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Artificial Intelligence for COVID-19 Diagnosis

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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: closed (31 October 2021) | Viewed by 23528

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Special Issue Editors

Prof. Dr. Antonella Santone

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Guest Editor

Department of Medicine and Health Sciences “Vincenzo Tiberio”, University of Molise, 86100 Campobasso, Italy
Interests: radiomics; artificial intelligence; formal methods; explainability

Prof. Dr. Emanuele Neri

E-Mail Website
Co-Guest Editor

Department of Diagnostic and Interventional Radiology, University of Pisa, Pisa, Italy
Interests: imaging biomarkers; imaging biobanks; oncologic imaging; imaging informatics; health technology assessment
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Special Issue Information

Dear Colleagues,

Recently, radiomics has developed to assisting specialists through an objective analysis of radiological examinations, especially in cancer prediction. Once the virus causing COVID-19 started to spread, the role of specialists became fundamental to the detection of the disease, the assessment of the severity of the disease, and the correct interpretation of follow-up until resolution. Recognizing COVID-19 could be very difficult and present obstacles, due in part to the possible inconsistency of different exams and a lack of the clinical expertise needed to interpret diagnostic results, in addition to the huge volume of cases requiring treatment in the shortest time possible.

The management of COVID-19 should comprise data combination. Hence, there is a need for software tools aimed at assisting in the detection, diagnosis and treatment of pathologies based on radiological image analysis. Artificial intelligence techniques, formal methods, statistical analysis and other techniques are currently being used for this purpose. An important aspect of a method is a capacity to explain the “reasoning” that has taken place to arrive at the results. In scientific research, the use of automated decision processes should require an explanation to ensure trust, the acceptance of results and the progress of research.

Prof. Dr. Antonella Santone
Prof. Dr. Emanuele Neri
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

Radiomics
Artificial intelligence
Machine learning
Data analytics
Image processing
Deep learning
Formal methods
Explainability

Published Papers (7 papers)

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Research

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11 pages, 3093 KiB

Open AccessArticle

COVID-19 Intelligence-Driven Operational Response Platform: Experience of a Large Tertiary Multihospital System in the Middle East

by Osama A. Alswailem, Bashar K. Horanieh, Arwa AlAbbad, Sarab AlMuhaideb, Abdulkarim AlMuhanna, Muhammad AlQuaid, Hisham ElMoaqet, Nuhad Abuzied and Ahmad AbuSalah

Diagnostics 2021, 11(12), 2283; https://0-doi-org.brum.beds.ac.uk/10.3390/diagnostics11122283 - 06 Dec 2021

Cited by 3 | Viewed by 2102

Abstract

The COVID-19 pandemic has resulted in global disruptions within healthcare systems, leading to quick dynamic fluctuations in hospital operations and supply chain management. During the early months of the pandemic, tertiary multihospital systems were highly viewed as the go-to hospitals for handling these rapid healthcare challenges caused by the rapidly increasing number of COVID-19 cases. Yet, this pandemic has created an urgent need for coordinated mechanisms to alleviate increasing pressures on these large multihospital systems and ensure services remain high-quality, accessible, and sustainable. Digital health solutions have been identified as promising approaches to address these challenges. This case report describes results for developing multidisciplinary visualizations to support digital health operations in one of the largest tertiary multihospital systems in the Middle East. The report concludes with some lessons and insights learned from the rapid development and delivery of this user-centric COVID-19 multihospital operations intelligent platform. Full article

(This article belongs to the Special Issue Artificial Intelligence for COVID-19 Diagnosis)

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18 pages, 2885 KiB

Open AccessArticle

DW-UNet: Loss Balance under Local-Patch for 3D Infection Segmentation from COVID-19 CT Images

by Cheng Chen, Jiancang Zhou, Kangneng Zhou, Zhiliang Wang and Ruoxiu Xiao

Diagnostics 2021, 11(11), 1942; https://0-doi-org.brum.beds.ac.uk/10.3390/diagnostics11111942 - 20 Oct 2021

Cited by 8 | Viewed by 1563

Abstract

(1) Background: COVID-19 has been global epidemic. This work aims to extract 3D infection from COVID-19 CT images; (2) Methods: Firstly, COVID-19 CT images are processed with lung region extraction and data enhancement. In this strategy, gradient changes of voxels in different directions respond to geometric characteristics. Due to the complexity of tubular tissues in lung region, they are clustered to the lung parenchyma center based on their filtered possibility. Thus, infection is improved after data enhancement. Then, deep weighted UNet is established to refining 3D infection texture, and weighted loss function is introduced. It changes cost calculation of different samples, causing target samples to dominate convergence direction. Finally, the trained network effectively extracts 3D infection from CT images by adjusting driving strategy of different samples. (3) Results: Using Accuracy, Precision, Recall and Coincidence rate, 20 subjects from a private dataset and eight subjects from Kaggle Competition COVID-19 CT dataset tested this method in hold-out validation framework. This work achieved good performance both in the private dataset (99.94–00.02%, 60.42–11.25%, 70.79–09.35% and 63.15–08.35%) and public dataset (99.73–00.12%, 77.02–06.06%, 41.23–08.61% and 52.50–08.18%). We also applied some extra indicators to test data augmentation and different models. The statistical tests have verified the significant difference of different models. (4) Conclusions: This study provides a COVID-19 infection segmentation technology, which provides an important prerequisite for the quantitative analysis of COVID-19 CT images. Full article

