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Digital dual test syphilis/HIV detection based on Fourier Descriptors of Cyclic Voltammetry curves.

Author information

Affiliations

  • 1. Laboratory of Technological Innovation in Health (LAIS), Hospital Universitário Onofre Lopes, Federal University of Rio Grande do Norte (UFRN), Natal/RN, Brazil.
      Authors
    • Sanchez-Gendriz I 1
    • Carvalho DDA 1
    • Galvão-Lima LJ 1
    • Sales-Moioli AIL 1
    • Brito T 1
    • Fernandes F 1
    • Cruz AS 1
    (7 authors)
  • 2. Department of Informatics Engineering, Center for Informatics and Systems of the University of Coimbra, Universidade de Coimbra, Coimbra, Portugal.
      Authors
    • Henriques J 2
    (1 author)
  • 3. Laboratory of Technological Innovation in Health (LAIS), Hospital Universitário Onofre Lopes, Federal University of Rio Grande do Norte (UFRN), Natal/RN, Brazil; Ministry of Health, Esplanada dos Ministérios, Brasília, Brazil.
      Authors
    • Lima T 3
    (1 author)
  • 4. Department of Computer Engineering and Automation, Federal University of Rio Grande do Norte (UFRN), Natal/RN, Brazil.
      Authors
    • Guedes LA 4
    (1 author)
  • 5. Laboratory of Technological Innovation in Health (LAIS), Hospital Universitário Onofre Lopes, Federal University of Rio Grande do Norte (UFRN), Natal/RN, Brazil; Advanced Nucleus of Technological Innovation (NAVI), Federal Institute of Rio Grande do Norte, Natal/RN, Brazil.
      Authors
    • Morais AHF 5
    • Santos JPQ 5
    (2 authors)
    • Show all (9)

ORCIDs linked to this article

Computers in Biology and Medicine, 09 Apr 2024, 174:108454
https://doi.org/10.1016/j.compbiomed.2024.108454 PMID: 38608326 

Abstract 

Background

Effective and timely detection is vital for mitigating the severe impacts of Sexually Transmitted Infections (STI), including syphilis and HIV. Cyclic Voltammetry (CV) sensors have shown promise as diagnostic tools for these STI, offering a pathway towards cost-effective solutions in primary health care settings.

Objective

This study aims to pioneer the use of Fourier Descriptors (FDs) in analyzing CV curves as 2D closed contours, targeting the simultaneous detection of syphilis and HIV.

Methods

Raw CV signals are filtered, resampled, and transformed into 2D closed contours for FD extraction. Essential shape characteristics are captured through selected coefficients. A complementary geometrical analysis further extracts features like curve areas and principal axes lengths from CV curves. A Mahalanobis Distance Classifier is employed for differentiation between patient and control groups.

Results

The evaluation of the proposed method revealed promising results with classification performance metrics such as Accuracy and F1-Score consistently achieving values rounded to 0.95 for syphilis and 0.90 for HIV. These results underscore the potential efficacy of the proposed approach in differentiating between patient and control samples for STI detection.

Conclusion

By integrating principles from biosensors, signal processing, image processing, machine learning, and medical diagnostics, this study presents a comprehensive approach to enhance the detection of both syphilis and HIV. This setts the stage for advanced and accessible STI diagnostic solutions.

Full text links 

Read article at publisher's site: https://doi.org/10.1016/j.compbiomed.2024.108454

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