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Tomography
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The Challenge of Advanced Medical Imaging Data Analysis in COVID-19
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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: closed (31 October 2023) | Viewed by 12448

Special Issue Editors

Dr. Pietro Andrea Bonaffini

E-Mail Website
Guest Editor
1. Department of Radiology, Papa Giovanni XXIII Hospital, 24127 Bergamo, Italy
2. School of Medicine, University Milano Bicocca, 20126 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
1. Department of Radiology, Papa Giovanni XXIII Hospital, 24127 Bergamo, Italy
2. School of Medicine, University Milano Bicocca, 20126 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 monthly 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 2400 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 (6 papers)

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Research

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14 pages, 3573 KiB  
Article
MSTAC: A Multi-Stage Automated Classification of COVID-19 Chest X-ray Images Using Stacked CNN Models
by Thanakorn Phumkuea, Thakerng Wongsirichot, Kasikrit Damkliang, Asma Navasakulpong and Jarutas Andritsch
Tomography 2023, 9(6), 2233-2246; https://0-doi-org.brum.beds.ac.uk/10.3390/tomography9060173 - 13 Dec 2023
Viewed by 1047
Abstract
This study introduces a Multi-Stage Automated Classification (MSTAC) system for COVID-19 chest X-ray (CXR) images, utilizing stacked Convolutional Neural Network (CNN) models. Suspected COVID-19 patients often undergo CXR imaging, making it valuable for disease classification. The study collected CXR images from public datasets [...] Read more.
This study introduces a Multi-Stage Automated Classification (MSTAC) system for COVID-19 chest X-ray (CXR) images, utilizing stacked Convolutional Neural Network (CNN) models. Suspected COVID-19 patients often undergo CXR imaging, making it valuable for disease classification. The study collected CXR images from public datasets and aimed to differentiate between COVID-19, non-COVID-19, and healthy cases. MSTAC employs two classification stages: the first distinguishes healthy from unhealthy cases, and the second further classifies COVID-19 and non-COVID-19 cases. Compared to a single CNN-Multiclass model, MSTAC demonstrated superior classification performance, achieving 97.30% accuracy and sensitivity. In contrast, the CNN-Multiclass model showed 94.76% accuracy and sensitivity. MSTAC’s effectiveness is highlighted in its promising results over the CNN-Multiclass model, suggesting its potential to assist healthcare professionals in efficiently diagnosing COVID-19 cases. The system outperformed similar techniques, emphasizing its accuracy and efficiency in COVID-19 diagnosis. This research underscores MSTAC as a valuable tool in medical image analysis for enhanced disease classification. Full article
(This article belongs to the Special Issue The Challenge of Advanced Medical Imaging Data Analysis in COVID-19)
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13 pages, 3216 KiB  
Article
Diagnostic Performance in Differentiating COVID-19 from Other Viral Pneumonias on CT Imaging: Multi-Reader Analysis Compared with an Artificial Intelligence-Based Model
by Francesco Rizzetto, Luca Berta, Giulia Zorzi, Antonino Cincotta, Francesca Travaglini, Diana Artioli, Silvia Nerini Molteni, Chiara Vismara, Francesco Scaglione, Alberto Torresin, Paola Enrica Colombo, Luca Alessandro Carbonaro and Angelo Vanzulli
Tomography 2022, 8(6), 2815-2827; https://0-doi-org.brum.beds.ac.uk/10.3390/tomography8060235 - 25 Nov 2022
Cited by 4 | Viewed by 3005
Abstract
Growing evidence suggests that artificial intelligence tools could help radiologists in differentiating COVID-19 pneumonia from other types of viral (non-COVID-19) pneumonia. To test this hypothesis, an R-AI classifier capable of discriminating between COVID-19 and non-COVID-19 pneumonia was developed using CT chest scans of [...] Read more.
Growing evidence suggests that artificial intelligence tools could help radiologists in differentiating COVID-19 pneumonia from other types of viral (non-COVID-19) pneumonia. To test this hypothesis, an R-AI classifier capable of discriminating between COVID-19 and non-COVID-19 pneumonia was developed using CT chest scans of 1031 patients with positive swab for SARS-CoV-2 (n = 647) and other respiratory viruses (n = 384). The model was trained with 811 CT scans, while 220 CT scans (n = 151 COVID-19; n = 69 non-COVID-19) were used for independent validation. Four readers were enrolled to blindly evaluate the validation dataset using the CO-RADS score. A pandemic-like high suspicion scenario (CO-RADS 3 considered as COVID-19) and a low suspicion scenario (CO-RADS 3 considered as non-COVID-19) were simulated. Inter-reader agreement and performance metrics were calculated for human readers and R-AI classifier. The readers showed good agreement in assigning CO-RADS score (Gwet’s AC2 = 0.71, p < 0.001). Considering human performance, accuracy = 78% and accuracy = 74% were obtained in the high and low suspicion scenarios, respectively, while the AI classifier achieved accuracy = 79% in distinguishing COVID-19 from non-COVID-19 pneumonia on the independent validation dataset. The R-AI classifier performance was equivalent or superior to human readers in all comparisons. Therefore, a R-AI classifier may support human readers in the difficult task of distinguishing COVID-19 from other types of viral pneumonia on CT imaging. Full article
(This article belongs to the Special Issue The Challenge of Advanced Medical Imaging Data Analysis in COVID-19)
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16 pages, 5238 KiB  
Article
Structural and Functional Pulmonary Assessment in Severe COVID-19 Survivors at 12 Months after Discharge
