Utilizing Artificial Intelligence to Determine Bone Mineral Density Via Chest Computed Tomography Article Swipe
Rock H. Savage
,
Marly van Assen
,
Simon S. Martin
,
Pooyan Sahbaee
,
Lewis Griffith
,
Dante A. Giovagnoli
,
Jonathan I. Sperl
,
Christian Hopfgartner
,
Rainer Kärgel
,
U. Joseph Schoepf
·
YOU?
·
· 2020
· Open Access
·
· DOI: https://doi.org/10.1097/rti.0000000000000484
YOU?
·
· 2020
· Open Access
·
· DOI: https://doi.org/10.1097/rti.0000000000000484
Purpose: The purpose of this study was to validate the accuracy of an artificial intelligence (AI) prototype application in determining bone mineral density (BMD) from chest computed tomography (CT), as compared with dual-energy x-ray absorptiometry (DEXA). Materials and Methods: In this Institutional Review Board–approved study, we analyzed the data of 65 patients (57 female, mean age: 67.4 y) who underwent both DEXA and chest CT (mean time between scans: 1.31 y). From the DEXA studies, T -scores for L1-L4 (lumbar vertebrae 1 to 4) were recorded. Patients were then divided on the basis of their T -scores into normal control, osteopenic, or osteoporotic groups. An AI algorithm based on wavelet features, AdaBoost, and local geometry constraints independently localized thoracic vertebrae from chest CT studies and automatically computed average Hounsfield Unit (HU) values with kVp-dependent spectral correction. The Pearson correlation evaluated the correlation between the T -scores and HU values. Mann-Whitney U test was implemented to compare the HU values of normal control versus osteoporotic patients. Results: Overall, the DEXA-determined T -scores and AI-derived HU values showed a moderate correlation ( r =0.55; P <0.001). This 65-patient population was divided into 3 subgroups on the basis of their T -scores. The mean T -scores for the 3 subgroups (normal control, osteopenic, osteoporotic) were 0.77±1.50, −1.51±0.04, and −3.26±0.59, respectively. The mean DEXA-determined L1-L4 BMD measures were 1.13±0.16, 0.88±0.06, and 0.68±0.06 g/cm 2 , respectively. The mean AI-derived attenuation values were 145±42.5, 136±31.82, and 103±16.28 HU, respectively. Using these AI-derived HU values, a significant difference was found between the normal control patients and osteoporotic group ( P =0.045). Conclusion: Our results show that this AI prototype can successfully determine BMD in moderate correlation with DEXA. Combined with other AI algorithms directed at evaluating cardiac and lung diseases, this prototype may contribute to future comprehensive preventative care based on a single chest CT.
Related Topics
Concepts
Medicine
Hounsfield scale
Bone mineral
Nuclear medicine
Osteoporosis
Osteopenia
Dual-energy X-ray absorptiometry
Lumbar vertebrae
Bone density
Correlation
Radiology
Computed tomography
Lumbar
Internal medicine
Mathematics
Geometry
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1097/rti.0000000000000484
- OA Status
- green
- Cited By
- 20
- References
- 37
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W3006955029
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W3006955029Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.1097/rti.0000000000000484Digital Object Identifier
- Title
-
Utilizing Artificial Intelligence to Determine Bone Mineral Density Via Chest Computed TomographyWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2020Year of publication
- Publication date
-
2020-02-19Full publication date if available
- Authors
-
Rock H. Savage, Marly van Assen, Simon S. Martin, Pooyan Sahbaee, Lewis Griffith, Dante A. Giovagnoli, Jonathan I. Sperl, Christian Hopfgartner, Rainer Kärgel, U. Joseph SchoepfList of authors in order
