Development and Validation of Deep Learning-Based Algorithms for Predicting Lumbar Herniated Nucleus Pulposus Using Lumbar X-rays

Article Clinical Neurology

Diagnostic triage in patients with central lumbar spinal stenosis using a deep learning system of radiographs

Tackeun Kim et al.

Summary: In this study, a deep learning model using a convolutional neural network (CNN) was developed to diagnose severe central LSS using radiography, and it showed high diagnostic accuracy. The model accurately localized stenotic lesions and assisted physicians in diagnosing LSS.

JOURNAL OF NEUROSURGERY-SPINE (2022)

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Article Biotechnology & Applied Microbiology

Lumbar Disc Herniation Automatic Detection in Magnetic Resonance Imaging Based on Deep Learning

Jen-Yung Tsai et al.

Summary: This study demonstrates the potential of using deep learning with a small-scale dataset to automate the detection of LDH, with data augmentation playing a key role in preventing under- or overfitting. The results show high accuracy and precision in diagnosing LDH using the YOLOv3 model with limited medical images.

FRONTIERS IN BIOENGINEERING AND BIOTECHNOLOGY (2021)

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Article Medical Informatics

Automatically Diagnosing Disk Bulge and Disk Herniation With Lumbar Magnetic Resonance Images by Using Deep Convolutional Neural Networks: Method Development Study

Qiong Pan et al.

Summary: The study introduced an automatic system for diagnosing disk bulge and herniation, utilizing deep convolutional neural networks to identify the position of intervertebral disks via analysis of geometrical relationship between sagittal and axial images. The system achieved high accuracy in locating vertebral bodies, identifying disks, and classifying axial lumbar disk MR images, showcasing its potential to significantly enhance diagnostic efficiency and standardize diagnosis reports in the field of lumbar intervertebral disk disorders.

JMIR MEDICAL INFORMATICS (2021)

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Article Clinical Neurology