A deep learning-based application for COVID-19 diagnosis on CT: The Imaging COVID-19 AI initiative

Topff, Laurens; Sánchez-García, José; López‐González, Rafael; Jiménez-Pastor, Ana; Visser, Jacob J.; Huisman, Merel; Guiot, Julien; Alberich‐Bayarri, Ángel; Fuster-Matanzo, Almudena; Ranschaert, Erik

doi:10.1371/journal.pone.0285121

Cited by 5 publications

(16 citation statements)

References 40 publications

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“…These studies also recommended the use of CDSS in future research. Finally, 68 articles met all the inclusion criteria 5,17,34–99 . The flowchart of the selection process is shown in Figure 1.…”

Section: Resultsmentioning

confidence: 99%

“…Types of CDSS to assist in diagnosing COVID‐19 are shown in Figure 4. Most of the studies used ICDSS based on ML (nonknowledge‐based CDSS) ( n = 52 [76.5%]) 34–85 . In these studies, the most common methods for designing CDSS were CNN ( n = 33), 38,40–42,45–47,49–52,54,56–69,71,72,78,82–85 SVM ( n = 8), 35,36,39,43,44,54,56,57 RF ( n = 7), 34,35,37,39,42,44,55 and KNN ( n = 7) 36,37,39,42,43,55,56 (Table 1 and Appendix ).…”

Section: Resultsmentioning

confidence: 99%

“…Most of the studies used ICDSS based on ML (nonknowledge‐based CDSS) ( n = 52 [76.5%]) 34–85 . In these studies, the most common methods for designing CDSS were CNN ( n = 33), 38,40–42,45–47,49–52,54,56–69,71,72,78,82–85 SVM ( n = 8), 35,36,39,43,44,54,56,57 RF ( n = 7), 34,35,37,39,42,44,55 and KNN ( n = 7) 36,37,39,42,43,55,56 (Table 1 and Appendix ). Some studies used a combination of ML methods to develop CDSS ( n = 14) 34–37,39,42–44,53,55–57,70,84 (Appendix ).…”

Section: Resultsmentioning

confidence: 99%

“…Most studies used a combination of performance metrics to evaluate CDSS ( n = 40) (Appendix ). In most of these studies, accuracy ( n = 43), 35–40,42–47,49–69,71,72,76–78,80–83,85 sensitivity (recall) ( n = 35) 35–37,39,41–44,46,47,49–54,56,57,61,63–69,71,72,76,78,81–85 and F1‐score ( n = 26) 34–36,39,41–43,46,47,50,52–54,56,57,60,61,63,64,66,68,69,81,82,84,85 were used (Table 2). Other metrics for evaluating ICDSS based on ML were the Matthews correlation coefficient (MCC), 52,56,58 receiver operating characteristic (ROC) curve, 34,36,70 and so on (Appendix ).…”

Section: Resultsmentioning

confidence: 99%

“…The most frequently used method for this purpose was the ICDSS based on ML (nonknowledge‐based CDSS), followed by ICDSS based on ES (knowledge‐based CDSS). Most studies have indicated that using CDSS have positively impacted the accurate diagnosis of COVID‐19 5,17,34–62,64–84,86–90,92–99 …”

Section: Discussionmentioning

confidence: 99%

See 4 more Smart Citations

Clinical decision support systems (CDSS) in assistance to COVID‐19 diagnosis: A scoping review on types and evaluation methods

