PERCEPTION OF HEALTH CARE WORKERS ON ARTIFICIAL INTELLIGENCE BASED MALARIA DIAGNOSIS IN SOUTHWESTERN NIGERIA

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O.S. Michael¹, E. Bukoye², P. Whiley², N. Idusuyi², P. Casserly^2,3, D. Ademola², A.O. Coker^2,4

1. Department of Pharmacology and Therapeutics, College of Medicine, University of Ibadan, Nigeria.
2. Department of Biomedical Engineering, Faculty of Technology, University of Ibadan, Nigeria.
3. Institute for Global Health Technologies, Rice University, Texas, United States of America.
4. Department of Civil Engineering, Faculty of Technology, University of Ibadan, Nigeria.

Abstract

Background: Effective control of malaria is anchored on accurate diagnosis. Conventional Methods of diagnosis include microscopy, and malaria rapid diagnosis. Many factors, particularly human error, diagnostic inaccuracies of microscopy due to human errors. The study reports the results of an online survey designed to assess the perception of health workers on artificial intelligence methods in the diagnosis of malaria.

Methodology: An online, cross-sectional survey, conducted in April to August 2022. The study was conducted using Google forms. The knowledge of conventional methods of malaria diagnosis and willingness to accept artificial intelligence-based automated malaria diagnosis and parasite counts were assessed. The form(questionnaire) was delivered to emails and several WhatsApp groups.

Results: Sixty seven responses were received over the study period, comprising medical doctors (30, 44.8%), medical laboratory scientists (18, 26.9%), postgraduate students (8, 11.9%), nurses (7, 10.4%), and students (4, 6.0%). All the respondents knew about conventional methods of malaria diagnosis. Majority of the respondents (41/67, 61.2%) reported that light microscopy was the most commonly used conventional method of malaria diagnosis. All the respondents reported that they were unaware of artificial intelligence-based malaria diagnosis. The respondents affirmed that artificial intelligence based malaria diagnosis will be a better alternative to the conventional methods and will improve the accuracy of malaria diagnosis.

Conclusion: None of the respondents had knowledge of artificial intelligence-based malaria diagnosis; however, respondents affirmed that artificial intelligence-based malaria diagnosis will be a better alternative to conventional methods of malaria diagnosis.

Keywords: Artificial intelligence, Malaria diagnosis, Nigeria

Introduction

In 2021, an estimated 619,000 malaria deaths were recorded worldwide, with most deaths recorded in sub-Saharan Africa.¹ In Nigeria, malaria is highly endemic, accounting for about 60% of out-patient visits to almost all hospitals.² Prompt and accurate diagnosis of Malaria is essential for effective management and control of the disease. To date, microscopy remains the gold standard and the most widely used method of diagnosis in malaria endemic countries.³ This method of diagnosis has many challenges.

In resource constrained settings microscopists are few and opportunities for qualitative training are scarce.⁴ Regular quality control measures (to ensure that screening results are not affected by the physical state of microscopes and expertise of microscopists) are often not in place at primary and secondary health centers. Light microscopy is time consuming and may be significantly operator dependent. The results are variable from one microscopist to another and may be affected by user fatigue and expertise. There may also be frequent power outages. All these factors, particularly human errors, affect the quality of results of malaria microscopy.

Over the years, there has been a gradual shift from microscopy to rapid diagnosis using antigen and monoclonal antibody based techniques.⁵ However, a potentially more powerful solution is the deployment of automated, artificial intelligence-based, techniques. The use of deep learning or artificial intelligence, software for image recognition and interpretation may hold the key to improving qualitative optical malaria diagnosis⁶. Computer vision and image analysis devices are increasingly being used to diagnosis malaria⁷ . Matthew P and colleagues in 2021⁸ tested a fully automated malaria diagnosis system on a World Health organization (WHO) validated set of malaria parasite positive slides. The system achieved a diagnostic accuracy of 94.3%. Emerging literature reveals that automated systems coupled with artificial intelligence are increasingly being developed as alternatives to manual laboratory procedures.⁹

With the rise in applications of artificial intelligence in optical-based diagnosis globally, to date, there is little research on the perception of their availability and use in the diagnosis of malaria among medical health practitioners in Nigeria. To address this gap, we conducted an online survey to assess the perception of health workers in southwestern Nigeria, on the availability and use of artificial intelligence techniques in the diagnosis of malaria. In this study, “perception” refers to the way respondents interpreted, understood, or viewed the emerging use of AI in malaria diagnosis based on their personal experiences, beliefs, and knowledge. The study aim included the assessment of how respondents perceived and made sense of various aspects of a AI in malaria diagnosis, their insights, attitudes, opinions, and behaviors. At the time of the study, there was scarcity of empirical data on health workers’ perceptions of AI in malaria diagnosis. The study also sought to identify misconceptions about AI in malaria diagnosis and to evaluate areas where further education is needed so as inform strategies that could enhance the adoption and effective use of AI technologies in malaria diagnosis.