Sa1981 Evaluation of Lynch Syndrome by Immunohistochemistry and Quantitative Scoring by Digital Image Analysis As a Screening Tool for the Diagnosis of Hereditary Colon Cancer and Correlation With Genetic Analysis

2014 ◽  
Vol 146 (5) ◽  
pp. S-346
Author(s):  
Emma Diaz de Leon ◽  
Linda Robinson ◽  
David Euhus ◽  
Ezra Burstein ◽  
Venetia R. Sarode
Keyword(s):  
Colon Cancer ◽  
Image Analysis ◽  
Genetic Analysis ◽  
Lynch Syndrome ◽  
Digital Image ◽  
Digital Image Analysis ◽  
Screening Tool ◽  
Hereditary Colon Cancer ◽  
Quantitative Scoring

Defoliation patterns and genetics of insect resistance in cottonwoods

2000 ◽  
Vol 30 (1) ◽  
pp. 85-90 ◽  
Author(s):  
R R James ◽  
G Newcombe
Keyword(s):  
Image Analysis ◽  
Genetic Analysis ◽  
Digital Image ◽  
Digital Image Analysis ◽  
Populus Deltoides ◽  
Populus Trichocarpa ◽  
Columbia River ◽  
Leaf Beetle ◽  
Broad Sense Heritability ◽  
The Difference

In 1995, an outbreak of a leaf beetle, Phratora californica Brown (Coleoptera: Chrysomelidae), began in a three-generation Populus trichocarpa Torr. & Gray × Populus deltoides Bartr. pedigree planting near the lower Columbia River in Oregon. This outbreak provided us with an opportunity to assess leaf beetle feeding patterns and the genetics of cottonwood resistance to defoliation. We developed a method for estimating damage levels by training personnel to visually estimate percent damage in leaf samples. Digital image analysis was used to measure damage to the leaves used in the training. Based on a sample of 300 trees from 100 genotypes, herbivory was found to be greatest in the upper canopy and in the fall. Broad-sense heritability was estimated to be 0.88 and 0.80 for July and October, respectively, demonstrating that resistance to P. californica is under relatively strong genetic control. Resistance in the F2 likely came from the P. trichocarpa parent, because this parent was less susceptible, on average, than the P. deltoides parent. However, the difference between parents was not great, and any further genetic analysis of resistance to Phratora californica should employ crosses between individuals with more strongly contrasting phenotypes.

scholarly journals Comparison of Semi-Quantitative Scoring and Artificial Intelligence Aided Digital Image Analysis of Chromogenic Immunohistochemistry

Biomolecules ◽  
2021 ◽  
Vol 12 (1) ◽  
pp. 19
Author(s):  
János Bencze ◽  
Máté Szarka ◽  
Balázs Kóti ◽  
Woosung Seo ◽  
Tibor G. Hortobágyi ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Image Analysis ◽  
Digital Image ◽  
Digital Image Analysis ◽  
Statistical Significance ◽  
Reference Method ◽  
Tissue Sections ◽  
Quantitative Scoring ◽  
Analysis Platform ◽  
Experimental Group

Semi-quantitative scoring is a method that is widely used to estimate the quantity of proteins on chromogen-labelled immunohistochemical (IHC) tissue sections. However, it suffers from several disadvantages, including its lack of objectivity and the fact that it is a time-consuming process. Our aim was to test a recently established artificial intelligence (AI)-aided digital image analysis platform, Pathronus, and to compare it to conventional scoring by five observers on chromogenic IHC-stained slides belonging to three experimental groups. Because Pathronus operates on grayscale 0-255 values, we transformed the data to a seven-point scale for use by pathologists and scientists. The accuracy of these methods was evaluated by comparing statistical significance among groups with quantitative fluorescent IHC reference data on subsequent tissue sections. The pairwise inter-rater reliability of the scoring and converted Pathronus data varied from poor to moderate with Cohen’s kappa, and overall agreement was poor within every experimental group using Fleiss’ kappa. Only the original and converted that were obtained from Pathronus original were able to reproduce the statistical significance among the groups that were determined by the reference method. In this study, we present an AI-aided software that can identify cells of interest, differentiate among organelles, protein specific chromogenic labelling, and nuclear counterstaining after an initial training period, providing a feasible and more accurate alternative to semi-quantitative scoring.