Ameri,

Salmanizadeh

et al. 2024

Health Science Reports

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Background and AimsDue to the COVID‐19 pandemic, a precise and reliable diagnosis of this disease is critical. The use of clinical decision support systems (CDSS) can help facilitate the diagnosis of COVID‐19. This scoping review aimed to investigate the role of CDSS in diagnosing COVID‐19.MethodsWe searched four databases (Web of Science, PubMed, Scopus, and Embase) using three groups of keywords related to CDSS, COVID‐19, and diagnosis. To collect data from studies, we utilized a data extraction form that consisted of eight fields. Three researchers selected relevant articles and extracted data using a data collection form. To resolve any disagreements, we consulted with a fourth researcher.ResultsA search of the databases retrieved 2199 articles, of which 68 were included in this review after removing duplicates and irrelevant articles. The studies used nonknowledge‐based CDSS (n = 52) and knowledge‐based CDSS (n = 16). Convolutional Neural Networks (CNN) (n = 33) and Support Vector Machine (SVM) (n = 8) were employed to design the CDSS in most of the studies. Accuracy (n = 43) and sensitivity (n = 35) were the most common metrics for evaluating CDSS.ConclusionCDSS for COVID‐19 diagnosis have been developed mainly through machine learning (ML) methods. The greater use of these techniques can be due to their availability of public data sets about chest imaging. Although these studies indicate high accuracy for CDSS based on ML, their novelty and data set biases raise questions about replacing these systems as clinician assistants in decision‐making. Further studies are needed to improve and compare the robustness and reliability of nonknowledge‐based and knowledge‐based CDSS in COVID‐19 diagnosis.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

See 3 more Smart Citations

Clinical decision support systems (CDSS) in assistance to COVID‐19 diagnosis: A scoping review on types and evaluation methods

Ameri,

Salmanizadeh

et al. 2024

Health Science Reports

View full text Add to dashboard Cite

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Robust Intelligent System for COVID-19 Detection using CT-Scan

Al Smadi,

Abugabah,

Al-smadi

2023

2023 International Conference Automatics and Informatics (ICAI)

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Multi-modal deep learning methods for classification of chest diseases using different medical imaging and cough sounds

Malik,

Anees

2024

PLoS ONE

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Chest disease refers to a wide range of conditions affecting the lungs, such as COVID-19, lung cancer (LC), consolidation lung (COL), and many more. When diagnosing chest disorders medical professionals may be thrown off by the overlapping symptoms (such as fever, cough, sore throat, etc.). Additionally, researchers and medical professionals make use of chest X-rays (CXR), cough sounds, and computed tomography (CT) scans to diagnose chest disorders. The present study aims to classify the nine different conditions of chest disorders, including COVID-19, LC, COL, atelectasis (ATE), tuberculosis (TB), pneumothorax (PNEUTH), edema (EDE), pneumonia (PNEU). Thus, we suggested four novel convolutional neural network (CNN) models that train distinct image-level representations for nine different chest disease classifications by extracting features from images. Furthermore, the proposed CNN employed several new approaches such as a max-pooling layer, batch normalization layers (BANL), dropout, rank-based average pooling (RBAP), and multiple-way data generation (MWDG). The scalogram method is utilized to transform the sounds of coughing into a visual representation. Before beginning to train the model that has been developed, the SMOTE approach is used to calibrate the CXR and CT scans as well as the cough sound images (CSI) of nine different chest disorders. The CXR, CT scan, and CSI used for training and evaluating the proposed model come from 24 publicly available benchmark chest illness datasets. The classification performance of the proposed model is compared with that of seven baseline models, namely Vgg-19, ResNet-101, ResNet-50, DenseNet-121, EfficientNetB0, DenseNet-201, and Inception-V3, in addition to state-of-the-art (SOTA) classifiers. The effectiveness of the proposed model is further demonstrated by the results of the ablation experiments. The proposed model was successful in achieving an accuracy of 99.01%, making it superior to both the baseline models and the SOTA classifiers. As a result, the proposed approach is capable of offering significant support to radiologists and other medical professionals.

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A deep learning-based application for COVID-19 diagnosis on CT: The Imaging COVID-19 AI initiative

Cited by 5 publications

References 40 publications

Clinical decision support systems (CDSS) in assistance to COVID‐19 diagnosis: A scoping review on types and evaluation methods

Clinical decision support systems (CDSS) in assistance to COVID‐19 diagnosis: A scoping review on types and evaluation methods

Robust Intelligent System for COVID-19 Detection using CT-Scan

Multi-modal deep learning methods for classification of chest diseases using different medical imaging and cough sounds

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