Utilizing Artificial intelligence (AI) for Predicting Early Disease for Animals
Artificial intelligence (AI) has been making waves in various industries, from healthcare to finance, and now it is emerging as a new ally in the fight against animal diseases. With the rapid advancement of technology, AI has the potential to revolutionize the way we approach the prevention, diagnosis, and treatment of diseases in animals, ultimately improving their overall health and welfare.
The use of artificial intelligence (AI) in livestock health management has revolutionized the dairy production industry. Farmers have struggled to identify and take timely action against animal diseases that threaten the lives of their livestock and significantly impact dairy production and food security. However, with AI-based early detection systems for livestock health management, farmers can easily identify sick cows and take prompt action to prevent further spread of disease. The dairy production industry has long faced the threat of animal diseases that can quickly spread and cause significant livestock and economic losses. Not only do these diseases threaten the lives of livestock, but they also have a severe impact on dairy production and food security. Farmers have always been challenged with the task of identifying sick animals and taking timely action to prevent the spread of disease. However, with the help of artificial intelligence (AI), farmers can now use an early detection system to identify sick livestock and take prompt action to prevent further spread of disease. In dairy production, AI-based early detection systems are a game-changer because they provide a non-invasive method of detecting potential health issues in cows. Using AI and infrared vision, the system can identify the profiled cattle and record the heat emitted. This allows for the detection of elevated temperatures that may indicate a fever or illness. A noninvasive approach to livestock health management ensures that no unnecessary procedures are carried out, making it a more humane and ethical method.
Artificial Intelligence (AI) is catching up its resurgence again after a dormancy period. AI is gaining traction across multi sectors including telecom, financial services and many other when compared to the situation an year or so ago. AI was core of chatbots since it was considered as a dangerous tool that will overtake human intelligence. Today adoption of AI by hundreds of companies move on it to utilize their data to the clouds and even it found a place in school curriculum and textbooks. The coming generations will be a springtime for artificial intelligence just like the need of driverless cars, as the intelligent machine can replicate human performance on any task. Artificial intelligence can tackle the problems with greater accuracy and speed when compared to human intelligence if wisely used. Artificial intelligence has a significant role in achieving One Health concept on a global basis by resolving the real worldwide issues by simulating the human knowledge and reasoning skills. The concept of one health emphasise that the health of human is linked with that of animals and environment. Application of artificial intelligence for obtaining one health enables to monitor and control public health threats at an earlier stage by recognising the changes in patterns of behaviour and its relationship between human, animal and environment. AI models are well developed for various healthcare programmes like diagnostic procedures, treatment protocol, drug development especially for the development of personalized medicines, monitoring and care of patients. Artificial Intelligence also allows faster data collection and processing due to its efficient computing power. AI technology aids clinicians to detect a minute change which may go unnoticed in a routine imaging process. It can also describe and evaluate the outcome of certain surgical procedures even. Constant monitoring of patient is possible by AI devices. Predictive modelling of electronic health records using AI in individualized treatment will be a promising tool to predict the course of disease and probable response in each patient. Adoption of artificial intelligence reduces the medical costs considerably due to its higher accuracy in diagnosis and better prediction in treatment plan as well as preventive strategies to be followed. Artificial Intelligence along with brain computer interfaces helps those patients with troubles in speaking, hearing or differently disabled by decoding the neural activities. Virtual nursing assistants can answer patient’s enquiry with the use of AI and can decrease their unnecessary hospital visits. AI models are now widely getting popular in livestock field too. Management of dairy cows based on artificial intelligence is practicing in some European countries due to its precise digital nature. With the adoption of artificial intelligence, the farm management tools becomes more specific. AI module identifies each cow by facial recognition technology and track the behaviour of them in the barn itself. The information collected is used for designing key animal and farm performance indicators, which are later informed to dairy entrepreneurs. The real time detailed analytics and daily notification help the dairy farmer to find out the loopholes and animal health issues for further action or modification of the management. Even the feeding behaviour of each animal can be monitored real time and the digitalized data can be stored for further stud ies. Thus the analytics addressing in time ensures the better welfare and productivity of the animals.
