormalities, or residual fibrotic abnormalities according to CT patterns classified following standard definitions and international guidelines. In 225/405 (55.6%) patients, follow-up CT scans were normal or barely normal, whereas in 152/405 (37.5%) and 18/405 (4.4%) patients, non-fibrotic and fibrotic abnormalities were respectively found, and 10/405 (2.5%) had post-ventilatory changes (cicatricial emphysema and bronchiectasis in the anterior regions of upper lobes). Among non-fibrotic changes, either barely visible (n = 110/152) or overt (n = 20/152) ground-glass opacities (GGO), resembling non-fibrotic nonspecific interstitial pneumonia (NSIP) with or without organizing pneumonia features, represented the most common findings. The most frequent fibrotic abnormalities were subpleural reticulation (15/18), traction bronchiectasis (16/18) and GGO (14/18), resembling a fibrotic NSIP pattern. When multiple timepoints were available until 12 months (n = 65), residual abnormalities extension decreased over time. NSIP, more frequently without fibrotic features, represents the most common CT appearance of post-severe COVID-19 pneumonia. Full article
(This article belongs to the Special Issue The Challenge of Advanced Medical Imaging Data Analysis in COVID-19)
Show Figures

Figure 1

Back to TopTop