Selection of density standard and X–ray tube settings for computed digital absorptiometry in horses using the k–means clustering algorithm
Background 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 decis...
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| Published in | BMC veterinary research Vol. 21; no. 1; p. 165 |
|---|---|
| Main Authors | , , , , , , |
| Format | Journal Article |
| Language | English |
| Published |
London
BioMed Central
13.03.2025
BioMed Central Ltd Springer Nature B.V BMC |
| Subjects | |
| Online Access | Get full text |
| ISSN | 1746-6148 1746-6148 |
| DOI | 10.1186/s12917-025-04591-5 |
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| Abstract | Background
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.
Methods and 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.
Conclusion
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. |
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| AbstractList | 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.
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.
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. Background 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. Methods and 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. Conclusion 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. BackgroundIn 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.Methods and resultsFive 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.ConclusionDensity 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. Abstract Background 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. Methods and 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. Conclusion 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. BACKGROUND: 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. METHODS AND 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. CONCLUSION: 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. Background 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. Methods and 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. Conclusion 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. Keywords: Computed digital absorptiometry, Bone mineral density, Radiological signs, Distal limbs, Horse 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.BACKGROUNDIn 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.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.METHODS AND RESULTSFive 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.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.CONCLUSIONDensity 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. 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. 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. 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. |
| ArticleNumber | 165 |
| Audience | Academic |
| Author | Pawlikowski, Marek Jankowski, Krzysztof Borowska, Marta Jasiński, Tomasz Domino, Małgorzata Turek, Bernard Skierbiszewska, Katarzyna |
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| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/40082938$$D View this record in MEDLINE/PubMed |
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| Keywords | Radiological signs Bone mineral density Computed digital absorptiometry Distal limbs Horse |
| Language | English |
| License | 2025. The Author(s). Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. cc-by |
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In veterinary medicine, conventional radiography is the first–choice method for most diagnostic imaging applications in both small animal and equine... In veterinary medicine, conventional radiography is the first-choice method for most diagnostic imaging applications in both small animal and equine practice.... Background In veterinary medicine, conventional radiography is the first-choice method for most diagnostic imaging applications in both small animal and equine... BackgroundIn veterinary medicine, conventional radiography is the first–choice method for most diagnostic imaging applications in both small animal and equine... BACKGROUND: In veterinary medicine, conventional radiography is the first–choice method for most diagnostic imaging applications in both small animal and... Abstract Background In veterinary medicine, conventional radiography is the first–choice method for most diagnostic imaging applications in both small animal... |
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| SubjectTerms | Absorptiometry Absorptiometry, Photon - methods Absorptiometry, Photon - veterinary Algorithms Aluminum aluminum alloys Animals Artificial intelligence Bone densitometry Bone Density Bone mineral density Bones Cancellous bone Cluster Analysis Clustering Clustering Algorithms Computed digital absorptiometry copper Cortical bone Data mining Decision making Distal limbs General anesthesia Horse Horse Diseases - diagnostic imaging Horses Long bone Machine learning Magnetic resonance imaging Medical examination Medical imaging Medical practices Medicine Medicine & Public Health Methods Osteoarthritis Radiography Radiological signs Software Tomography Transgenics Ultrasonic imaging veterinary clinics Veterinary medicine Veterinary Medicine/Veterinary Science X-radiation Zoology |
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| Title | Selection of density standard and X–ray tube settings for computed digital absorptiometry in horses using the k–means clustering algorithm |
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