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April 10, 2026RSC Advances4 citationsOpen Access

Toward sustainable diagnostics for Candida albicans : the role of artificial intelligence in analytical chemistry from data processing to Python-based blueness and redness evaluation metrics

ASAhmed M. SalehRHRabeay Y. A. HassanABAmr M. Badawey

Key Points

  • The aim is to explore the role of artificial intelligence in enhancing diagnostic methods for Candida albicans.
  • Evaluated eighteen diagnostic techniques for Candida albicans.
  • Used Python-based tools for analytical evaluation.
  • Employed BAGI and RAPI metrics within a sustainability framework.
  • Demonstrated systematic comparison of diagnostic methods.
  • Aligned techniques with GAC and WAC principles.

Abstract

Python-based tools enable analytical evaluation of Candida diagnostic methods by systematically comparing eighteen techniques using BAGI and RAPI within a unified, sustainability-driven decision-making framework aligned with GAC and WAC principles.

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Cite This Study

Saleh et al. (2026) studied this question.

synapsesocial.com/papers/69d895ea6c1944d70ce07165https://doi.org/10.1039/d6ra00286b
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