Alejandro Guerrero-López

The paper creates a synthetic MALDI-TOF MS atlas to improve microbial classification across hospitals by addressing dataset fragmentation and variability.

This review evaluates ML applications for MALDI-TOF MS in clinical microbiology, identifying trends and challenges in bacterial identification, resistance prediction, and strain typing.

This paper develops automated tools using MALDI-TOF MS and machine learning to accurately classify C. difficile ribotypes from clinical isolates.

The paper introduces a large MALDI-TOF MS dataset from Spain to support AI-driven clinical microbiology research and improve antimicrobial resistance detection.

The paper introduces an open-source model for automatically segmenting osseous structures of the paranasal sinuses, achieving high accuracy in ex vivo CT scans.

This paper develops a Bayesian method for pneumonia and lung lesion localization in CT scans, combining uncertainty measures with object detection to improve explainability and reduce automation bias.

This paper introduces an unsupervised method for detecting novel Escherichia coli strains using MALDI-TOF MS data and machine learning techniques.

This study improves ML-based C. difficile typing from MALDI-TOF MS spectra by using data augmentation to reduce performance variability across different experimental conditions.