What question did this study set out to answer?

This work aims to develop a method for predicting bone fracture risk using neural networks in settings with limited data.

January 1, 2017Open Access

Handling limited datasets with neural networks in medical applications: A small-data approach

Key Points

This work aims to develop a method for predicting bone fracture risk using neural networks in settings with limited data.
Application of regression neural networks to predict outcomes.
Utilization of a novel methodology that addresses challenges of small dataset sizes.
Demonstrated successful non-destructive prediction of bone fracture risk.
Framework developed can be applied to various medical problems with limited datasets.

Abstract

The significance of this work is two-fold: the practical application allows for non-destructive prediction of bone fracture risk, while the novel methodology extends beyond the task considered in this study and provides a general framework for application of regression NNs to medical problems characterised by limited dataset sizes.

Handling limited datasets with neural networks in medical applications: A small-data approach

Key Points

Abstract

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