(This article belongs to the Special Issue Artificial Intelligence for COVID-19 Diagnosis)

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14 pages, 2098 KiB

Open AccessArticle

Fine-Tuning Convolutional Neural Networks for COVID-19 Detection from Chest X-ray Images

by Wentao Zhao, Wei Jiang and Xinguo Qiu

Diagnostics 2021, 11(10), 1887; https://0-doi-org.brum.beds.ac.uk/10.3390/diagnostics11101887 - 13 Oct 2021

Cited by 9 | Viewed by 2054

Abstract

As the COVID-19 pandemic continues to ravage the world, the use of chest X-ray (CXR) images as a complementary screening strategy to reverse transcription-polymerase chain reaction (RT-PCR) testing continues to grow owing to its routine clinical application to respiratory diseases. We performed extensive convolutional neural network (CNN) fine-tuning experiments and identified that models pretrained on larger out-of-domain datasets show an improved performance. This suggests that a priori knowledge of models from out-of-field training should also apply to X-ray images. With appropriate hyperparameters selection, we found that higher resolution images carry more clinical information, and the use of mixup in training improved the performance of the model. The experimental showed that our proposed transfer learning present state-of-the-art results. Furthermore, we evaluated the performance of our model with a small amount of downstream training data and found that the model still performed well in COVID-19 identification. We also explored the mechanism of model detection using a gradient-weighted class activation mapping (Grad-CAM) method for CXR imaging to interpret the detection of radiology images. The results helped us understand how the model detects COVID-19, which can be used to discover new visual features and assist radiologists in screening. Full article

(This article belongs to the Special Issue Artificial Intelligence for COVID-19 Diagnosis)

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21 pages, 4411 KiB

Open AccessArticle

Mortality Prediction Utilizing Blood Biomarkers to Predict the Severity of COVID-19 Using Machine Learning Technique

by Tawsifur Rahman, Fajer A. Al-Ishaq, Fatima S. Al-Mohannadi, Reem S. Mubarak, Maryam H. Al-Hitmi, Khandaker Reajul Islam, Amith Khandakar, Ali Ait Hssain, Somaya Al-Madeed, Susu M. Zughaier and Muhammad E. H. Chowdhury

Diagnostics 2021, 11(9), 1582; https://0-doi-org.brum.beds.ac.uk/10.3390/diagnostics11091582 - 31 Aug 2021

Cited by 29 | Viewed by 2962

Abstract

Healthcare researchers have been working on mortality prediction for COVID-19 patients with differing levels of severity. A rapid and reliable clinical evaluation of disease intensity will assist in the allocation and prioritization of mortality mitigation resources. The novelty of the work proposed in this paper is an early prediction model of high mortality risk for both COVID-19 and non-COVID-19 patients, which provides state-of-the-art performance, in an external validation cohort from a different population. Retrospective research was performed on two separate hospital datasets from two different countries for model development and validation. In the first dataset, COVID-19 and non-COVID-19 patients were admitted to the emergency department in Boston (24 March 2020 to 30 April 2020), and in the second dataset, 375 COVID-19 patients were admitted to Tongji Hospital in China (10 January 2020 to 18 February 2020). The key parameters to predict the risk of mortality for COVID-19 and non-COVID-19 patients were identified and a nomogram-based scoring technique was developed using the top-ranked five parameters. Age, Lymphocyte count, D-dimer, CRP, and Creatinine (ALDCC), information acquired at hospital admission, were identified by the logistic regression model as the primary predictors of hospital death. For the development cohort, and internal and external validation cohorts, the area under the curves (AUCs) were 0.987, 0.999, and 0.992, respectively. All the patients are categorized into three groups using ALDCC score and death probability: Low (probability < 5%), Moderate (5% < probability < 50%), and High (probability > 50%) risk groups. The prognostic model, nomogram, and ALDCC score will be able to assist in the early identification of both COVID-19 and non-COVID-19 patients with high mortality risk, helping physicians to improve patient management. Full article

(This article belongs to the Special Issue Artificial Intelligence for COVID-19 Diagnosis)

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19 pages, 3944 KiB

Open AccessArticle

Detection and Severity Classification of COVID-19 in CT Images Using Deep Learning

by Yazan Qiblawey, Anas Tahir, Muhammad E. H. Chowdhury, Amith Khandakar, Serkan Kiranyaz, Tawsifur Rahman, Nabil Ibtehaz, Sakib Mahmud, Somaya Al Maadeed, Farayi Musharavati and Mohamed Arselene Ayari

Diagnostics 2021, 11(5), 893; https://0-doi-org.brum.beds.ac.uk/10.3390/diagnostics11050893 - 17 May 2021

