Quantitative analysis of ultrasonographic images and cytology in relation to histopathology of canine and feline liver: An ex-vivo study

ORNIPURAL® as conventional therapy versus mixture of Curcuma longa extract and pomegranate peel extract as homeotherapy in dogs with dexamethasone-induced hepatopathy: clinicolaboratory, ultrasonographic, and histopathological monitoring

Introduction

Curcuma longa extract and pomegranate peel extract as homeotherapy have numerous therapeutic uses, mainly for anti-inflammatory, immunomodulatory, and hepatoprotective efficacy. The current study compared ORNIPURAL® (as a commercial hepatoprotective drug) and a herbal mixture of Curcuma longa extract and pomegranate peel extract [as homeotherapy] in dogs with dexamethasone-induced hepatopathy throughout a 42-day long-term study.

Methods

The study was conducted on mongrel dogs (n = 30) throughout three phases of the experiment: an acclimatization phase (14 days), a steroidal-induced hepatopathy phase (14 days), and a treatment phase (14 days, i.e., either with ORNIPURAL® or with herbal mixtures). The investigated dogs undergoing complete clinical and ultrasonographic examinations as well as hematological analysis and serum hepatorenal biomarkers that were estimated in days 0 (control group), 7 (hepatopathy group), 14 (hepatopathy group), 21 (treatment group), and 28 (treatment group). Histopathology of the liver was conducted for some dogs on days 0, 14, and 28 after the euthanization of these animals.

Results and conclusion

The present study reported the most remarkable efficacy of both ORNIPURAL® and a herbal mixture of Curcuma longa extract and pomegranate peel extract as hepatoprotective medicaments in the therapy of dexamethasone-induced fatty liver in dogs. Therefore, a 14-day treatment with either a herbal mixture or ORNIPURAL® in treated dogs (treatment groups) induced an unmistakable improvement in their clinical status, blood pictures, and serum hepatorenal parameters as well as characteristic sonographic and histopathological findings compared with those in dexamethasone-induced hepatic lipidosis (hepatopathy groups). Compared to dogs treated with ORNIPURAL®, this clinical improvement was more evident in dogs treated with an herbal mixture. Moreover, no significant alterations in blood pictures and serum hepatorenal indices were demonstrated between ORNIPURAL® and herbal-treated dogs. Overall, the herbal mix of Curcuma longa extract and pomegranate peel extract had higher efficacy and greater potency than conventional therapy that uses ORNIPURAL® in treating dogs with hepatopathy. The study also recommended the parallel use of this herbal mixture as well as ORNIPURAL® in long-term therapeutic strategies in dogs with dexamethasone-induced fatty liver as both minimized dexamethasone side effects. Ultrasonography alone was not enough to evaluate hepatobiliary disorders in canines.

Artificial intelligence in veterinary diagnostic imaging: Perspectives and limitations

The field of veterinary diagnostic imaging is undergoing significant transformation with the integration of artificial intelligence (AI) tools. This manuscript provides an overview of the current state and future prospects of AI in veterinary diagnostic imaging. The manuscript delves into various applications of AI across different imaging modalities, such as radiology, ultrasound, computed tomography, and magnetic resonance imaging. Examples of AI applications in each modality are provided, ranging from orthopaedics to internal medicine, cardiology, and more. Notable studies are discussed, demonstrating AI's potential for improved accuracy in detecting and classifying various abnormalities. The ethical considerations of using AI in veterinary diagnostics are also explored, highlighting the need for transparent AI development, accurate training data, awareness of the limitations of AI models, and the importance of maintaining human expertise in the decision-making process. The manuscript underscores the significance of AI as a decision support tool rather than a replacement for human judgement. In conclusion, this comprehensive manuscript offers an assessment of the current landscape and future potential of AI in veterinary diagnostic imaging. It provides insights into the benefits and challenges of integrating AI into clinical practice while emphasizing the critical role of ethics and human expertise in ensuring the wellbeing of veterinary patients.

Ultrasonographic evaluation of the liver and gallbladder and hepatic histogram of non-venomous snakes

This study aimed to describe sonographic features of the liver, gallbladder and hepatic histogram from grey-scale ultrasound in three species of healthy non-venomous snakes. Twenty-eight adult snakes were enrolled in the study, including 10 common boas (Boa constrictor), eight black-tailed pythons (Python molurus) and 10 rainbow boas (Epicrates crassus). The snakes fasted for 30 days and were manually restrained while conscious. For B. constrictor and P. molurus the liver and gallbladder were best visualized in ventral recumbency, and E. crassus in dorsal recumbency. A single elongated hepatic lobe was identified in all snakes. The gallbladder was positioned caudal and separated from the liver, with an oval shape and homogeneous anechoic content in the lumen, and thin and regular walls. A region of interest by pixel number was chosen for the liver, fat bodies, left kidney, and splenopancreas. The mean grey level (G) of the organs had significant differences within each species. Standard deviation of grey levels (SG ) had significant differences within B. constrictor and E. crassus. P. molurus had no significant difference among organs. The comparison among snakes showed that E. crassus had G of liver and splenopancreas lower than B. constrictor and P. molurus. The SG of the liver in E. crassus was lowest compared to B. constrictor and P. molurus. P. molurus showed the highest values in mean of G and SG . In conclusion, despite the liver and gallbladder having similar sonographic features, the grey-level histogram showed that liver echotexture and echogenicity differ among species.

