Machine learning Techniques for Spondylolisthesis Diagnosis: a review

Authors

  • Rahma Rabee Aziz Department of Medical Instrumentation Techniques Engineering, Technical Engineering College of Mosul, Northern Technical University, Mosul, Iraq
  • Mohammed S. Jarjees Department of Medical Instrumentation Techniques Engineering, Technical Engineering College of Mosul, Northern Technical University, Mosul, Iraq
  • Mohammad R. Aziz Ministry Of Health, Mosul Health Directorate, Radiology Institute, Mosul, Iraq
  • Ali Asim Hameed Ministry Of Health, Mosul Health Directorate, Ibn Sina hospital, Mosul, Iraq

DOI:

https://doi.org/10.56286/ntujet.v3i2.768

Keywords:

Spondylolisthesis, Artificial Intelligence, Computer-assisted diagnosis, Deep learning, Grading.

Abstract

Spondylolisthesis, a condition marked by vertebral slippage, presents a challenge in medical diagnosis and grading. This study examines previous research on image processing for spondylolisthesis severity evaluation. Methodologies, sample sizes, algorithms, and measurement accuracy are the main topics of interest. The study shows the potential of computer-assisted methods for diagnosing spondylolisthesis, particularly in situations where qualified medical personnel are scarce. Machine learning techniques and deep learning models, including convolutional neural networks (CNNs), are utilized to accurately detect and assess spondylolisthesis. Notably, these findings address a gap in previous research by measuring spondylolisthesis severity and distinguishing between normal and abnormal spines. The analysis emphasizes the significance of selecting the appropriate modality and data quality, with X-rays predominating as the preferred imaging technique. This review highlights how deep learning and machine learning models can improve spondylolisthesis diagnosis, enabling enhanced diagnosis and treatment methods.

Author Biographies

  • Mohammed S. Jarjees, Department of Medical Instrumentation Techniques Engineering, Technical Engineering College of Mosul, Northern Technical University, Mosul, Iraq

    Asst. prof. at Medical Instrumentation Techniques Department, Technical Engineering College, Mosul, NTU

  • Mohammad R. Aziz, Ministry Of Health, Mosul Health Directorate, Radiology Institute, Mosul, Iraq

    specialist diagnostic radiologist at Radiology Institute, Mosul Health Directorate, Health Ministry, Iraq

    Arabic Board in Diagnostic Radiology

  • Ali Asim Hameed, Ministry Of Health, Mosul Health Directorate, Ibn Sina hospital, Mosul, Iraq

    Specialist diagnostic radiologist at Ibn Sina hospital , Mosul health directorate, Health ministry, Iraq

Additional Files

Published

2024-07-01

Issue

Section

Review

How to Cite

[1]
“Machine learning Techniques for Spondylolisthesis Diagnosis: a review”, NTU-JET, vol. 3, no. 2, Jul. 2024, doi: 10.56286/ntujet.v3i2.768.

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