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Estimation of Contact Regions Between Hands and Objects During Human Multi-Digit Grasping

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

Zhiyue J Wang¹

¹Department of Radiology, Children's Health and University of Texas Southwestern Medical Center, 1935 Medical District Drive, F1-02, Dallas, TX, 75235, USA. zhiyue.wang@utsouthwestern.edu.

Scientific Reports

|May 20, 2021

Summary

This summary is machine-generated.

Gradient-based saliency mapping reveals how artificial intelligence (AI) models determine hand bone age from X-rays. The AI uses a holistic approach, with wrist importance decreasing as age increases.

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Area of Science:

Medical Imaging
Artificial Intelligence
Radiology

Background:

Understanding AI decision-making is crucial for clinical applications.
AI regression models are increasingly used for medical image analysis, including bone age determination.
Gradient-based saliency mapping offers a method to interpret "black box" AI models.

Purpose of the Study:

To apply gradient-based saliency mapping to an AI regression model for hand bone age determination.
To identify which anatomical regions of X-ray images are most influential in the AI's age predictions.
To assess the AI model's decision-making process and identify potential vulnerabilities.

Main Methods:

Utilized gradient-based saliency mapping on an AI regression model analyzing hand X-ray radiographs.
Calculated partial derivatives (PD) of inferred age with respect to pixel intensity as saliency markers.
Evaluated 100 test images, calculating the mean of absolute PD values across five anatomical regions of interest.

Main Results:

Saliency maps indicated a holistic AI approach to bone age determination.
The wrist area was most important for early bone ages, with decreasing importance for older ages.
The middle metacarpal region was least important; a muscular region showed high PD but no age information, indicating a vulnerability.

Conclusions:

Gradient-based saliency maps provide valuable insights into the decision-making processes of AI regression models in medical imaging.
The AI model's reliance on specific regions changes with age, highlighting the complexity of bone age assessment.
Identifying vulnerable regions in AI models is essential for improving accuracy and reliability in clinical practice.