One of the most significant challenges in managing animal diseases is the early detection and diagnosis of infections. Traditional methods of diagnosis, such as laboratory tests and clinical examinations, can be time-consuming and costly. Moreover, these methods may not always be accurate, leading to misdiagnoses and delayed treatment. AI has the potential to overcome these limitations by offering rapid, accurate, and cost-effective diagnostic solutions.
Machine learning, a subset of AI, has been particularly useful in this regard. By analyzing large amounts of data, machine learning algorithms can identify patterns and make predictions, allowing for early detection of diseases. For example, researchers have developed an AI-based system that can analyze the vocalizations of pigs to detect signs of respiratory diseases. This system can accurately identify sick pigs, enabling farmers to take appropriate action and prevent the spread of disease.
Another area where AI can make a significant impact is in the development of new vaccines and treatments for animal diseases. Traditionally, the process of developing a new vaccine or treatment can take years and involve extensive testing on animals. AI can help streamline this process by predicting the effectiveness of potential vaccines and treatments, reducing the need for animal testing and speeding up the development process.
For instance, researchers have used AI to predict the structure of proteins in the foot-and-mouth disease virus, which could lead to the development of new vaccines. Similarly, AI has been used to identify potential drug candidates for treating African swine fever, a highly contagious and deadly disease affecting pigs.
AI can also play a crucial role in monitoring and controlling the spread of animal diseases. By analyzing data from various sources, such as satellite imagery, social media, and veterinary reports, AI can help identify potential outbreaks and predict their spread. This information can be invaluable for governments and organizations working to prevent and control animal diseases, allowing them to allocate resources more effectively and implement targeted interventions.
For example, AI has been used to predict the spread of avian influenza, enabling authorities to implement targeted surveillance and control measures. In another instance, researchers have developed an AI-based system that can predict the risk of rabies outbreaks in wildlife populations, helping to inform vaccination campaigns and other control measures.
In addition to these applications, AI can also help improve animal welfare by identifying and addressing issues related to animal health and well-being. For example, AI-based systems can monitor the behavior and physiological parameters of animals, such as heart rate and body temperature, to detect signs of stress or discomfort. This information can be used to improve animal housing and management practices, ultimately enhancing the welfare of animals.
Identification of symptoms, cattle diseases and providing proper treatments is difficult in the contemporary medical industry. Real-time management of the symptoms of cow illness and disease types as animals can’t explain their problems or pain that they are facing. In medical sector finding the cattle disease symptoms, diseases are a challenging task. Manual process of identifying the cattle disease and treatment is too complex and time consuming and also expensive. These technologies only gather information, store it in databases, and then retrieve it in the future; they do not extract any helpful data that enables medical professionals to manage the cattle disease in a better way. Existing system is a manual process where doctors diagnosis animals and identifies the diseases and gives the treatment. In foreign countries they use some advanced system such as IBM Watson, the MYCIN expert system , etc. These technologies merely gather information, store it in databases, and retrieves the same in the future, but no important information that aids medical professionals in handling the cattle disease in a better way.
