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Updated: Sep 12, 2025

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A Multimodal Deep Learning Ensemble Framework for Building a Spine Surgery Triage System.

Mahvash Siavashpour1, Erin McCabe2, Andrew Nataraj3

  • 1Computing Science, University of Alberta.

Studies in Health Technology and Informatics
|August 8, 2025
PubMed
Summary
This summary is machine-generated.

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This study introduces an AI framework to quickly identify patients needing spinal surgery using radiology reports and questionnaires. The approach helps speed up critical care decisions for low back and neck pain patients.

Area of Science:

  • Medical Informatics
  • Artificial Intelligence in Healthcare
  • Spinal Surgery Decision Support

Background:

  • Spinal radiology reports and physician questionnaires are vital for diagnosing low back and neck pain.
  • Current processes are time-consuming, potentially delaying care for severe cases.

Purpose of the Study:

  • To develop an ensemble framework using BERT-based models to classify patients' need for surgery.
  • To improve the efficiency of surgical decision-making for spinal conditions.

Main Methods:

  • Utilized pre-trained BERT-based models for an ensemble framework.
  • Integrated multiple data modalities: radiology reports and physician questionnaires.
  • Classified patients based on their likelihood of requiring surgery.
Keywords:
BERTMedical QuestionnairesRadiology ReportTransformer

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Main Results:

  • The proposed ensemble framework demonstrated superior performance compared to previous studies.
  • Effectively integrated information from diverse data sources.
  • Showcased the potential to expedite surgical recommendations.

Conclusions:

  • The developed framework offers a valuable tool for assisting physicians in surgical decision-making for spinal conditions.
  • Highlights the efficacy of AI in processing multimodal data for clinical applications.
  • Suggests a pathway for more timely interventions in spinal pain management.