OBJECTIVE: To predict the hand-wrist maturation stages based on the cervical vertebrae (CV) images, and to analyse the accuracy of the proposed algorithms.
Background Previous studies suggest that use of artificial intelligence (AI) algorithms as diagnostic aids may improve the quality of skeletal age assessment, though these studies lack evidence from clinical practice. Purpose To compare the accuracy ...
OBJECTIVE: To evaluate the accuracy and clinical efficacy of a hybrid Greulich-Pyle (GP) and modified Tanner-Whitehouse (TW) artificial intelligence (AI) model for bone age assessment.
Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
34891787
Conventional methods for artificial age determination of skeletal bones have several problems, such as strong subjectivity, large random errors, complex evaluation processes, and long evaluation cycles. In this study, an automated age determination o...
Artificial intelligence (AI) is increasingly being used in bone-age (BA) assessment due to its complicated and lengthy nature. We aimed to evaluate the clinical performance of a commercially available deep learning (DL)-based software for BA assessme...
BACKGROUND: The autonomous artificial intelligence (AI) system for bone age rating (BoneXpert) was designed to be used in clinical radiology practice as an AI-replace tool, replacing the radiologist completely.
PURPOSE: The purpose of this study is to construct an automatic carpal bone age evaluation system for Chinese children based on TW3-C Carpal method by deep learning and to evaluate the accuracies in test set and clinical test set.
OBJECTIVE: The purpose of this study is to determine whether or not the ImageJ program can be used to automatically determine the growth period of the hand and wrist which have different growth-development periods according to the density values in t...
PURPOSE: To evaluate the applicability of Greulich-Pyle (GP) standards to bone age (BA) assessment in healthy Korean children using manual and deep learning-based methods.
IEEE journal of biomedical and health informatics
35653451
The Cervical Vertebral Maturation (CVM) method aims to determine the craniofacial skeletal maturational stage, which is crucial for orthodontic and orthopedic treatment. In this paper, we explore the potential of deep learning for automatic CVM asses...