Staff Publications

Staff Publications

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    'Staff publications' is the digital repository of Wageningen University & Research

    'Staff publications' contains references to publications authored by Wageningen University staff from 1976 onward.

    Publications authored by the staff of the Research Institutes are available from 1995 onwards.

    Full text documents are added when available. The database is updated daily and currently holds about 240,000 items, of which 72,000 in open access.

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Record number 529669
Title Assisting differential clinical diagnosis of cattle diseases using smartphone-based technology in low resource settings : A pilot study
Author(s) Beyene, Tariku Jibat; Eshetu, Amanuel; Abdu, Amina; Wondimu, Etenesh; Beyi, Ashenafi Feyisa; Tufa, Takele Beyene; Ibrahim, Sami
Source BMC Veterinary Research 13 (2017)1. - ISSN 1746-6148
DOI https://doi.org/10.1186/s12917-017-1249-3
Department(s) Business Economics
Publication type Refereed Article in a scientific journal
Publication year 2017
Keyword(s) Bayesian inference - Cattle disease - Differential diagnosis - Ethiopia - Smartphone-based application
Abstract Background: The recent rise in mobile phone use and increased signal coverage has created opportunities for growth of the mobile Health sector in many low resource settings. This pilot study explores the use of a smartphone-based application, VetAfrica-Ethiopia, in assisting diagnosis of cattle diseases. We used a modified Delphi protocol to select important diseases and Bayesian algorithms to estimate the related disease probabilities based on various clinical signs being present in Ethiopian cattle. Results: A total of 928 cases were diagnosed during the study period across three regions of Ethiopia, around 70% of which were covered by diseases included in VetAfrica-Ethiopia. Parasitic Gastroenteritis (26%), Blackleg (8.5%), Fasciolosis (8.4%), Pasteurellosis (7.4%), Colibacillosis (6.4%), Lumpy skin disease (5.5%) and CBPP (5.0%) were the most commonly occurring diseases. The highest (84%) and lowest (30%) levels of matching between diagnoses made by student practitioners and VetAfrica-Ethiopia were for Babesiosis and Pasteurellosis, respectively. Multiple-variable logistic regression analysis indicated that the putative disease indicated, the practitioner involved, and the level of confidence associated with the prediction made by VetAfrica-Ethiopia were major determinants of the likelihood that a diagnostic match would be obtained. Conclusions: This pilot study demonstrated that the use of such applications can be a valuable means of assisting less experienced animal health professionals in carrying out disease diagnosis which may lead to increased animal productivity through appropriate treatment.
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