Rights statement: This is an Accepted Manuscript of an article published by Taylor & Francis in Expert Review of Molecular Diagnostics on 27/06/2020 available online: https://www.tandfonline.com/doi/abs/10.1080/14737159.2020.1784008
Accepted author manuscript, 733 KB, PDF document
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Final published version
Research output: Contribution to Journal/Magazine › Journal article › peer-review
Research output: Contribution to Journal/Magazine › Journal article › peer-review
}
TY - JOUR
T1 - Role of artificial intelligence and vibrational spectroscopy in cancer diagnostics
AU - Rehman, I.U.
AU - Khan, R.S.
AU - Rehman, S.
N1 - This is an Accepted Manuscript of an article published by Taylor & Francis in Expert Review of Molecular Diagnostics on 27/06/2020 available online: https://www.tandfonline.com/doi/abs/10.1080/14737159.2020.1784008
PY - 2020/8/1
Y1 - 2020/8/1
N2 - Introduction: Raman and Infrared spectroscopic techniques are being used for the analysis of different types of cancers and other biological molecules. It is possible to identify cancers from normal tissues both in fresh and fixed tissues. These techniques can be used not only for the early diagnosis of cancer but also for monitoring the progression of the disease. Furthermore, chemical pathways to the progression of the disease process can be understood and followed. Areas covered: More recently, Artificial Intelligence (AI), Neural Network (NN), and Machine Learning are being combined with spectroscopy, which is making it easier to understand the chemical structural details of cancers and biological molecules more precisely and accurately. In this report, these aspects are being outlined by using breast cancer as a specific example. Expert opinion: A pathway showing to combine vibrational spectroscopy with AI and ML has immense potential in predicting various stages of different disease processes, in particular, in cancer diagnosis, staging, and designing treatment. This will result in improved patient care pathways.
AB - Introduction: Raman and Infrared spectroscopic techniques are being used for the analysis of different types of cancers and other biological molecules. It is possible to identify cancers from normal tissues both in fresh and fixed tissues. These techniques can be used not only for the early diagnosis of cancer but also for monitoring the progression of the disease. Furthermore, chemical pathways to the progression of the disease process can be understood and followed. Areas covered: More recently, Artificial Intelligence (AI), Neural Network (NN), and Machine Learning are being combined with spectroscopy, which is making it easier to understand the chemical structural details of cancers and biological molecules more precisely and accurately. In this report, these aspects are being outlined by using breast cancer as a specific example. Expert opinion: A pathway showing to combine vibrational spectroscopy with AI and ML has immense potential in predicting various stages of different disease processes, in particular, in cancer diagnosis, staging, and designing treatment. This will result in improved patient care pathways.
KW - artificial intelligence
KW - breast cancer
KW - machine learning
KW - neural network
KW - principal component analysis
KW - Raman spectroscopy
U2 - 10.1080/14737159.2020.1784008
DO - 10.1080/14737159.2020.1784008
M3 - Journal article
VL - 20
SP - 749
EP - 755
JO - Expert Review of Molecular Diagnostics
JF - Expert Review of Molecular Diagnostics
SN - 1473-7159
IS - 8
ER -