Digital Image Analysis of the Larynx

2000 ◽  
Vol 10 (2) ◽  
pp. 7-9
Author(s):  
Yaser Natour ◽  
Christine Sapienza ◽  
Mark Schmalz ◽  
Savita Collins
Keyword(s):  
Image Analysis ◽  
Digital Image ◽  
Digital Image Analysis

scholarly journals Digital Image Analysis of BAP-1 Accurately Predicts Uveal Melanoma Metastasis

2019 ◽  
Vol 8 (3) ◽  
pp. 11 ◽  
Author(s):  
Gustav Stålhammar ◽  
Thonnie Rose O. See ◽  
Stephen Phillips ◽  
Stefan Seregard ◽  
Hans E. Grossniklaus
Keyword(s):  
Image Analysis ◽  
Uveal Melanoma ◽  
Digital Image ◽  
Digital Image Analysis ◽  
Melanoma Metastasis

Quantitative evaluation of elderly skin based on digital image analysis

2008 ◽  
Vol 14 (2) ◽  
pp. 192-200 ◽  
Author(s):  
Hiromasa Tanaka ◽  
Gojiro Nakagami ◽  
Hiromi Sanada ◽  
Yunita Sari ◽  
Hiroshi Kobayashi ◽  
...  
Keyword(s):  
Image Analysis ◽  
Quantitative Evaluation ◽  
Digital Image ◽  
Digital Image Analysis

scholarly journals In vitro validation of Digital Image Analysis Sequence (DIAS) for the assessment of the marginal fit of cement-retained implant-supported experimental crowns

2021 ◽  
Vol 7 (1) ◽  
Author(s):  
Aristeidis A. Villias ◽  
Stefanos G. Kourtis ◽  
Hercules C. Karkazis ◽  
Gregory L. Polyzois
Keyword(s):  
Image Analysis ◽  
Digital Image ◽  
Confidence Level ◽  
Digital Image Analysis ◽  
Statistical Tests ◽  
Interobserver Reliability ◽  
Intraclass Correlation ◽  
Marginal Fit ◽  
Smallest Detectable Change ◽  
The Mean

Abstract Background The replica technique with its modifications (negative replica) has been used for the assessment of marginal fit (MF). However, identification of the boundaries between prosthesis, cement, and abutment is challenging. The recently developed Digital Image Analysis Sequence (DIAS) addresses this limitation. Although DIAS is applicable, its reliability has not yet been proven. The purpose of this study was to verify the DIAS as an acceptable method for the quantitative assessment of MF at cemented crowns, by conducting statistical tests of agreement between different examiners. Methods One hundred fifty-one implant-supported experimental crowns were cemented. Equal negative replicas were produced from the assemblies. Each replica was sectioned in six parts, which were photographed under an optical microscope. From the 906 standardized digital photomicrographs (0.65 μm/pixel), 130 were randomly selected for analysis. DIAS included tracing the profile of the crown and the abutment and marking the margin definition points before cementation. Next, the traced and marked outlines were superimposed on each digital image, highlighting the components’ boundaries and enabling MF measurements. One researcher ran the analysis twice and three others once, independently. Five groups of 130 measurements were formed. Intra- and interobserver reliability was evaluated with intraclass correlation coefficient (ICC). Agreement was estimated with the standard error of measurement (SEM), the smallest detectable change at the 95% confidence level (SDC95%), and the Bland and Altman method of limits of agreement (LoA). Results Measured MF ranged between 22.83 and 286.58 pixels. Both the intra- and interobserver reliability were excellent, ICC = 1 at 95% confidence level. The intra- and interobserver SEM and SDC95% were less than 1 and 3 pixels, respectively. The Bland–Altman analysis presented graphically high level of agreement between the mean measurement of the first observer and each of the three other observers’ measurements. Differences between observers were normally distributed. In all three cases, the mean difference was less than 1 pixel and within ± 3 pixels LoA laid at least 95% of differences. T tests of the differences did not reveal any fixed bias (P > .05, not significant). Conclusion The DIAS is an objective and reliable method able to detect and quantify MF at ranges observed in clinical practice.

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