Artificial Intelligence and its Application in Animal Disease Diagnosis
Abstract
Present paper reviews about the origin, subfields, mandates and application of artificial intelligence in animal disease diagnosis. Artificial intelligence (AI) is intelligence manifested by machines and has developed into subfields; Machine and Deep learning. Machine learning (ML) provides application of algorithms for identification of patterns of data and creates a model to make future predictions. Commonly used algorithms are linear regression, random forest, decision tree, K-nearest and support vector machines. In deep learning, algorithms are classified into categories; Convolutional neural network, Restricted Boltzmann Machines, Auto encoder and Sparse Coding. The Convolutional Neural Networks (CNN) is one of the most notable approaches, doesn't require human supervision and automatically detects the significant features. Some of the commendable CNN architectures presented at ILSVRC (ImageNet Large Scale Visual Recognition Challenge (ILSVRC); AlexNet, ZFNet, VGG-16, GoogLeNet etc. Regarding use of AI technique in veterinary sciences, this paper reviewed some of the documented data of its application in disease prediction and diagnosis; The National Animal Disease Referral Expert System (NADRES) of ICAR-NIVEDI, detection of left atrial enlargement on canine thoracic radiology (Li et al., 2021), Predicting survivability and need for surgery in Horses with Colic (Fraiwan et al., 2020), detection of sub clinical mastitis in cows with the help of machine learning by
How strong is the evidence?
Evidence Hub reads this as a review, done in animal, not in people. Its evidence level is 5 of 5 (Very low · case / narrative / animal / in vitro / in silico). Case reports, narrative reviews, and animal, cell or computer-model studies give the weakest evidence for human health.
Read the paper
Related studies
- Improving the translation of search strategies using the Polyglot Search Translator: a randomized controlled trial
- Nutritional, Physical, Cognitive, and Combination Interventions and Frailty Reversal Among Older Adults: A Randomized Controlled Trial
- Clinical characteristics and risk factors associated with COVID-19 severity in patients with haematological malignancies in Italy: a retrospective, multicentre, cohort study
- A randomized, controlled trial of oral propranolol in infantile hemangioma
- Clinical phenotypes of delirium during critical illness and severity of subsequent long-term cognitive impairment: a prospective cohort study
- Increased physical activity improves sleep and mood outcomes in inactive people with insomnia: a randomized controlled trial
Go deeper in Evidence Hub
Evidence Hub adds a study data sheet (design, sample size, evidence level, key numbers), an AI summary and a chat grounded in the paper's text.