Detection and prediction of osteoporosis using impulse response technique and artificial neural network

Tejaswini, E; Vaishnavi, P.; Sunitha, R

doi:10.1109/icacci.2016.7732272

Cited by 14 publications

(15 citation statements)

References 5 publications

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“…There is a clustering of peaks in the very low frequency region, which we assume to be the resonances of the stethoscope as described previously. A second group of peaks are found at 75-110 Hz, which would agree with [8,19]. Further along there is a cluster of much more damped peaks in the 200 -250 Hz region, which would match the findings in much of the previous bio-mechanics literature [9,15,16].…”

Section: Frequency Domain Feature Extractionsupporting

confidence: 78%

“…Previous research had used impact hammers and accelerometers to study the bone response, e.g. [8]. Our Taylor hammer and stethoscope must be able to repeat these findings and show equivalency.…”

Section: The Methods 31 Reflex Hammer and Stethoscope Methodsmentioning

confidence: 86%

“…Machine learning was used in the diagnosis of osteoporosis from the tibia's lowest resonant frequency and other physiological information of patients [8]. The algorithm mimics the alreadyestablished fracture prediction algorithm 'FRAX' rather than signal processing and information extraction.…”

Section: Background and Rationale 21 Backgroundmentioning

confidence: 99%

“…The only physical measure adopted was the lowest resonant frequency, which was known to be inadequate to reliably determine the quality and fragility of bones. The complex set-up and procedure, including an accelerometer, a dedicated charge amplifier and an analogue to digital converter, and the use of an impact hammer as described in [8], are unlikely to find adoption for front-line screening tests.…”

Section: Background and Rationale 21 Backgroundmentioning

confidence: 99%

See 3 more Smart Citations

Machine Learning and DSP Algorithms for Screening of Possible Osteoporosis Using Electronic Stethoscopes

Scanlan

Umnova

et al. 2018

Proceedings of the 2018 3rd International Conference on Biomedical Imaging, Signal Processing

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Section: Frequency Domain Feature Extractionsupporting

confidence: 78%

Section: The Methods 31 Reflex Hammer and Stethoscope Methodsmentioning

confidence: 86%

Section: Background and Rationale 21 Backgroundmentioning

confidence: 99%

Section: Background and Rationale 21 Backgroundmentioning

confidence: 99%

See 2 more Smart Citations

Machine Learning and DSP Algorithms for Screening of Possible Osteoporosis Using Electronic Stethoscopes

Scanlan

Umnova

et al. 2018

Proceedings of the 2018 3rd International Conference on Biomedical Imaging, Signal Processing

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show abstract

“…It is recommended that the estimations of sound and osteoporotic models support our hypothesis that the trabecular parallel to the roots are diminished more than those inverse to the roots by osteoporosis. In paper [9] author has proposed a review to create and describe two new districts of intrigue (ROIs) for DXA at the hip, one concentrating on cortical and other for trabecular bone. Magnetic resonance imaging (MRI) is a promising therapeutic imaging procedure that is utilized to survey femoral neck cortical geometry.…”

Section: A Causes Of Osteoporosismentioning

confidence: 99%

A Hybrid Classification and Prediction Methodology for the Diagonosis of Osteoporosis

Kumar¹,

Sharma²

2019

IJITEE

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With the advent of technology medical science is growing very fast . Disease diagonosis using machine learning technique is quite cumbersome. But efforts are made by the innovative minds to develope an optimal and efficient prediction model for the prediction of the disease viz, bone disease . Bone disease prediction is also a broad area of research where machine learning techniques can be used. Better prediction and analysis can be helpful to cure such disease . Osteoporosis is an osteo- metabolic disease characterized by low bone mineral density (BMD) and deterioration of the micro- architecture of the bone tissues, causing an increase in bone fragility and consequently leading to an increased risk of fractures.. Machine learning algorithms play important role for predicting and analyzing such disease using available algorithms and by modifying them . There are many algorithms such as SVM (Support vector machine), Genetic Algorithm, Naive bayes classifier and other tree based classifiers which are proposed in traditional scenario for prediction of expected data from an image. The goal of this paper is to discuss about a proposed hybrid analysis and prediction approach for diagonosing osteoporosis. This paper also discuss about various prediction models viz, svm prediction model as the efficient one while processing the textual data for symptoms analysis. Here symptoms are called predictors . A comparison using the Accuracy and training time is performed. The approach shows the efficiency of proposed model over textual data as well as graphical image data analysis.

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The Influence of the Normalisation of Spinal CT Images on the Significance of Textural Features in the Identification of Defects in the Spongy Tissue Structure

Dzierżak

Omiotek

Tkacz

et al. 2019

Advances in Intelligent Systems and Computing

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Detection and prediction of osteoporosis using impulse response technique and artificial neural network

Cited by 14 publications

References 5 publications

Machine Learning and DSP Algorithms for Screening of Possible Osteoporosis Using Electronic Stethoscopes

Machine Learning and DSP Algorithms for Screening of Possible Osteoporosis Using Electronic Stethoscopes

A Hybrid Classification and Prediction Methodology for the Diagonosis of Osteoporosis

The Influence of the Normalisation of Spinal CT Images on the Significance of Textural Features in the Identification of Defects in the Spongy Tissue Structure

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