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

Abstract

Candida albicans remains one of the most clinically significant fungal pathogens, contributing substantially to morbidity and mortality among immunocompromised and hospitalized patients. In response to the growing analytical complexity of fungal diagnostics, this review presents a unified Python-based framework structured around three fundamental and interdependent axes of contemporary C. albicans diagnostics: (i) accurate pathogen detection, (ii) high-throughput data processing, and (iii) analytical method evaluation. Together, these three dimensions form an integrated analytical architecture, herein conceptualized as the Candida Diagnostic Triad. Within the detection and data-processing axes, recent advances in artificial intelligence, particularly convolutional neural networks, transfer-learning strategies, and hybrid machine-learning models have markedly enhanced the sensitivity, selectivity, and interpretability of analytical outputs derived from complex biological matrices. However, the most distinctive contribution of the present framework lies in the third axis, namely method evaluation, where Python-based open-source tools now enable fully automated, quantitative, and reproducible assessment of diagnostic methods within the principles of Green Analytical Chemistry (GAC) and White Analytical Chemistry (WAC). By systematically examining eighteen advanced diagnostic methodologies applied to clinically relevant matrices, including blood, urine, and vaginal samples, this review demonstrates how Python-driven analytical software tools such as the Blue Applicability Grade Index (BAGI) and the Red Analytical Performance Index (RAPI) to establish a mathematically transparent and decision-oriented workflow for comparative method assessment. This unified framework supports evidence-based selection and optimization of diagnostic strategies that are not only analytically robust, but also practically applicable and environmentally responsible. The resulting Python-enabled Candida Diagnostic Triad provides an evidence-based roadmap for selecting and optimizing diagnostic strategies that are analytically robust, practically feasible and environmentally sustainable, thereby supporting United Nations Sustainable Development Goals 3 and 9.