A systematic review and meta-analysis on the diagnostic accuracy of artificial intelligence and computer-aided diagnosis of lumbar prolapsed intervertebral disc
Introduction: Lumbar prolapsed intervertebral disc (PIVD) is a debilitating lower back condition, whose accurate and timely diagnosis is crucial for its effective management. Artificial intelligence (AI) and computer-aided diagnosis (CAD) techniques have the potential to revolutionise diagnosis by i...
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| Language: | English |
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Elsevier
2025-09-01
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| Series: | Neuroscience Informatics |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2772528625000366 |
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| author | Sandeep Pattnaik Manu Goyal Rajneesh Kumar Gujral Amit Mittal |
| author_facet | Sandeep Pattnaik Manu Goyal Rajneesh Kumar Gujral Amit Mittal |
| author_sort | Sandeep Pattnaik |
| collection | DOAJ |
| description | Introduction: Lumbar prolapsed intervertebral disc (PIVD) is a debilitating lower back condition, whose accurate and timely diagnosis is crucial for its effective management. Artificial intelligence (AI) and computer-aided diagnosis (CAD) techniques have the potential to revolutionise diagnosis by improving accuracy, efficiency, and objectivity. This systematic review with meta-analysis thus aims to thoroughly assess the available knowledge on the usability of different AI and CAD-based tools in lumbar PIVD diagnosis. Methods: A systematic search of electronic databases, between June and August 2024 for relevant full-text studies. The primary outcomes for review included the diagnostic accuracy (of each AI and CAD system. Subsequently, a meta-analysis was conducted to synthesise the results of the included studies. Result: A total of eight studies were identified, evaluating thirteen CAD or AI systems. The meta-analysis involved three of the studies, and it demonstrated a high pooled sensitivity (0.901, 95% CI: 0.871–0.924) and specificity (0.919, 95% CI: 0.898–0.936) for lumbar PIVD diagnosis. Conclusion: To conclude, these findings strongly support the potential of AI/CAD systems to improve the accuracy and efficiency of lumbar PIVD diagnosis. Prospero ID: CRD42023444785 |
| format | Article |
| id | doaj-art-7b6b920ddc5c444891e336704406bc5f |
| institution | Kabale University |
| issn | 2772-5286 |
| language | English |
| publishDate | 2025-09-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Neuroscience Informatics |
| spelling | doaj-art-7b6b920ddc5c444891e336704406bc5f2025-08-22T04:58:50ZengElsevierNeuroscience Informatics2772-52862025-09-015310022110.1016/j.neuri.2025.100221A systematic review and meta-analysis on the diagnostic accuracy of artificial intelligence and computer-aided diagnosis of lumbar prolapsed intervertebral discSandeep Pattnaik0Manu Goyal1Rajneesh Kumar Gujral2Amit Mittal3Maharishi Markandeshwar Institute of Physiotherapy and Rehabilitation, Maharishi Markandeshwar (Deemed to be University), Mullana-Ambala, Haryana, IndiaMaharishi Markandeshwar Institute of Physiotherapy and Rehabilitation, Maharishi Markandeshwar (Deemed to be University), Mullana-Ambala, Haryana, IndiaMaharishi Markandeshwar Engineering College, Maharishi Markandeshwar (Deemed to be University), Mullana-Ambala, Haryana, IndiaMaharishi Markandeshwar Institute of Medical Sciences and Research, Maharishi Markandeshwar (Deemed to be University), Mullana-Ambala, Haryana, India; Corresponding author.Introduction: Lumbar prolapsed intervertebral disc (PIVD) is a debilitating lower back condition, whose accurate and timely diagnosis is crucial for its effective management. Artificial intelligence (AI) and computer-aided diagnosis (CAD) techniques have the potential to revolutionise diagnosis by improving accuracy, efficiency, and objectivity. This systematic review with meta-analysis thus aims to thoroughly assess the available knowledge on the usability of different AI and CAD-based tools in lumbar PIVD diagnosis. Methods: A systematic search of electronic databases, between June and August 2024 for relevant full-text studies. The primary outcomes for review included the diagnostic accuracy (of each AI and CAD system. Subsequently, a meta-analysis was conducted to synthesise the results of the included studies. Result: A total of eight studies were identified, evaluating thirteen CAD or AI systems. The meta-analysis involved three of the studies, and it demonstrated a high pooled sensitivity (0.901, 95% CI: 0.871–0.924) and specificity (0.919, 95% CI: 0.898–0.936) for lumbar PIVD diagnosis. Conclusion: To conclude, these findings strongly support the potential of AI/CAD systems to improve the accuracy and efficiency of lumbar PIVD diagnosis. Prospero ID: CRD42023444785http://www.sciencedirect.com/science/article/pii/S2772528625000366Artificial intelligenceDeep learningMachine learningMagnetic resonance imagingIntervertebral disc |
| spellingShingle | Sandeep Pattnaik Manu Goyal Rajneesh Kumar Gujral Amit Mittal A systematic review and meta-analysis on the diagnostic accuracy of artificial intelligence and computer-aided diagnosis of lumbar prolapsed intervertebral disc Neuroscience Informatics Artificial intelligence Deep learning Machine learning Magnetic resonance imaging Intervertebral disc |
| title | A systematic review and meta-analysis on the diagnostic accuracy of artificial intelligence and computer-aided diagnosis of lumbar prolapsed intervertebral disc |
| title_full | A systematic review and meta-analysis on the diagnostic accuracy of artificial intelligence and computer-aided diagnosis of lumbar prolapsed intervertebral disc |
| title_fullStr | A systematic review and meta-analysis on the diagnostic accuracy of artificial intelligence and computer-aided diagnosis of lumbar prolapsed intervertebral disc |
| title_full_unstemmed | A systematic review and meta-analysis on the diagnostic accuracy of artificial intelligence and computer-aided diagnosis of lumbar prolapsed intervertebral disc |
| title_short | A systematic review and meta-analysis on the diagnostic accuracy of artificial intelligence and computer-aided diagnosis of lumbar prolapsed intervertebral disc |
| title_sort | systematic review and meta analysis on the diagnostic accuracy of artificial intelligence and computer aided diagnosis of lumbar prolapsed intervertebral disc |
| topic | Artificial intelligence Deep learning Machine learning Magnetic resonance imaging Intervertebral disc |
| url | http://www.sciencedirect.com/science/article/pii/S2772528625000366 |
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