A Deep Framework for Bone Age Assessment based on Finger Joint Localization

TL;DR

This paper introduces a deep learning framework that improves bone age assessment accuracy by localizing finger joints and combining this information with full hand images, reducing variability in clinical evaluations.

Contribution

The study presents a novel finger joint localization strategy integrated with deep neural networks to enhance bone age prediction accuracy over traditional methods.

Findings

Improved accuracy in bone age assessment using joint localization.

Enhanced performance when combining joint images with full hand images.

Reduction in observer variability in skeletal maturity evaluation.

Abstract

Bone age assessment is an important clinical trial to measure skeletal child maturity and diagnose of growth disorders. Conventional approaches such as the Tanner-Whitehouse (TW) and Greulich and Pyle (GP) may not perform well due to their large inter-observer and intra-observer variations. In this paper, we propose a finger joint localization strategy to filter out most non-informative parts of images. When combining with the conventional full image-based deep network, we observe a much-improved performance. % Our approach utilizes full hand and specific joints images for skeletal maturity prediction. In this study, we applied powerful deep neural network and explored a process in the forecast of skeletal bone age with the specifically combine joints images to increase the performance accuracy compared with the whole hand images.