Role of AI in diagnostic imaging error reduction.

The topic of diagnostic imaging error and the tools and strategies for error mitigation are poorly investigated in veterinary medicine. The increasing popularity of diagnostic imaging and the high demand for teleradiology make mitigating diagnostic imaging errors paramount in high-quality services. The different sources of error have been thoroughly investigated in human medicine, and the use of AI-based products is advocated as one of the most promising strategies for error mitigation.

Development of an artificial intelligence-based algorithm for predicting the severity of myxomatous mitral valve disease from thoracic radiographs by using two grading systems.

A heart-convolutional neural network (heart-CNN) was designed and tested for the automatic classification of chest radiographs in dogs affected by myxomatous mitral valve disease (MMVD) at different stages of disease severity. A retrospective and multicenter study was conducted. Lateral radiographs of dogs with concomitant X-ray and echocardiographic examination were selected from the internal databases of two institutions.

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.

Preliminary Evaluation of Cortical and Medullary Echogenicity in Normal Canine Fetal Kidneys during the Last 10 Days of Pregnancy.

The objective of this study was to assess changes in the echogenicity of the cortex and medulla of canine fetal kidneys in relation to days before parturition (dbp), maternal size and litter size. Monitoring of 10 healthy pregnant bitches (2-8 years old, 8.8-40.

An AI-based algorithm for the automatic evaluation of image quality in canine thoracic radiographs.

The aim of this study was to develop and test an artificial intelligence (AI)-based algorithm for detecting common technical errors in canine thoracic radiography. The algorithm was trained using a database of thoracic radiographs from three veterinary clinics in Italy, which were evaluated for image quality by three experienced veterinary diagnostic imagers. The algorithm was designed to classify the images as correct or having one or more of the following errors: rotation, underexposure, overexposure, incorrect limb positioning, incorrect neck positioning, blurriness, cut-off, or the presence of foreign objects, or medical devices.

Development of an artificial intelligence-based method for the diagnosis of the severity of myxomatous mitral valve disease from canine chest radiographs.

An algorithm based on artificial intelligence (AI) was developed and tested to classify different stages of myxomatous mitral valve disease (MMVD) from canine thoracic radiographs. The radiographs were selected from the medical databases of two different institutions, considering dogs over 6 years of age that had undergone chest X-ray and echocardiographic examination. Only radiographs clearly showing the cardiac silhouette were considered.

Contrast-enhanced ultrasound features of adrenal lesions in dogs.

Background: The contrast-enhanced ultrasound (CEUS) features of adrenal lesions are poorly reported in veterinary literature.

Methods: Qualitative and quantitative B-mode ultrasound and CEUS features of 186 benign (adenoma) and malignant (adenocarcinoma and pheochromocytoma) adrenal lesions were evaluated.

Results: Adenocarcinomas (n = 72) and pheochromocytomas (n = 32) had mixed echogenicity with B-mode, and a non-homogeneous aspect with a diffused or peripheral enhancement pattern, hypoperfused areas, intralesional microcirculation and non-homogeneous wash-out with CEUS.

Contrast-enhanced ultrasound features of focal pancreatic lesions in cats.

A large overlap in the ultrasound (US) features of focal pancreatic lesions (FPLs) in cats is reported. Furthermore, only a small number of studies describing the contrast-enhanced ultrasound (CEUS) features of FPLs in cats have been conducted today. The aim of this study is to describe the B-mode US and CEUS features of FPLs in cats.

Contrast-enhanced ultrasound features of focal pancreatic lesions in dogs.

Background: Contrast-enhanced ultrasound (CEUS) features of pancreatic lesions are poorly reported in veterinary literature.

Methods: Qualitative and quantitative features of pancreatic benign (nodular hyperplasia [NH], cyst and abscess) and malignant (adenocarcinoma and insulinoma) lesions during B-mode and CEUS examinations are described in 75 dogs.

Results: Adenocarcinomas (n = 23) had mixed echogenicity at B-mode, and they were hypoenhancing or non-enhancing at CEUS, with a non-homogeneous and cystic enhancement pattern.

A Machine Learning-Based Approach for Classification of Focal Splenic Lesions Based on Their CT Features.

The aim of the study was to describe the CT features of focal splenic lesions (FSLs) in dogs in order to predict lesion histotype. Dogs that underwent a CT scan and had a FSL diagnosis by cytology or histopathology were retrospectively included in the study. For the statistical analysis the cases were divided into four groups, based on the results of cytopatholoy or hystopathology, namely: nodular hyperplasia (NH), other benign lesions (OBLs), sarcoma (SA), round cell tumour (RCT).

Undifferentiated laryngeal carcinoma with hyaline bodies in a cat.

Background: Primary laryngeal neoplasms are rare in cats, with lymphoma and squamous cell carcinoma being the most commonly diagnosed tumour types. These tumours are usually highly aggressive, difficult to treat, and have a poor prognosis. Here an undifferentiated laryngeal carcinoma with hyaline bodies in a cat is reported.

