Evidence map›Paper›PMID 31600691›Full record

ArticleComputers in biology and medicine2019

Artificial neural network to estimate micro-architectural properties of cortical bone using ultrasonic attenuation: A 2-D numerical study.

Kaustav Mohanty, Omid Yousefian, Yasamin Karbalaeisadegh, Micah Ulrich, Quentin Grimal, Marie Muller

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Computers in biology and medicine, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.

0numbers the graph read from it
0cells of the map it votes in
13citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

13 citing papers in PubMed.

  1. Review
  2. Article
  3. Article
  4. Review
  5. Article
  6. Article
  7. Article
  8. Article
  9. New Horizons: Artificial Intelligence Tools for Managing Osteoporosis.The Journal of clinical endocrinology and metabolism · 2023
    Article
  10. Review
  11. Axial Transmission: Techniques, Devices and Clinical Results.Advances in experimental medicine and biology · 2022
    Article
  12. Article
  13. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors.

Kaustav MohantyDepartment of Mechanical and Aerospace Engineering, NC State University, Raleigh, NC, 27695, USA. Electronic address: kmohant@ncsu.edu.
Omid YousefianDepartment of Mechanical and Aerospace Engineering, NC State University, Raleigh, NC, 27695, USA. Electronic address: oyousef@ncsu.edu.
Yasamin KarbalaeisadeghDepartment of Mechanical and Aerospace Engineering, NC State University, Raleigh, NC, 27695, USA. Electronic address: ykarbal@ncsu.edu.
Micah UlrichDepartment of Mechanical and Aerospace Engineering, NC State University, Raleigh, NC, 27695, USA. Electronic address: mulrich@ncsu.edu.
Quentin GrimalSorbonne Université, INSERM UMR S 1146, CNRS UMR 7371, Laboratoire d'Imagerie Biomédicale, 75006, Paris, France. Electronic address: quentin.grimal@sorbonne-universite.fr.
Marie MullerDepartment of Mechanical and Aerospace Engineering, NC State University, Raleigh, NC, 27695, USA. Electronic address: mmuller2@ncsu.edu.

Funding

Assessment of bone micro-structure using ultrasonic methodsR03EB022743 · NIBIB · NORTH CAROLINA STATE UNIVERSITY RALEIGH · PI MULLER, MARIE · 2016 to 2017
$139k
NIBIB NIH HHS R03 EB022743
6 · The paper itself

Abstract

The goal of this study is to estimate micro-architectural parameters of cortical porosity such as pore diameter (φ), pore density (ρ) and porosity (ν) of cortical bone from ultrasound frequency dependent attenuation using an artificial neural network (ANN). First, heterogeneous structures with controlled pore diameters and pore densities (mono-disperse) were generated, to mimic simplified structure of cortical bone. Then, more realistic structures were obtained from high resolution CT scans of human cortical bone. 2-D finite-difference time-domain simulations were conducted to calculate the frequency-dependent attenuation in the 1-8 MHz range. An ANN was then trained with the ultrasonic attenuation at different frequencies as the input feature vectors while the output was set as the micro-architectural parameters (pore diameter, pore density and porosity). The ANN is composed of three fully connected dense layers with 24, 12 and 6 neurons, connected to the output layer. The dataset was trained over 6000 epochs with a batch size of 16. The trained ANN exhibits the ability to predict the micro-architectural parameters with high accuracy and low losses. ANN approaches could potentially be used as a tool to help inform physics-based modelling of ultrasound propagation in complex media such as cortical bone. This will lead to the solution of inverse-problems to retrieve bone micro-architectural parameters from ultrasound measurements for the non-invasive diagnosis and monitoring osteoporosis.

Indexed as

Neural Networks, ComputerCortical BoneHumansImage Processing, Computer-AssistedOsteoporosisPorosityTomography, X-Ray ComputedUltrasonographyCortical boneMultiple scatteringNeural networksOsteoporosisQuantitative ultrasound

Identifiers

PMID31600691
PMCPMC6817400

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.