An Explainable-AI approach for Diagnosis of COVID-19 using MALDI-ToF Mass Spectrometry

TL;DR

This paper introduces an explainable AI method for COVID-19 diagnosis using MALDI-ToF mass spectrometry, providing accurate, biologically interpretable results to support clinical decision-making.

Contribution

It is among the first to apply explainable AI to COVID-19 diagnosis with mass spectrometry, emphasizing biological relevance in the decision process.

Findings

Achieved 94.12% accuracy in COVID-19 detection

Provided biologically relevant explanations for AI decisions

Enhanced trustworthiness of AI-based diagnostics

Abstract

The severe acute respiratory syndrome coronavirus type-2 (SARS-CoV-2) caused a global pandemic and immensely affected the global economy. Accurate, cost-effective, and quick tests have proven substantial in identifying infected people and mitigating the spread. Recently, multiple alternative platforms for testing coronavirus disease 2019 (COVID-19) have been published that show high agreement with current gold standard real-time polymerase chain reaction (RT-PCR) results. These new methods do away with nasopharyngeal (NP) swabs, eliminate the need for complicated reagents, and reduce the burden on RT-PCR test reagent supply. In the present work, we have designed an artificial intelligence-based (AI) testing method to provide confidence in the results. Current AI applications for COVID-19 studies often lack a biological foundation in the decision-making process, and our AI approach is one of the earliest to leverage explainable AI (X-AI) algorithms for COVID-19 diagnosis using mass spectrometry. Here, we have employed X-AI to explain the decision-making process on a local (per-sample) and global (all samples) basis underscored by biologically relevant features. We evaluated our technique with data extracted from human gargle samples and achieved a testing accuracy of 94.12%. Such techniques would strengthen the relationship between AI and clinical diagnostics by providing biomedical researchers and healthcare workers with trustworthy and, most importantly, explainable test results