Dataset Information

Digital imaging analysis to assess scar phenotype.

ABSTRACT: In order to understand the link between the genetic background of patients and wound clinical outcomes, it is critical to have a reliable method to assess the phenotypic characteristics of healed wounds. In this study, we present a novel imaging method that provides reproducible, sensitive, and unbiased assessments of postsurgical scarring. We used this approach to investigate the possibility that genetic variants in orofacial clefting genes are associated with suboptimal healing. Red-green-blue digital images of postsurgical scars of 68 patients, following unilateral cleft lip repair, were captured using the 3dMD imaging system. Morphometric and colorimetric data of repaired regions of the philtrum and upper lip were acquired using ImageJ software, and the unaffected contralateral regions were used as patient-specific controls. Repeatability of the method was high with intraclass correlation coefficient score > 0.8. This method detected a very significant difference in all three colors, and for all patients, between the scarred and the contralateral unaffected philtrum (p ranging from 1.20(-05) to 1.95(-14) ). Physicians' clinical outcome ratings from the same images showed high interobserver variability (overall Pearson coefficient = 0.49) as well as low correlation with digital image analysis results. Finally, we identified genetic variants in TGFB3 and ARHGAP29 associated with suboptimal healing outcome.

SUBMITTER: Smith BJ

PROVIDER: S-EPMC4411947 | biostudies-literature | 2014 Mar-Apr

REPOSITORIES: biostudies-literature

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Publications

Digital imaging analysis to assess scar phenotype.

Smith Brian J BJ Nidey Nichole N Miller Steven F SF Moreno Uribe Lina M LM Baum Christian L CL Hamilton Grant S GS Wehby George L GL Dunnwald Martine M

Wound repair and regeneration : official publication of the Wound Healing Society [and] the European Tissue Repair Society 20140301 2

In order to understand the link between the genetic background of patients and wound clinical outcomes, it is critical to have a reliable method to assess the phenotypic characteristics of healed wounds. In this study, we present a novel imaging method that provides reproducible, sensitive, and unbiased assessments of postsurgical scarring. We used this approach to investigate the possibility that genetic variants in orofacial clefting genes are associated with suboptimal healing. Red-green-blue ...[more]

PMID: 24635173

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Project description:Alzheimer's disease (AD) is a progressive neurological disorder that affects more than 30 million people worldwide. While various dementia-related losses in cognitive functioning are its hallmark clinical symptoms, ultimate diagnosis is based on manual neuropathological assessments using various schemas, including Braak staging, CERAD (Consortium to Establish a Registry for Alzheimer's Disease) and Thal phase scoring. Since these scoring systems are based on subjective assessment, there is inevitably some degree of variation between readers, which could affect ultimate neuropathology diagnosis. Here, we report a pilot study investigating the applicability of computer-driven image analysis for characterizing neuropathological features, as well as its potential to supplement or even replace manually derived ratings commonly performed in medical settings. In this work, we quantitatively measured amyloid beta (A?) plaque in various brain regions from 34 patients using a robust digital quantification algorithm. We next verified these digitally derived measures to the manually derived pathology ratings using correlation and ordinal logistic regression methods, while also investigating the association with other AD-related neuropathology scoring schema commonly used at autopsy, such as Braak and CERAD. In addition to successfully verifying our digital measurements of A? plaques with respective categorical measurements, we found significant correlations with most AD-related scoring schemas. Our results demonstrate the potential for digital analysis to be adapted to more complex staining procedures commonly used in neuropathological diagnosis. As the efficiency of scanning and digital analysis of histology images increases, we believe that the basis of our semi-automatic approach may better standardize quantification of neuropathological changes and AD diagnosis, ultimately leading to a more comprehensive understanding of neurological disorders and more efficient patient care.

Dataset Information

Digital imaging analysis to assess scar phenotype.

Publications

Digital imaging analysis to assess scar phenotype.

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