Standing magnetic resonance imaging of distal phalanx fractures in 6 cases of Thoroughbred racehorse

Mizobe, Fumiaki; Nomura, Motoi; Kanai, Kota; Ishikawa, Yuhiro; Yamada, Kazutaka

doi:10.1292/jvms.18-0183

Cited by 1 publication

(2 citation statements)

References 12 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This work suggests that radiomic features from resampled μCT data comparable to the voxel dimensions in conventional CT could be used in modeling the risk of fracture in PSBs. Other non-invasive imaging methods such as MRI, nuclear imaging, and infrared spectroscopy are able to detect lesions and abnormalities before the onset of gross disease [ 8 , 9 , 33 , 34 , 35 , 36 ]. Recently, a commercial option has been proposed for obtaining CT images in anesthetized standing horses [ 7 ].…”

Section: Discussionmentioning

confidence: 99%

“…Some technical advances in standing CT have been suggested but whether these devices can achieve sufficient spatial resolution for diagnosis, mitigate the risk of motion and beam hardening artifacts, and whether these devices could be used in a screening capacity in the horseracing environment remains to be seen [ 7 ]. There have been similar advancements in standing MRI and PET technologies, but operationalizing them as a screening tool in the clinical and commercial setting remains a challenge [ 7 , 8 , 9 ].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Radiomics Modeling of Catastrophic Proximal Sesamoid Bone Fractures in Thoroughbred Racehorses Using μCT

Basran

McDonough

Palmer

et al. 2022

Animals

View full text Add to dashboard Cite

Proximal sesamoid bone (PSB) fractures are the most common musculoskeletal injury in race-horses. X-ray CT imaging can detect expressed radiological features in horses that experienced catastrophic fractures. Our objective was to assess whether expressed radiomic features in the PSBs of 50 horses can be used to develop machine learning models for predicting PSB fractures. The μCTs of intact contralateral PSBs from 50 horses, 30 of which suffered catastrophic fractures, and 20 controls were studied. From the 129 intact μCT images of PSBs, 102 radiomic features were computed using a variety of voxel resampling dimensions. Decision Trees and Wrapper methods were used to identify the 20 top expressed features, and six machine learning algorithms were developed to model the risk of fracture. The accuracy of all machine learning models ranged from 0.643 to 0.903 with an average of 0.754. On average, Support Vector Machine, Random Forest (RUS Boost), and Log-regression models had higher performance than K-means Nearest Neighbor, Neural Network, and Random Forest (Bagged Trees) models. Model accuracy peaked at 0.5 mm and decreased substantially when the resampling resolution was greater than or equal to 1 mm. We find that, for this in vitro dataset, it is possible to differentiate between unfractured PSBs from case and control horses using μCT images. It may be possible to extend these findings to the assessment of fracture risk in standing horses.

show abstract