AI can be used for prediction of the occurrences of livestock disease outbreaks at an early stage itself. Seasonal and climatic forecasts based on AI for the prediction and better management of infectious diseases is a promising tool for animal healthcare sector. Analysis of disease patterns, disease maps, distribution of livestock population and study of disease impact in the environment can be achieved more effectively by AI models. The animal health management can be improved by an early detection of any disease like laminitis or mastitis before the appearance of clinical or subclinical stages of diseases. The data regarding prediction of events such as oestrus, dietary changes and behaviour tracking can be obtained from AI models implemented collar sensors. Accurate prediction of rumen fermentation pattern plays significant role for the evaluation of diets which has a role in milk production. One interesting application of AI is the prediction of carcass weight of food animals based on its zoometric measurement features and live bodyweight before slaughtering. Artificial intelligence models can be effectively explored in other species of animals also like beef cattle, hogs or poultry. Early detection of problems in commercial production of eggs is also possible by AI technology. Convolutional neural networks based on face recognition are used in pigs to identify the animal, making them tags and distress free. Even in aquaculture, application of AI has an exciting opportunity for the effective management of fish, thereby improving our nutrition. Artificial intelligence technology also adds up the value to the supply chain of livestock products, addressing the growing interest in animal welfare, traceability and sustainability. The progressive agricultural entrepreneurs are very much interested in investment in those technologies which make a boom in near future. The application of AI models for sustainable development and reduction of environmental deterioration will be fruitful in case of food security operations. Microsoft Corporation is rendering various agriculture services in India based on AI facility. The precision agriculture using AI technology helps to improve plants health as well as crop production. The detection of plant diseases, the causative pest, the nutrition deficiency, the identification of readiness of crop for harvesting are some areas where AI is using to optimise the resources. AI sensors can detect and target weeds and then decide which herbicides to be applied within the right buffer zone. Scanning of large crop fields by imaging enables the real time monitoring of crops in order to take rapid and appropriate actions by the farmer. Automatic irrigation techniques by AI probed machines add up the overall yield by conserving the water. Personalized recommendation for each farmer based on his land and soil parameters, weather forecast, pest infection at a specific area and the external factors like trends in marketplace, crop prices and consumer needs enable farmers to take rapid and real decisions for successful farming. Artificial neural networks with applications like differentiation of weeds from crops, forecasting of water resource variables, prediction of nutritional level in crops, prediction of crop quality and yield etc are in use in various agricultural farms across the world.
AI’s potential for human medicine
AI was first introduced in the 1950s. However, it took decades before the required computing power matured and became cost-effective enough for researchers to explore its many applications. Now, almost daily, researchers are unearthing new ways to apply AI and machine learning to diagnose, treat, and even predict human diseases. These innovative applications promise earlier, more precise patient care, and, ultimately, better quality of life and longevity.
The potential for AI to radically improve human health by finding disease—even predicting it before it occurs—is undeniable. Its success offers physicians an unprecedented opportunity to prepare for disease before it occurs by helping patients achieve the best health possible, to delay disease through proven interventions, or, in some cases, even to prevent it. The possibilities are both extraordinary and endless.
AI’s promise for veterinary medicine
The early success of artificial intelligence in improving human lives and helping physicians better understand complicated diseases makes its foray into animal health unsurprising. Pets also face devastating diseases for which diagnosis is sometimes too late to have a meaningful impact. In terms of complexity, chronic kidney disease (CKD) is among the frontrunners. As veterinarians know all too well, CKD is a multifactorial disease that is discouragingly difficult to detect in time to impact an animal’s health and longevity. Once it’s present, kidney damage is irreversible. CKD is increasingly prevalent in cats aged five and older. In fact, one third of this population could be affected. Traditionally available laboratory parameters—namely creatinine levels—detect loss of kidney function after 75 percent or greater is gone.2 And that’s only if pet owners maintain strict adherence to preventive care guidelines and visit annually for an exam. Cats with advancing chronic kidney disease often show no clinical signs until at least 50 to 67 percent of renal mass is lost. In an effort to find disease earlier, symmetric dimethylarginine (SDMA), a biomarker of renal function, was introduced in 2015. While an improvement, even this more sensitive biomarker finds disease only after 25 percent of kidney function is lost.
Large data sets of patient images and results are being used to train AI software to detect complex diseases with increasing accuracy. Some practice management systems employ AI systems to interpret veterinary results to recommend diagnostic alternatives and next steps. There are even AI-powered stethoscopes that help identify arrhythmias and other cardiac-related disorders. And these are just a few of AI’s numerous use cases in veterinary medicine. In a nutshell, veterinarians can now look to technology for intelligent and reliable clinical decision support.
Detect Rare Animals’ Diseases
One of AI’s primary uses in veterinary medicine is detecting potential animal diseases. Our fluffy friends cannot talk, which makes it difficult for doctors to diagnose their health conditions. Fortunately, the AI application used in veterinary medicine can help doctors correctly identify more intractable diseases for animals.
For instance, AI can be used to detect Addison’s disease in dogs, which can have very serious consequences for dogs. Addison’s disease is usually hard to diagnose in dogs. Since the symptoms of the disease are similar to many common dog illnesses, this often leads doctors to overlook the possibility of Addison’s disease. A dog with Addison’s disease may have symptoms such as:
- Losing weight
- Unhappy and depressed
- Drinking too much water
- Unable to face stress
- Frequently diarrhea
- Poor appetite
- Even though properly timed treatment can help dogs with Addison’s disease have normal lifespans, all these vague and common symptoms have become hindrances for doctors trying to accurately diagnose the disease.
