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Using artificial intelligence on dermatology conditions in Uganda: A case for diversity in training data sets for machine learning

View ORCID ProfileLouis Henry Kamulegeya, Mark Okello, John Mark Bwanika, Davis Musinguzi, William Lubega, Davis Rusoke, Faith Nassiwa, View ORCID ProfileAlexander Börve
doi: https://doi.org/10.1101/826057
Louis Henry Kamulegeya
1The Medical Concierge Group, Kampala- Uganda
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  • For correspondence: louiskamu@gmail.com
Mark Okello
1The Medical Concierge Group, Kampala- Uganda
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John Mark Bwanika
1The Medical Concierge Group, Kampala- Uganda
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Davis Musinguzi
1The Medical Concierge Group, Kampala- Uganda
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William Lubega
1The Medical Concierge Group, Kampala- Uganda
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Davis Rusoke
1The Medical Concierge Group, Kampala- Uganda
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Faith Nassiwa
1The Medical Concierge Group, Kampala- Uganda
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Alexander Börve
2Departments of Orthopaedics, Sahlgrenska University Hospital, Institute of Clinical Sciences at the Sahlgrenska Academy, University of Gothenburg, Sweden
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Abstract

Introduction Artificial intelligence (AI) in healthcare has gained momentum with advances in affordable technology that has potential to help in diagnostics, predictive healthcare and personalized medicine. In pursuit of applying universal non-biased AI in healthcare, it is essential that data from different settings (gender, age and ethnicity) is represented. We present findings from beta-testing an AI-powered dermatological algorithm called Skin Image Search, by online dermatology company First Derm on Fitzpatrick 6 skin type (dark skin) dermatological conditions.

Methods 123 dermatological images selected from a total of 173 images retrospectively extracted from the electronic database of a Ugandan telehealth company, The Medical Concierge Group (TMCG) after getting their consent. Details of age, gender and dermatological clinical diagnosis were analyzed using R on R studio software to assess the diagnostic accuracy of the AI app along disease diagnosis and body part. Predictability levels of the AI app was graded on a scale of 0 to 5, where 0-no prediction made and 1-5 demonstrating reducing correct prediction.

Results 76 (62%) of the dermatological images were from females and 47 (38%) from males. The 5 most reported body parts were; genitals (20%), trunk (20%), lower limb (14.6%), face (12%) and upper limb (12%) with the AI app predicting a diagnosis in 62% of image body parts uploaded. Overall diagnostic accuracy of the AI app was low at 17% (21 out of 123 predictable images) with varying predictability levels correctness i.e. 1-8.9%, 2-2.4%, 3-2.4%, 4-1.6%, 5-1.6% with performance along individual diagnosis highest with dermatitis (80%).

Conclusion There is a need for diversity in the image datasets used when training dermatology algorithms for AI applications in clinical decision support as a means to increase accuracy and thus offer correct treatment across skin types and geographies.

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY 4.0 International license.
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Posted October 31, 2019.
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Using artificial intelligence on dermatology conditions in Uganda: A case for diversity in training data sets for machine learning
Louis Henry Kamulegeya, Mark Okello, John Mark Bwanika, Davis Musinguzi, William Lubega, Davis Rusoke, Faith Nassiwa, Alexander Börve
bioRxiv 826057; doi: https://doi.org/10.1101/826057
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Using artificial intelligence on dermatology conditions in Uganda: A case for diversity in training data sets for machine learning
Louis Henry Kamulegeya, Mark Okello, John Mark Bwanika, Davis Musinguzi, William Lubega, Davis Rusoke, Faith Nassiwa, Alexander Börve
bioRxiv 826057; doi: https://doi.org/10.1101/826057

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