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

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. Automated methods using deep learning models have been explored to overcome this limitation.

Development of a deep learning model for automatic detection of narrowed intervertebral disc space sites in caudal thoracic and lumbar lateral X-ray images of dogs.

Intervertebral disc disease is the most common spinal cord-related disease in dogs, caused by disc material protrusion or extrusion that compresses the spinal cord, leading to clinical symptoms. Diagnosis involves identifying radiographic signs such as intervertebral disc space narrowing, increased opacity of the intervertebral foramen, spondylosis deformans, and magnetic resonance imaging findings like spinal cord compression and lesions, alongside clinical symptoms and neurological examination findings. Intervertebral disc space narrowing on radiographs is the most common finding in intervertebral disc extrusion.

Case report: Echocardiographic and computed tomographic features of congenital bronchoesophageal artery hypertrophy and fistula in a dog.

Introduction: Studies on aberrant bronchoesophageal arteries are limited. Herein, we report a case of a multi-origin systemic-to-pulmonary shunt with suspected bronchoesophageal artery hypertrophy and fistula in a dog.

Case Report: A 4-year-old castrated male beagle weighing 11 kg underwent routine medical screening.

Detection of spondylosis deformans in thoracolumbar and lumbar lateral X-ray images of dogs using a deep learning network.

Introduction: Spondylosis deformans is a non-inflammatory osteophytic reaction that develops to re-establish the stability of weakened joints between intervertebral discs. However, assessing these changes using radiography is subjective and difficult. In human medicine, attempts have been made to use artificial intelligence to accurately diagnose difficult and ambiguous diseases in medical imaging.

A deep learning model for automated kidney calculi detection on non-contrast computed tomography scans in dogs.

Nephrolithiasis is one of the most common urinary disorders in dogs. Although a majority of kidney calculi are non-obstructive and are likely to be asymptomatic, they can lead to parenchymal loss and obstruction as they progress. Thus, early diagnosis of kidney calculi is important for patient monitoring and better prognosis.

Case report: Imaging features of aorta-right atrial tunnel in a dog using two-dimensional echocardiography and computed tomography.

A 7-year-old castrated male Pomeranian dog weighing 5 kg presented with a right-sided continuous murmur without any clinical signs. Thoracic radiographs indicated cardiomegaly and right atrial (RA) bulging. Echocardiography revealed a tunnel originating from the right coronary sinus of Valsalva and terminating in the RA.

Evaluation of urethral thickness using ultrasonography in healthy small-breed dogs.

Introduction: Urethral thickness measurements can be indicative of the pathological state of a patient; however to the best of our knowledge, no measurement reference range has been established in small-breed dogs. This study aimed to establish reference ranges for total urethral thickness and urethral wall thickness in healthy small-breed dogs; "urethral wall thickness" was assumed to be 1/2 of the "total urethral thickness."

Methods: Total urethral thickness was measured by ultrasonography in 240 healthy small-breed dogs.

A deep learning model for CT-based kidney volume determination in dogs and normal reference definition.

Kidney volume is associated with renal function and the severity of renal diseases, thus accurate assessment of the kidney is important. Although the voxel count method is reported to be more accurate than several methods, its laborious and time-consuming process is considered as a main limitation. In need of a new technology that is fast and as accurate as the manual voxel count method, the aim of this study was to develop the first deep learning model for automatic kidney detection and volume estimation from computed tomography (CT) images of dogs.