- Thankfully, the application of AI provides a solution to this dilemma. Nowadays, an AI-based algorithm developed by the veterinarians at the University of California, Davis School of Veterinary Medicine, plays a significant role in disease diagnosis. Addison’s disease leads to insufficient hormone secretion in dogs, which will show some slight differences in dogs’ blood test results. The AI-based algorithm is trained to identify the differences and report abnormal blood tests.
- In this situation, the algorithm is working as an alarm, telling veterinarians which medical cases are suspected of potential Addison’s disease. Then, veterinarians will proceed with further diagnostic testing for these cases. With learning and training, the accuracy rate of this AI-powered algorithm can reach 99 percent.
Predict Potential Animal Disease
In addition to detecting rare diseases, AI applications in veterinary medicine can also help doctors to predict animals’ healthy conditions and the risk of getting potential diseases in the following years and provide proactive care.
For example, AI is largely used in predicting Chronic kidney disease (CKD) for cats. According to AAHA, CKD has become the 1st cause of death in cats older than five, and about 30% of cats over 12 years old suffer from this disease. The usual symptoms of CKD include:
- Bad breath
- Poor hair quality
- Weight loss
- Depression
- Variable appetite
Well, an AI-powered algorithm was developed by the American College of Veterinary Internal Medicine. By analyzing the data from more than 150,000 cats, the algorithm now has the ability to predict the potential risk of a cat developing CKD. By analyzing and learning from the huge amount of health data, the algorithm can predict whether a cat will get the disease in the next two years or not with an accuracy of 95%.
Even though the prediction can not prevent the occurrence of CKD, it allows veterinarians to take proactive care of the “future patients,” which will help the cats to suffer less and live longer and happier lives.
Automatically Code Notes for Doctors
Taking notes is significant and necessary for both doctors and vets. The records provide the health background and medical experience of pets, which work as accurate resources for vets to diagnose the condition of animal patients.
However, taking vet notes is not that simple. Before AI intervention, the traditional way of taking vet clinical notes was by handwriting, which made the work time-consuming, messy, and hard to copy. The difficulty had been bothering vets for decades until James Zou, an assistant professor of biomedical data science, invented an AI-powered algorithm called DeepTag.
Using artificial intelligence and applying natural language processing, the software is able to understand the texts of doctor’s notes and transform the textual information into codes that represent specific symptoms and diseases. In this way, it becomes easier to extract information from clinical databases, compare medical cases, and identify suspicious cases with potential disease risks.
Better Interpretation of Medical Images
Another way AI benefits veterinary medicine is by providing a better interpretation of medical images, such as radiology results. AI-based software can take over the simple and tedious work of veterinary radiologists, such as analyzing data, collecting information, and classifying cases. More importantly, it can provide suggestions to prioritize serious cases based on its interpretation of medical images.
Even though the AI-based software cannot fully replace the doctor’s role, it can streamline the process and improve the efficiency of diagnosis.
SignalPET is an AI-powered software that is widely accepted in veterinary medicine. The software aims to provide intelligent image interpretations for both veterinarians and patients in an innovative way. By applying machine learning classification techniques, the software can provide objective, consistent, and data-driven results within 10 minutes after it accesses the animal radiographs.
In conclusion, artificial intelligence has the potential to revolutionize the way we approach the prevention, diagnosis, and treatment of animal diseases. By offering rapid, accurate, and cost-effective solutions, AI can help improve the health and welfare of animals, reduce the economic impact of diseases on farmers and the wider economy, and ultimately contribute to a more sustainable and resilient food system. As technology continues to advance, it is essential that we harness the power of AI to protect the health of animals and ensure their well-being.
Compiled & Shared by- This paper is a compilation of groupwork provided by the
Team, LITD (Livestock Institute of Training & Development)
Image-Courtesy-Google
Reference-On Request.
APPLICATION OF ARTIFICIAL INTELLIGENCE (AI) IN ANIMAL HEALTH AND VETERINARY SCIENCES
