Age estimation based on bone length using 12 regression models of left hand X-ray images for Asian children below 19 years old

2015 ◽  
Vol 17 (2) ◽  
pp. 71-78 ◽  
Author(s):  
M.F. Darmawan ◽  
Suhaila M. Yusuf ◽  
M.R. Abdul Kadir ◽  
H. Haron
Keyword(s):  
Age Estimation ◽  
Regression Models ◽  
X Ray ◽  
Left Hand ◽  
Bone Length ◽  
Asian Children

Age Estimation of Asian Using Soft Computing Model Based on Bone Length of Left Hand

2018 ◽  
Vol 24 (10) ◽  
pp. 7559-7565 ◽  
Author(s):  
Mohd Faaizie Darmawan ◽  
Mohd Zamri Osman ◽  
Kohbalan Moorthy
Keyword(s):  
Soft Computing ◽  
Age Estimation ◽  
Left Hand ◽  
Computing Model ◽  
Model Based ◽  
Bone Length

Ultrasonographic evaluation of the wrist and elbow joints: A pilot study to explore a non-invasive technique for age estimation

2020 ◽  
pp. 002580242095509
Author(s):  
Rutwik Shedge ◽  
Tanuj Kanchan ◽  
Krit Pal Singh Kushwaha ◽  
Kewal Krishan
Keyword(s):  
Age Estimation ◽  
Elbow Joint ◽  
Regression Models ◽  
Wrist Joint ◽  
Invasive Technique ◽  
Linear Regression Models ◽  
X Ray ◽  
Non Invasive ◽  
Multiple Linear Regression Models ◽  
Wrist Joints

Age estimation is a vital aspect of the process of identification. Studying the appearance and fusion of long bones is one of the most commonly used methods for age estimation. Most research conducted on age estimation using the study of the appearance and fusion of ossification centres has been roentgenographic in nature. However, X-ray examination and computed tomography examination are associated with ionisation radiation. The present study investigated the use of ultrasonography (USG) as a means of visualising ossification centres of the elbow and wrist joints for age estimation in 31 Maharashtrian boys from Ahmednagar, India. The Schmeling et al. method of grading was used to score the fusion of ossification centres, and simple and multiple linear regression models were developed for age estimation. It was found that the ossification centres of the elbow and wrist joints followed a set pattern of maturation and fusion. The ossification centres of the elbow joint fused before the ossification centres of the wrist joint. The fusion scores of proximal radial epiphyses had the highest correlation with the decimal age of the participants, making its fusion the best indicator among all the ossification centres examined in this study for age estimation. Regression models to estimate age were generated using all the ossification centres. USG was found to be suitable for the purpose of age estimation based on ease of examination, minimal ionisation risks, its non-invasive nature and clear visualisation of ossification centres.

Identification of Asbestos Fibres in a Water Sample by X-Ray Microanalysis

1975 ◽  
Vol 33 ◽  
pp. 274-275
Author(s):  
K. A. Brookes ◽  
D. Finbow ◽  
Madeleine Samuel
Keyword(s):  
Water Sample ◽  
Background Radiation ◽  
X Ray ◽  
Left Hand ◽  
Transmission Imaging ◽  
Different Types ◽  
Normal Specimen ◽  
Liquid Nitrogen Cooling ◽  
Normal Transmission ◽  
Additional Port

Investigation of the particulate matter contained in the water sample, revealed the presence of a number of different types and certain of these were selected for analysis.An A.E.I. Corinth electron microscope was modified to accept a Kevex Si (Li) detector. To allow for existing instruments to be readily modified, this was kept to a minimum. An additional port is machined in the specimen region to accept the detector, with the liquid nitrogen cooling dewar conveniently housed in the left hand cupboard adjacent to the microscope column. Since background radiation leads to loss in the sensitivity of the instrument, great care has been taken to reduce this effect by screening and manufacturing components that are near the specimen from material of low atomic number. To change from normal transmission imaging to X-ray analysis, the special 4-position specimen rod is inserted through the normal specimen airlock.

scholarly journals Turner Syndrome, Uncommonly Diagnosed Cause of Short Stature: Case Report and Review of Literature

