Purpose: Foraminal stenosis is commonly investigated with radiological methods in patients with radiating pain in extremities. However, there is a lack of consensus regarding the methodology to assess compression of the nerve roots. This systematic review was performed to identify validated classification systems for foraminal stenosis in the lumbar and cervical spine based on magnetic resonance imaging (MRI).
Methods: A systematic search was conducted according to the PRISMA guidelines. The search included Cochrane, Embase, Medline and PubMed databases going back 30 years and up to September 2021. Three categories of words were used in different variations; foraminal stenosis, MRI and scoring. For inclusion, at least one word from each category had to be present. Articles suggesting classification systems or reporting on their validation were selected for inclusion.
Results: A total of 823 articles were identified and all abstracts were reviewed. Subsequently, a full-text review of 64 articles was performed and finally 14 articles were included. A total of three validated classification systems were found for the cervical and lumbar spine. The remaining 11 articles reported on validation or suggested modifications of the classification systems.
Conclusion: The three classification systems demonstrated moderate to good reliability and have all been shown feasible in the clinical setting. There is however a need for further studies testing the validity of these classifications in relation to both clinical findings and to surgical outcome data.
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http://dx.doi.org/10.1007/s00586-022-07147-5 | DOI Listing |
Sci Rep
December 2024
The School of Nursing, Fujian Medical University, No. 1 Xuefu North Road, Fuzhou, 350122, Fujian, China.
Diabetes Mellitus combined with Mild Cognitive Impairment (DM-MCI) is a high incidence disease among the elderly. Patients with DM-MCI have considerably higher risk of dementia, whose daily self-care and life management (i.e.
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December 2024
National Centre for Diseases Prevention and Health Promotion, Istituto Superiore di Sanità, Rome, Italy.
This study aimed to calculate Italy's first national maternal mortality ratio (MMR) through an innovative record-linkage approach within the enhanced Italian Obstetric Surveillance System (ItOSS). A record-linkage retrospective cohort study was conducted nationwide, encompassing all women aged 11-59 years with one or more hospitalizations related to pregnancy or pregnancy outcomes from 2011 to 2019. Maternal deaths were identified by integrating data from the Death Registry and national and regional Hospital Discharge Databases supported by the integration of findings from confidential enquiries conducted through active surveillance.
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December 2024
Department of Computer Science, Birzeit University, P.O. Box 14, Birzeit, West Bank, Palestine.
Accurate classification of logos is a challenging task in image recognition due to variations in logo size, orientation, and background complexity. Deep learning models, such as VGG16, have demonstrated promising results in handling such tasks. However, their performance is highly dependent on optimal hyperparameter settings, whose fine-tuning is both labor-intensive and time-consuming.
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December 2024
Faculty of Dental Medicine and Oral Health Sciences, McGill University, Montreal, Canada.
Accurate diagnosis of oral lesions, early indicators of oral cancer, is a complex clinical challenge. Recent advances in deep learning have demonstrated potential in supporting clinical decisions. This paper introduces a deep learning model for classifying oral lesions, focusing on accuracy, interpretability, and reducing dataset bias.
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December 2024
School of Engineering and Technology, Sunway University, No. 5, Jalan Universiti, Bandar Sunway, Petaling Jaya, 47500, Selangor Darul Ehsan, Malaysia.
Cervical cancer is a deadly disease in women globally. There is a greater chance of getting rid of cervical cancer in case of earliest diagnosis. But for some patients, there is a chance of recurrence.
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