Leprosy Screening Based on Artificial Intelligence: Development of a Cross-Platform App

• Type of Material: Article
• Authors: Márcio Luís Moreira De Souza, Federal University of Juiz de Fora; Gabriel A Lopes, Federal University of Juiz de Fora; Alexandre C Branco, Reference Ctr Endem Dis & Special Programs SMS GV; Jessica Fairley, Emory University; Lucia Alves De Oliveira Fraga, Federal University of Juiz de Fora
• Language: English
• Date: 2021-04-07
• Publisher: JMIR PUBLICATIONS, INC
• Publication Version: Final Published Version
• Copyright Statement: ©Márcio Luís Moreira De Souza, Gabriel Ayres Lopes, Alexandre Castelo Branco, Jessica K Fairley, Lucia Alves De Oliveira Fraga. Originally published in JMIR mHealth and uHealth (http://mhealth.jmir.org), 07.04.2021.
• License: Creative Commons Attribution 4.0 International
• Final Published Version (URL): http://dx.doi.org/10.2196/23718
• Title of Journal or Parent Work: JMIR MHEALTH AND UHEALTH
• Volume: 9
• Issue: 4
• Start Page: e23718
• End Page: e23718
• Grant/Funding Information: This study received financial support from the Conselho de Desenvolvimento Tecnológico e Científico/CNPq/BRAZIL, FAPEMIG. This study was also financed in part by the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior – Brasil (CAPES; Finance Code 001, file number: 88881.361990/2019-01 [Migrated-SICAPES3]). The funding sources had no role in the design of the study; in the collection, analysis, implementation, and interpretation of data; and in writing the manuscript.
• Keywords: R; CLASSIFICATION; leprosy; random forest; apps; artificial intelligence; Medical Informatics; shinyApp; mHealth; Python; Science & Technology; Life Sciences & Biomedicine; Health Care Sciences & Services
• Research Categories: Health Sciences, Medicine and Surgery; Chemistry, Biochemistry; Biology, Molecular

Abstract

Background: According to the World Health Organization, achieving targets for control of leprosy by 2030 will require disease elimination and interruption of transmission at the national or regional level. India and Brazil have reported the highest leprosy burden in the last few decades, revealing the need for strategies and tools to help health professionals correctly manage and control the disease. Objective: The main objective of this study was to develop a cross-platform app for leprosy screening based on artificial intelligence (AI) with the goal of increasing accessibility of an accurate method of classifying leprosy treatment for health professionals, especially for communities further away from major diagnostic centers. Toward this end, we analyzed the quality of leprosy data in Brazil on the National Notifiable Diseases Information System (SINAN). Methods: Leprosy data were extracted from the SINAN database, carefully cleaned, and used to build AI decision models based on the random forest algorithm to predict operational classification in paucibacillary or multibacillary leprosy. We used Python programming language to extract and clean the data, and R programming language to train and test the AI model via cross-validation. To allow broad access, we deployed the final random forest classification model in a web app via shinyApp using data available from the Brazilian Institute of Geography and Statistics and the Department of Informatics of the Unified Health System. Results: We mapped the dispersion of leprosy incidence in Brazil from 2014 to 2018, and found a particularly high number of cases in central Brazil in 2014 that further increased in 2018 in the state of Mato Grosso. For some municipalities, up to 80% of cases showed some data discrepancy. Of a total of 21,047 discrepancies detected, the most common was "operational classification does not match the clinical form."After data processing, we identified a total of 77,628 cases with missing data. The sensitivity and specificity of the AI model applied for the operational classification of leprosy was 93.97% and 87.09%, respectively. Conclusions: The proposed app was able to recognize patterns in leprosy cases registered in the SINAN database and to classify new patients with paucibacillary or multibacillary leprosy, thereby reducing the probability of incorrect assignment by health centers. The collection and notification of data on leprosy in Brazil seem to lack specific validation to increase the quality of the data for implementations via AI. The AI models implemented in this work had satisfactory accuracy across Brazilian states and could be a complementary diagnosis tool, especially in remote areas with few specialist physicians.