Hwang TS, et al. J Vet Sci. 2018 Sep;19(5):660-666. https://doi.org/10.4142/jvs.2018.19.5.660

The present study aimed to investigate the technical feasibility of percutaneous endoscopic mini-hemilaminectomy via a uniportal approach, and to evaluate the possibility of decompression and endoscopic examination of the thoracic and lumbar spinal canals in small dogs during such procedures. Fresh canine cadavers of mixed-breed dogs (n=7) were used in this study. Following injection of a barium and agarose mixture (BA-gel) to stimulate intervertebral disc herniation, percutaneous endoscopic mini-hemilaminectomy was performed using a lateral approach to the thoracic and lumbar vertebrae. BA-gel was removed to decompress the spinal cord using an elevator and rongeurs after mini-hemilaminectomy. Pre and post-operative computed tomography (CT) scans were obtained to evaluate surgical outcomes. Intra-operative complications, incision length, and procedure time were recorded. All procedures were completed with clear visualization of the spinal cord and floor of the spinal canal. The mean total operating time was 58.00 ± 18.06 min. Lengths of incision were under 1 cm in all dogs. Intra-operative complications included iatrogenic nerve root injuries caused by the micro-rongeur in two dogs. CT imaging revealed that removal of BA-gel resulted in sufficient spinal cord decompression. Our findings indicated that percutaneous endoscopic thoracolumbar mini-hemilaminectomy is feasible for spinal cord decompression and allows for adequate observation of the spinal canal. Thus, this technique may be an alternative surgical option for treatment of thoracolumbar disk disease in dogs.

Kim SY, et al. J Vet Sci. 2014 Jun;15(2):195-198. https://doi.org/10.4142/jvs.2014.15.2.195

This study evaluated the feasibility of using texture analysis and machine learning to distinguish radiographic lung patterns. A total of 1200 regions of interest (ROIs) including four specific lung patterns (normal, alveolar, bronchial, and unstructured interstitial) were obtained from 512 thoracic radiographs of 252 dogs and 65 cats. Forty-four texture parameters based on eight methods of texture analysis (first-order statistics, spatial gray-level-dependence matrices, gray-level-difference statistics, gray-level run length image statistics, neighborhood gray-tone difference matrices, fractal dimension texture analysis, Fourier power spectrum, and Law's texture energy measures) were used to extract textural features from the ROIs. The texture parameters of each lung pattern were compared and used for training and testing of artificial neural networks. Classification performance was evaluated by calculating accuracy and the area under the receiver operating characteristic curve (AUC). Forty texture parameters showed significant differences between the lung patterns. The accuracy of lung pattern classification was 99.1% in the training dataset and 91.9% in the testing dataset. The AUCs were above 0.98 in the training set and above 0.92 in the testing dataset. Texture analysis and machine learning algorithms may potentially facilitate the evaluation of medical images.

Objective—To evaluate the use of color Doppler imaging (CDI) for determining the resistive index (RI) of the medial long posterior ciliary artery (mLPCA) in clinically normal conscious dogs. Animal...