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Related Concept Videos

Bone Disorders01:29

Bone Disorders

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Aging and its effect on bone remodeling is the most common cause of bone disorders. In young and healthy people, bone deposition and resorption happen at an equal rate to maintain optimal bone health.
Bone deposition is also affected by the levels of sex hormones like estrogen and testosterone that promote osteoblast activity and bone matrix synthesis. When the level of these hormones decreases due to aging, it causes a reduction in bone deposition. As a result, bone resorption by osteoclasts...
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Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model
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Prediction of Low Bone Mass for Japanese Female Athletes Using Machine Learning.

Joao Gabriel Segato Kruse, Miki Kaneko, Sayaka Nose-Ogura

    Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
    |March 5, 2025
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    Summary

    This study developed a classifier using questionnaire data to identify low bone mass in female athletes. Key factors like amenorrhea duration and sport impact help predict bone density, aiding in injury prevention.

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    Area of Science:

    • Sports Medicine
    • Biomedical Engineering
    • Public Health

    Background:

    • Optimal bone mineral density (BMD) is crucial for female athletes' lifelong health and preventing fractures.
    • Existing BMD assessment tools lack practicality for continuous monitoring or are unsuitable for young female athletes.

    Purpose of the Study:

    • Develop a binary classifier to distinguish between normal and low bone mass in young female athletes.
    • Utilize features extracted from a questionnaire for BMD assessment.

    Main Methods:

    • Compared five machine learning models: logistic regression, decision tree, random forest, multi-layer perceptron, and XGBoost.
    • Employed cross-validation for model validation and permutation importance for feature assessment.
    • Utilized data from 213 female athletes.

    Main Results:

    • XGBoost demonstrated the most balanced sensitivity (0.93) and specificity (0.62), with an AUC of 0.73 and accuracy of 0.68.
    • Amenorrhea duration and sport impact were the most significant predictors of low bone mass.
    • Thinness level, training frequency, and age at menarche also showed high feature importance.

    Conclusions:

    • Questionnaire-based features show promise for evaluating low BMD in female athletes.
    • The developed classifier can aid in identifying athletes at risk for low bone mass, facilitating early intervention.
    • This approach offers a practical method for monitoring bone health in this demographic.