- Landing page
-
https://doi.org/10.1097/rti.0000000000000484Publisher landing page
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
-
https://research.rug.nl/en/publications/eae62526-f09e-491a-91a4-74d9eb56a4ceDirect OA link when available
- Concepts
-
Medicine, Hounsfield scale, Bone mineral, Nuclear medicine, Osteoporosis, Osteopenia, Dual-energy X-ray absorptiometry, Lumbar vertebrae, Bone density, Correlation, Radiology, Computed tomography, Lumbar, Internal medicine, Mathematics, GeometryTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
20Total citation count in OpenAlex
- Citations by year (recent)
-
2025: 6, 2024: 6, 2023: 5, 2022: 1, 2021: 1Per-year citation counts (last 5 years)
- References (count)
-
37Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.L1-L4 | 78, 220 |
| abstract_inverted_index.Using | 244 |
| abstract_inverted_index.based | 107, 303 |
| abstract_inverted_index.basis | 92, 194 |
| abstract_inverted_index.chest | 25, 63, 121, 307 |
| abstract_inverted_index.found | 253 |
| abstract_inverted_index.group | 261 |
| abstract_inverted_index.local | 113 |
| abstract_inverted_index.other | 284 |
| abstract_inverted_index.study | 5 |
| abstract_inverted_index.their | 94, 196 |
| abstract_inverted_index.these | 245 |
| abstract_inverted_index.x-ray | 33 |
| abstract_inverted_index.=0.55; | 181 |
| abstract_inverted_index.Review | 42 |
| abstract_inverted_index.future | 299 |
| abstract_inverted_index.normal | 98, 160, 256 |
| abstract_inverted_index.scans: | 68 |
| abstract_inverted_index.showed | 175 |
| abstract_inverted_index.single | 306 |
| abstract_inverted_index.study, | 44 |
| abstract_inverted_index.values | 131, 158, 174, 236 |
| abstract_inverted_index.versus | 162 |
| abstract_inverted_index.(DEXA). | 35 |
| abstract_inverted_index.(lumbar | 79 |
| abstract_inverted_index.(normal | 207 |
| abstract_inverted_index.-scores | 76, 96, 145, 170, 202 |
| abstract_inverted_index.Pearson | 137 |
| abstract_inverted_index.average | 127 |
| abstract_inverted_index.between | 67, 142, 254 |
| abstract_inverted_index.cardiac | 290 |
| abstract_inverted_index.compare | 155 |
| abstract_inverted_index.control | 161, 257 |
| abstract_inverted_index.density | 22 |
| abstract_inverted_index.divided | 89, 188 |
| abstract_inverted_index.female, | 53 |
| abstract_inverted_index.groups. | 103 |
| abstract_inverted_index.mineral | 21 |
| abstract_inverted_index.purpose | 2 |
| abstract_inverted_index.results | 267 |
| abstract_inverted_index.studies | 123 |
| abstract_inverted_index.values, | 248 |
| abstract_inverted_index.values. | 148 |
| abstract_inverted_index.wavelet | 109 |
| abstract_inverted_index.-scores. | 198 |
| abstract_inverted_index.=0.045). | 264 |
| abstract_inverted_index.Combined | 282 |
| abstract_inverted_index.Methods: | 38 |
| abstract_inverted_index.Overall, | 166 |
| abstract_inverted_index.Patients | 86 |
| abstract_inverted_index.Purpose: | 0 |
| abstract_inverted_index.Results: | 165 |
| abstract_inverted_index.accuracy | 10 |
| abstract_inverted_index.analyzed | 46 |
| abstract_inverted_index.compared | 30 |
| abstract_inverted_index.computed | 26, 126 |
| abstract_inverted_index.control, | 99, 208 |
| abstract_inverted_index.directed | 287 |
| abstract_inverted_index.geometry | 114 |
| abstract_inverted_index.measures | 222 |
| abstract_inverted_index.moderate | 177, 278 |
| abstract_inverted_index.patients | 51, 258 |
| abstract_inverted_index.spectral | 134 |
| abstract_inverted_index.studies, | 74 |