by Andrea Corsi, Anna Caroli, Pietro Andrea Bonaffini, Caterina Conti, Alberto Arrigoni, Elisa Mercanzin, Gianluca Imeri, Marisa Anelli, Maurizio Balbi, Marina Pace, Adriana Zanoletti, Milena Capelli, Fabiano Di Marco and Sandro Sironi
Tomography 2022, 8(5), 2588-2603; https://0-doi-org.brum.beds.ac.uk/10.3390/tomography8050216 - 13 Oct 2022
Cited by 9 | Viewed by 1998
Abstract
Long-term pulmonary sequelae in COVID-19 patients are currently under investigation worldwide. Potential relationships between blood sampling and functional and radiological findings are crucial to guide the follow-up. In this study, we collected and evaluated clinical status, namely symptoms and patients’ reported outcome, pulmonary [...] Read more.
Long-term pulmonary sequelae in COVID-19 patients are currently under investigation worldwide. Potential relationships between blood sampling and functional and radiological findings are crucial to guide the follow-up. In this study, we collected and evaluated clinical status, namely symptoms and patients’ reported outcome, pulmonary function tests (PFT), laboratory tests, and radiological findings at 3- and 12-months post-discharge in patients admitted between 25 February and 2 May 2020, and who survived severe COVID-19 pneumonia. A history of chronic pulmonary disease or COVID-19-unrelated complications were used as exclusion criteria. Unenhanced CTs were analyzed quantitatively (compromising lung volume %) and qualitatively, with main patterns of: ground-glass opacity (GGO), consolidation, and reticular configuration. Patients were subsequently divided into groups based on their radiological trends and according to the evolution in the percentage of compromised lung volume. At 12 months post-discharge, seventy-one patients showed significantly improved laboratory tests and PFT. Among them, 63 patients also underwent CT examination: all patients with negative CT findings at three months (n = 14) had negative CT also at 12 months; among the 49/63 patients presenting CT alterations at three months, 1/49 (2%) normalized, 40/49 (82%) improved, 7/49 (14%) remained stably abnormal, and 1/49 (2%) worsened. D-dimer values were low in patients with normal CT and higher in cases with improved or stably abnormal CT (median values 213 vs. 329 vs. 1000 ng/mL, respectively). The overall compromised lung volume was reduced compared with three months post-discharge (12.3 vs. 14.4%, p < 0.001). In stably abnormal CT, the main pulmonary pattern changed, showing a reduction in GGO and an increase in reticular configuration. To summarize, PFT are normal in most COVID-19 survivors 12 months post-discharge, but CT structural abnormalities persist (although sensibly improved over time) and are associated with higher D-dimer values. Full article
(This article belongs to the Special Issue The Challenge of Advanced Medical Imaging Data Analysis in COVID-19)
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8 pages, 646 KiB  
Article
Quantitative Chest CT Analysis to Measure Short-Term Sequelae of COVID-19 Pneumonia: A Monocentric Prospective Study
by Ezio Lanza, Angela Ammirabile, Maddalena Casana, Daria Pocaterra, Federica Maria Pilar Tordato, Benedetta Varisco, Costanza Lisi, Gaia Messana, Luca Balzarini and Paola Morelli
Tomography 2022, 8(3), 1578-1585; https://0-doi-org.brum.beds.ac.uk/10.3390/tomography8030130 - 17 Jun 2022
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Abstract
(1) Background: Quantitative CT analysis (QCT) has demonstrated promising results in the prognosis prediction of patients affected by COVID-19. We implemented QCT not only at diagnosis but also at short-term follow-up, pairing it with a clinical examination in search of a correlation between [...] Read more.
(1) Background: Quantitative CT analysis (QCT) has demonstrated promising results in the prognosis prediction of patients affected by COVID-19. We implemented QCT not only at diagnosis but also at short-term follow-up, pairing it with a clinical examination in search of a correlation between residual respiratory symptoms and abnormal QCT results. (2) Methods: In this prospective monocentric trial performed during the “first wave” of the Italian pandemic, i.e., from March to May 2020, we aimed to test the relationship between %deltaCL (variation of %CL-compromised lung volume) and variations of symptoms-dyspnea, cough and chest pain-at follow-up clinical assessment after hospitalization. (3) Results: 282 patients (95 females, 34%) with a median age of 60 years (IQR, 51–69) were included. We reported a correlation between changing lung abnormalities measured by QCT, and residual symptoms at short-term follow up after COVID-19 pneumonia. Independently from age, a low percentage of surviving patients (1–4%) may present residual respiratory symptoms at approximately two months after discharge. QCT was able to quantify the extent of residual lung damage underlying such symptoms, as the reduction of both %PAL (poorly aerated lung) and %CL volumes was correlated to their disappearance. (4) Conclusions QCT may be used as an objective metric for the measurement of COVID-19 sequelae. Full article
(This article belongs to the Special Issue The Challenge of Advanced Medical Imaging Data Analysis in COVID-19)
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12 pages, 4485 KiB  
Article
Follow-Up CT Patterns of Residual Lung Abnormalities in Severe COVID-19 Pneumonia Survivors: A Multicenter Retrospective Study
by Giulia Besutti, Filippo Monelli, Silvia Schirò, Francesca Milone, Marta Ottone, Lucia Spaggiari, Nicola Facciolongo, Carlo Salvarani, Stefania Croci, Pierpaolo Pattacini and Nicola Sverzellati
Tomography 2022, 8(3), 1184-1195; https://0-doi-org.brum.beds.ac.uk/10.3390/tomography8030097 - 20 Apr 2022
Cited by 19 | Viewed by 2756
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)
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Review