Radiographic and sonographic features, and histogram parameters of the liver and spleen in healthy Toco toucans (Ramphastos toco, Müller 1976)

This study aimed to evaluate radiographic and sonographic features, and histogram parameters based on grayscale ultrasound of the liver and spleen in healthy toco toucans. Fifteen adult toco toucans (Ramphastos toco), seven females and eight males, weighing approximately 650 g, were enrolled in the study. On the right lateral radiographic view, the liver was visualized in the midventral region of the coelomic cavity; ultrasonographically, the liver was located in the middle portion of the coelomic cavity in close relationship to the heart, and thoracic and abdominal air sacs. Two hepatic lobes were identified; the right lobe was larger than the left one. The spleen was visualized in 10 toco toucans on radiographs and only in eight toucans on ultrasound exams. The gallbladder was identified only on ultrasound. On the right lateral radiographic view, the spleen was visualized dorsal to the proventriculus and ventral to the lungs/air sacs as an oval shape. Ultrasonographically, the spleen was observed caudal to the liver, cranial to the proventriculus and craniodorsal to the ventriculus. There was no significant difference in spleen length for either radiographic or ultrasound measurements. The brightness intensity (BI) for mean gray level (G) and standard deviation of gray levels (SG ) in the liver were 101.315 (± 16.170) and 12.453 (± 2.616), respectively. Mean G and SG levels in the spleen were 63.940 (± 18.321) and 7.494 (± 3.595), respectively. In conclusion, the sonographic features and histogram parameters indicated that the liver is more echogenic and heterogeneous than the spleen, which must be considered for diagnosing alterations in these organs.

Use of deep learning to detect cardiomegaly on thoracic radiographs in dogs

The purpose of this study was to develop a computer-aided detection (CAD) device based on convolutional neural networks (CNNs) to detect cardiomegaly from plain radiographs in dogs. Right lateral chest radiographs (n = 1465) were retrospectively selected from archives. The radiographs were classified as having a normal cardiac silhouette (No-vertebral heart scale [VHS]-Cardiomegaly) or an enlarged cardiac silhouette (VHS-Cardiomegaly) based on the breed-specific VHS. The database was divided into a training set (1153 images) and a test set (315 images). The diagnostic accuracy of four different CNN models in the detection of cardiomegaly was calculated using the test set. All tested models had an area under the curve >0.9, demonstrating high diagnostic accuracy. There was a statistically significant difference between Model C and the remainder models (Model A vs. Model C, P = 0.0298; Model B vs. Model C, P = 0.003; Model C vs. Model D, P = 0.0018), but there were no significant differences between other combinations of models (Model A vs. Model B, P = 0.395; Model A vs. Model D, P = 0.128; Model B vs. Model D, P = 0.373). Convolutional neural networks could therefore assist veterinarians in detecting cardiomegaly in dogs from plain radiographs.

Application of texture analysis of b-mode ultrasound images for the quantification and prediction of intramuscular fat in living beef cattle: A methodological study

Intramuscular fat (IMF) contributes significantly to the aroma and tenderness of the meat, therefore playing a key role in quality determination. Yet, IMF determination methods rely on visual inspection or on fat extraction from meat samples after animals' slaughter. The aim of this methodological study was the elaboration of a process capable of predicting IMF% using real-time ultrasound (RTU) images in live beef cattle. The longissimus dorsi (LD) muscle of 26 Charolaise heifers was investigated. In vivo ultrasound images were taken and texture analysis was performed. One week after the animals' slaughter, the whole twelfth rib cut was collected, and IMF% was determined by extraction with petrol ether (Randall) method. Animals were divided in 3 groups depending on their mean lipid content percentage in 100 g meat (Group 1: IMF ≤ 4.24%; Group 2: 4.25% ≤ IMF ≤ 5.75%; Group 3: IMF ≥ 5.76%). Texture parameters were selected by a stepwise linear discriminant analysis using IMF% measured by chemical extraction (IMFqa) as the dependent variable, and the results of the texture analysis as explanatory variables. 6 variables were found predictive and molded into a multiple regression equation, this equation was then validated using IMFqa as ground truth. A high linear correlation between IMFqa and IMFpred was evident (r² = 0.8504), ROC analysis perfomed on IMFpred comparing it to IMFqa showed a sensitivity of 80% and a specificity of 93.7%, while results from the Bland-Altman plot were ± 1.96 (±1.11SD).