| abstract_inverted_index.thoracic | 118 |
| abstract_inverted_index.validate | 8 |
| abstract_inverted_index.AdaBoost, | 111 |
| abstract_inverted_index.Materials | 36 |
| abstract_inverted_index.algorithm | 106 |
| abstract_inverted_index.determine | 275 |
| abstract_inverted_index.diseases, | 293 |
| abstract_inverted_index.evaluated | 139 |
| abstract_inverted_index.features, | 110 |
| abstract_inverted_index.localized | 117 |
| abstract_inverted_index.patients. | 164 |
| abstract_inverted_index.prototype | 16, 272, 295 |
| abstract_inverted_index.recorded. | 85 |
| abstract_inverted_index.subgroups | 191, 206 |
| abstract_inverted_index.underwent | 59 |
| abstract_inverted_index.vertebrae | 80, 119 |
| abstract_inverted_index.0.68±0.06 | 227 |
| abstract_inverted_index.103±16.28 | 241 |
| abstract_inverted_index.145±42.5, | 238 |
| abstract_inverted_index.65-patient | 185 |
| abstract_inverted_index.AI-derived | 172, 234, 246 |
| abstract_inverted_index.Hounsfield | 128 |
| abstract_inverted_index.algorithms | 286 |
| abstract_inverted_index.artificial | 13 |
| abstract_inverted_index.contribute | 297 |
| abstract_inverted_index.difference | 251 |
| abstract_inverted_index.evaluating | 289 |
| abstract_inverted_index.population | 186 |
| abstract_inverted_index.tomography | 27 |
| abstract_inverted_index.<0.001). | 183 |
| abstract_inverted_index.0.77±1.50, | 212 |
| abstract_inverted_index.0.88±0.06, | 225 |
| abstract_inverted_index.1.13±0.16, | 224 |
| abstract_inverted_index.136±31.82, | 239 |
| abstract_inverted_index.Conclusion: | 265 |
| abstract_inverted_index.application | 17 |
| abstract_inverted_index.attenuation | 235 |
| abstract_inverted_index.constraints | 115 |
| abstract_inverted_index.correction. | 135 |
| abstract_inverted_index.correlation | 138, 141, 178, 279 |
| abstract_inverted_index.determining | 19 |
| abstract_inverted_index.dual-energy | 32 |
| abstract_inverted_index.implemented | 153 |
| abstract_inverted_index.osteopenic, | 100, 209 |
| abstract_inverted_index.significant | 250 |
| abstract_inverted_index.Mann-Whitney | 149 |
| abstract_inverted_index.intelligence | 14 |
| abstract_inverted_index.osteoporotic | 102, 163, 260 |
| abstract_inverted_index.preventative | 301 |
| abstract_inverted_index.successfully | 274 |
| abstract_inverted_index.Institutional | 41 |
| abstract_inverted_index.automatically | 125 |
| abstract_inverted_index.comprehensive | 300 |
| abstract_inverted_index.independently | 116 |
| abstract_inverted_index.kVp-dependent | 133 |
| abstract_inverted_index.osteoporotic) | 210 |
| abstract_inverted_index.respectively. | 216, 231, 243 |
| abstract_inverted_index.absorptiometry | 34 |
| abstract_inverted_index.−1.51±0.04, | 213 |
| abstract_inverted_index.−3.26±0.59, | 215 |
| abstract_inverted_index.DEXA-determined | 168, 219 |
| abstract_inverted_index.Board–approved | 43 |
| cited_by_percentile_year.max | 99 |
| cited_by_percentile_year.min | 89 |
| corresponding_author_ids | https://openalex.org/A5090779510, https://openalex.org/A5087250976, https://openalex.org/A5034218505, https://openalex.org/A5020326587, https://openalex.org/A5103880769, https://openalex.org/A5010260914, https://openalex.org/A5043077874 |
| countries_distinct_count | 3 |
| institutions_distinct_count | 10 |
| corresponding_institution_ids | https://openalex.org/I114090438, https://openalex.org/I1334415907, https://openalex.org/I153297377, https://openalex.org/I4210132578 |
| citation_normalized_percentile.value | 0.7795732 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |