Artificial intelligence in veterinary medicine is an emerging field. Machine learning, a subfield of artificial intelligence, allows computer programs to analyze large imaging datasets and learn to perform tasks relevant to veterinary diagnostic imaging. This review summarizes the small, yet growing body of artificial intelligence literature in veterinary imaging, provides necessary background to understand these papers, and provides author commentary on the state of the field. To date, less than 40 peer-reviewed publications have utilized machine learning to perform imaging-associated tasks across multiple anatomic regions in veterinary clinical and biomedical research. Major challenges in this field include collection and cleaning of sufficient image data, selection of high-quality ground truth labels, formation of relationships between veterinary and machine learning professionals, and closure of the gap between academic uses of artificial intelligence and currently available commercial products. Further development of artificial intelligence has the potential to help meet the growing need for radiological services through applications in workflow, quality control, and image interpretation for both general practitioners and radiologists.

Artificial intelligence in veterinary diagnostic imaging: A literature review.

The most common cardiac measurements on thoracic radiography in veterinary medicine are vertebral heart size (VHS) and vertebral left atrial size (VLAS). Previously published studies have demonstrated evidence that reference ranges for these measures vary among different dog breeds, however reference ranges for Maltese breed dogs are currently lacking. We hypothesized that VHS and VLAS in healthy Maltese dogs would not be influenced by sex, age, or bodyweight. This retrospective, reference interval study aimed to describe reference intervals for VHS and VLAS in a sample of clinically healthy Maltese dogs and assess the intra- and interobserver variabilities for VLAS and VHS measurements. A total of 81 clinically healthy Maltese dogs were included, with healthy status being based on a complete cardiologic examination. Thoracic radiography in right lateral recumbency was performed for all dogs. The intra- and interobserver variability assessments of VHS and VLAS were based on measurements independently performed by three observers. The mean VHS was 9.53 ± 0.46 vertebrae (v). The median and interquartile range (IQR) for VLAS was 2 v (IQR, 1.8-2.1v). A statistically significant moderate positive correlation was observed between VHS and VLAS (P < .01, r = 0.416). Both VHS and VLAS showed an almost perfect intraobserver agreement. Interobserver agreement was almost perfect for VHS and substantial for VLAS. Findings supported previous studies recommending the use of breed-specific reference values for VHS and VLAS, and provided background data for future radiographic evaluations of Maltese dogs with clinical signs of cardiac disease.

Vertebral heart size and vertebral left atrial size reference ranges in healthy Maltese dogs

Simple Summary The goal of this paper is to provide an overview of current radiomic and AI applications in veterinary diagnostic imaging. We discuss the essential elements of AI for veterinary practitioners with the aim of helping them make informed decisions in applying AI technologies to their practices and that veterinarians will play an integral role in ensuring the appropriate uses and suitable curation of data. The expertise of veterinary professionals will be vital to ensuring suitable data and, subsequently, AI that meets the needs of the profession. Abstract Great advances have been made in human health care in the application of radiomics and artificial intelligence (AI) in a variety of areas, ranging from hospital management and virtual assistants to remote patient monitoring and medical diagnostics and imaging. To improve accuracy and reproducibility, there has been a recent move to integrate radiomics and AI as tools to assist clinical decision making and to incorporate it into routine clinical workflows and diagnosis. Although lagging behind human medicine, the use of radiomics and AI in veterinary diagnostic imaging is becoming more frequent with an increasing number of reported applications. The goal of this paper is to provide an overview of current radiomic and AI applications in veterinary diagnostic imaging.

https://www.mdpi.com/2306-7381/9/11/620/pdf?version=1667906709

A Review of Radiomics and Artificial Intelligence and Their Application in Veterinary Diagnostic Imaging

Artificial intelligence (AI) is a fast-paced technological advancement in terms of its application to various fields of science and technology. In particular, AI has the potential to play various roles in veterinary clinical practice, enhancing the way veterinary care is delivered, improving outcomes for animals and ultimately humans. Also, in recent years, the emergence of AI has led to a new direction in biomedical research, especially in translational research with great potential, promising to revolutionize science. AI is applicable in antimicrobial resistance (AMR) research, cancer research, drug design and vaccine development, epidemiology, disease surveillance, and genomics. Here, we highlighted and discussed the potential impact of various aspects of AI in veterinary clinical practice and biomedical research, proposing this technology as a key tool for addressing pressing global health challenges across various domains.

The potential application of artificial intelligence in veterinary clinical practice and biomedical research

