Increasing Complexity to Simplify Clinical Care: High Resolution Mass Spectrometry as an Enabler of AI Guided Clinical and Therapeutic Monitoring

Abstract

Clinical care increasingly relies on individualized, highly multidimensional data for patient phenotyping. This review focusses on two technologies which are contributing to this trend and, in their mutual overlap, may provide a novel and powerful contribution to future clinical care. Artificial intelligence (AI), in particular the technique of deep learning, is steadily making inroads to clinical care and has demonstrated the ability to extract clinically relevant features from complex datasets. Likewise, mass spectrometry (MS), in particular high-resolution MS, is gaining the ability to provide highly multidimensional datasets from patient samples with the quality necessary for clinical decision making. The convergence of these trends has resulted in the nascent application of deep learning analytics to high resolution mass spectra. Due to the large volumes of content provided by high resolution mass spectra and the ability of multilayer neural networks to extract subtle features from complex datasets, future integration of their mutual application is identified as a research area of high priority for clinical care. Factors are identified that have contributed to historical progress for each technology and recommendations are provided to accelerate the mutual application of deep learning and mass spectrometry techniques to patient care.