A methodological approach for deep learning to distinguish between meningiomas and gliomas on canine MR-images

Background

Distinguishing between meningeal-based and intra-axial lesions by means of magnetic resonance (MR) imaging findings may occasionally be challenging. Meningiomas and gliomas account for most of the total primary brain neoplasms in dogs, and differentiating between these two forms is mandatory in choosing the correct therapy. The aims of the present study are: 1) to determine the accuracy of a deep convolutional neural network (CNN, GoogleNet) in discriminating between meningiomas and gliomas in pre- and post-contrast T1 images and T2 images; 2) to develop an image classifier, based on the combination of CNN and MRI sequence displaying the highest accuracy, to predict whether a lesion is a meningioma or a glioma.

Results

Eighty cases with a final diagnosis of meningioma (n = 56) and glioma (n = 24) from two different institutions were included in the study. A pre-trained CNN was retrained on our data through a process called transfer learning. To evaluate CNN accuracy in the different imaging sequences, the dataset was divided into a training, a validation and a test set. The accuracy of the CNN was calculated on the test set. The combination between post-contrast T1 images and CNN was chosen in developing the image classifier (trCNN). Ten images from challenging cases were excluded from the database in order to test trCNN accuracy; the trCNN was trained on the remainder of the dataset of post-contrast T1 images, and correctly classified all the selected images. To compensate for the imbalance between meningiomas and gliomas in the dataset, the Matthews correlation coefficient (MCC) was also calculated. The trCNN showed an accuracy of 94% (MCC = 0.88) on post-contrast T1 images, 91% (MCC = 0.81) on pre-contrast T1-images and 90% (MCC = 0.8) on T2 images.

Conclusions

The developed trCNN could be a reliable tool in distinguishing between different meningiomas and gliomas from MR images.

Estimation of fetal lung development using quantitative analysis of ultrasonographic images in normal canine pregnancy

We investigated the quantitative analysis of sonographic images to predict fetal lung maturity of the canine foetus in normal pregnancy. Twelve bitches were recruited in the present study. Serial ultrasonographic exams were performed at three pre-determined time periods corresponding to the pseudoglandular (40-48 days of pregnancy), canalicular (49-56 days of pregnancy) and saccular phase (57-63 days of pregnancy) of lung development. Mean grey level (MGL) and the standard deviation of the histogram (SDH) of fetal lung and liver sonographic images were measured with dedicated software. The lung-to-liver ratio (LLR) for both parameters was also calculated. Measurements were taken on the two caudal-most foetuses and then averaged. SDH did not show any statistically significant difference between the three time periods in the lungs or in the liver. MGL measured in the lungs significantly increased in the first period and reached a plateau during the last two periods. Liver echogenicity was constant during the first two periods and significantly increased during the last week of gestation. The LLR of MGL significantly decreased during the last week of pregnancy. The LLR was a very good test to detect fetal lung maturity (area under the receiver operator curve (AUROC) = 0.875); using a cut-off value of LLR < 1.541, sensitivity was 83.33% and specificity was 83.33%, positive likelihood ratio = 5. LLR of MGL is an accurate test to estimate lung development in normal canine pregnancies.

Texture analysis of B-mode ultrasound images to stage hepatic lipidosis in the dairy cow: A methodological study

Hepatic lipidosis is the most diffused hepatic disease in the lactating cow. A new methodology to estimate the degree of fatty infiltration of the liver in lactating cows by means of texture analysis of B-mode ultrasound images is proposed. B-mode ultrasonography of the liver was performed in 48 Holstein Friesian cows using standardized ultrasound parameters. Liver biopsies to determine the triacylglycerol content of the liver (TAGqa) were obtained from each animal. A large number of texture parameters were calculated on the ultrasound images by means of a free software. Based on the TAGqa content of the liver, 29 samples were classified as mild (TAGqa<50mg/g), 6 as moderate (50mg/g<TAGqa>100mg/g) and 13 as severe (TAG>100mg/g) in steatosis. Stepwise linear regression analysis was performed to predict the TAGqa content of the liver (TAGpred) from the texture parameters calculated on the ultrasound images. A five-variable model was used to predict the TAG content from the ultrasound images. The regression model explained 83.4% of the variance. An area under the curve (AUC) of 0.949 was calculated for <50mg/g vs >50mg/g of TAGqa; using an optimal cut-off value of 72mg/g TAGpred had a sensitivity of 86.2% and a specificity of 84.2%. An AUC of 0.978 for <100mg/g vs >100mg/g of TAGqa was calculated; using an optimal cut-off value of 89mg/g, TAGpred sensitivity was 92.3% and specificity was 88.6%. Texture analysis of B-mode ultrasound images may therefore be used to accurately predict the TAG content of the liver in lactating cows.