Clinical applications of artificial intelligence in identification and management of bacterial infection: Systematic review and meta-analysis
Pneumonia is declared a global emergency public health crisis in children less than five age and the geriatric population. Recent advancements in deep learning models could be utilized effectively for the timely and early diagnosis of pneumonia in immune-compromised patients to avoid complications....
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| Format: | Article |
| Language: | English |
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Elsevier
2024-03-01
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| Series: | Saudi Journal of Biological Sciences |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S1319562X24000123 |
| _version_ | 1797316013419659264 |
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| author | Mohammad Zubair |
| author_facet | Mohammad Zubair |
| author_sort | Mohammad Zubair |
| collection | DOAJ |
| description | Pneumonia is declared a global emergency public health crisis in children less than five age and the geriatric population. Recent advancements in deep learning models could be utilized effectively for the timely and early diagnosis of pneumonia in immune-compromised patients to avoid complications. This systematic review and meta-analysis utilized PRISMA guidelines for the selection of ten articles included in this study. The literature search was done through electronic databases including PubMed, Scopus, and Google Scholar from 1st January 2016 till 1 July 2023. Overall studies included a total of 126,610 images and 1706 patients in this meta-analysis. At a 95% confidence interval, for pooled sensitivity was 0.90 (0.85–0.94) and I2 statistics 90.20 (88.56 – 91.92). The pooled specificity for deep learning models' diagnostic accuracy was 0.89 (0.86–––0.92) and I2 statistics 92.72 (91.50 – 94.83). I2 statistics showed low heterogeneity across studies highlighting consistent and reliable estimates, and instilling confidence in these findings for researchers and healthcare practitioners. The study highlighted the recent deep learning models single or in combination with high accuracy, sensitivity, and specificity to ensure reliable use for bacterial pneumonia identification and differentiate from other viral, fungal pneumonia in children and adults through chest x-rays and radiographs. |
| first_indexed | 2024-03-08T03:13:12Z |
| format | Article |
| id | doaj.art-d759a9c4f0b0469ab86ee1eda53c7799 |
| institution | Directory Open Access Journal |
| issn | 1319-562X |
| language | English |
| last_indexed | 2024-03-08T03:13:12Z |
| publishDate | 2024-03-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Saudi Journal of Biological Sciences |
| spelling | doaj.art-d759a9c4f0b0469ab86ee1eda53c77992024-02-13T04:06:30ZengElsevierSaudi Journal of Biological Sciences1319-562X2024-03-01313103934Clinical applications of artificial intelligence in identification and management of bacterial infection: Systematic review and meta-analysisMohammad Zubair0Department of Medical Microbiology, Faculty of Medicine, University of Tabuk, Tabuk 71491, Kingdom of Saudi ArabiaPneumonia is declared a global emergency public health crisis in children less than five age and the geriatric population. Recent advancements in deep learning models could be utilized effectively for the timely and early diagnosis of pneumonia in immune-compromised patients to avoid complications. This systematic review and meta-analysis utilized PRISMA guidelines for the selection of ten articles included in this study. The literature search was done through electronic databases including PubMed, Scopus, and Google Scholar from 1st January 2016 till 1 July 2023. Overall studies included a total of 126,610 images and 1706 patients in this meta-analysis. At a 95% confidence interval, for pooled sensitivity was 0.90 (0.85–0.94) and I2 statistics 90.20 (88.56 – 91.92). The pooled specificity for deep learning models' diagnostic accuracy was 0.89 (0.86–––0.92) and I2 statistics 92.72 (91.50 – 94.83). I2 statistics showed low heterogeneity across studies highlighting consistent and reliable estimates, and instilling confidence in these findings for researchers and healthcare practitioners. The study highlighted the recent deep learning models single or in combination with high accuracy, sensitivity, and specificity to ensure reliable use for bacterial pneumonia identification and differentiate from other viral, fungal pneumonia in children and adults through chest x-rays and radiographs.http://www.sciencedirect.com/science/article/pii/S1319562X24000123Artificial IntelligenceConvolutional Neural NetworkNeural Network Algorithms PediatricsPneumonia |
| spellingShingle | Mohammad Zubair Clinical applications of artificial intelligence in identification and management of bacterial infection: Systematic review and meta-analysis Saudi Journal of Biological Sciences Artificial Intelligence Convolutional Neural Network Neural Network Algorithms Pediatrics Pneumonia |
| title | Clinical applications of artificial intelligence in identification and management of bacterial infection: Systematic review and meta-analysis |
| title_full | Clinical applications of artificial intelligence in identification and management of bacterial infection: Systematic review and meta-analysis |
| title_fullStr | Clinical applications of artificial intelligence in identification and management of bacterial infection: Systematic review and meta-analysis |
| title_full_unstemmed | Clinical applications of artificial intelligence in identification and management of bacterial infection: Systematic review and meta-analysis |
| title_short | Clinical applications of artificial intelligence in identification and management of bacterial infection: Systematic review and meta-analysis |
| title_sort | clinical applications of artificial intelligence in identification and management of bacterial infection systematic review and meta analysis |
| topic | Artificial Intelligence Convolutional Neural Network Neural Network Algorithms Pediatrics Pneumonia |
| url | http://www.sciencedirect.com/science/article/pii/S1319562X24000123 |
| work_keys_str_mv | AT mohammadzubair clinicalapplicationsofartificialintelligenceinidentificationandmanagementofbacterialinfectionsystematicreviewandmetaanalysis |