2013 ◽  
Vol 33 (1) ◽  
pp. 74-76
Author(s):  
S Basnet ◽  
A Eleena ◽  
AK Sharma
Keyword(s):  
Growth Hormone ◽  
Short Stature ◽  
Turner Syndrome ◽  
Age Estimation ◽  
Bone Age ◽  
Review Of Literature ◽  
X Ray ◽  
Dysmorphic Features ◽  
The Past ◽  
Hormone Analysis

Many children are frequently brought to the paediatric clinic for evaluation of short stature. Evaluation for these children does not go beyond x-ray for bone age estimation and growth hormone analysis. Most of them are considered having constitutional or genetic cause for their short stature. However, shuttle dysmorphic features could be missed in many of them. Hence, many children might be having chromosomal anomaly as an underlying cause. We report a case of 40 months who had been evaluated several times in the past for pneumonia, otitis media and short stature is finally diagnosed to have Turner syndrome. DOI: http://dx.doi.org/10.3126/jnps.v33i1.8174 J Nepal Paediatr Soc. 2013;33(1):74-76

Forensic age estimation for pelvic X-ray images using deep learning

2018 ◽  
Vol 29 (5) ◽  
pp. 2322-2329 ◽  
Author(s):  
Yuan Li ◽  
Zhizhong Huang ◽  
Xiaoai Dong ◽  
Weibo Liang ◽  
Hui Xue ◽  
...  
Keyword(s):  
Deep Learning ◽  
Age Estimation ◽  
Forensic Age Estimation ◽  
X Ray

scholarly journals Bigdata and Deep Learning: Using Python Keras Predict Patient Diabetes and Employee‟s Wages per Hour

2019 ◽  
Vol 9 (1S6) ◽  
pp. 175-178
Keyword(s):  
Big Data ◽  
Deep Learning ◽  
Regression Models ◽  
Medical Sciences ◽  
X Ray ◽  
Data Analytic ◽  
Patient Diabetes

In this paper we analyze big data analytic & Deep Learning is not supposing as two entire various concept. BigData mean extreme simple larger data into set in that may be analyzes as finding into pattern, trend. The first techniques in that may useful with data analyzed therefore in capable to helping to finding abstract pattern into Big Data is DeepLearning. It is applying into DeepLearning into Big Data, it can be find out nameless & useful pattern in that not possible up to now. This is technique as present into extra active areas into researches in the medical sciences. From increases sizes & complex into medical data’s such as X-ray, deeplearning gain into small success to prediction as several diseases such as pneumonia, diabetes. The project is proposed into two deeplearning model used to Keras & too we can be building in a regression models in to predicted as employee pay per hour, & we are builds in a classifications models in predict when it is na patient have been diabetes.

Age determination in dogs using ocular light reflection, dental abrasion and tartar

2015 ◽  
Vol 43 (05) ◽  
pp. 317-322
Author(s):  
K. Failing ◽  
R. Neiger ◽  
K. Gesierich
Keyword(s):  
Logistic Regression ◽  
Standard Deviation ◽  
Age Estimation ◽  
Regression Models ◽  
Age Determination ◽  
Light Reflection ◽  
Animal Shelters ◽  
Large Standard Deviation ◽  
Logistic Regression Models ◽  
Final Equation

SummaryObjective: The knowledge of an animal’s age is important for disease probability, prognoses, or epidemiological questions, but unfortunately, it is often unknown for dogs in animal shelters. A simple estimating procedure is preferable being quick and easy to perform, even for nonveterinarians. Material and methods: In 295 dogs the dimension of light reflection (diameter in millimetres), visible on the posterior lens capsule using a penlight, the grade of dental abrasion and dental tartar were documented photographically and the exact weight and age in days were obtained. These photographs were evaluated blinded. The dogs were divided randomly into two groups. The first group was used to establish a model for age determination using linear and logistic regression models considering the documented parameters, which was then validated with the data of the second group. Results: The size of ocular light reflection and age correlated significantly (r = 0.781; p < 0.001; sy,x = 2.45 years [SD of y for given x]). The linear regression model gave the final equation: Estimated age [months] = 13.954 + 33.400 × lens reflection [mm] + 8.406 × dental abrasion [grade] + 8.871 × tartar [grade] with a standard error of estimation of 2.26 years. Conclusion and clinical relevance: Age determination, even based on three parameters results in a large standard deviation making age estimation in dogs very crude.

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