Corrigendum: Diagnostic Accuracy of Delayed Phase Post Contrast Computed Tomographic Images in the Diagnosis of Focal Liver Lesions in Dogs: 69 Cases.

[This corrects the article DOI: 10.3389/fvets.2021.

Computed tomography features for differentiating malignant and benign focal liver lesions in dogs: A meta-analysis.

Computed tomography (CT) is often performed to complement ultrasound following detection of focal liver lesions (FLL). There is no consensus in the literature regarding the CT features that might be helpful in the distinction between benign and malignant FLL. The aim of this meta-analysis was to identify, based on the available literature, the qualitative and quantitative CT features able to distinguish between benign and malignant FLL.

An AI-Based Algorithm for the Automatic Classification of Thoracic Radiographs in Cats.

An artificial intelligence (AI)-based computer-aided detection (CAD) algorithm to detect some of the most common radiographic findings in the feline thorax was developed and tested. The database used for training comprised radiographs acquired at two different institutions. Only correctly exposed and positioned radiographs were included in the database used for training.

Diagnostic Accuracy of Delayed Phase Post Contrast Computed Tomographic Images in the Diagnosis of Focal Liver Lesions in Dogs: 69 Cases.

To describe the computed tomographic (CT) features of focal liver lesions (FLLs) in dogs, that could enable predicting lesion histotype. Dogs diagnosed with FLLs through both CT and cytopathology and/or histopathology were retrospectively collected. Ten qualitative and 6 quantitative CT features have been described for each case.

Automatic classification of canine thoracic radiographs using deep learning.

The interpretation of thoracic radiographs is a challenging and error-prone task for veterinarians. Despite recent advancements in machine learning and computer vision, the development of computer-aided diagnostic systems for radiographs remains a challenging and unsolved problem, particularly in the context of veterinary medicine. In this study, a novel method, based on multi-label deep convolutional neural network (CNN), for the classification of thoracic radiographs in dogs was developed.

Training Deep Neural Networks for Small and Highly Heterogeneous MRI Datasets for Cancer Grading.

Using medical images recorded in clinical practice has the potential to be a game-changer in the application of machine learning for medical decision support. Thousands of medical images are produced in daily clinical activity. The diagnosis of medical doctors on these images represents a source of knowledge to train machine learning algorithms for scientific research or computer-aided diagnosis.

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.

Progesterone-responsive vaginal leiomyoma and hyperprogesteronemia due to ovarian luteoma in an older bitch.

Background: This is the first report about a vaginal leiomyoma concomitant with an ovarian luteoma in a bitch.

Case Presentation: A 11-year-old intact female Labrador retriever was referred because of anuria, constipation and protrusion of a vaginal mass through the vulvar commissure. The bitch had high serum progesterone concentration (4.

Antiproliferative Flavanoid Dimers Isolated from Brazilian Red Propolis.

Herein reported are results of the chemical and biological investigation of red propolis collected at the Brazilian Northeast coastline. New propolones A-D (-), with a 3-{3-[(2-phenylbenzofuran-3-yl)methyl]phenyl}chromane skeleton; propolonones A-C (-), with a 3-[3-(3-benzylbenzofuran-2-yl)phenyl]chromane skeleton; and propolol A (), with a 6-(3-benzylbenzofuran-2-yl)-3-phenylchromane skeleton, were isolated as constituents of Brazilian red propolis by cytotoxicity-guided assays and structurally identified by analysis of their spectroscopic data. Propolone B () and propolonone A () display significant cytotoxic activities against an ovarian cancer cell line expressing a multiple drug resistance phenotype when compared with doxorubicin.

Antioxidant, antiproliferative and healing properties of araticum (Annona crassiflora Mart.) peel and seed.

Araticum (Annona crassiflora Mart.) is a native fruit from Brazilian Cerrado widely used by folk medicine. Nevertheless, the biological effects of its seeds and peel have not been extensively evaluated.

Contrast-enhanced ultrasound features of malignant focal liver masses in dogs.

A total of 185 cases (150 retrospectively and 35 prospectively) of malignant liver masses were collected. In the retrospectively collected cases hyperenhancement during wash-in was the most common feature in HCCs but there was a high percentage of cases showing no enhancement or hypo/isoenhancement. ICCs displayed a large variety of contrast enhancement patterns and, although statically significant differences between ICCs and HCCs were evident, no clear distinction between these two pathologies was possible based only on their CEUS appearance.

Correlation between renal histopathology and renal ultrasound in dogs.

Fifty-three privately owned dogs were included in the study. Ultrasonography of the kidneys was performed ante mortem. All the dogs died or were euthanized for reasons unrelated to this study.

A Frailty Index based on clinical data to quantify mortality risk in dogs.

Frailty is defined as a decline in an organism's physiological reserves resulting in increased vulnerability to stressors. In humans, a single continuous variable, the so-called Frailty Index (FI), can be obtained by multidimensionally assessing the biological complexity of an ageing organism. Here, we evaluate this variability in dogs and compare it to the data available for humans.