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

a pilot study

Tariku Jibat Beyene, Amanuel Eshetu, Amina Abdu, Etenesh Wondimu, Ashenafi Feyisa Beyi, Takele Beyene Tufa, Sami Ibrahim, Crawford W. Revie

Research output: Contribution to journalArticle

3 Citations (Scopus)
12 Downloads (Pure)

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.

Original languageEnglish
Article number323
Number of pages11
JournalBMC Veterinary Research
Volume13
Issue number1
DOIs
Publication statusPublished - 9 Nov 2017

Fingerprint

Cattle Diseases
cattle diseases
Smartphones
Ethiopia
Differential Diagnosis
Technology
Resources
pasteurellosis
Pasteurella Infections
blackleg (animal disease)
lumpy skin disease
Lumpy Skin Disease
application coverage
fascioliasis
babesiosis
Babesiosis
gastroenteritis
disease diagnosis
Animals
health care workers

Keywords

  • cattle disease
  • differential diagnosis
  • Ethiopia
  • smartphone-based applications
  • Bayesian inference

Cite this

Beyene, Tariku Jibat ; Eshetu, Amanuel ; Abdu, Amina ; Wondimu, Etenesh ; Beyi, Ashenafi Feyisa ; Tufa, Takele Beyene ; Ibrahim, Sami ; Revie, Crawford W. / Assisting differential clinical diagnosis of cattle diseases using smartphone-based technology in low resource settings : a pilot study. In: BMC Veterinary Research. 2017 ; Vol. 13, No. 1.
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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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Assisting differential clinical diagnosis of cattle diseases using smartphone-based technology in low resource settings : a pilot study. / Beyene, Tariku Jibat; Eshetu, Amanuel; Abdu, Amina; Wondimu, Etenesh; Beyi, Ashenafi Feyisa; Tufa, Takele Beyene; Ibrahim, Sami; Revie, Crawford W.

In: BMC Veterinary Research, Vol. 13, No. 1, 323, 09.11.2017.

Research output: Contribution to journalArticle

TY - JOUR

T1 - Assisting differential clinical diagnosis of cattle diseases using smartphone-based technology in low resource settings

T2 - a pilot study

AU - Beyene, Tariku Jibat

AU - Eshetu, Amanuel

AU - Abdu, Amina

AU - Wondimu, Etenesh

AU - Beyi, Ashenafi Feyisa

AU - Tufa, Takele Beyene

AU - Ibrahim, Sami

AU - Revie, Crawford W.

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Y1 - 2017/11/9

N2 - 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.

AB - 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.

KW - cattle disease

KW - differential diagnosis

KW - Ethiopia

KW - smartphone-based applications

KW - Bayesian inference

U2 - 10.1186/s12917-017-1249-3

DO - 10.1186/s12917-017-1249-3

M3 - Article

VL - 13

JO - BMC Veterinary Research

JF - BMC Veterinary Research

SN - 1746-6148

IS - 1

M1 - 323

ER -