Impact of Breathing Exercises on Low Back Pain and the Diagnostic Role of Artificial Intelligence: A Narrative Review | ||
| Deraya International Journal for Medical Sciences and Rehabilitation | ||
| Volume 2, Issue 1, October 2025 PDF (675.84 K) | ||
| Document Type: Review Article | ||
| DOI: 10.21608/dijms.2025.390497.1025 | ||
| Authors | ||
| Wael Abdelnaeem Gomaa1; Tarek Hanfy Mahmoud* 2; Menna Elramly Amir3; Moamen Abdelraheem Nageh3; Malak Abd El-Halim Mohamed4 | ||
| 1Taha Hussein st, Elminya_Egypt | ||
| 2Assistant professor of physical therapy at Deraya University | ||
| 3student at faculty of physical therapy, Deraya University, Minya, Egypt. | ||
| 4Student at faculty of physical therapy, Deraya University, Minya, Egypt. | ||
| Abstract | ||
| Background: Low back pain (LBP) is one of the most common clinical problems. Its prevalence is 60 to 80%. Mostly, while evidence supports breathing exercises for low back pain (LBP), AI-driven diagnostics can enhance clinical precision by identifying biomechanical or structural abnormalities. Objective: This study investigates the impact of breathing exercises on pain intensity, respiratory function, and spinal stability with emerging role of AI in improving diagnostic accuracy for LBP. Methods: The author conducted a PubMed search Google Scholar, Science Direct, Scopus, Web of Science and Consensus databases with keywords "breathing exercises,” “respiratory exercises,” “low back pain,” “AI in spinal diagnostics,” “machine learning,” “biomechanical analysis"". “The review includes studies between January 2010 and January 2025, focusing on back pain, exercise, almost 45 articles that fulfilled the inclusion criteria. Results: This review consists of 45 articles 17 systematic reviews and meta analyses, and 28 RCTs. Most of studies showed a statistically significant improvement in total lung capacity (TLC), forced vital capacity (FVC), forced expiratory volume in 1 second (FEV1), and the FEV1/FVC ratio. Also studies showed a statistically significant reduce pain intensity, improve lumbar movement control and physical function. AI algorithms (e.g., convolutional neural networks) achieved 73–92% accuracy in classifying patients with LBP by using spinal motion and muscle activity data. Conclusion: The findings of this review indicate that Breathing exercises significantly reduce pain and improve functional outcomes in LBP patients. Separately, AI demonstrates high diagnostic accuracy (73–92%) in identifying spinal pathologies. Keywords: Breathing exercises, low back pain, Artificial intelligence. | ||
| Keywords | ||
| Breathing exercises; low back pain; Artificial intelligence | ||
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