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12 pages, 1596 KiB  
Review
Residual Lung Abnormalities in Survivors of Severe or Critical COVID-19 at One-Year Follow-Up Computed Tomography: A Narrative Review Comparing the European and East Asian Experiences
by Andrea Borghesi, Pietro Ciolli, Elisabetta Antonelli, Alessandro Monti, Alessandra Scrimieri, Marco Ravanelli, Roberto Maroldi and Davide Farina
Tomography 2024, 10(1), 25-36; https://0-doi-org.brum.beds.ac.uk/10.3390/tomography10010003 - 30 Dec 2023
Viewed by 845
Abstract
The literature reports that there was a significant difference in the medical impact of the coronavirus disease (COVID-19) pandemic between European and East Asian countries; specifically, the mortality rate of COVID-19 in Europe was significantly higher than that in East Asia. Considering such [...] Read more.
The literature reports that there was a significant difference in the medical impact of the coronavirus disease (COVID-19) pandemic between European and East Asian countries; specifically, the mortality rate of COVID-19 in Europe was significantly higher than that in East Asia. Considering such a difference, our narrative review aimed to compare the prevalence and characteristics of residual lung abnormalities at one-year follow-up computed tomography (CT) after severe or critical COVID-19 in survivors of European and East Asian countries. A literature search was performed to identify articles focusing on the prevalence and characteristics of CT lung abnormalities in survivors of severe or critical COVID-19. Database analysis identified 16 research articles, 9 from Europe and 7 from East Asia (all from China). Our analysis found a higher prevalence of CT lung abnormalities in European than in Chinese studies (82% vs. 52%). While the most prevalent lung abnormalities in Chinese studies were ground-glass opacities (35%), the most prevalent lung abnormalities in European studies were linear (59%) and reticular opacities (55%), followed by bronchiectasis (46%). Although our findings required confirmation, the higher prevalence and severity of lung abnormalities in European than in Chinese survivors of COVID-19 may reflect a greater architectural distortion due to a more severe lung damage. Full article
(This article belongs to the Special Issue The Challenge of Advanced Medical Imaging Data Analysis in COVID-19)
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