  This literature review presents a comprehensive overview of machine learning
(ML) applications in proton magnetic resonance spectroscopy (MRS). As the use
of ML techniques in MRS continues to grow, this review aims to provide the MRS
community with a structured overview of the state-of-the-art methods.
Specifically, we examine and summarize studies published between 2017 and 2023
from major journals in the magnetic resonance field. We categorize these
studies based on a typical MRS workflow, including data acquisition,
processing, analysis, and artificial data generation. Our review reveals that
ML in MRS is still in its early stages, with a primary focus on processing and
analysis techniques, and less attention given to data acquisition. We also
found that many studies use similar model architectures, with little comparison
to alternative architectures. Additionally, the generation of artificial data
is a crucial topic, with no consistent method for its generation. Furthermore,
many studies demonstrate that artificial data suffers from generalization
issues when tested on in-vivo data. We also conclude that risks related to ML
models should be addressed, particularly for clinical applications. Therefore,
output uncertainty measures and model biases are critical to investigate.
Nonetheless, the rapid development of ML in MRS and the promising results from
the reviewed studies justify further research in this field.
