Detection of spinal ataxia in horses using fuzzy clustering of body position uncertainty.

SummaryReasons for performing study: Subjective neurologicalevaluation in horses is prone to bias. An objective method ofspinal ataxia detection is not subject to these limitations andcould be of use in equine practice and research.Hypothesis : Kinematic data in the walking horse candifferentiate normal and spinal ataxic horses.Methods : Twelve normal and 12 spinal ataxic horses wereevaluated by kinematic analysis walking on a treadmill.Each body position signal was reduced to a scalarmeasure ofuncertainty then fuzzy clustered into normal or ataxicgroups. Correct classification percentage (CCP) was thencalculated using membership values of each horse in the 2 groups. Subsequently, a guided search for measurecombinations with high CCPwas performed.Results : Eight measures of body position resulted in CCP ! 70%.Several combinations of 4Ð5 measures resulted in 100% CCP.All combinations with 100% CCPcould be obtained with onebody marker on the back measuring vertical and horizontalmovement and one body marker each on the right fore- andhindlimb measuring vertical movement.Conclusions and potential relevance: Kinematic gait analysisusing simple body marker combinations can be usedobjectively to detect spinal ataxia in horses.IntroductionSpinal ataxia is a significant problem for horses in North America,with diverse aetiology. In cases of mild to moderate severity,spinal ataxia may be suspected by the veterinarian initially fromowner complaints that their horse is weak or unable to perform.Spinal ataxia can then be determined more definitively after asubjective neurological examination. During this, the horseOsability to move and resist movement during and after a number ofdifferent manipulations and manoeuvres by the examiner isassessed and graded. The accuracy of the neurologicalexamination is limited by its subjective nature and the experientialbias of the examiner (Mackay 1997).Further diagnostic tests ranging from contrast myelograpy to avariety of spinal fluid analyses can be performed to help confirmthe existence of spinal ataxia by finding a plausible aetiology, aswell as to guide appropriate medical or surgical treatment.However, these techniques, although generally expensive andsomewhat invasive, are not very sensitive or accurate and a largepercentage of equine spinal ataxia cases remain without a specificdiagnosis. Because of this tendency for failure to find a specificdiagnosis, it is difficult for an equine practitioner to recommendthese more in-depth neurological diagnostic tests, especially if thehorse has mild, equivocal clinical signs.Clinical signs of many neurological dysfunctions have beenquantified in man and other animals by a variety of procedures,many using some form of computer-assisted kinematic analysis ofbody movements (Davis et al.2000). Kinematic evaluation ofspinal ataxia in horses has not previously been reported. Thepurpose of this study was to establish an objective, kinematicspinal ataxia detection system for use in horses.Materials and methodsT wenty-four mature horses were selected; 12 normal and 12 with spinal ataxia (Table 1). The normal horses were selectedfrom the University of Missouri teaching herd. Horses with spinalataxia were donated to the University of Missouri VeterinaryT eaching Hospital by clients after presentation for evaluation ofneurological dysfunction. All horses in the spinal ataxia groupunderwent neurological evaluation by 3 independent, experiencedexaminers and were accepted into the study if 2 or more examinersgave neurological scores of at least grade 1 . Ataxia was scored ona scale of 0Ð5+, modified from Mayhew et al. (1978), with 0 beingnormal and 5 recumbent. Since all horses were evaluated whilewalking on a treadmill, no horses affected with spinal ataxia of aseverity that prevented them from easily loading onto and off andsafely walking on the treadmill could be used in the study.All horses were trained over 3 days to load onto and walk onthe treadmill (Sato Equine Treadmill)