What question did this study set out to answer?

The aim is to explore the role of artificial intelligence in enhancing diagnostic methods for Candida albicans.

April 10, 2026Open 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

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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Authors

Ahmed M. Saleh

Rabeay Y. A. Hassan

Amr M. Badawey

Journals

RSC Advances

Actions

Institutions

Cairo University

National Research Centre

Zewail City of Science and Technology

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Building similarity graph...

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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

Abstract

Citation Network

Connected Papers

Discussion

Authors

Journals

Actions

Institutions

References and Citations

Citation Network

Connected Papers

Discussion

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