AIMC Topic: Age Determination by Skeleton

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External validation of deep learning-based bone-age software: a preliminary study with real world data.

Scientific reports Jan 24, 2022
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...
Humans Female Male Radiography Feasibility Studies Age Determination by Skeleton Hand Bones Deep Learning Adolescent Child, Preschool Child
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Clinical Validation of a Deep Learning-Based Hybrid (Greulich-Pyle and Modified Tanner-Whitehouse) Method for Bone Age Assessment.

Korean journal of radiology Oct 1, 2021
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.
Age Determination by Skeleton Deep Learning Adolescent Child Radiography Humans Artificial Intelligence Female Reproducibility of Results Male
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Artificial Intelligence Algorithm Improves Radiologist Performance in Skeletal Age Assessment: A Prospective Multicenter Randomized Controlled Trial.

Radiology Sep 28, 2021
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 ...
Artificial Intelligence Prospective Studies Female Reproducibility of Results Humans Radiologists Radiographic Image Interpretation, Computer-Assisted Age Determination by Skeleton Adolescent Child, Preschool Child Radiography Adult Sensitivity and Specificity Male Infant
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Prediction of hand-wrist maturation stages based on cervical vertebrae images using artificial intelligence.

Orthodontics & craniofacial research Aug 17, 2021
OBJECTIVE: To predict the hand-wrist maturation stages based on the cervical vertebrae (CV) images, and to analyse the accuracy of the proposed algorithms.
Artificial Intelligence Child Cephalometry Cervical Vertebrae Humans Wrist Age Determination by Skeleton Bone Development
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Probing an AI regression model for hand bone age determination using gradient-based saliency mapping.

Scientific reports May 19, 2021
Understanding how a neural network makes decisions holds significant value for users. For this reason, gradient-based saliency mapping was tested on an artificial intelligence (AI) regression model for determining hand bone age from X-ray radiographs...
Color Age Determination by Skeleton Hand Bones Humans Artificial Intelligence Algorithms Child Regression Analysis
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Performance of an artificial intelligence system for bone age assessment in Tibet.

The British journal of radiology Feb 9, 2021
OBJECTIVE: To investigate whether bone age (BA) of children living in Tibet Highland could be accurately assessed using a fully automated artificial intelligence (AI) system.
Hand Tibet Age Determination by Skeleton Child, Preschool Child Reproducibility of Results Image Processing, Computer-Assisted Male Radiography Adolescent Humans Artificial Intelligence Female
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Automated Bone Age Assessment Using Artificial Intelligence: The Future of Bone Age Assessment.

Korean journal of radiology Jan 19, 2021
Bone age assessments are a complicated and lengthy process, which are prone to inter- and intra-observer variabilities. Despite the great demand for fully automated systems, developing an accurate and robust bone age assessment solution has remained ...
Artificial Intelligence Wrist Hand Automation Age Determination by Skeleton Humans Neural Networks, Computer
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Automated age estimation of young individuals based on 3D knee MRI using deep learning.

International journal of legal medicine Dec 17, 2020
Age estimation is a crucial element of forensic medicine to assess the chronological age of living individuals without or lacking valid legal documentation. Methods used in practice are labor-intensive, subjective, and frequently comprise radiation e...
Humans Neural Networks, Computer Machine Learning Algorithms Young Adult Male Deep Learning Adolescent Knee Age Determination by Skeleton Imaging, Three-Dimensional Image Processing, Computer-Assisted Magnetic Resonance Imaging Automation
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Training confounder-free deep learning models for medical applications.

Nature communications Nov 26, 2020
The presence of confounding effects (or biases) is one of the most critical challenges in using deep learning to advance discovery in medical imaging studies. Confounders affect the relationship between input data (e.g., brain MRIs) and output variab...
Sex Factors Image Interpretation, Computer-Assisted Datasets as Topic Young Adult Age Determination by Skeleton Adult Predictive Value of Tests Humans Male Radiography Hand Cohort Studies Middle Aged Case-Control Studies Sex Characteristics Child Deep Learning Brain Age Factors Adolescent Female Magnetic Resonance Imaging HIV Infections Aged Aged, 80 and over
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Age verification using random forests on facial 3D landmarks.

Forensic science international Nov 21, 2020
Three-dimensional facial images are becoming more and more widespread. As such images provide more information about facial morphology than 2D imagery, they show great promise for use in future forensic applications, including age estimation and veri...
Adolescent Young Adult Humans Child Anatomic Landmarks Imaging, Three-Dimensional Adult Machine Learning Face Image Processing, Computer-Assisted Cross-Sectional Studies Female Age Determination by Skeleton Male
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