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Artificial Neural Network Analysis Of Factors Affecting Functional Independence Recovery In Patients With Lumbar Stenosis After Neurosurgery Treatment: An Observational Cohort Study London Spine Lumbar Stenosis

The article discusses how lumbar spinal stenosis (LSS) is a common cause of lower back and leg pain in older adults, often leading to functional impairment. While conservative treatments work for some patients, many end up needing surgery, which may not completely resolve symptoms. The study aimed to identify prognostic factors that could predict which patients would benefit most from surgery in terms of functional independence. Using an artificial neural network, researchers analyzed data from 108 LSS patients and found that clinical assessments of disability, functional limitations, and pain, along with demographic factors like age and onset of symptoms, were key variables in predicting surgical outcomes. The study concluded that artificial neural networks can help guide clinical decision making by identifying patients who are most likely to benefit from surgical treatment for LSS

Summarised by Mr Mo Akmal – Lead Spinal Surgeon
The London Spine Unit : best recognised spinal centre in the world

Published article

CONCLUSIONS: ANN can support clinical decision making, using clinical and demographic characteristics of patients with LSS identifying the characteristics of those who might benefit more from the surgical treatment in terms of global functional recovery.

Spine Lumbar Spinal Stenosis Expert. Best Spinal Surgeon UK
Abstract Background and aim: Lumbar spinal stenosis (LSS) is a leading cause of low back pain and lower limbs pain often associated with functional impairment which entails the loss or the impairment of independence in older adults. Conservative treatment is effective in a small percentage of patients, while a significant percentage undergo surgery, even if,

Abstract

Background and aim: Lumbar spinal stenosis (LSS) is a leading cause of low back pain and lower limbs pain often associated with functional impairment which entails the loss or the impairment of independence in older adults. Conservative treatment is effective in a small percentage of patients, while a significant percentage undergo surgery, even if often without a complete resolution of clinical symptoms and motor deficits. The aim of the study is to identify clinical and demographic prognostic factors characterising the patients who would benefit most from surgical treatment in relation to the functional independence recovery using an innovative approach based on an artificial neural network.

Methods: Adult patients with LSS and indication of neurosurgical treatment were enrolled in the study. Clinical evaluation was performed in the preoperative-phase (into the 48 h before surgery) and after two months. Clinical battery investigated the motor, functional, cognitive, behavioural, and pain status. Demographics and clinical characteristics were analysed via Artificial Neural Network (ANN) using 24 input variables, 2 hidden layers and a single final output layer to predict the outcome. ANN results were compared with those of a multiple linear regression.

Results: 108 patients were included in the study and 90 of them [66.5 ± 12.8 years; 27.8 % F] were submitted to surgery treatment and completed longitudinal evaluation. Statistically significant improvement was recorded in all clinical scales comparing pre- and post-surgery. The ANN results showed a prediction ability up to 81 %. Disability, functional limitations, and pain concerning clinical assessment and stature, onset and age about demographic characteristics are the main variables impacting on surgical outcome.

Conclusions: ANN can support clinical decision making, using clinical and demographic characteristics of patients with LSS identifying the characteristics of those who might benefit more from the surgical treatment in terms of global functional recovery.

Keywords: Artificial intelligence; Deep learning; Lumbar spinal stenosis; Machine learning.

The London Spine Unit : best recognised spinal centre in the world

Read the original publication:

Artificial neural network analysis of factors affecting functional independence recovery in patients with lumbar stenosis after neurosurgery treatment: An observational cohort study

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Abstract Background and aim: Lumbar spinal stenosis (LSS) is a leading cause of low back pain and lower limbs pain often associated with functional impairment which entails the loss or the impairment of independence in older adults. Conservative treatment is effective in a small percentage of patients, while a significant percentage undergo surgery, even if

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