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

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  • 1Multicentre Biochemistry and Molecular Biology Program, Federal University of Juiz de Fora, Governador Valadares-MG, Brazil.

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Summary

This study developed an AI-powered app to classify leprosy cases, improving diagnosis accuracy for health professionals. The tool demonstrated high sensitivity and specificity, aiding leprosy control efforts in Brazil.

Keywords:
PythonRappsartificial intelligenceleprosymHealthrandom forestshinyApp

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Area of Science:

  • Medical Informatics
  • Artificial Intelligence in Healthcare
  • Public Health

Background:

  • Leprosy elimination requires interrupting transmission, with India and Brazil facing the highest burdens.
  • Effective disease management and control necessitate improved tools for health professionals.

Purpose of the Study:

  • To develop a cross-platform artificial intelligence (AI) app for leprosy screening and classification.
  • To enhance accessibility of accurate leprosy diagnosis for healthcare providers, particularly in remote areas.
  • To analyze leprosy data quality in Brazil using the National Notifiable Diseases Information System (SINAN).

Main Methods:

  • Extracted and cleaned leprosy data from the SINAN database.
  • Developed AI decision models using the random forest algorithm for paucibacillary/multibacillary classification.
  • Deployed the AI model in a web app for broad accessibility.

Main Results:

  • Mapped leprosy incidence in Brazil (2014-2018), noting increased cases in Mato Grosso.
  • Identified significant data discrepancies in the SINAN database, with "operational classification does not match the clinical form" being most common.
  • The AI model achieved 93.97% sensitivity and 87.09% specificity for leprosy classification.

Conclusions:

  • The AI app accurately classifies leprosy cases, reducing misassignment probability.
  • Data quality and validation in Brazil's leprosy reporting systems need improvement for AI implementation.
  • The AI models offer a reliable complementary diagnostic tool, especially for underserved remote regions.