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Home / Archives / Volume-6 / Issue-2 / Article-5

Volume - 6 | Issue - 2 | june 2024

Automated X-Ray Image Analysis for Lumbar Spondylolisthesis Detection and Severity Grading
Balaji N  , Sunitha R, Pavithra H C, Bhuvan A, Jay Suhas
Pages: 133-153
Cite this article
N, Balaji, Sunitha R, Pavithra H C, Bhuvan A, and Jay Suhas. "Automated X-Ray Image Analysis for Lumbar Spondylolisthesis Detection and Severity Grading." Journal of Innovative Image Processing 6, no. 2 (2024): 133-153
Published
25 May, 2024
Abstract

Spondylolisthesis, characterized by the anterior displacement of a vertebra, significantly impacts spinal health diagnosis and treatment. This study introduces a groundbreaking machine learning strategy for automated detection and grading of lumbar spondylolisthesis from X-ray images, utilizing Roboflow for data management and a customized convolutional neural network (CNN). This CNN accurately identifies lumbar vertebral segments and objectively grades vertebral slippage. The evaluations show a mean average precision (mAP) of 98.5%, with precision at 96.8% and recall at 97.2%, underscoring the model's accuracy and reliability. Additionally, we developed a user-friendly interface for healthcare professionals, enhancing the tool's clinical applicability. The method offers a significant improvement over existing diagnostic approaches, providing a reliable, efficient solution for the early detection and management of lumbar spondylolisthesis.

Keywords

Lumbar Spondylolisthesis P-Grade Spine Lumbar Vertebrae Classes

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