Simple Summary Infiltrative brain tumours are common in dogs. Although different treatments have been used, such as surgery, radiotherapy, chemotherapy, or combinations, guidelines for the most effective management are lacking. In this study, we report the effect of combining surgery and chemotherapy on the survival of 14 dogs with infiltrative gliomas. Four dogs were operated on two or three times to remove the tumors, and only one of these dogs died shortly after the second surgery. All tolerated the surgery with minimal or no deterioration, and all were euthanized between 6 months to 2 years after diagnosis due to tumour progression. To conclude, surgery and chemotherapy, although not curative, can prolong survival in dogs with infiltrative brain tumours. This information may help future research into the most appropriate treatment for this debilitating condition. Abstract Intracranial gliomas are associated with a poor prognosis, and the most appropriate treatment is yet to be defined. The objectives of this retrospective study are to report the time to progression and survival times of a group of dogs with histologically confirmed intracranial gliomas treated with surgical debulking and adjuvant temozolomide chemotherapy. All cases treated in a single referral veterinary hospital from 2014 to 2021 were reviewed. Inclusion criteria comprised a histopathological diagnosis of intracranial glioma, adjunctive chemotherapy, and follow-up until death. Cases were excluded if the owner declined chemotherapy or there was insufficient follow-up information in the clinical records. Fourteen client-owned dogs were included with a median time to progression (MTP) of 156 days (95% CI 133–320 days) and median survival time (MST) of 240 days (95% CI 149–465 days). Temozolomide was the first-line adjuvant chemotherapy but changed to another chemotherapy agent (lomustine, toceranib phosphate, or melphalan) when tumour relapse was either suspected by clinical signs or confirmed by advanced imaging. Of the fourteen dogs, three underwent two surgical resections and one, three surgeries. Survival times (ST) were 241, 428, and 468 days for three dogs treated twice surgically and 780 days for the dog treated surgically three times. Survival times for dogs operated once was 181 days. One case was euthanized after developing aspiration pneumonia, and all other cases after progression of clinical signs due to suspected or confirmed tumour relapse. In conclusion, the results of this study suggest that debulking surgery and adjuvant chemotherapy are well-tolerated options in dogs with intracranial gliomas in which surgery is a possibility and should be considered a potential treatment option. Repeated surgery may be considered for selected cases.

/pdf/survival-time-after-surgical-debulking-and-temozolomide-1nu3cio2.pdf

Survival Time after Surgical Debulking and Temozolomide Adjuvant Chemotherapy in Canine Intracranial Gliomas

Radiographic assessment of heart size is important for clinical management of dogs with cardiovascular disease (CVDz). We sought to compare the ability of vertebral heart size (VHS), vertebral left atrial size (VLAS), and radiologists' assessment of left atrial size (RadLAE) to predict echocardiographic left atrial size (EchoLAE), an important marker of left heart disease severity. We also compared the ability of VHS and VLAS to predict echocardiographic criteria for ACVIM stage B2 (EchoB2) in dogs with myxomatous mitral valve disease (MMVD). This prospective observational study enrolled 183 dogs with known or suspected CVDz that had an echocardiographic examination and thoracic radiographs obtained within 24 h. Compared to increased VHS, VLAS >2.3 was a more accurate predictor of EchoLAE (P = .002). VLAS >2.3 and RadLAE (both P 2.3 vertebrae (sensitivity [Sn] = 90.3%, specificity [Sp] = 73.6%) and >11.1 vertebrae (Sn = 75.8%, Sp = 76.0%), respectively. Diagnostic accuracy of VLAS (AUC 0.84, 95% CI 0.73-0.92) and VHS (AUC 0.78, 95% CI 0.66-0.88) to predict EchoB2 in dogs with subclinical MMVD (n = 64) were not significantly different (P = .17). Results demonstrate that VLAS and RadLAE were superior indicators of EchoLAE compared to VHS in dogs with known or suspected CVDz. Both VLAS and VHS are useful predictors of EchoB2 in dogs with subclinical MMVD. When echocardiography is unavailable, VLAS represents a useful radiographic measurement to aid clinical management of dogs with known or suspected CVDz.

Evaluation of radiographic predictors of left heart enlargement in dogs with known or suspected cardiovascular disease.

Magnetic resonance imaging is the primary method used to diagnose canine glial cell neoplasia and noninfectious inflammatory meningoencephalitis. Subjective differentiation of these diseases can be difficult due to overlapping imaging characteristics. This study utilizes texture analysis (TA) of intra-axial lesions both as a means to quantitatively differentiate these broad categories of disease and to help identify glial tumor grade/cell type and specific meningoencephalitis subtype in a group of 119 dogs with histologically confirmed diagnoses. Fifty-nine dogs with gliomas and 60 dogs with noninfectious inflammatory meningoencephalitis were retrospectively recruited and randomly split into training (n = 80) and test (n = 39) cohorts. Forty-five of 120 texture metrics differed significantly between cohorts after correcting for multiple testing (false discovery rate < 0.05). After training the random forest algorithm, the classification accuracy for the test set was 85% (sensitivity 89%, specificity 81%). TA was only partially able to differentiate the inflammatory subtypes (granulomatous meningoencephalitis [GME], necrotizing meningoencephalitis [NME], and necrotizing leukoencephalitis [NLE]) (out-of-bag error rate of 35.0%) and was unable to identify metrics that could correctly classify glioma grade or cell type (out-of-bag error rate of 59.6% and 47.5%, respectively). Multiple demographic differences, such as patient age, sex, weight, and breed were identified between disease cohorts and subtypes which may be useful in prioritizing differential diagnoses. TA of MR images with a random forest algorithm provided classification accuracy of inflammatory and neoplastic brain disease approaching the accuracy of previously reported subjective radiologist evaluation.

Classification of neoplastic and inflammatory brain disease using MRI texture analysis in 119 dogs

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Evaluation of radiographic predictors of left heart enlargement in dogs with known or suspected cardiovascular disease.

Classification of neoplastic and inflammatory brain disease using MRI texture analysis in 119 dogs