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Multi-Pose-Based Convolutional Neural Network Model For Diagnosis Of Patients With Central Lumbar Spinal Stenosis London Spine Lumbar Stenosis

The article discusses the use of deep learning and multi-pose radiographs to improve the diagnostic ability of plain radiographs in diagnosing lumbar spinal stenosis (LSS). The researchers developed a multi-pose-based convolutional neural network (MP-CNN) model using the encoders of three single-pose-based CNN models. They compared the performance of the MP-CNN model using four algorithms pretrained with ImageNet. The results showed that the ResNet50-based MP-CNN model achieved a high area under the receiver operating characteristic curve (AUROC) of 91.4% for internal validation. The AUROC for extra-internal and external validations were 91.3% and 79.5%, respectively. The MP-CNN model also provided a logical decision-making direction through optimized visualization. The study suggests that this model has potential as a screening tool for LSS diagnosis, providing an explainable rationale for its predictions

Summarised by Mr Mo Akmal – Lead Spinal Surgeon
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Published article

Although the role of plain radiographs in diagnosing lumbar spinal stenosis (LSS) has declined in importance since the advent of magnetic resonance imaging (MRI), diagnostic ability of plain radiographs has improved dramatically when combined with deep learning. Previously, we developed a convolutional neural network (CNN) model using a radiograph for diagnosing LSS. In this study, we aimed to improve and generalize the performance of CNN models and overcome the limitation of the…

Spine Lumbar Spinal Stenosis Expert. Best Spinal Surgeon UK
Abstract Although the role of plain radiographs in diagnosing lumbar spinal stenosis (LSS) has declined in importance since the advent of magnetic resonance imaging (MRI), diagnostic ability of plain radiographs has improved dramatically when combined with deep learning. Previously, we developed a convolutional neural network (CNN) model using a radiograph for diagnosing LSS. In this,

Abstract

Although the role of plain radiographs in diagnosing lumbar spinal stenosis (LSS) has declined in importance since the advent of magnetic resonance imaging (MRI), diagnostic ability of plain radiographs has improved dramatically when combined with deep learning. Previously, we developed a convolutional neural network (CNN) model using a radiograph for diagnosing LSS. In this study, we aimed to improve and generalize the performance of CNN models and overcome the limitation of the single-pose-based CNN (SP-CNN) model using multi-pose radiographs. Individuals with severe or no LSS, confirmed using MRI, were enrolled. Lateral radiographs of patients in three postures were collected. We developed a multi-pose-based CNN (MP-CNN) model using the encoders of the three SP-CNN model (extension, flexion, and neutral postures). We compared the validation results of the MP-CNN model using four algorithms pretrained with ImageNet. The MP-CNN model underwent additional internal and external validations to measure generalization performance. The ResNet50-based MP-CNN model achieved the largest area under the receiver operating characteristic curve (AUROC) of 91.4% (95% confidence interval [CI] 90.9-91.8%) for internal validation. The AUROC of the MP-CNN model were 91.3% (95% CI 90.7-91.9%) and 79.5% (95% CI 78.2-80.8%) for the extra-internal and external validation, respectively. The MP-CNN based heatmap offered a logical decision-making direction through optimized visualization. This model holds potential as a screening tool for LSS diagnosis, offering an explainable rationale for its prediction.

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Multi-pose-based convolutional neural network model for diagnosis of patients with central lumbar spinal stenosis

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Abstract Although the role of plain radiographs in diagnosing lumbar spinal stenosis (LSS) has declined in importance since the advent of magnetic resonance imaging (MRI), diagnostic ability of plain radiographs has improved dramatically when combined with deep learning. Previously, we developed a convolutional neural network (CNN) model using a radiograph for diagnosing LSS. In this

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