Probing an AI regression model for hand bone age determination using gradient-based saliency mapping

Abstract Understanding how a neural network makes decisions holds significant value for users. For this reason, gradient-based saliency mapping was tested on an artificial intelligence (AI) regression model for determining hand bone age from X-ray radiographs. The partial derivative (PD) of the infe...

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Autor principal:	Zhiyue J. Wang
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/da32c9ad4d2b4e23944040ab85b496cd
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id	oai:doaj.org-article:da32c9ad4d2b4e23944040ab85b496cd
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spelling	oai:doaj.org-article:da32c9ad4d2b4e23944040ab85b496cd2021-12-02T15:52:25ZProbing an AI regression model for hand bone age determination using gradient-based saliency mapping10.1038/s41598-021-90157-y2045-2322https://doaj.org/article/da32c9ad4d2b4e23944040ab85b496cd2021-05-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-90157-yhttps://doaj.org/toc/2045-2322Abstract Understanding how a neural network makes decisions holds significant value for users. For this reason, gradient-based saliency mapping was tested on an artificial intelligence (AI) regression model for determining hand bone age from X-ray radiographs. The partial derivative (PD) of the inferred age with respect to input image intensity at each pixel served as a saliency marker to find sensitive areas contributing to the outcome. The mean of the absolute PD values was calculated for five anatomical regions of interest, and one hundred test images were evaluated with this procedure. The PD maps suggested that the AI model employed a holistic approach in determining hand bone age, with the wrist area being the most important at early ages. However, this importance decreased with increasing age. The middle section of the metacarpal bones was the least important area for bone age determination. The muscular region between the first and second metacarpal bones also exhibited high PD values but contained no bone age information, suggesting a region of vulnerability in age determination. An end-to-end gradient-based saliency map can be obtained from a black box regression AI model and provide insight into how the model makes decisions.Zhiyue J. WangNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-7 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Zhiyue J. Wang Probing an AI regression model for hand bone age determination using gradient-based saliency mapping
description	Abstract Understanding how a neural network makes decisions holds significant value for users. For this reason, gradient-based saliency mapping was tested on an artificial intelligence (AI) regression model for determining hand bone age from X-ray radiographs. The partial derivative (PD) of the inferred age with respect to input image intensity at each pixel served as a saliency marker to find sensitive areas contributing to the outcome. The mean of the absolute PD values was calculated for five anatomical regions of interest, and one hundred test images were evaluated with this procedure. The PD maps suggested that the AI model employed a holistic approach in determining hand bone age, with the wrist area being the most important at early ages. However, this importance decreased with increasing age. The middle section of the metacarpal bones was the least important area for bone age determination. The muscular region between the first and second metacarpal bones also exhibited high PD values but contained no bone age information, suggesting a region of vulnerability in age determination. An end-to-end gradient-based saliency map can be obtained from a black box regression AI model and provide insight into how the model makes decisions.
format	article
author	Zhiyue J. Wang
author_facet	Zhiyue J. Wang
author_sort	Zhiyue J. Wang
title	Probing an AI regression model for hand bone age determination using gradient-based saliency mapping
title_short	Probing an AI regression model for hand bone age determination using gradient-based saliency mapping
title_full	Probing an AI regression model for hand bone age determination using gradient-based saliency mapping
title_fullStr	Probing an AI regression model for hand bone age determination using gradient-based saliency mapping
title_full_unstemmed	Probing an AI regression model for hand bone age determination using gradient-based saliency mapping
title_sort	probing an ai regression model for hand bone age determination using gradient-based saliency mapping
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/da32c9ad4d2b4e23944040ab85b496cd
work_keys_str_mv	AT zhiyuejwang probinganairegressionmodelforhandboneagedeterminationusinggradientbasedsaliencymapping
_version_	1718385601499103232

Probing an AI regression model for hand bone age determination using gradient-based saliency mapping

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