Benchmarking deep learning architectures for MALDI-TOF mass spectrometry in infectious disease diagnostics and vector-borne disease surveillance.

Emerging global health threats, from antimicrobial resistance to vector-borne diseases, require scalable diagnostic solutions. Matrix-assisted laser desorption/ionization time-of-flight (MALDI-TOF) mass spectrometry has revolutionized microbial diagnostics but remains underutilized in public health due to algorithmic challenges in interpreting and generalizing complex spectra. Here, we present a systematic benchmarking framework to expand MALDI-TOF's utility for field-deployable diagnostics using diverse deep learning architectures and preprocessing strategies, including approaches newly applied to spectral data. Using a total of 7424 mass spectra from clinical and field-collected datasets, we evaluated Mycobacterium abscessus subspecies and antimicrobial resistance classification (814 training/187 test spectra from 33/8 isolates); Anopheles mosquito species identification across anatomical parts (legs: 1047/188 spectra from 211/40 specimens; head: 1089/214 spectra; thorax: 999/279 spectra); and mosquito age estimation (legs: 806/144 spectra from 202/36 specimens; head: 727/124 spectra; thorax: 689/117 spectra). Results demonstrate that balanced accuracy ranged from 84% to 95% for species/subspecies identification and from 91% to 99% for resistance prediction, and that mean absolute error ranged from 1.9 to 3.8 days for age estimation. Key to this performance are adaptations managing instrumental and biological variability. We further assess computational efficiency and energy consumption, highlighting trade-offs between accuracy and deployment feasibility. These findings support AI-enhanced MALDI-TOF as a practical tool for global health surveillance and precision diagnostics in resource-limited settings.