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Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography
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DL4Burn: Burn Surgical Candidacy Prediction using Multimodal Deep Learning.

Sirisha Rambhatla1, Samantha Huang2, Loc Trinh1

  • 1Computer Science Department, University of Southern California, Los Angeles, CA, U.S.A.

AMIA ... Annual Symposium Proceedings. AMIA Symposium
|March 21, 2022
PubMed
Summary

This study introduces DL4Burn, a deep learning tool to objectively predict burn wound surgical candidacy. It uses patient data and wound images to aid clinical decisions, improving upon subjective visual assessments.

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Area of Science:

  • Medical technology
  • Artificial intelligence in healthcare
  • Burn surgery

Background:

  • Current burn wound surgical candidacy assessment relies on subjective visual inspection, which can be inconsistent.
  • Provider experience significantly influences the interpretation of burn depth and patient factors for surgical decisions.
  • Objective, data-driven methods are needed to enhance the accuracy and consistency of surgical candidacy evaluation.

Purpose of the Study:

  • To develop a multimodal deep learning model for predicting burn wound surgical candidacy.
  • To create a complementary mobile application, DL4Burn, to assist clinicians in decision-making.
  • To emulate the comprehensive, multi-factored clinical approach using artificial intelligence.

Main Methods:

  • A ResNet50-based multimodal deep learning architecture was proposed.
  • The model was trained and validated using retrospective data.
  • Input data included patient burn images, demographic information, and injury characteristics.

Main Results:

  • The developed multimodal deep learning model demonstrated potential in predicting burn surgical candidacy.
  • The DL4Burn application provides a tool to integrate AI-driven predictions into clinical workflows.
  • Validation using retrospective data supports the model's efficacy in analyzing complex burn characteristics.

Conclusions:

  • Deep learning offers a promising avenue for objective burn wound surgical candidacy assessment.
  • The DL4Burn system can augment clinical judgment, potentially leading to more consistent surgical decisions.
  • Further prospective validation is warranted to confirm the clinical utility and impact of this AI approach.