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Mobile application prototyping using Artificial Intelligence to support childhood tuberculosis diagnosis.

Katerine Souza Picoli1, Flávia Regina Souza Ramos1,2, Denise Maria Guerreiro Vieira da Silva1

  • 1Universidade do Estado do Amazonas. Manaus, Amazonas, Brazil.

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A new mobile app, TB Kids, uses Artificial Intelligence (AI) to aid in diagnosing childhood tuberculosis. This AI-powered tool supports early detection and risk assessment for better patient outcomes.

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Area of Science:

  • Medical Informatics
  • Artificial Intelligence in Healthcare
  • Pediatric Infectious Diseases

Background:

  • Pulmonary tuberculosis (TB) diagnosis in children presents unique challenges.
  • There is a need for innovative tools to support early and accurate diagnosis.
  • Existing diagnostic software for pediatric TB is limited.

Purpose of the Study:

  • To develop a mobile application prototype named TB Kids.
  • To leverage Artificial Intelligence (AI) for predicting and supporting the diagnosis of pulmonary tuberculosis in children.
  • To address the gap in AI-driven diagnostic software for pediatric TB.

Main Methods:

  • Technological development research utilizing a prototyping approach.
  • Adherence to the Rational Unified Process (RUP) model with four distinct stages: conception, elaboration, construction, and transition.
  • Development timeline: November 2022 to July 2023.

Main Results:

  • The TB Kids prototype integrates features for comprehensive patient assessment, including risk and nutritional evaluation.
  • Includes functionalities for tuberculin skin testing, antibiotic therapy, and contact tracing.
  • Features AI-driven interpretation of chest X-rays with risk graphing and decision support, alongside clinical guidance and recording.

Conclusions:

  • The high-fidelity TB Kids mobile application prototype offers an innovative solution for diagnosing pediatric tuberculosis.
  • The application demonstrates a consistent and creative interface, aligning with Sustainable Development Goal 3.
  • Addresses the critical need for AI-based prediction software in the diagnosis of children at risk for tuberculosis.