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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 |
Appears in Collections: | Staff Publications Elements |
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