Recent Applications of Artificial Intelligence and Related Technical Challenges in MALDI MS and MALDI-MSI: A Mini Review

Abstract

Artificial intelligence (AI) has provided viable methods for retrieving, organizing, and analyzing mass spectrometry (MS) data in various applications. However, several challenges remain as this technique is still in its early, preliminary stages. Critical limitations include the need for more effective methods for identification, quantification, and interpretation to ensure rapid and accurate results. Recently, high-throughput MS data have been leveraged to advance machine learning (ML) techniques, particularly in matrix-assisted laser desorption/ionization time-of-flight (MALDI-TOF) MS and MS imaging (MSI). The accuracy of AI models is intricately linked to the sampling techniques used in MALDI and MALDI imaging measurements. With the help of artificial neural networks, traditional barriers are being overcome, accelerating data acquisition for different applications. AI-driven analysis of chemical specificity and spatial mapping in two-dimensional datasets has gained significant attention, highlighting its potential impact. This review focuses on recent AI applications, particularly supervised ML in MALDI-TOF MS and MALDI-MSI data analysis. Additionally, this review provides an overview of sample preparation methods and sampling techniques essential for ensuring high-quality data in deep learning-based models.

Keywords: MALDI; MS imaging; artificial intelligence; deep learning; machine learning.

Fig. 1. Number of publications employing ML and DL techniques for MALDI and MSI data analysis from 1997 to 2024. Data were retrieved from the PubMed database in February 2025. DL, deep learning; MALDI, matrix-assisted laser desorption/ionization; ML, machine learning; MSI, mass spectrometry imaging.

Fig. 2. Analysis of complex signals in in vitro diagnosis can be effectively addressed using clustering and regression algorithms powered by AI. The AI-driven techniques facilitate automatic processing and analysis of big data acquired using mass spectrometry and optical spectroscopic methods. Reprinted by permission from Chen et al.³⁾ AI, artificial intelligence.

Fig. 3. Common applications of ML in MS. (A) Gradient-boosted decision trees applied for preprocessing in classification of peaks containing isotopic clusters; (B) prediction of structural features in analytes using feed-forward neural networks for spectral analysis; (C) prediction of peptide ion abundances by DL-based DeepScp pipeline; (D) classification of tissue samples analyzed through mass cytometry; (E) CNN propagated for MSI data analysis. Reprinted by permission from Beck et al.⁷⁵⁾ CNN, convolutional neural network; DL, deep learning; ML, machine learning; MS, mass spectrometry; MSI, mass spectrometry imaging.

Fig. 4. Training and testing of DL models on tissue sections from several intracranial GBM PDX models. (A) Distribution of eight tissue sections for training and testing of DL models; (B) the number of spectra from normal and tumor regions and representative images for model training and testing; (C) H&E annotated tissue sections and a representative mass spectrum. Reprinted by permission from Abdelmoula et al.⁷⁷⁾ DL, deep learning; GBM, glioblastoma multiforme; H&E, hematoxylin and eosin; PDX, patient-derived xenograft.

Fig. 5. Chronological development of AI, ML, and DL in relation to algorithm/model design and their integration into technological innovations for MS. This schematic illustrates how advancements in AI methodologies have progressively influenced various phases of MS and MALDI-TOF and MALDI-MS data acquisition and analysis. The transition from basic algorithmic approaches to sophisticated DL models has facilitated a shift from molecular-level analytics to comprehensive applications in clinical diagnostics. AI, artificial intelligence; DL, deep learning; MALDI, matrix-assisted laser desorption/ionization; ML, machine learning; MS, mass spectrometry; MSI, mass spectrometry imaging; TOF, time of flight.

Fig. 6. Hyperspectral modeling results using a RGB color-coding scheme to visualize rat brain images in MALDI-MSI analysis. (A) Schematic of the rat brain anatomy that was subjected to MALDI MSI with a 2-mm scale bar for reference; (B) randomly selected ion images of three distinct m/z values; (C) RGB color intensities overlaid for each pixel using the three images in (B); (D) the result in PCA space, where the position of each pixel determines the corresponding RGB intensity values; (E) the pixels within the highlighted box in the PCA plot (D) shown in the same color; (F) the image showing pixels with RGB color-coding in (D); (G) the result in a 20 × 10 × 5 3D SOM space based on unsupervised artificial neural network¹²⁴⁾ in which the level of the RGB colors determines their locations in the SOM; (H) the color pixels corresponding to the marked section in (G) in the original image; (I) full rat brain image reconstructed using SOM-based color-coding; (J) t-SNE model used to produce a scatter plot showing clusters in which pixels are color-coded in RGB according to their positions; (K) pixels highlighted in the boxed region in (J) visualized as corresponding colors; (L) final image output showing RGB color coding derived from t-SNE manifold learning. MALDI, matrix-assisted laser desorption/ionization; MSI, mass spectrometry imaging; PCA, principal component analysis; RGB, red–green–blue; SOM, self-organizing map; t-SNE, t-distributed stochastic neighbor embedding.