An online real-time DICOM web-based computer-aided diagnosis system for bone age assessment of children in a PACS environment

2009 ◽  
Author(s):  
Kevin C. Ma ◽  
Aifeng Zhang ◽  
Paymann Moin ◽  
Mariam Fleshman ◽  
Linda Vachon ◽  
...  
Keyword(s):  
Real Time ◽  
Bone Age ◽  
Computer Aided Diagnosis ◽  
Age Assessment ◽  
Web Based ◽  
Diagnosis System ◽  
Computer Aided ◽  
Bone Age Assessment ◽  
Aided Diagnosis

Web-based computer-aided-diagnosis (CAD) system for bone age assessment (BAA) of children

2008 ◽  
Author(s):  
Aifeng Zhang ◽  
Joshua Uyeda ◽  
Sinchai Tsao ◽  
Kevin Ma ◽  
Linda A. Vachon ◽  
...  
Keyword(s):  
Bone Age ◽  
Computer Aided Diagnosis ◽  
Age Assessment ◽  
Web Based ◽  
Cad System ◽  
Computer Aided ◽  
Bone Age Assessment ◽  
Aided Diagnosis

Evaluation of a Computer-Aided Diagnosis System for Automated Bone Age Assessment in Comparison to the Greulich-Pyle Atlas Method

2019 ◽  
Vol 43 (1) ◽  
pp. 39-45 ◽  
Author(s):  
Christian Booz ◽  
Julian L. Wichmann ◽  
Sabine Boettger ◽  
Ahmed Al Kamali ◽  
Simon S. Martin ◽  
...  
Keyword(s):  
Bone Age ◽  
Computer Aided Diagnosis ◽  
Age Assessment ◽  
Diagnosis System ◽  
Computer Aided ◽  
Bone Age Assessment ◽  
Atlas Method ◽  
Aided Diagnosis

scholarly journals A deep learning-based computer-aided diagnosis method of X-ray images for bone age assessment

2021 ◽  
Author(s):  
Shaowei Li ◽  
Bowen Liu ◽  
Shulian Li ◽  
Xinyu Zhu ◽  
Yang Yan ◽  
...  
Keyword(s):  
Deep Learning ◽  
Bone Age ◽  
Computer Aided Diagnosis ◽  
Experimental Comparison ◽  
Age Assessment ◽  
X Ray ◽  
Computer Aided ◽  
Bone Age Assessment ◽  
Diagnosis Method ◽  
Aided Diagnosis

AbstractBone age assessment using hand-wrist X-ray images is fundamental when diagnosing growth disorders of a child or providing a more patient-specific treatment. However, as clinical procedures are a subjective assessment, the accuracy depends highly on the doctor’s experience. Motivated by this, a deep learning-based computer-aided diagnosis method was proposed for performing bone age assessment. Inspired by clinical approaches and aimed to reduce expensive manual annotations, informative regions localization based on a complete unsupervised learning method was firstly performed and an image-processing pipeline was proposed. Subsequently, an image model with pre-trained weights as a backbone was utilized to enhance the reliability of prediction. The prediction head was implemented by a Multiple Layer Perceptron with one hidden layer. In compliance with clinical studies, gender information was an additional input to the prediction head by embedded into the feature vector calculated from the backbone model. After the experimental comparison study, the best results showed a mean absolute error of 6.2 months on the public RSNA dataset and 5.1 months on the additional dataset using MobileNetV3 as the backbone.

A web-based computer aided diagnosis system on liver disease

2020 ◽  
Author(s):  
Xuejun Zhang ◽  
Ju Huang ◽  
Huining Huang ◽  
Bijiang Li ◽  
Binmei Liang ◽  
...  
Keyword(s):  
Liver Disease ◽  
Computer Aided Diagnosis ◽  
Web Based ◽  
Diagnosis System ◽  
Computer Aided ◽  
Aided Diagnosis
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