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. In this study, a machine learning-based quality classification model is suggested for canine and feline thoracic radiographs captured in both ventrodorsal and dorsoventral positions. The problem of quality classification was divided into collimation, positioning, and exposure, and then an automatic classification method was proposed for each based on deep learning and machine learning. We utilized a dataset of 899 radiographs of dogs and cats. Evaluations using fivefold cross-validation resulted in an F1 score and AUC score of 91.33 (95% CI: 88.37-94.29) and 91.10 (95% CI: 88.16-94.03), respectively. Results indicated that the proposed automatic quality classification has the potential to be implemented in radiology clinics to improve radiograph quality and reduce nondiagnostic images.

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. Shear wave elastography is one novel ultrasound method, which has shown promise in identifying increases in tissue stiffness and identifying CKD in people. As CKD is often histologically characterized by tubulointerstitial fibrosis, shear wave elastography has the potential to identify CKD and differentiate between stages of CKD in cats. This prospective observational case-control study with 78 cats found no difference in shear wave velocities between groups (P = 0.33), a contradictory finding to one prior publication. There was no effect of weight (P = 0.65), nor the presence of mineralization (P = 0.31) or infarction (P = 0.52) on cortical shear wave velocities. There was a significant effect of age on shear wave velocity (P = 0.018) where velocities increased with age. The intraclass correlation coefficient was only moderate (0.62). Possible reasons for the difference in results between our work and that published prior, include differences in methodology and differences in instrumentation. Variability in measurements in our population may be due to the effects of respiratory motion or limitations in shear wave elastography software. As such, shear wave elastography is not currently recommended as a tool to evaluate CKD in cats and further work is necessary.

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. The optic nerve sheath is continuous with the meninges of the brain and becomes distended with intracranial hypertension. Optic nerve sheath diameter is a reliable and consistent measure of intracranial pressure and has been utilized in humans to evaluate patients for intracranial hypertension secondary to laparoscopy and capnoperitoneum. No thorough evaluation of the effects of laparoscopy on intracranial pressure has been performed in dogs. Ultrasonographic evaluation of the optic nerve sheath is a safe, non-invasive, and inexpensive procedure that may allow for the evaluation of intracranial pressure without the need for invasive monitoring systems. As laparoscopic procedures are performed increasingly often, this review aims to inform the reader on the effects of capnoperitoneum and to facilitate appropriate patient selection, anesthetic considerations, and surgical planning.

L'effet de la laparoscopie sur la pression intracrânienne mesurée par le diamètre de la gaine du nerf optique : une revue. La chirurgie laparoscopique présente de nombreux avantages par rapport à la chirurgie ouverte, notamment des taux de complications plus faibles, une durée d'hospitalisation plus courte et un coût moindre. Cependant, la littérature humaine récente suggère que la laparoscopie et l'insufflation de dioxyde de carbone peuvent entraîner une hypertension intracrânienne. La surveillance invasive de la pression intracrânienne n'est pas systématiquement effectuée en médecine vétérinaire, et l'évaluation échographique de la gaine du nerf optique a été utilisée comme mesure indirecte de la pression intracrânienne chez de nombreuses espèces. La gaine du nerf optique est continue avec les méninges du cerveau et se distend avec l'hypertension intracrânienne. Le diamètre de la gaine du nerf optique est une mesure fiable et cohérente de la pression intracrânienne et a été utilisé chez l'homme pour évaluer les patients atteints d'hypertension intracrânienne secondaire à la laparoscopie et au capnopéritoine. Aucune évaluation approfondie des effets de la laparoscopie sur la pression intracrânienne n'a été réalisée chez le chien. L'évaluation échographique de la gaine du nerf optique est une procédure sûre, non invasive et peu coûteuse qui peut permettre l'évaluation de la pression intracrânienne sans avoir besoin de systèmes de surveillance invasifs. Les procédures laparoscopiques étant de plus en plus pratiquées, cette revue vise à informer le lecteur sur les effets du pneumopéritoine et à faciliter la sélection appropriée des patients, les considérations anesthésiques et la planification chirurgicale.(Traduit par Dr Serge Messier).

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. Incorporating AI into veterinary medicine requires addressing unique ethical and legal considerations, including data privacy, owner consent, and the impact of AI outputs on decision-making. Effective change management principles aid in avoiding disruptions and building trust in AI technology. Furthermore, continuous evaluation of AI's relevance in veterinary practice ensures that the benefits of AI translate into meaningful improvements in patient care.

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. Asymmetrical hemithoraces are one of the indications of obliquity for which we propose an automatic classification method. Approach: We propose a hemithoraces segmentation method based on convolutional neural networks and active contours. We utilized the U-Net model to segment the ribs and spine and then utilized active contours to find left and right hemithoraces. We then extracted features from the left and right hemithoraces to train an ensemble classifier, which include support vector machine, gradient boosting, and multi-layer perceptron. Five-fold cross-validation was used, thorax segmentation was evaluated by intersection over union (IoU), and symmetry classification was evaluated using precision, recall, area under curve, and F1 score. Results: Classification of symmetry for 900 radiographs reported an F1 score of 82.8%. To test the robustness of the proposed thorax segmentation method to underexposure and overexposure, we synthetically corrupted properly exposed radiographs and evaluated results using IoU. The results showed that the model's IoU for underexposure and overexposure dropped by 2.1% and 1.2%, respectively. Conclusions: Our results indicate that the proposed thorax segmentation method is robust to poor exposure radiographs. The proposed thorax segmentation method can be applied to human radiography with minimal changes.

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. A machine learning model was trained to identify the appropriate inclusion of the entire lung field at both the cranial and caudal borders. Both individual models and a combined overall inclusion model were assessed based on the combined results of both the cranial and caudal border assessments. RESULTS: The combined overall inclusion model showed a precision of 91.21% (95% CI [91, 91.4]), accuracy of 83.17% (95% CI [83, 83.4]), and F1 score of 87% (95% CI [86.8, 87.2]) for classification when compared with the radiologist's quality assessment. The model took on average 6 ± 1 second to run. CLINICAL RELEVANCE: Deep learning-based methods can classify small animal thoracic radiographs as appropriately or inappropriately collimated. These methods could be deployed in a clinical setting to improve the diagnostic quality of thoracic radiographs in small animal practice.

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. Given the rapid pace of research and commercial product developments in this area, the next several years will pose challenges to understanding, interpreting, and adopting this powerful and evolving technology. Artificial intelligence has the potential to enable veterinarians to perform tasks more efficiently while providing new insights for the management and treatment of disorders. It is our hope that this will translate to better quality of life for animals and those who care for them.

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. Veterinarians will play an integral role in ensuring the appropriate uses and good curation of data. The expertise of veterinary professionals will be vital to ensuring good data and, subsequently, AI that meets the needs of the profession. Readers interested in an in-depth description of AI and veterinary medicine are invited to explore a complementary manuscript of this Currents in One Health available in the May 2022 issue of the American Journal of Veterinary Research.