Combining ultrasound radiomics, complete blood count, and serum biochemical biomarkers for diagnosing intestinal disorders in cats using machine learning.

This retrospective analytical observational cohort study aimed to model and predict the classification of feline intestinal diseases from segmentations of a transverse section from small intestine ultrasound (US) image, complete blood count (CBC), and serum biochemical profile data using a variety of machine-learning approaches. In 149 cats from three institutions, images were obtained from cats with biopsy-confirmed small cell epitheliotropic lymphoma (lymphoma), inflammatory bowel disease (IBD), no pathology ("healthy"), and other conditions (warrant a biopsy for further diagnosis). CBC, blood serum chemistry, small intestinal ultrasound, and small intestinal biopsy were obtained within a 2-week interval. CBC and serum biomarkers and radiomic features were combined for modeling. Four classification schemes were investigated: (1) normal versus abnormal; (2) warranting or not warranting a biopsy; (3) lymphoma, IBD, healthy, or other conditions; and (4) lymphoma, IBD, or other conditions. Two feature selection methods were used to identify the top 3, 5, 10, and 20 features, and six machine learning models were trained. The average (95% CI) performance of models for all combinations of features, numbers of features, and types of classifiers was 0.886 (0.871-0.912) for Model 1 (normal vs. abnormal), 0.751 (0.735-0.818) for Model 2 (biopsy vs. no biopsy), 0.504 (0.450-0.556) for Model 3 (lymphoma, IBD, healthy, or other), and 0.531 (0.426-0.589), for Model 4 (lymphoma, IBD, or other). Our findings suggest model accuracies above 0.85 can be achieved in Model 1 and 2, and that including CBC and biochemistry data with US radiomics data did not significantly improve accuracy in our models.

Intra- and interobserver assessments of intestinal wall thickness and segmentations from transverse sections of feline abdominal ultrasound images.

Measurements of intestinal wall thicknesses from ultrasound imaging (US) are routinely used to support diagnoses of intestinal disorders in cats, however published studies describing observer agreement are currently lacking. The aim of this retrospective, observer agreement study was to quantify inter- and intraobserver repeatability and agreement in the measurement of intestinal wall layer thicknesses and the segmentation of transverse sections of small intestines in US images of 20 cats. Intestinal wall layer thickness measurements of the mucosa, submucosa, muscularis, serosa layer, and total thickness of these layers were performed on five cats with small cell epitheliotropic lymphoma, five with inflammatory bowel disease, and 10 with other conditions. Thickness measurements and the segmentation encompassing the serosa layer were obtained from five observers four times non-sequentially. The average standard deviation in thickness measurements (95% confidence interval) in the mucosa, submucosa, muscularis, serosa, and total thickness were 0.35 (0.07-0.95), 0.24 (0.07-0.52), 0.22 (0.06-0.49), 0.20 (0.05-0.49), and 0.57 (0.11-1.60) mm, respectively. The average intraclass correlation coefficients, which estimates the degree of consistency in thickness measurements and segmentation areas for each observer, ranged from 0.355 to 0.870 and 0.850 to 0.993, respectively. The interclass correlation coefficient, which estimates the degree of consistency when measuring a thickness or segmentation area over all observers ranged from 0.115 to 0.753, and 0.811 to 0.902, respectively. The overall average Dice Coefficient, which estimates the extent of overlap of the segmentations for all observers was 0.957 (0.933 to 0.972). Our results suggest segmentations of small intestines have a higher interobserver agreement than measurements of intestinal wall thicknesses.

Composition, Technological, and Microstructural Aspects of Concrete Modified with Finely Ground Mussel Shell Powder.

Reducing the negative environmental impact of a widely spread building material such as concrete is possible by decreasing the amount of cement in this composite material, especially when specific waste is included as a substitution for the binder. Another important environmental issue is accumulated aquaculture waste. This work justifies the possibility of achieving modified concrete with improved properties based on sea mussel shell powder (MSP). An improved environmentally friendly concrete was obtained and modified with MSP as a result of experimental studies. The dosage of MSP in the amount of 6% instead of part of the cement turned out to be optimal and most effective. Because of the modification, it was possible to increase the strength properties: the increments were up to 12% for the compressive strength (CS), up to 13% for the axial CS, up to 14% for the tensile strength (TS) in bending, and up to 12% for the axial TS. The ultimate strains under axial compression and tension decreased to 9% and 12%, respectively, and the elastic modulus increased to 15%. SEM analysis showed a more integral microstructure without voids and cracks in this composite with a modifier content of 6% compared with the sample of the ordinary composition. Economic efficiency is expressed in reducing the total cost of new concrete compared to traditional ones by about 17% and the cost of building construction by up to 15% due to a decrease in the percentage of defects.