Cited by 78 | Viewed by 6137

Abstract

Detecting COVID-19 at an early stage is essential to reduce the mortality risk of the patients. In this study, a cascaded system is proposed to segment the lung, detect, localize, and quantify COVID-19 infections from computed tomography images. An extensive set of experiments were performed using Encoder–Decoder Convolutional Neural Networks (ED-CNNs), UNet, and Feature Pyramid Network (FPN), with different backbone (encoder) structures using the variants of DenseNet and ResNet. The conducted experiments for lung region segmentation showed a Dice Similarity Coefficient (DSC) of 97.19% and Intersection over Union (IoU) of 95.10% using U-Net model with the DenseNet 161 encoder. Furthermore, the proposed system achieved an elegant performance for COVID-19 infection segmentation with a DSC of 94.13% and IoU of 91.85% using the FPN with DenseNet201 encoder. The proposed system can reliably localize infections of various shapes and sizes, especially small infection regions, which are rarely considered in recent studies. Moreover, the proposed system achieved high COVID-19 detection performance with 99.64% sensitivity and 98.72% specificity. Finally, the system was able to discriminate between different severity levels of COVID-19 infection over a dataset of 1110 subjects with sensitivity values of 98.3%, 71.2%, 77.8%, and 100% for mild, moderate, severe, and critical, respectively. Full article

(This article belongs to the Special Issue Artificial Intelligence for COVID-19 Diagnosis)

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12 pages, 2063 KiB

Open AccessArticle

A Novel Computational Model for Detecting the Severity of Inflammation in Confirmed COVID-19 Patients Using Chest X-ray Images

by Mohammed S. Alqahtani, Mohamed Abbas, Ali Alqahtani, Mohammad Alshahrani, Abdulhadi Alkulib, Magbool Alelyani, Awad Almarhaby and Abdullah Alsabaani

Diagnostics 2021, 11(5), 855; https://0-doi-org.brum.beds.ac.uk/10.3390/diagnostics11050855 - 10 May 2021

Cited by 3 | Viewed by 2569

Abstract

Since late 2019, Coronavirus Disease 2019 (COVID-19) has spread all over the world. The disease is highly contagious, and it may lead to acute respiratory distress (ARD). Medical imaging can play an important role in classifying, detecting, and measuring the severity of the virus. This study aims to provide a novel auto-detection tool that can detect abnormal changes in conventional X-ray images for confirmed COVID-19 cases. X-ray images from patients diagnosed with COVID-19 were converted into 19 different colored layers. Each layer represented objects with similar contrast that could be defined as a specific color. The objects with similar contrasts were formed in a single layer. All the objects from all the layers were extracted as a single-color image. Based on the differentiation of colors, the prototype model was able to recognize a wide spectrum of abnormal changes in the image texture. This was true even if there was minimal variation of the contrast values of the detected uncleared abnormalities. The results indicate that the proposed novel method can detect and determine the degree of lung infection from COVID-19 with an accuracy of 91%, compared to the opinions of three experienced radiologists. The method can also efficiently determine the sites of infection and the severity of the disease by classifying the X-rays into five levels of severity. Thus, the proposed COVID-19 autodetection method can identify locations and indicate the degree of severity of the disease by comparing affected tissue with healthy tissue, and it can predict where the disease may spread. Full article

(This article belongs to the Special Issue Artificial Intelligence for COVID-19 Diagnosis)

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Review

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30 pages, 3232 KiB

Open AccessReview

The Applications of Artificial Intelligence in Chest Imaging of COVID-19 Patients: A Literature Review

by Maria Elena Laino, Angela Ammirabile, Alessandro Posa, Pierandrea Cancian, Sherif Shalaby, Victor Savevski and Emanuele Neri

Diagnostics 2021, 11(8), 1317; https://0-doi-org.brum.beds.ac.uk/10.3390/diagnostics11081317 - 22 Jul 2021

Cited by 18 | Viewed by 4464

Abstract

Diagnostic imaging is regarded as fundamental in the clinical work-up of patients with a suspected or confirmed COVID-19 infection. Recent progress has been made in diagnostic imaging with the integration of artificial intelligence (AI) and machine learning (ML) algorisms leading to an increase in the accuracy of exam interpretation and to the extraction of prognostic information useful in the decision-making process. Considering the ever expanding imaging data generated amid this pandemic, COVID-19 has catalyzed the rapid expansion in the application of AI to combat disease. In this context, many recent studies have explored the role of AI in each of the presumed applications for COVID-19 infection chest imaging, suggesting that implementing AI applications for chest imaging can be a great asset for fast and precise disease screening, identification and characterization. However, various biases should be overcome in the development of further ML-based algorithms to give them sufficient robustness and reproducibility for their integration into clinical practice. As a result, in this literature review, we will focus on the application of AI in chest imaging, in particular, deep learning, radiomics and advanced imaging as quantitative CT. Full article

(This article belongs to the Special Issue Artificial Intelligence for COVID-19 Diagnosis)

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