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Development Of A Deep Learning Model For Diagnosing Lumbar Spinal Stenosis Based On CT Images London Spine Lumbar Stenosis

The article discusses a retrospective study that aimed to develop a deep learning model based on CT scans for diagnosing lumbar spinal stenosis. The study highlights the benefits of using CT scans, which are more portable, cost-effective, and provide wider regional coverage compared to MRI. The dataset used in the study consisted of axial lumbar spine CT scans labeled by two spine surgeons. The deep learning model was trained and evaluated using different sets of data, and its performance was compared to that of specialists with varying levels of experience. The results showed that the deep learning model had accuracies similar to those of experienced specialist physicians in assessing the degree of lumbar spinal stenosis in CT scans. The study concludes that the developed deep learning system has potential for further development and clinical application

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

Published article

CONCLUSIONS: Our preliminarily developed deep learning system for assessing the degree of lumbar spinal stenosis in CT, including the central canal and lateral recess, has shown similar accuracy to experienced specialist physicians. This holds great value for further development and clinical application.

Spine Lumbar Spinal Stenosis Expert. Best Spinal Surgeon UK
Abstract Study design: Retrospective study. Objectives: This study aimed to develop an initial deep learning model based on CT scans for diagnosing lumbar spinal stenosis. Summary of background data: MRI is commonly used for diagnosing lumbar spinal stenosis due to its high soft tissue resolution, but CT is more portable, cost-effective, and has wider regional,

Abstract

Study design: Retrospective study.

Objectives: This study aimed to develop an initial deep learning model based on CT scans for diagnosing lumbar spinal stenosis.

Summary of background data: MRI is commonly used for diagnosing lumbar spinal stenosis due to its high soft tissue resolution, but CT is more portable, cost-effective, and has wider regional coverage. Using deep learning models to improve the accuracy of CT diagnosis can effectively reduce missed diagnoses and misdiagnoses in clinical practice.

Methods: Axial lumbar spine CT scans obtained between March 2022 and September 2023 were included. The dataset was divided into a training set (62.3%), a validation set (22.9%), and a control set (14.8%). All data were labeled by two spine surgeons using the widely accepted grading system for lumbar spinal stenosis. The training and validation sets were used to annotate the ROIs by the two spine surgeons. First, an ROI detection model and a CNN classifier were trained using the training set. After training, the model was preliminarily evaluated using a validation set. Finally, the performance of the deep learning model was evaluated on the control set, and a comparison was made between the model and classification performance of specialists with varying levels of experience.

Results: The central stenosis grading accuracies of DL Model Version 1 and DL Model Version 2 were 88% and 83%, respectively. The lateral recess grading accuracies of DL Model Version 1 and DL Model Version 2 were 75% and 71%, respectively.

Conclusions: Our preliminarily developed deep learning system for assessing the degree of lumbar spinal stenosis in CT, including the central canal and lateral recess, has shown similar accuracy to experienced specialist physicians. This holds great value for further development and clinical application.

The London Spine Unit : best recognised spine hospital in the world

Read the original publication:

Development of a Deep Learning Model for Diagnosing Lumbar Spinal Stenosis Based on CT Images

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Abstract Study design: Retrospective study. Objectives: This study aimed to develop an initial deep learning model based on CT scans for diagnosing lumbar spinal stenosis. Summary of background data: MRI is commonly used for diagnosing lumbar spinal stenosis due to its high soft tissue resolution, but CT is more portable, cost-effective, and has wider regional

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