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Related Experiment Video

Updated: Jan 16, 2026

Semiautomated Longitudinal Microcomputed Tomography-based Quantitative Structural Analysis of a Nude Rat Osteoporosis-related Vertebral Fracture Model
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Semiautomated Longitudinal Microcomputed Tomography-based Quantitative Structural Analysis of a Nude Rat Osteoporosis-related Vertebral Fracture Model

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Deep Learning-Based Prediction Model of Surgical Indication of Nasal Bone Fracture Using Waters' View.

Dong Yun Lee1, Soo A Lim1, Su Rak Eo1

  • 1Department of Plastic and Reconstructive Surgery, Dongguk University Ilsan Hospital, Dongguk University College of Medicine, Seoul 10326, Republic of Korea.

Diagnostics (Basel, Switzerland)
|September 27, 2025
PubMed
Summary

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A novel artificial intelligence (AI) algorithm accurately detects nasal bone fractures using radiographic images. This AI tool aids in determining surgical indications, improving emergency department diagnostics for facial trauma.

Area of Science:

  • Medical Imaging
  • Artificial Intelligence in Medicine
  • Orthopedics

Background:

  • Nasal bone fractures are common facial injuries presenting to emergency departments.
  • Accurate identification and management decisions are challenging, especially for less experienced clinicians.
  • The nasal bone's importance in facial structure necessitates precise fracture assessment.

Purpose of the Study:

  • To develop and validate a deep learning-based artificial intelligence (AI) algorithm for detecting nasal bone fractures.
  • To assess the AI model's performance in identifying fractures on radiographic images.
  • To create a predictive algorithm for guiding conservative versus surgical management of nasal bone fractures.

Main Methods:

  • Retrospective analysis of 2099 cranial radiography (Waters' view) images from facial trauma patients.
Keywords:
Waters’ viewartificial intelligencedeep learningnasal bone fracturesurgical indication

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Last Updated: Jan 16, 2026

Semiautomated Longitudinal Microcomputed Tomography-based Quantitative Structural Analysis of a Nude Rat Osteoporosis-related Vertebral Fracture Model
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  • Development and validation of a deep learning AI algorithm for fracture detection.
  • Quantification of model performance using accuracy, precision, recall, and F1 score.

    • Main Results:

    • The AI model achieved 97.68% accuracy, 82.2% precision, 88.9% recall, and an 85.4% F1 score in identifying nasal bone fractures.
    • The algorithm demonstrated high diagnostic accuracy and operational efficiency.
    • Outcomes informed a predictive algorithm for surgical management decisions.

    Conclusions:

    • The AI-driven algorithm accurately detects nasal bone fractures and predicts surgical indications.
    • The tool serves as a valuable clinical decision-support system in emergency settings.
    • This AI approach enhances diagnostic capabilities for nasal bone fractures.