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

Ahmed M. Saleh; Rabeay Y. A. Hassan; Amr M. Badawey; Hoda M. Marzouk

doi:10.1039/d6ra00286b

January 1, 2026RSC AdvancesOpen 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

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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.

Implication

Analytical evaluation of Candida diagnostic methods demonstrates sustainable decision-making using AI tools.

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/69d895ea6c1944d70ce07165 https://doi.org/10.1039/d6ra00286b

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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

Key Points

Implication

Abstract

Discussion

Cite This Study

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

Key Points

Implication

Abstract

Discussion

Cite This Study

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