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Development of a Machine Learning Algorithm to Correlate Lumbar Disc Height on X-rays with Disc Bulging or Herniation.
Lin, Pao-Chun; Chang, Wei-Shan; Hsiao, Kai-Yuan; Liu, Hon-Man; Shia, Ben-Chang; Chen, Ming-Chih; Hsieh, Po-Yu; Lai, Tseng-Wei; Lin, Feng-Huei; Chang, Che-Cheng.
Affiliation
  • Lin PC; Department of Biomedical Engineering, National Taiwan University, Taipei City 10617, Taiwan.
  • Chang WS; Department of Neurosurgery, Fu Jen Catholic University Hospital, Fu Jen Catholic University, New Taipei City 24352, Taiwan.
  • Hsiao KY; Graduate Institute of Business Administration, College of Management, Fu Jen Catholic University, New Taipei City 24352, Taiwan.
  • Liu HM; Artificial Intelligence Development Center, Fu Jen Catholic University, New Taipei City 24352, Taiwan.
  • Shia BC; Graduate Institute of Business Administration, College of Management, Fu Jen Catholic University, New Taipei City 24352, Taiwan.
  • Chen MC; Artificial Intelligence Development Center, Fu Jen Catholic University, New Taipei City 24352, Taiwan.
  • Hsieh PY; Department of Radiology, Fu Jen Catholic University Hospital, Fu Jen Catholic University, New Taipei City 24352, Taiwan.
  • Lai TW; Graduate Institute of Business Administration, College of Management, Fu Jen Catholic University, New Taipei City 24352, Taiwan.
  • Lin FH; Artificial Intelligence Development Center, Fu Jen Catholic University, New Taipei City 24352, Taiwan.
  • Chang CC; Graduate Institute of Business Administration, College of Management, Fu Jen Catholic University, New Taipei City 24352, Taiwan.
Diagnostics (Basel) ; 14(2)2024 Jan 06.
Article in En | MEDLINE | ID: mdl-38248010
ABSTRACT
Lumbar disc bulging or herniation (LDBH) is one of the major causes of spinal stenosis and related nerve compression, and its severity is the major determinant for spine surgery. MRI of the spine is the most important diagnostic tool for evaluating the need for surgical intervention in patients with LDBH. However, MRI utilization is limited by its low accessibility. Spinal X-rays can rapidly provide information on the bony structure of the patient. Our study aimed to identify the factors associated with LDBH, including disc height, and establish a clinical diagnostic tool to support its diagnosis based on lumbar X-ray findings. In this study, a total of 458 patients were used for analysis and 13 clinical and imaging variables were collected. Five machine-learning (ML) methods, including LASSO regression, MARS, decision tree, random forest, and extreme gradient boosting, were applied and integrated to identify important variables for predicting LDBH from lumbar spine X-rays. The results showed L4-5 posterior disc height, age, and L1-2 anterior disc height to be the top predictors, and a decision tree algorithm was constructed to support clinical decision-making. Our study highlights the potential of ML-based decision tools for surgeons and emphasizes the importance of L1-2 disc height in relation to LDBH. Future research will expand on these findings to develop a more comprehensive decision-supporting model.
Key words

Full text: 1 Collection: 01-internacional Database: MEDLINE Type of study: Prognostic_studies Language: En Journal: Diagnostics (Basel) Year: 2024 Document type: Article Affiliation country: Taiwán

Full text: 1 Collection: 01-internacional Database: MEDLINE Type of study: Prognostic_studies Language: En Journal: Diagnostics (Basel) Year: 2024 Document type: Article Affiliation country: Taiwán