Assisting differential clinical diagnosis of cattle diseases using smartphone-based technology in low resource settings: a pilot study

BMC Veterinary Research

Research Article

Sami Ibrahim¹  Amanuel Eshetu²  Amina Abdu²  Takele Beyene Tufa²  Etenesh Wondimu²  Tariku Jibat Beyene³  Ashenafi Feyisa Beyi⁴  Crawford W. Revie⁵ 

[1] Cojengo Ltd, Glasgow, UK;College of Veterinary Medicine and Agriculture, Addis Ababa University, POBox 34, Bishoftu/Debre Zeit, Ethiopia;College of Veterinary Medicine and Agriculture, Addis Ababa University, POBox 34, Bishoftu/Debre Zeit, Ethiopia;Business Economics Group, Wageningen University, Hollandseweg 1, 6706 KN, Wageningen, The Netherlands;College of Veterinary Medicine and Agriculture, Addis Ababa University, POBox 34, Bishoftu/Debre Zeit, Ethiopia;Department of Animal Sciences, University of Florida, Gainesville, FL, USA;Department of Health Management, Atlantic Veterinary College, University of Prince Edward Island, Charlottetown, PEI, Canada;

关键词: Cattle disease;    Differential diagnosis;    Ethiopia;    Smartphone-based application;    Bayesian inference;   

DOI  :  10.1186/s12917-017-1249-3

 received in 2017-05-09, accepted in 2017-10-31,  发布年份 2017

来源: Springer

【 摘 要 】

BackgroundThe 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.ResultsA 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.ConclusionsThis 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.

【 授权许可】

CC BY   
© The Author(s). 2017