1
|
Rabaan AA, Bakhrebah MA, Alotaibi J, Natto ZS, Alkhaibari RS, Alawad E, Alshammari HM, Alwarthan S, Alhajri M, Almogbel MS, Aljohani MH, Alofi FS, Alharbi N, Al-Adsani W, Alsulaiman AM, Aldali J, Ibrahim FA, Almaghrabi RS, Al-Omari A, Garout M. Unleashing the power of artificial intelligence for diagnosing and treating infectious diseases: A comprehensive review. J Infect Public Health 2023; 16:1837-1847. [PMID: 37769584 DOI: 10.1016/j.jiph.2023.08.021] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/11/2023] [Revised: 07/19/2023] [Accepted: 08/27/2023] [Indexed: 10/03/2023] Open
Abstract
Infectious diseases present a global challenge, requiring accurate diagnosis, effective treatments, and preventive measures. Artificial intelligence (AI) has emerged as a promising tool for analysing complex molecular data and improving the diagnosis, treatment, and prevention of infectious diseases. Computer-aided detection (CAD) using convolutional neural networks (CNN) has gained prominence for diagnosing tuberculosis (TB) and other infectious diseases such as COVID-19, HIV, and viral pneumonia. The review discusses the challenges and limitations associated with AI in this field and explores various machine-learning models and AI-based approaches. Artificial neural networks (ANN), recurrent neural networks (RNN), support vector machines (SVM), multilayer neural networks (MLNN), CNN, long short-term memory (LSTM), and random forests (RF) are among the models discussed. The review emphasizes the potential of AI to enhance the accuracy and efficiency of diagnosis, treatment, and prevention of infectious diseases, highlighting the need for further research and development in this area.
Collapse
Affiliation(s)
- Ali A Rabaan
- Molecular Diagnostic Laboratory, Johns Hopkins Aramco Healthcare, Dhahran 31311, Saudi Arabia; College of Medicine, Alfaisal University, Riyadh 11533, Saudi Arabia; Department of Public Health and Nutrition, The University of Haripur, Haripur 22610, Pakistan.
| | - Muhammed A Bakhrebah
- Life Science and Environment Research Institute, King Abdulaziz City for Science and Technology (KACST), Riyadh 11442, Saudi Arabia
| | - Jawaher Alotaibi
- Infectious Diseases Unit, Department of Medicine, King Faisal Specialist Hospital and Research Center, Riyadh 11564, Saudi Arabia
| | - Zuhair S Natto
- Department of Dental Public Health, Faculty of Dentistry, King Abdulaziz University, Jeddah 21589, Saudi Arabia
| | - Rahaf S Alkhaibari
- Molecular Diagnostic Laboratory, Dammam Regional Laboratory and Blood Bank, Dammam 31411, Saudi Arabia
| | - Eman Alawad
- Adult Infectious Diseases Department, Prince Mohammed Bin Abdulaziz Hospital, Riyadh 11474, Saudi Arabia
| | - Huda M Alshammari
- Clinical Pharmacy Department, College of Pharmacy, Northern Border University, Arar 9280, Saudi Arabia
| | - Sara Alwarthan
- Department of Internal Medicine, College of Medicine, Imam Abdulrahman Bin Faisal University, Dammam 34212, Saudi Arabia
| | - Mashael Alhajri
- Department of Internal Medicine, College of Medicine, Imam Abdulrahman Bin Faisal University, Dammam 34212, Saudi Arabia
| | - Mohammed S Almogbel
- Department of Medical Laboratory Sciences, College of Applied Medical Sciences, University of Hail, Hail 4030, Saudi Arabia
| | - Maha H Aljohani
- Department of Infectious Diseases, King Fahad Hospital, Madinah 42351, Saudi Arabia
| | - Fadwa S Alofi
- Department of Infectious Diseases, King Fahad Hospital, Madinah 42351, Saudi Arabia
| | - Nada Alharbi
- Department of Basic Medical Sciences, College of Medicine, Princess Nourah bint Abdulrahman University, Riyadh 11671, Saudi Arabia.
| | - Wasl Al-Adsani
- Department of Medicine, Infectious Diseases Hospital, Kuwait City 63537, Kuwait; Department of Infectious Diseases, Hampton Veterans Administration Medical Center, Hampton, VA 23667, USA
| | | | - Jehad Aldali
- Department of Pathology, College of Medicine, Imam Mohammad Ibn Saud Islamic University, Riyadh 13317, Saudi Arabia
| | - Fatimah Al Ibrahim
- Infectious Disease Division, Department of Internal Medicine, Dammam Medical Complex, Dammam 32245, Saudi Arabia
| | - Reem S Almaghrabi
- Organ Transplant Center of Excellence, King Faisal Specialist Hospital and Research Center, Riyadh 11211, Saudi Arabia
| | - Awad Al-Omari
- College of Medicine, Alfaisal University, Riyadh 11533, Saudi Arabia; Research Center, Dr. Sulaiman Al Habib Medical Group, Riyadh 11372, Saudi Arabia
| | - Mohammed Garout
- Department of Community Medicine and Health Care for Pilgrims, Faculty of Medicine, Umm Al-Qura University, Makkah 21955, Saudi Arabia.
| |
Collapse
|
2
|
Sinha A, Sangeet S, Roy S. Evolution of Sequence and Structure of SARS-CoV-2 Spike Protein: A Dynamic Perspective. ACS OMEGA 2023; 8:23283-23304. [PMID: 37426203 PMCID: PMC10324094 DOI: 10.1021/acsomega.3c00944] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/12/2023] [Accepted: 06/01/2023] [Indexed: 07/11/2023]
Abstract
Novel coronavirus (SARS-CoV-2) enters its host cell through a surface spike protein. The viral spike protein has undergone several modifications/mutations at the genomic level, through which it modulated its structure-function and passed through several variants of concern. Recent advances in high-resolution structure determination and multiscale imaging techniques, cost-effective next-generation sequencing, and development of new computational methods (including information theory, statistical methods, machine learning, and many other artificial intelligence-based techniques) have hugely contributed to the characterization of sequence, structure, function of spike proteins, and its different variants to understand viral pathogenesis, evolutions, and transmission. Laying on the foundation of the sequence-structure-function paradigm, this review summarizes not only the important findings on structure/function but also the structural dynamics of different spike components, highlighting the effects of mutations on them. As dynamic fluctuations of three-dimensional spike structure often provide important clues for functional modulation, quantifying time-dependent fluctuations of mutational events over spike structure and its genetic/amino acidic sequence helps identify alarming functional transitions having implications for enhanced fusogenicity and pathogenicity of the virus. Although these dynamic events are more difficult to capture than quantifying a static, average property, this review encompasses those challenging aspects of characterizing the evolutionary dynamics of spike sequence and structure and their implications for functions.
Collapse
|
3
|
Yakimovich A. Sequencing meets machine learning to fight emerging pathogens: A preview. PATTERNS 2022; 3:100448. [PMID: 35169757 PMCID: PMC8832723 DOI: 10.1016/j.patter.2022.100448] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Indexed: 12/02/2022]
Abstract
In searching for SARS-CoV variants-of-concern, pathogen sequencing is generating an impressive amount of data. However, beyond epidemiological use, these data contain cues fundamental to our understanding of pathogen evolution in the human population. Yet, to harness them, further development of computational methodology, such as machine learning, may be required. This preview discusses updates in machine learning to understand emerging pathogens.
Collapse
|