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Current Osteoporosis Reports
Published in: Current Osteoporosis Reports 3/2018

01-06-2018 | Bone and Diabetes (A Schwartz and P Vestergaard, Section Editors)

The New Possibilities from “Big Data” to Overlooked Associations Between Diabetes, Biochemical Parameters, Glucose Control, and Osteoporosis

Author: Christian Kruse

Published in: Current Osteoporosis Reports | Issue 3/2018

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Abstract

Purpose of Review

To review current practices and technologies within the scope of “Big Data” that can further our understanding of diabetes mellitus and osteoporosis from large volumes of data. “Big Data” techniques involving supervised machine learning, unsupervised machine learning, and deep learning image analysis are presented with examples of current literature.

Recent Findings

Supervised machine learning can allow us to better predict diabetes-induced osteoporosis and understand relative predictor importance of diabetes-affected bone tissue. Unsupervised machine learning can allow us to understand patterns in data between diabetic pathophysiology and altered bone metabolism. Image analysis using deep learning can allow us to be less dependent on surrogate predictors and use large volumes of images to classify diabetes-induced osteoporosis and predict future outcomes directly from images.

Summary

“Big Data” techniques herald new possibilities to understand diabetes-induced osteoporosis and ascertain our current ability to classify, understand, and predict this condition.
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Metadata
Title
The New Possibilities from “Big Data” to Overlooked Associations Between Diabetes, Biochemical Parameters, Glucose Control, and Osteoporosis
Author
Christian Kruse
Publication date
01-06-2018
Publisher
Springer US
Published in
Current Osteoporosis Reports / Issue 3/2018
Print ISSN: 1544-1873
Electronic ISSN: 1544-2241
DOI
https://doi.org/10.1007/s11914-018-0445-9

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