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

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.

A Bibliometric Analysis of Leprosy during 2000–2021 from Web of Science Database

“…Recently, WHO announced its new strategy toward zero leprosy with the Global Leprosy (Hansen’s Disease) Strategy, 2021–2030 [ 46 ]. Some specialists indicated that closer cooperation between countries/individuals is the best way to achieve the goal, while more high-quality papers should be published to provide references for the plan toward zero leprosy.…”

Alterações Fonoaudiológicas na Hanseníase: Revisão da Literatura

“…Além de iniciativas de disseminação de conhecimento 15 , outras estratégias procuram estabelecer dados epidemiológicos mais precisos da hanseníase, como o estudo que utilizou um aplicativo para rastreamento de hanseníase baseado em inteligência artificial para aumentar a acessibilidade de um método preciso de classificação do tratamento da hanseníase para profissionais de saúde no Brasil 18 .…”

Advances in the Diagnosis of Leprosy

“…Several electrophysiological, ultrasonographic, Electroneuromyography (ENMG) and histological techniques are also developed and adapted to diagnose PNL (80,81). Subsequently, many other sensitive techniques have been used previously or in development that can be updated and further used as a point of care tools in the laboratory as well as field settings as diagnostic tools such as variants of PCR (8,24,71,72,82), colorimetric assays (83), Biomarkers (66), Filter paper (84), peptide-nucleic-acid-ELISA (85,86), reverse line probe (87), synthetic peptide reagents (88)(89)(90), Ultrasonography (50)(51)(52), Fingerstick test (91), Electrochemical biosensors (53), LAMP (61)(62)(63), MRI (54)(55)(56), LF Assays (92,93), Fluorescent staining (47)(48)(49) and Artificial Intelligence to combat leprosy (44,45). Here we will be discussing budding techniques that have the potential to be used in the field settings easily.…”

“…They have named it AI4Leprosy project which aims to develop an easy-to-use AI-powered solution to accelerate the diagnosis of leprosy with skin images. Recently, a cross-platform app for leprosy screening based on artificial intelligence has been developed which can recognize patterns of leprosy cases in a database with ~94% sensitivity and 87% specificity (45). Recently, Barbieri et al described that AI models have the potential to become a complementary diagnosis tool, especially for the remote areas where very few clinicians are available known as AI4Leprosy (46).…”