AccScience Publishing / GTM / Volume 2 / Issue 3 / DOI: 10.36922/gtm.337
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PERSPECTIVE ARTICLE

Nuclear magnetic resonance-biochemical correlation toward deep learning of theranosis and precision medicine

Rakesh Sharma1* ,  Arvind Trivedi2
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1 Plastic Surgery Scholar, Surgery NMR Lab, Shriners Children Hospital, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
2 Department of Medicine, Government Medical College, Saharanpur, Uttar Pradesh, India
Global Translational Medicine 2023, 2(3), 337 https://doi.org/10.36922/gtm.337
Submitted: 23 January 2023 | Accepted: 7 June 2023 | Published: 16 August 2023
© 2023 by the Author(s). This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution 4.0 International License ( https://creativecommons.org/licenses/by/4.0/ )
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Abstract

Efforts have been made to employ the nuclear magnetic resonance (NMR)-biochemical correlation concept or a combination of MR imaging (MRI) and MR spectroscopy (MRS) as an established diagnostic tool for medical practice in clinical settings. Recent reviews and meta-analyses indicate the great possibility of using integrated multimodal multiparametric MRI and MRS for deep learning (DL) of soft-tissue pathophysiology, enabling improved decision-making and disease progression monitoring in precision medicine. Recent guidelines and clinical trials suggest the need for DL of the biophysical and biochemical nature of the brain, breast, prostate, liver, and heart tissue from digital spectromics analysis, along with other molecular imaging modalities. The current opinions, based on recent recommendations, available literature on evidence-based MR spectromics, clinical trials, and meta-analyses on high-resolution MRI and MRS suggest that utilizing MRI and MRS signals as theranostic biomarkers for various soft tissues can demonstrate NMR-biochemical correlation and employ MRI with MRS as adjunct real-time tools, generating robust, and fast tissue digital images with metabolic screening. The integration of DL features can aid in evaluating patient disease diagnosis and therapy within a clinical setting, considering the available medical practices and their limitations.

Keywords
Nuclear magnetic resonance-biochemical correlation
Magnetic resonance imaging
Magnetic resonance spectroscopy
Deep learning of disease nature
Clinical trials
Magnetic resonance meta-analysis
Funding
None.
Conflict of interest
The authors declare no conflict of interest.
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Global Translational Medicine, Electronic ISSN: 2811-0021 Print ISSN: 3060-8600, Published by AccScience Publishing
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