ABSTRACT
Background Degenerative spinal conditions (DSCs) involve a diverse set of pathologies that significantly impact health and quality of life, affecting many individuals at least once during their lifetime. Treatment approaches are varied and complex, reflecting the intricacy of spinal anatomy and kinetics. Diagnosis and management pose challenges, with the accurate detection of lesions further complicated by age-related degeneration and surgical implants. Technological advancements, particularly in artificial intelligence (AI) and deep learning, have demonstrated the potential to enhance detection of spinal lesions. Despite challenges in dataset creation and integration into clinical settings, further research holds promise for improved patient outcomes. Methods This study aimed to develop a DSC detection and classification model using a Kaggle dataset of 967 spinal X-ray images at the Department of Neurosurgery of Arrowhead Regional Medical Center, Colton, California, USA. Our entire workflow, including data preprocessing, training, validation, and testing, was performed by utilizing an online-cloud based AI platform. The model's performance was evaluated based on its ability to accurately classify certain DSCs (osteophytes, spinal implants, and foraminal stenosis) and distinguish these from normal X-rays. Evaluation metrics, including accuracy, precision, recall, and confusion matrix, were calculated. Results The model achieved an average precision of 0.88, with precision and recall values of 87% and 83.3%, respectively, indicating its high accuracy in classifying DSCs and distinguishing these from normal cases. Sensitivity and specificity values were calculated as 94.12% and 96.68%, respectively. The overall accuracy of the model was calculated to be 89%. Conclusion These findings indicate the utility of deep learning algorithms in enhancing early DSC detection and screening. Our platform is a cost-effective tool that demonstrates robust performance given a heterogeneous dataset. However, additional validation studies are required to evaluate the model's generalizability across different populations and optimize its seamless integration into various types of clinical practice.
ABSTRACT
Exercise is a critical factor that impacts arterial stiffness. In this narrative review, we noted multiple findings that could not be reconciled with one another. Some studies indicated that arterial stiffness increases after a regimen of resistance training. However, such studies were limited by a lack of specification of the resistance training protocols, as well as varying results reported from different areas of the body, undermining the internal validity of the studies. Another factor explored in this review was how the order of performing exercises can affect arterial stiffness. Low-intensity resistance training before high-intensity resistance training resulted in increased arterial stiffness, whereas vice versa showed no change in arterial stiffness. Other studies indicated that resistance exercise results in reduced arterial stiffness. Intensity is a variable in studies that produces inconsistent results of arterial stiffness, with some studies suggesting high-intensity resistance training increases arterial stiffness and low-intensity resistance training decreases arterial stiffness, while other studies pointing to a significant decrease in arterial stiffness, regardless of the intensity of resistance training. Demographic factors such as gender, age, and diet play an important role in explaining these differences. In terms of future implications, there is potential clinical significance as increased arterial stiffness serves as a prognostic marker in diagnosing coronary heart disease.
