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BMC veterinary research2025; 21(1); 165; doi: 10.1186/s12917-025-04591-5

Selection of density standard and X-ray tube settings for computed digital absorptiometry in horses using the k-means clustering algorithm.

Abstract: In veterinary medicine, conventional radiography is the first-choice method for most diagnostic imaging applications in both small animal and equine practice. One direction in its development is the integration of bone density evaluation and artificial intelligence-assisted clinical decision-making, which is expected to enhance and streamline veterinarians' daily practices. One such decision-support method is k-means clustering, a machine learning and data mining technique that can be used clinically to classify radiographic signs into healthy or affected clusters. The study aims to investigate whether the k-means clustering algorithm can differentiate cortical and trabecular bone in both healthy and affected horse limbs. Therefore, identifying the optimal computed digital absorptiometry parameters was necessary. Results: Five metal-made density standards, made of pure aluminum, aluminum alloy (duralumin), cuprum alloy, iron-nickel alloy, and iron-silicon alloy, and ten X-ray tube settings were evaluated for the radiographic imaging of equine distal limbs, including six healthy limbs and six with radiographic signs of osteoarthritis. Density standards were imaged using ten combinations of X-ray tube settings, ranging from 50 to 90 kV and 1.2 to 4.0 mAs. The relative density in Hounsfield units was firstly returned for both bone types and density standards, then compared, and finally used for clustering. In both healthy and osteoarthritis-affected limbs, the relative density of the long pastern bone (the proximal phalanx) differed between bone types, allowing the k-means clustering algorithm to successful differentiate cortical and trabecular bone. Conclusions: Density standard made of duralumin, along with the 60 kV, 4.0 mAs X-ray tube settings, yielded the highest clustering metric values and was therefore considered optimal for further research. We believe that the identified optimal computed digital absorptiometry parameters may be recommended for further researches on the relative quantification of conventional radiographs and for distal limb examination in equine veterinary practice.
© 2025. The Author(s).
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Publication Date: 2025-03-13 PubMed ID: 40082938PubMed Central: PMC11905476DOI: 10.1186/s12917-025-04591-5Google Scholar: Lookup
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  • Journal Article

Summary

This research summary has been generated with artificial intelligence and may contain errors and omissions. Refer to the original study to confirm details provided. Submit correction.

This research study investigates the use of the k-means clustering algorithm, a machine learning technique, to differentiate between different types of bone in both healthy and disease-affected horse limbs, using computed digital absorptiometry. The study found that a density standard made from duralumin, alongside specific X-ray tube settings, was the most effective for this purpose.

Research Purpose and Methodology

  • The study had two main objectives. The first was to determine if the k-means clustering algorithm could differentiate between cortical and trabecular bone types in the limbs of healthy and disease-affected horses. The second objective was to identify the best computed digital absorptiometry parameters for achieving this.
  • The researchers used five metal-made density standards and ten X-ray tube settings to image the distal limbs of six healthy horses and six horses with signs of osteoarthritis.
  • The ability of the k-means clustering algorithm to differentiate between the bone types was then assessed using the relative density Hounsfield units of both bone types and the density standards.

Results

  • Both sets of horse limbs showed different relative densities in the long pastern bone, which allowed the k-means clustering algorithm to distinguish between cortical and trabecular bone types.
  • Optimal imaging was achieved with a duralumin density standard and X-ray tube settings of 60 kV and 4.0 mAs. This combination resulted in the highest clustering metric values.

Implications and Conclusions

  • The findings suggest that the identified computed digital absorptiometry parameters could be used in future research into the relative quantification of conventional radiographs.
  • This could also prove useful in equine veterinary practice for examinations of distal limb health, as it enables the differentiation of healthy and disease-affected structures.
  • As such, the incorporation of the k-means clustering algorithm and optimal imaging settings could well enhance and streamline daily practice for veterinarians treating horses.

Cite This Article

APA
Turek B, Pawlikowski M, Jankowski K, Borowska M, Skierbiszewska K, Jasiński T, Domino M. (2025). Selection of density standard and X-ray tube settings for computed digital absorptiometry in horses using the k-means clustering algorithm. BMC Vet Res, 21(1), 165. https://doi.org/10.1186/s12917-025-04591-5

Publication

BMC veterinary research
ISSN: 1746-6148
NlmUniqueID: 101249759
Country: England
Language: English
Volume: 21
Issue: 1
Pages: 165
PII: 165

Researcher Affiliations

Turek, Bernard
  • Department of Large Animal Diseases and Clinic, Institute of Veterinary Medicine, Warsaw University of Life Sciences (WULS - SGGW), Nowoursynowska 100, Warsaw, 02-797, Poland.
Pawlikowski, Marek
  • Institute of Mechanics and Printing, Warsaw University of Technology, Narbutta 85, Warsaw, 02-524, Poland.
Jankowski, Krzysztof
  • Institute of Mechanics and Printing, Warsaw University of Technology, Narbutta 85, Warsaw, 02-524, Poland.
Borowska, Marta
  • Institute of Biomedical Engineering, Faculty of Mechanical Engineering, Białystok University of Technology, Wiejska 45C, Bialystok, 15-351, Poland.
Skierbiszewska, Katarzyna
  • Department of Large Animal Diseases and Clinic, Institute of Veterinary Medicine, Warsaw University of Life Sciences (WULS - SGGW), Nowoursynowska 100, Warsaw, 02-797, Poland.
Jasiński, Tomasz
  • Department of Large Animal Diseases and Clinic, Institute of Veterinary Medicine, Warsaw University of Life Sciences (WULS - SGGW), Nowoursynowska 100, Warsaw, 02-797, Poland.
Domino, Małgorzata
  • Department of Large Animal Diseases and Clinic, Institute of Veterinary Medicine, Warsaw University of Life Sciences (WULS - SGGW), Nowoursynowska 100, Warsaw, 02-797, Poland. malgorzata_domino@sggw.edu.pl.

MeSH Terms

  • Animals
  • Horses
  • Absorptiometry, Photon / veterinary
  • Bone Density
  • Algorithms
  • Horse Diseases / diagnostic imaging
  • Cluster Analysis
  • Osteoarthritis / veterinary
  • Osteoarthritis / diagnostic imaging

Conflict of Interest Statement

Declarations. Ethics approval and consent to participate: Not applicable. The research, using the samples collected postmortem at a commercial slaughterhouse, does not fall under the legislation for the protection of animals used for scientific purposes, national decree–law (Dz. U. 2015 poz. 266 and 2010–63–EU directive). No ethical approval was needed. Consent for publication: Not applicable. Competing interests: The authors declare no competing interests.

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Citations

This article has been cited 1 times.
  1. Kozłowska N, Borowska M, Jasiński T, Wierzbicka M, Domino M. Computer-Aided Diagnosis of Equine Pharyngeal Lymphoid Hyperplasia Using the Object Detection-Based Processing Technique of Digital Endoscopic Images. Animals (Basel) 2025 Sep 22;15(18).
    doi: 10.3390/ani15182758pubmed: 41008003google scholar: lookup
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