A Comprehensive Review of Machine Learning Used to Combat COVID-19

Gomes, Rahul; Kamrowski, Connor; Langlois, Jordan; Rozario, Papia; Dircks, Ian; Grottodden, Keegan; T., Martinez, Matthew; Tee, Wei Zhong; LaFleur, Corbin; Haley, Mitchell

doi:10.3390/diagnostics12081853

Cited by 19 publications

(12 citation statements)

References 143 publications

(131 reference statements)

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“…Lastly, all selected articles used English language in the manuscripts. Some studies that reviewed the significance of AI in COVID-19 diagnosis included [ 7 , 15 , 16 , 17 , 18 , 19 , 20 , 21 , 22 , 23 , 24 , 25 , 26 , 27 ] and to mention but a few.…”

Section: Methodsmentioning

confidence: 99%

“…Prepossessing includes noise reduction and segmenting images to find a region of interest (ROI), while image classification extracts details or features from ROI, which is the basis for diagnosis [ 22 ]. Generally, medical images provide excellent pathological details; however, these details can be ignored, due to the lack of automation, which allows for both quantitative and qualitative assessments [ 24 ]. The details that are ignored are a thin line between a false positive or negative.…”

Section: Ai For Covid-19 Diagnosismentioning

confidence: 99%

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The Capacity of Artificial Intelligence in COVID-19 Response: A Review in Context of COVID-19 Screening and Diagnosis

2022

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Artificial intelligence (AI) has been shown to solve several issues affecting COVID-19 diagnosis. This systematic review research explores the impact of AI in early COVID-19 screening, detection, and diagnosis. A comprehensive survey of AI in the COVID-19 literature, mainly in the context of screening and diagnosis, was observed by applying the preferred reporting items for systematic reviews and meta-analyses (PRISMA) guidelines. Data sources for the years 2020, 2021, and 2022 were retrieved from google scholar, web of science, Scopus, and PubMed, with target keywords relating to AI in COVID-19 screening and diagnosis. After a comprehensive review of these studies, the results found that AI contributed immensely to improving COVID-19 screening and diagnosis. Some proposed AI models were shown to have comparable (sometimes even better) clinical decision outcomes, compared to experienced radiologists in the screening/diagnosing of COVID-19. Additionally, AI has the capacity to reduce physician work burdens and fatigue and reduce the problems of several false positives, associated with the RT-PCR test (with lower sensitivity of 60–70%) and medical imaging analysis. Even though AI was found to be timesaving and cost-effective, with less clinical errors, it works optimally under the supervision of a physician or other specialists.

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Section: Methodsmentioning

confidence: 99%

Section: Ai For Covid-19 Diagnosismentioning

confidence: 99%

The Capacity of Artificial Intelligence in COVID-19 Response: A Review in Context of COVID-19 Screening and Diagnosis

2022

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“…The first module is a 3D-region proposal network that detects suspicious nodules in the scan while the second module evaluates the cancer probability based on the most suspicious nodules using a leaky noisy-or model. CNNs have also been used extensively for detection of tuberculosis [ 20 , 34 , 35 ], cancer detection [ 36 , 37 , 38 , 39 , 40 ] as well as COVID-19 [ 41 , 42 , 43 , 44 ]. Domain adaptation using unsupervised machine learning have also been successfully applied for knowledge extraction and organ segmentation [ 45 , 46 , 47 ].…”

Section: Artificial Intelligence and Medical Imagingmentioning

confidence: 99%

Application of Deep Learning to IVC Filter Detection from CT Scans

et al. 2022

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IVC filters (IVCF) perform an important function in select patients that have venous blood clots. However, they are usually intended to be temporary, and significant delay in removal can have negative health consequences for the patient. Currently, all Interventional Radiology (IR) practices are tasked with tracking patients in whom IVCF are placed. Due to their small size and location deep within the abdomen it is common for patients to forget that they have an IVCF. Therefore, there is a significant delay for a new healthcare provider to become aware of the presence of a filter. Patients may have an abdominopelvic CT scan for many reasons and, fortunately, IVCF are clearly visible on these scans. In this research a deep learning model capable of segmenting IVCF from CT scan slices along the axial plane is developed. The model achieved a Dice score of 0.82 for training over 372 CT scan slices. The segmentation model is then integrated with a prediction algorithm capable of flagging an entire CT scan as having IVCF. The prediction algorithm utilizing the segmentation model achieved a 92.22% accuracy at detecting IVCF in the scans.

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“…Artificial intelligence is used in wide applications in the medical field and proved its efficiency. Machine learning is a branch of AI and computer science that is concerned with designing algorithms to allow computers to have the ability to learn without programming rules for each issue [ 1 , 10 , 12 , 32 ]. The advantages of using ML classifiers are: Quickly identifies trends and patterns.…”

Section: Introductionmentioning

confidence: 99%

COVID-19 CT-images diagnosis and severity assessment using machine learning algorithm

2023

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As a pandemic, the primary evaluation tool for coronavirus (COVID-19) still has serious flaws. To improve the existing situation, all facilities and tools available in this field should be used to combat the pandemic. Reverse transcription polymerase chain reaction is used to evaluate whether or not a person has this virus, but it cannot establish the severity of the illness. In this paper, we propose a simple, reliable, and automatic system to diagnose the severity of COVID-19 from the CT scans into three stages: mild, moderate, and severe, based on the simple segmentation method and three types of features extracted from the CT images, which are ratio of infection, statistical texture features (mean, standard deviation, skewness, and kurtosis), GLCM and GLRLM texture features. Four machine learning techniques (decision trees (DT), K-nearest neighbors (KNN), support vector machines (SVM), and Naïve Bayes) are used to classify scans. 1801 scans are divided into four stages based on the CT findings in the scans and the description file found with the datasets. Our proposed model divides into four steps: preprocessing, feature extraction, classification, and performance evaluation. Four machine learning algorithms are used in the classification step: SVM, KNN, DT, and Naive Bayes. By SVM method, the proposed model achieves 99.12%, 98.24%, 98.73%, and 99.9% accuracy for COVID-19 infection segmentation at the normal, mild, moderate, and severe stages, respectively. The area under the curve of the model is 0.99. Finally, our proposed model achieves better performance than state-of-art models. This will help the doctors know the stage of the infection and thus shorten the time and give the appropriate dose of treatment for this stage.

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A Comprehensive Review of Machine Learning Used to Combat COVID-19

Cited by 19 publications

References 143 publications

The Capacity of Artificial Intelligence in COVID-19 Response: A Review in Context of COVID-19 Screening and Diagnosis

The Capacity of Artificial Intelligence in COVID-19 Response: A Review in Context of COVID-19 Screening and Diagnosis

Application of Deep Learning to IVC Filter Detection from CT Scans

COVID-19 CT-images diagnosis and severity assessment using machine learning algorithm

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