Deep learning-based automated bone age estimation for Saudi patients on hand radiograph images: a retrospective study.

ABSTRACT: In pediatric medicine, precise estimation of bone age is essential for skeletal maturity evaluation, growth disorder diagnosis, and therapeutic intervention planning. Conventional techniques for determining bone age depend on radiologists' subjective judgments, which may lead to non-negligible differences in the estimated bone age. This study proposes a deep learning-based model utilizing a fully connected convolutional neural network(CNN) to predict bone age from left-hand radiographs. The data set used in this study, consisting of 473 patients, was retrospectively retrieved from the PACS (Picture Achieving and Communication System) of a single institution. We developed a fully connected CNN consisting of four convolutional blocks, three fully connected layers, and a single neuron as outp

SUBMITTER: Hamd ZY 

PROVIDER: S-EPMC11295702 | biostudies-literature | 2024 Aug

REPOSITORIES: biostudies-literature