Advanced mass spectrometric and spectroscopic methods coupled with machine learning for in vitro diagnosis

• Published in: View (Beijing, China) Vol. 4; no. 1
• Main Authors: Chen, Xiaonan, Shu, Weikang, Zhao, Liang, Wan, Jingjing
• Format: Journal Article
• Language: English
• Published: Beijing John Wiley & Sons, Inc 01.02.2023; Wiley
• Subjects: Accuracy; Algorithms; Artificial intelligence; Clustering; Datasets; Discriminant analysis; Enzymes; in vitro diagnosis; Ions; Machine learning; Mass spectrometry; Medical research; Metabolism; multi‐modal analysis; Scientific imaging; spectroscopy
• ISSN: 2688-3988; 2688-268X; 2688-268X
• DOI: 10.1002/VIW.20220038

In vitro diagnosis (IVD) is one vital component of medical tests that detects biological samples of tissues or bio‐fluids. Recently, mass spectrometry and spectroscopy have been increasingly employed in the field of IVD, due to their high accuracy, facile sample preparation, and rapid detection. Notably, the large datasets generated by these two technology methods provide a wealth of information but subsequently involve complex and time‐consuming processing works. Machine learning (ML), an important branch of artificial intelligence (AI), has emerged as a promising solution for the decoding of big data. ML imitates the human brain to process data, significantly improving accuracy and efficiency compared with traditional processing methods. In this review, we first introduce the commonly used ML algorithms and advanced mass spectrometry and spectroscopy techniques in the field of IVD, respectively. The ML algorithms are summarized as four aspects according to different learning tasks. Then, the combinations of ML with mass spectrometry, spectroscopy, and multi‐modal analysis for IVD are presented, and the roles of ML in these combinations are elucidated by some representative examples. This review aims to provide a systematic and comprehensive summary of the literature on ML‐assisted mass spectrometry or spectroscopy. We believe that it will facilitate researchers to select suitable ML algorithms for supplementing existing detection techniques or to develop the potential of coupling more detection techniques with ML, thus promoting the development of mass spectrometry and spectroscopy in IVD. Recently, mass spectrometric and spectroscopic methods coupled with machine learning have been increasingly employed in the field of in vitro diagnoses, such as pathogen identification, cancer diagnosis, and cell classification. In this review, the authors focus on the combinations of machine learning with mass spectrometry, spectroscopy, and multi‐modal analysis for in vitro diagnoses, and they highlight the roles of machine learning in these combinations through some representative examples. The authors furthermore discuss the challenges and perspectives of mass spectrometry, spectroscopy, multi‐modal analysis, and machine learning.