Please use this identifier to cite or link to this item: https://doi.org/10.1371/journal.pone.0125183
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dc.titleComputational chemical imaging for cardiovascular pathology: Chemical microscopic imaging accurately determines cardiac transplant rejection
dc.contributor.authorTiwari S.
dc.contributor.authorReddy V.B.
dc.contributor.authorBhargava R.
dc.contributor.authorRaman J.
dc.date.accessioned2019-11-06T01:31:52Z
dc.date.available2019-11-06T01:31:52Z
dc.date.issued2015
dc.identifier.citationTiwari 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
dc.identifier.issn19326203
dc.identifier.urihttps://scholarbank.nus.edu.sg/handle/10635/161517
dc.description.abstractRejection 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.
dc.rightsAttribution 4.0 International
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/
dc.sourceUnpaywall 20191101
dc.subjectArticle
dc.subjectautomation
dc.subjectcardiac graft rejection
dc.subjectclinical article
dc.subjectclinical effectiveness
dc.subjectcomputer assisted diagnosis
dc.subjectcontrolled study
dc.subjectdiagnostic accuracy
dc.subjectdiagnostic procedure
dc.subjectdiagnostic test accuracy study
dc.subjectheart muscle biopsy
dc.subjecthistopathology
dc.subjecthuman
dc.subjecthuman tissue
dc.subjectimage quality
dc.subjectimaging system
dc.subjectimmune response
dc.subjectinfrared spectroscopy
dc.subjectintermethod comparison
dc.subjectmolecular recognition
dc.subjectbiopsy
dc.subjectCardiovascular Diseases
dc.subjectcomputer simulation
dc.subjectdiagnostic imaging
dc.subjectgraft rejection
dc.subjectheart transplantation
dc.subjectmicroscopy
dc.subjectpathology
dc.subjectprobability
dc.subjectprocedures
dc.subjectreceiver operating characteristic
dc.subjectreproducibility
dc.subjectBiopsy
dc.subjectCardiovascular Diseases
dc.subjectComputer Simulation
dc.subjectDiagnostic Imaging
dc.subjectGraft Rejection
dc.subjectHeart Transplantation
dc.subjectHumans
dc.subjectMicroscopy
dc.subjectProbability
dc.subjectReproducibility of Results
dc.subjectROC Curve
dc.typeArticle
dc.contributor.departmentSURGERY
dc.description.doi10.1371/journal.pone.0125183
dc.description.sourcetitlePLoS ONE
dc.description.volume10
dc.description.issue5
dc.description.pagee0125183
dc.published.statePublished
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