AIMC Topic: Dog Diseases

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Bulldogs stenosis degree classification using synthetic images created by generative artificial intelligence.

Scientific reports
Nasal stenosis in bulldogs significantly impacts their quality of life, making early diagnosis crucial for effective treatment. This study developed an automated deep learning model to classify the severity of nasal stenosis using 1020 images of bull...

Detection of canine external ear canal lesions using artificial intelligence.

Veterinary dermatology
BACKGROUND: Early and accurate diagnosis of otitis externa is crucial for correct management yet can often be challenging. Artificial intelligence (AI) is a valuable diagnostic tool in human medicine. Currently, no such tool is available in veterinar...

PFSH-Net: Parallel frequency-spatial hybrid network for segmentation of kidney stones in pre-contrast computed tomography images of dogs.

Computers in biology and medicine
Kidney stone is a common urological disease in dogs and can lead to serious complications such as pyelonephritis and kidney failure. However, manual diagnosis involves a lot of burdens on radiologists and may cause human errors due to fatigue. Automa...

Machine learning approach in canine mammary tumour classification using rapid evaporative ionization mass spectrometry.

Analytical and bioanalytical chemistry
Rapid evaporative ionization mass spectrometry (REIMS) coupled with a monopolar handpiece used for surgical resection and combined with chemometrics has been previously explored by our research group (Mangraviti et al. in Int J Mol Sci 23(18):10562, ...

A machine-learning algorithm to grade heart murmurs and stage preclinical myxomatous mitral valve disease in dogs.

Journal of veterinary internal medicine
BACKGROUND: The presence and intensity of heart murmurs are sensitive indicators of several cardiac diseases in dogs, particularly myxomatous mitral valve disease (MMVD), but accurate interpretation requires substantial clinical expertise.

Artificial intelligence can be trained to predict -11 mutational status of canine mast cell tumors from hematoxylin and eosin-stained histological slides.

Veterinary pathology
Numerous prognostic factors are currently assessed histologically and immunohistochemically in canine mast cell tumors (MCTs) to evaluate clinical behavior. In addition, polymerase chain reaction (PCR) is often performed to detect internal tandem dup...

Utilizing machine learning and hemagglutinin sequences to identify likely hosts of influenza H3Nx viruses.

Preventive veterinary medicine
Influenza is a disease that represents both a public health and agricultural risk with pandemic potential. Among the subtypes of influenza A virus, H3 influenza virus can infect many avian and mammalian species and is therefore a virus of interest to...

Neural network analysis of pharyngeal sounds can detect obstructive upper respiratory disease in brachycephalic dogs.

PloS one
Brachycephalic obstructive airway syndrome (BOAS) is a highly prevalent respiratory disease affecting popular short-faced dog breeds such as Pugs and French bulldogs. BOAS causes significant morbidity, leading to poor exercise tolerance, sleep disord...

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

Research in veterinary science
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 mul...

Enhancing electrochemical detection through machine learning-driven prediction for canine mammary tumor biomarker with green silver nanoparticles.

Analytical and bioanalytical chemistry
This study developed an innovative biosensor strategy for the sensitive and selective detection of canine mammary tumor biomarkers, cancer antigen 15-3 (CA 15-3) and mucin 1 (MUC-1), integrating green silver nanoparticles (GAgNPs) with machine learni...