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Convolutional Neural Network Algorithm Trained On Lumbar Spine Radiographs To Predict Outcomes Of Transforaminal Epidural Steroid Injection For Lumbosacral Radicular Pain From Spinal Stenosis London Spine Lumbar Stenosis

The article discusses the use of a convolutional neural network (CNN) to predict therapeutic outcomes of transforaminal epidural steroid injection (TFESI) in patients with lumbosacral radicular pain due to lumbar spinal stenosis (LSS). The study included 193 patients and utilized lumbar spine radiographs to train the CNN model. The model was able to accurately predict outcomes, with an area under the curve of 0.920 and an accuracy of 87.2%. This suggests that the CNN model has the potential to effectively predict therapeutic outcomes after TFESI in patients with lumbosacral radicular pain caused by LSS

Summarised by Mr Mo Akmal – Lead Spinal Surgeon
The London Spine Unit : best rated day surgery spinal centre in UK

Published article

Little is known about the therapeutic outcomes of transforaminal epidural steroid injection (TFESI) in patients with lumbosacral radicular pain due to lumbar spinal stenosis (LSS). Using lumbar spine radiographs as input data, we trained a convolutional neural network (CNN) to predict therapeutic outcomes after lumbar TFESI in patients with lumbosacral radicular pain caused by LSS. We retrospectively recruited 193 patients for this study. The lumbar spine radiographs included anteroposterior,…

Spine Lumbar Spinal Stenosis Expert. Best Spinal Surgeon UK
Abstract Little is known about the therapeutic outcomes of transforaminal epidural steroid injection (TFESI) in patients with lumbosacral radicular pain due to lumbar spinal stenosis (LSS). Using lumbar spine radiographs as input data, we trained a convolutional neural network (CNN) to predict therapeutic outcomes after lumbar TFESI in patients with lumbosacral radicular pain caused by,

Abstract

Little is known about the therapeutic outcomes of transforaminal epidural steroid injection (TFESI) in patients with lumbosacral radicular pain due to lumbar spinal stenosis (LSS). Using lumbar spine radiographs as input data, we trained a convolutional neural network (CNN) to predict therapeutic outcomes after lumbar TFESI in patients with lumbosacral radicular pain caused by LSS. We retrospectively recruited 193 patients for this study. The lumbar spine radiographs included anteroposterior, lateral, and bilateral (left and right) oblique views. We cut each lumbar spine radiograph image into a square shape that included the vertebra corresponding to the level at which the TFESI was performed and the vertebrae juxta below and above that level. Output data were divided into “favorable outcome” (≥ 50% reduction in the numeric rating scale [NRS] score at 2 months post-TFESI) and “poor outcome” (< 50% reduction in the NRS score at 2 months post-TFESI). Using these input and output data, we developed a CNN model for predicting TFESI outcomes. The area under the curve of our model was 0.920. Its accuracy was 87.2%. Our CNN model has an excellent capacity for predicting therapeutic outcomes after lumbar TFESI in patients with lumbosacral radicular pain induced by LSS.

Keywords: Convolutional neural network; Deep learning; Lumbar spine; Spinal stenosis; Transforaminal epidural steroid injection.

The London Spine Unit : best rated day surgery spinal centre in UK

Read the original publication:

Convolutional neural network algorithm trained on lumbar spine radiographs to predict outcomes of transforaminal epidural steroid injection for lumbosacral radicular pain from spinal stenosis

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Abstract Little is known about the therapeutic outcomes of transforaminal epidural steroid injection (TFESI) in patients with lumbosacral radicular pain due to lumbar spinal stenosis (LSS). Using lumbar spine radiographs as input data, we trained a convolutional neural network (CNN) to predict therapeutic outcomes after lumbar TFESI in patients with lumbosacral radicular pain caused by

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