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

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

BMC Vet Res. 2017 Nov 09;13(1):323

Authors: Beyene TJ, Eshetu A, Abdu A, Wondimu E, Beyi AF, Tufa TB, Ibrahim S, Revie CW

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.

PMID: 29121922 [PubMed - indexed for MEDLINE]