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

arXiv: https://arxiv.org/abs/2109.14099 · HTML: https://mlsanity.com/paper/2109.14099/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 105 references that are in the map.
- First posted on arXiv 28 Sep 2021.

## Builds on (most cited first, 7 of 7)

- [Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization](https://mlsanity.com/paper/1610.02391/) (2016)
- [A Unified Approach to Interpreting Model Predictions](https://mlsanity.com/paper/1705.07874/) (2017)
- [XGBoost: A Scalable Tree Boosting System](https://mlsanity.com/paper/1603.02754/) (2016)
- ["Why Should I Trust You?": Explaining the Predictions of Any Classifier](https://mlsanity.com/paper/1602.04938/) (2016)
- [Score-CAM: Score-Weighted Visual Explanations for Convolutional Neural Networks](https://mlsanity.com/paper/1910.01279/) (2019)
- [Explainable AI For COVID-19 CT Classifiers: An Initial Comparison Study](https://mlsanity.com/paper/2104.14506/) (2021)
- [An Explainable AI System for Automated COVID-19 Assessment and Lesion Categorization from CT-scans](https://mlsanity.com/paper/2101.11943/) (2021)

## Built on by (most cited first, 4 of 4)

- [EAFP-Med: An Efficient Adaptive Feature Processing Module Based on Prompts for Medical Image Detection](https://mlsanity.com/paper/2311.15540/) (2023)
- [The Role of Explainable AI in Revolutionizing Human Health Monitoring: A Review](https://mlsanity.com/paper/2409.07347/) (2024)
- [Artificial Intelligence for Pediatric Height Prediction Using Large-Scale Longitudinal Body Composition Data](https://mlsanity.com/paper/2504.06979/) (2025)
- [Towards a Transparent and Interpretable AI Model for Medical Image Classifications](https://mlsanity.com/paper/2509.16685/) (2025)

## Abstract (arXiv)

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