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publication name Lung Ultrasonography and Computer-Aided Scoring System as a Diagnostic Aid for Bovine Respiratory Disease in Feedlot Cattle
Authors Mohamed M. Zeineldin, Yassein M. Abd El-Raof, Hassam A. El-attar and Mohamed M. Ghanem
year 2016
keywords Feedlot cattle Lung score Ultrasonography Bovine respiratory disease
journal Global Veterinaria
volume 17
issue 6
pages 588-594
publisher Not Available
Local/International International
Paper Link https://www.idosi.org/gv/gv17(6)16/14.pdf
Full paper download
Supplementary materials Not Available
Abstract

: Bovine respiratory disease (BRD) is one of the most important diseases in feedlot calves especially in intensely raised and newly transported calves. With the recent availability of modern portable diagnostic techniques, the veterinary practitioner can now be quickly examined the surface and superficial lung parenchyma at farm. The purpose of our study was to investigate the diagnostic utility of computer-aided lung scoring system and lung ultrasonography at initial diagnosis of naturally occurring BRD in feedlot cattle. A total of one hundred thirty-five feedlot calves at high risk of developing BRD were monitored daily during the first month after entry at the feedlot for clinical signs of BRD. Calves with visual signs of BRD identified by pen checkers were examined by computer-aided stethoscope and portable ultrasonography. For each calf examined for BRD, one pen matched apparently healthy calf was selected as control and examined in the same manner. Of the 135 steers, 24 BRD cases with 24 pen matched healthy controls calves were enrolled in our study. All feedlot calves with visual signs of BRD had abnormal lung sounds with lung scores ranging from 2 to 5. Moreover, lung ultrasonography identified lung lesions in 17/24 (70.83%) from BRD affected calves and 3/24 (12.5%) from pen matched controls. Our study results revealed that, using computer aided lung score at initial diagnosis of BRD with lung ultrasonography could help in improve case definition, treatment decision and evaluating disease risk during the initial identification of the diseased animals.

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