Diagnosing COVID-19 using artificial intelligence: a comprehensive review

Abstract

In early March 2020, the World Health Organization (WHO) proclaimed the novel COVID-19 as a global pandemic. The coronavirus went on to be a life-threatening infection and is still wreaking havoc all around the globe. Though vaccines have been rolled out, a section of the population (the elderly and people with comorbidities) still succumb to this deadly illness. Hence, it is imperative to diagnose this infection early to prevent a potential severe prognosis. This contagious disease is usually diagnosed using a conventional technique called the Reverse Transcription Polymerase Chain Reaction (RT-PCR). However, this procedure leads to a number of wrong and false-negative results. Moreover, it might also not diagnose the newer variants of this mutating virus. Artificial Intelligence has been one of the most widely discussed topics in recent years. It is widely used to tackle various issues across multiple domains in the modern world. In this extensive review, the applications of Artificial Intelligence in the detection of coronavirus using modalities such as CT-Scans, X-rays, Cough sounds, MRIs, ultrasound and clinical markers are explored in depth. This review also provides data enthusiasts and the broader health community with a complete assessment of the current state-of-the-art approaches in diagnosing COVID-19. The key issues and future directions are also provided for upcoming researchers.

AI in MRI: Computational Frameworks for a Faster, Optimized, and Automated Imaging Workflow

“…Another medical imaging regime where AI has brought considerable benefits is automated diagnosis and prognosis. AI has been found useful, for example, for the diagnosis of breast and prostate cancer from MRI [ 79 , 80 ], the diagnosis of COVID-19 from medical images [ 81 , 82 ], and fault detection in health management [ 83 ]. Furthermore, AI-based methods have led to state-of-the-art results in lesion detection and classification [ 84 , 85 , 86 , 87 , 88 ].…”

An overview of deep learning techniques for COVID-19 detection: methods, challenges, and future works

“…In addition, in respiratory diseases, the voices of the patient and the lungs change significantly. Auscultation is a method of detecting abnormal lung sounds such as crackles, wheezes, and high-pitched sounds that can help determine the presence of lung disease [ 17 ]. With the development of ML and DL methods, it is possible to diagnose COVID-19 based on the patient’s voice, breath sounds, vibration, heart sounds, MRI, and ultrasound.…”

“…In ML, supervised classification algorithms of different categories such as the linear logistic regression model (LR), the K-nearest neighbor classifier (KNN), tree-based ensemble models such as random forest (RF), and boosting methods (XGBoost and AdaBoost) are also used for COVID-19 detection [ 17 , 32 ].…”

“…If the dataset has an imbalance of classes, the training does not prove that the model is reliable. Evaluation criteria such as precision, accuracy, AUC, F1 score, Cohen Kappa, and other criteria should also be used [ 17 ].…”

The Impact of Artificial Intelligence in the Odyssey of Rare Diseases

“…Since the beginning of the pandemic, artificial intelligence (AI) has played a crucial role in the battle against the virus, and several methods have been applied for various purposes [ 18 ]. Machine learning (ML) and deep learning (DL) models have been used for the early detection and diagnosis of COVID-19 by monitoring the demographic, clinical, and epidemiological characteristics of patients, and for developing diagnostic tools that can quickly analyze CT scans and X-rays to identify patterns indicative of the disease [ 19 ]. AI has also been used to predict patient vulnerability, in order to administer appropriate drugs and treatments [ 20 ], as well as being decisive in accelerating the discovery of potential vaccines.…”