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

arXiv: https://arxiv.org/abs/1905.13124 · HTML: https://mlsanity.com/paper/1905.13124/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 May 2019.

## Built on by (most cited first, 1 of 1)

- [Doctor Imitator: Hand-Radiography-based Bone Age Assessment by Imitating Scoring Methods](https://mlsanity.com/paper/2102.05424/) (2021)

## Abstract (arXiv)

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.