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      Spectroscopic and deep learning-based approaches to identify and quantify cerebral microhemorrhages

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          Abstract

          Cerebral microhemorrhages (CMHs) are associated with cerebrovascular disease, cognitive impairment, and normal aging. One method to study CMHs is to analyze histological sections (5–40 μm) stained with Prussian blue. Currently, users manually and subjectively identify and quantify Prussian blue-stained regions of interest, which is prone to inter-individual variability and can lead to significant delays in data analysis. To improve this labor-intensive process, we developed and compared three digital pathology approaches to identify and quantify CMHs from Prussian blue-stained brain sections: (1) ratiometric analysis of RGB pixel values, (2) phasor analysis of RGB images, and (3) deep learning using a mask region-based convolutional neural network. We applied these approaches to a preclinical mouse model of inflammation-induced CMHs. One-hundred CMHs were imaged using a 20 × objective and RGB color camera. To determine the ground truth, four users independently annotated Prussian blue-labeled CMHs. The deep learning and ratiometric approaches performed better than the phasor analysis approach compared to the ground truth. The deep learning approach had the most precision of the three methods. The ratiometric approach has the most versatility and maintained accuracy, albeit with less precision. Our data suggest that implementing these methods to analyze CMH images can drastically increase the processing speed while maintaining precision and accuracy.

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          Most cited references45

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          A Guideline of Selecting and Reporting Intraclass Correlation Coefficients for Reliability Research.

          Intraclass correlation coefficient (ICC) is a widely used reliability index in test-retest, intrarater, and interrater reliability analyses. This article introduces the basic concept of ICC in the content of reliability analysis.
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            The meaning and use of the area under a receiver operating characteristic (ROC) curve.

            A representation and interpretation of the area under a receiver operating characteristic (ROC) curve obtained by the "rating" method, or by mathematical predictions based on patient characteristics, is presented. It is shown that in such a setting the area represents the probability that a randomly chosen diseased subject is (correctly) rated or ranked with greater suspicion than a randomly chosen non-diseased subject. Moreover, this probability of a correct ranking is the same quantity that is estimated by the already well-studied nonparametric Wilcoxon statistic. These two relationships are exploited to (a) provide rapid closed-form expressions for the approximate magnitude of the sampling variability, i.e., standard error that one uses to accompany the area under a smoothed ROC curve, (b) guide in determining the size of the sample required to provide a sufficiently reliable estimate of this area, and (c) determine how large sample sizes should be to ensure that one can statistically detect differences in the accuracy of diagnostic techniques.
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              Understanding Bland Altman analysis

              In a contemporary clinical laboratory it is very common to have to assess the agreement between two quantitative methods of measurement. The correct statistical approach to assess this degree of agreement is not obvious. Correlation and regression studies are frequently proposed. However, correlation studies the relationship between one variable and another, not the differences, and it is not recommended as a method for assessing the comparability between methods.
In 1983 Altman and Bland (B&A) proposed an alternative analysis, based on the quantification of the agreement between two quantitative measurements by studying the mean difference and constructing limits of agreement.
The B&A plot analysis is a simple way to evaluate a bias between the mean differences, and to estimate an agreement interval, within which 95% of the differences of the second method, compared to the first one, fall. Data can be analyzed both as unit differences plot and as percentage differences plot.
The B&A plot method only defines the intervals of agreements, it does not say whether those limits are acceptable or not. Acceptable limits must be defined a priori, based on clinical necessity, biological considerations or other goals.
The aim of this article is to provide guidance on the use and interpretation of Bland Altman analysis in method comparison studies.
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                Author and article information

                Contributors
                choib@uci.edu
                Journal
                Sci Rep
                Sci Rep
                Scientific Reports
                Nature Publishing Group UK (London )
                2045-2322
                21 May 2021
                21 May 2021
                2021
                : 11
                : 10725
                Affiliations
                [1 ]GRID grid.266093.8, ISNI 0000 0001 0668 7243, Beckman Laser Institute and Medical Clinic, , University of California-Irvine, ; Irvine, CA USA
                [2 ]GRID grid.266093.8, ISNI 0000 0001 0668 7243, Department of Biomedical Engineering, , University of California-Irvine, ; Irvine, CA USA
                [3 ]GRID grid.411982.7, ISNI 0000 0001 0705 4288, Department of Biomedical Engineering, Beckman Laser Institute Korea, , Dankook University, ; Cheonan, 31116 Republic of Korea
                [4 ]GRID grid.116068.8, ISNI 0000 0001 2341 2786, Massachusetts Institute of Technology, ; Cambridge, MA USA
                [5 ]GRID grid.251990.6, ISNI 0000 0000 9562 8554, Albany State University, ; Albany, GA USA
                [6 ]GRID grid.266093.8, ISNI 0000 0001 0668 7243, Department of Medicine, Division of Nephrology, , University of California-Irvine, ; Irvine, CA USA
                [7 ]GRID grid.266093.8, ISNI 0000 0001 0668 7243, Institute for Memory Impairments and Neurological Disorders, , University of California-Irvine, ; Irvine, CA USA
                [8 ]GRID grid.266093.8, ISNI 0000 0001 0668 7243, Neurology and Pathology and Laboratory Medicine, , University of California-Irvine, ; Irvine, CA USA
                [9 ]GRID grid.266093.8, ISNI 0000 0001 0668 7243, Department of Surgery, , University of California-Irvine, ; Irvine, CA USA
                [10 ]GRID grid.266093.8, ISNI 0000 0001 0668 7243, Edwards Lifesciences Center for Advanced Cardiovascular Technology, , University of California-Irvine, ; Irvin, CA USA
                Article
                88236
                10.1038/s41598-021-88236-1
                8140127
                34021170
                d467aea4-bd72-4849-928b-864e9ab59fd4
                © The Author(s) 2021

                Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.

                History
                : 24 December 2020
                : 25 March 2021
                Funding
                Funded by: FundRef http://dx.doi.org/10.13039/100000002, National Institutes of Health;
                Award ID: 5TL1TR001415
                Award ID: R01NS020989
                Award ID: P41EB015890
                Award Recipient :
                Funded by: FundRef http://dx.doi.org/10.13039/501100003725, National Research Foundation of Korea;
                Award ID: NRF-2018K1A4A3A02060572
                Funded by: National Institutes of Health,United States
                Award ID: R21AG066000
                Award ID: R21NS111984
                Funded by: FundRef http://dx.doi.org/10.13039/100000997, Arnold and Mabel Beckman Foundation;
                Categories
                Article
                Custom metadata
                © The Author(s) 2021

                Uncategorized
                neurology,optical techniques
                Uncategorized
                neurology, optical techniques

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