Please use this identifier to cite or link to this item: https://doi.org/10.1371/journal.pone.0125183
Title: Computational chemical imaging for cardiovascular pathology: Chemical microscopic imaging accurately determines cardiac transplant rejection
Authors: Tiwari S.
Reddy V.B.
Bhargava R.
Raman J. 
Keywords: Article
automation
cardiac graft rejection
clinical article
clinical effectiveness
computer assisted diagnosis
controlled study
diagnostic accuracy
diagnostic procedure
diagnostic test accuracy study
heart muscle biopsy
histopathology
human
human tissue
image quality
imaging system
immune response
infrared spectroscopy
intermethod comparison
molecular recognition
biopsy
Cardiovascular Diseases
computer simulation
diagnostic imaging
graft rejection
heart transplantation
microscopy
pathology
probability
procedures
receiver operating characteristic
reproducibility
Biopsy
Cardiovascular Diseases
Computer Simulation
Diagnostic Imaging
Graft Rejection
Heart Transplantation
Humans
Microscopy
Probability
Reproducibility of Results
ROC Curve
Issue Date: 2015
Citation: Tiwari S., Reddy V.B., Bhargava R., Raman J. (2015). Computational chemical imaging for cardiovascular pathology: Chemical microscopic imaging accurately determines cardiac transplant rejection. PLoS ONE 10 (5) : e0125183. ScholarBank@NUS Repository. https://doi.org/10.1371/journal.pone.0125183
Rights: Attribution 4.0 International
Abstract: Rejection is a common problem after cardiac transplants leading to significant number of adverse events and deaths, particularly in the first year of transplantation. The gold standard to identify rejection is endomyocardial biopsy. This technique is complex, cumbersome and requires a lot of expertise in the correct interpretation of stained biopsy sections. Traditional histopathology cannot be used actively or quickly during cardiac interventions or surgery. Our objective was to develop a stain-less approach using an emerging technology, Fourier transform infrared (FT-IR) spectroscopic imaging to identify different components of cardiac tissue by their chemical and molecular basis aided by computer recognition, rather than by visual examination using optical microscopy.We studied this technique in assessment of cardiac transplant rejection to evaluate efficacy in an example of complex cardiovascular pathology.We recorded data from human cardiac transplant patients' biopsies, used a Bayesian classification protocol and developed a visualization scheme to observe chemical differences without the need of stains or human supervision. Using receiver operating characteristic curves, we observed probabilities of detection greater than 95% for four out of five histological classes at 10% probability of false alarm at the cellular level while correctly identifying samples with the hallmarks of the immune response in all cases. The efficacy of manual examination can be significantly increased by observing the inherent biochemical changes in tissues, which enables us to achieve greater diagnostic confidence in an automated, label-free manner. We developed a computational pathology system that gives high contrast images and seems superior to traditional staining procedures. This study is a prelude to the development of real time in situ imaging systems, which can assist interventionists and surgeons actively during procedures. © 2015 Tiwari et al.
Source Title: PLoS ONE
URI: https://scholarbank.nus.edu.sg/handle/10635/161517
ISSN: 19326203
DOI: 10.1371/journal.pone.0125183
Rights: Attribution 4.0 International
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