Classification of the quality of canine and feline ventrodorsal and dorsoventral thoracic radiographs through machine learning.

Thoracic radiographs are an essential diagnostic tool in companion animal medicine and are frequently used as a part of routine workups in patients presenting for coughing, respiratory distress, cardiovascular diseases, and for staging of neoplasia. Quality control is a critical aspect of radiology practice in preventing misdiagnosis and ensuring consistent, accurate, and reliable diagnostic imaging. Implementing an effective quality control procedure in radiology can impact patient outcomes, facilitate clinical decision-making, and decrease healthcare costs.

What's in the box? A toolbox for safe deployment of artificial intelligence in veterinary medicine.

This report describes a comprehensive framework for applying artificial intelligence (AI) in veterinary medicine. Our framework draws on existing research on AI implementation in human medicine and addresses the challenges of limited technology expertise and the need for scalability. The critical components of this framework include assembling a diverse team of experts in AI, promoting a foundational understanding of AI among veterinary professionals, identifying relevant use cases and objectives, ensuring data quality and availability, creating an effective implementation plan, providing team training, fostering collaboration, considering ethical and legal obligations, integrating AI into existing workflows, monitoring and evaluating performance, managing change effectively, and staying up-to-date with technological advancements.

Automatic classification of symmetry of hemithoraces in canine and feline radiographs.

Purpose: Thoracic radiographs are commonly used to evaluate patients with confirmed or suspected thoracic pathology. Proper patient positioning is more challenging in canine and feline radiography than in humans due to less patient cooperation and body shape variation. Improper patient positioning during radiograph acquisition has the potential to lead to a misdiagnosis.

Machine learning can appropriately classify the collimation of ventrodorsal and dorsoventral thoracic radiographic images of dogs and cats.

Objectives: To determine the feasibility of machine learning algorithms for the classification of appropriate collimation of the cranial and caudal borders in ventrodorsal and dorsoventral thoracic radiographs.

Samples: 900 ventrodorsal and dorsoventral canine and feline thoracic radiographs were retrospectively acquired from the Picture Archiving and Communication system (PACs) system of the Ontario Veterinary College.

Procedures: Radiographs acquired from April 2020 to May 2021 were labeled by 1 radiologist in Summer of 2022 as either appropriately or inappropriately collimated for the cranial and caudal borders.

Shear wave elastography evaluation of cats with chronic kidney disease.

Chronic kidney disease (CKD) is a major health condition in cats that can lead to poor quality of life and financial implications for therapy. Currently staging and identification of CKD is limited by diagnostic testing such as creatinine and urine-specific gravity, which do not change until late in the disease course. Other methods to evaluate CKD would be valuable in the clinical setting.

The effect of laparoscopy on intracranial pressure as measured by optic nerve sheath diameter: A review.

Laparoscopic surgery has many benefits over open surgery including lower complication rates, and shorter duration and lower cost of hospitalization. However, recent human literature suggests laparoscopy and carbon dioxide insufflation can result in intracranial hypertension. Invasive monitoring of intracranial pressure is not routinely performed in veterinary medicine, and ultrasonographic evaluation of the optic nerve sheath has been employed as an indirect measure of intracranial pressure in many species.

Artificial intelligence in veterinary medicine.

Artificial intelligence (AI) is a branch of computer science in which computer systems are designed to perform tasks that mimic human intelligence. Today, AI is reshaping day-to-day life and has numerous emerging medical applications poised to profoundly reshape the practice of veterinary medicine. In this Currents in One Health, we discuss the essential elements of AI for veterinary practitioners with the aim to help them make informed decisions in applying AI technologies into their practices.

The unmet potential of artificial intelligence in veterinary medicine.

Veterinary medicine is a broad and growing discipline that includes topics such as companion animal health, population medicine and zoonotic diseases, and agriculture. In this article, we provide insight on how artificial intelligence works and how it is currently applied in veterinary medicine. We also discuss its potential in veterinary medicine.