Multimodal machine learning in precision health: A sco** review

A Kline, H Wang, Y Li, S Dennis, M Hutch, Z Xu… - npj Digital …, 2022 - nature.com
Abstract Machine learning is frequently being leveraged to tackle problems in the health
sector including utilization for clinical decision-support. Its use has historically been focused …

Emerging applications of deep learning in bone tumors: current advances and challenges

X Zhou, H Wang, C Feng, R Xu, Y He, L Li… - Frontiers in oncology, 2022 - frontiersin.org
Deep learning is a subfield of state-of-the-art artificial intelligence (AI) technology, and
multiple deep learning-based AI models have been applied to musculoskeletal diseases …

[PDF][PDF] Traditional and new methods of bone age assessment-an overview

M Prokop-Piotrkowska… - Journal of Clinical …, 2021 - jag.journalagent.com
Bone age is one of biological indicators of maturity used in clinical practice and it is a very
important parameter of a child's assessment, especially in paediatric endocrinology. The …

Pulmonary nodule classification based on heterogeneous features learning

C Tong, B Liang, Q Su, M Yu, J Hu… - IEEE Journal on …, 2020 - ieeexplore.ieee.org
Pulmonary cancer is one of the most dangerous cancers with a high incidence and mortality.
An early accurate diagnosis and treatment of pulmonary cancer can observably increase the …

Ridge regression neural network for pediatric bone age assessment

I Salim, AB Hamza - Multimedia Tools and Applications, 2021 - Springer
Bone age is an important measure for assessing the skeletal and biological maturity of
children. Delayed or increased bone age is a serious concern for pediatricians, and needs …

Faster region-convolutional neural network oriented feature learning with optimal trained recurrent neural network for bone age assessment for pediatrics

S Deshmukh, A Khaparde - Biomedical Signal Processing and Control, 2022 - Elsevier
This paper tactics to develop the novel Tanner-Whitehouse 3 (TW3)-based automated Bone
Age Assessment (BAA) model for children with the assistance of Faster Region …

Rethinking Greulich and Pyle: a deep learning approach to pediatric bone age assessment using pediatric trauma hand radiographs

I Pan, GL Baird, S Mutasa, D Merck… - Radiology: Artificial …, 2020 - pubs.rsna.org
Purpose To develop a deep learning approach to bone age assessment based on a training
set of developmentally normal pediatric hand radiographs and to compare this approach …

Texture analysis for the bone age assessment from MRI images of adolescent wrists in boys

R Obuchowicz, K Nurzynska, M Pierzchala… - Journal of Clinical …, 2023 - mdpi.com
Currently, bone age is assessed by X-rays. It enables the evaluation of the child's
development and is an important diagnostic factor. However, it is not sufficient to diagnose a …

A deep automated skeletal bone age assessment model via region-based convolutional neural network

B Liang, Y Zhai, C Tong, J Zhao, J Li, X He… - Future Generation …, 2019 - Elsevier
Skeletal bone age assessment is widely applied in growth prediction and auxiliary diagnosis
of medical problems. X-ray images of hands are observed in the evaluation of bone age …