* Corresponding authors
a Department of Chemistry, University at Albany, SUNY, 1400 Washington Avenue, Albany, NY 12222, USA
E-mail: ilednev@albany.edu
Tel: +1 518 591 8863
b The RNA Institute, University at Albany, SUNY, 1400 Washington Avenue, Albany, NY 12222, USA
AbstractMany problems exist within the myriad of currently employed screening and diagnostic methods. Further, an incredibly wide variety of procedures are used to identify an even greater number of diseases which exist in the world. There is a definite unmet clinical need to improve diagnostic capabilities of these procedures, including improving test sensitivity and specificity, objectivity and definitiveness, and reducing cost and invasiveness of the test, with an interest in replacing multiple diagnostic methods with one powerful tool. There has been a recent surge in the literature which focuses on utilizing Raman spectroscopy in combination with machine learning analyses to improve diagnostic measures for identifying an assortment of diseases, including cancers, viral and bacterial infections, neurodegenerative and autoimmune disorders, and more. This review highlights the work accomplished since 2018 which focuses on using Raman spectroscopy and machine learning to address the need for better screening and medical diagnostics in all areas of disease. A critical evaluation considers both the benefits and obstacles of utilizing the method for universal diagnostics. It is clear based on the evidence provided herein Raman spectroscopy in combination with machine learning provides the first glimmer of hope for the development of an accurate, inexpensive, fast, and non-invasive method for universal medical diagnostics.
You have access to this article
Please wait while we load your content... Something went wrong. Try again? Supplementary files Article informationChem. Soc. Rev., 2020,49, 7428-7453
N. M. Ralbovsky and I. K. Lednev, Chem. Soc. Rev., 2020, 49, 7428 DOI: 10.1039/D0CS01019G
To request permission to reproduce material from this article, please go to the Copyright Clearance Center request page.
If you are an author contributing to an RSC publication, you do not need to request permission provided correct acknowledgement is given.
If you are the author of this article, you do not need to request permission to reproduce figures and diagrams provided correct acknowledgement is given. If you want to reproduce the whole article in a third-party publication (excluding your thesis/dissertation for which permission is not required) please go to the Copyright Clearance Center request page.
Read more about how to correctly acknowledge RSC content.
Fetching data from CrossRef.
This may take some time to load.
RetroSearch is an open source project built by @garambo | Open a GitHub Issue
Search and Browse the WWW like it's 1997 | Search results from DuckDuckGo
HTML:
3.2
| Encoding:
UTF-8
| Version:
0.7.3