The research team buried two foreign bodies into cadaver equine legs: one at the sole and one at the coronary band. The materials included dry wood, soaked wood, glass, slate and plastic. They then asked three equine veterinarians to examine the images produced by computed tomography (CT), magnetic resonance imaging (MRI) and digital X-rays.

They determined that there was minimal variation between the vet's findings on all the images. CT was found to be the most useful imaging modality. CT was able to detect all materials; it was able to pick out slate, glass and dry wood better than the other imaging modalities.

The foreign bodies were able to be seen on MRI, but these images were not clear enough for the vets to determine what type of material it was. Plastic and wood were difficult to determine on digital X-rays.

The team notes that though it is not traditionally necessary to determine what material is involved, it is important to use an appropriate imaging technique to make sure the foreign body can be detected.

Read the full investigation here.

Read more at Equine Science Update.

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Pain causes behavior changes in horses; understanding normal and pain-induced behaviors in horses is critical to properly evaluating pain levels. Though horses may work to mask pain in an unfamiliar surrounding like a clinic, even subtle variations become apparent when behavior is thoroughly analyzed.

Though there are multiple pain assessment scales available, they are all scored manually and can be skewed by multiple things, including inexperience and the amount of time spent viewing the horse.

To test their video tracker theory, researchers used 34 horses at the University of Veterinary Medicine in Vienna's teaching hospital. All the horses were housed in box stalls with water and were fed four times a day. The horses were recorded on an action camera and in time-lapse mode. The videos were then processed to look for an automated prediction of three body parts: the tail, nose and withers.

The technology was able to identify the horse's stance with an accuracy and sensitivity of more than 80 percent, meaning that it could more often than not detect when a horse was exhibiting pain behaviors. The research team concluded that this technology will improve the detection of equine pain and provide insight for equine behavioral research.

Read the study here.

Read more at HorseTalk.

The post Study Shows Machines Can Detect Equine Pain From Daily Behavior appeared first on Horse Racing News | Paulick Report.

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