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Related Concept Videos

Fractures: Bone Repair01:27

Fractures: Bone Repair

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Treatment for a fracture is based on the type of break, the bone affected, and the patient's age.
Minor fractures with no bone displacement are treated by immobilizing the fractured bone using a cast or splint. However, in the case of fractures with displaced bones, the broken bones are repositioned before immobilization to ensure successful healing without deformation and loss of function. The realignment of fractured bone ends is performed through a process called reduction. If the...
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Classification of Bones01:18

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The bones of the human skeletal system are of varied shapes, sizes, and functions. They can be classified based on their shape and function into four major classes: long bones, short bones, flat bones, and irregular bones. Some classifications include a fifth type, the sesamoid bones, as a separate class, whereas others categorize them under short bones.
Long and Short Bones
The appendicular skeleton, particularly the upper and lower limbs, is primarily made of long and short bones. The...
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Updated: Dec 8, 2025

Assessment of Bone Fracture Healing Using Micro-Computed Tomography
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Deep Learning With Electronic Health Records for Short-Term Fracture Risk Identification: Crystal Bone Algorithm

Yasmeen Adar Almog1, Angshu Rai1, Patrick Zhang1

  • 1Digital Health & Innovation, Amgen Inc, Thousand Oaks, CA, United States.

Journal of Medical Internet Research
|September 21, 2020
PubMed
Summary
This summary is machine-generated.

This study developed an AI algorithm, Crystal Bone, to predict short-term fracture risk in patients over 50. The tool uses electronic health records to identify high-risk individuals for timely intervention.

Keywords:
AIEHRNLPartificial intelligencebonedeep learningelectronic health recordfracturelow bone massmachine learningnatural language processingosteoporosisprediction

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Area of Science:

  • Artificial Intelligence in Healthcare
  • Machine Learning for Medical Prediction
  • Natural Language Processing in Clinical Data Analysis

Background:

  • Osteoporosis-related fractures pose a significant burden, yet high fracture risk is often underdiagnosed and undertreated.
  • Current fracture risk assessment tools have limitations, including reliance on manual data entry and long-term predictions.
  • There is a critical need for tools that provide short-term fracture risk estimates for timely intervention.

Purpose of the Study:

  • To develop and evaluate an algorithm for identifying patients at high risk of fracture within a 1- to 2-year period.
  • To overcome limitations of existing tools by focusing on short-term risk, automated data entry, and longitudinal patient data.
  • To create a predictive model that aids in the early identification and management of individuals susceptible to fractures.

Main Methods:

  • Developed Crystal Bone, an algorithm using machine learning and natural language processing on retrospective electronic health record data from over 1,000,000 patients.
  • Applied temporal analysis of patient histories to generate short-term fracture risk predictions, analogous to language models predicting future text.
  • Validated the algorithm's accuracy using a holdout set of 192,590 patients, comparing performance against baseline models and approximated human performance.

Main Results:

  • The Crystal Bone algorithm accurately predicted 1- to 2-year fracture risk in patients over 50 with an AUROC of 0.81.
  • The model significantly outperformed experimental baseline models (AUROC 0.67).
  • It correctly identified 70% of at-risk patients who had not received preventative interventions, highlighting its potential for clinical application.

Conclusions:

  • A patient's evolving medical history can be effectively utilized to predict short-term fracture risk.
  • Implementing and validating such a tool in healthcare systems can enable automated, widespread fracture risk prediction.
  • This technology may significantly improve the identification of patients at very high risk for fracture, facilitating proactive care.