Veterinary AI Technology New Integrations

Veterinary AI Technology New Integrations

San Diego, April 1, 2023. Vetology.ai now pushes its 5 minute Virtual Radiology reports to more PIMS systems adding automatic report importing for DaySmart and RocketPACS to its growing list of PIMS integrations. Besides email, text message, and report display directly in the DICOM viewer and through Vetology’s website access, direct PIMS integration gives your clinic another way to easily integrate Vetology AI into your practice without changing workflow.

DaySmart Logo
VetRocket Logo

Per Eric Goldman, President of Vetology AI, “Our goal with the Vetology AI product is to integrate directly into a practices’ current workflow. Everyone is too busy to go searching for reports. It is up to us at Vetology AI to provide our report information when and where veterinarians want to view it.”

Vetology.ai strives to remove complexities to leveraging these technologies.  

Contact us for a demo! We look forward to learning how we can help.

Clinical Review: Accuracy of AI Software for the Detection of Confirmed Pleural Effusion in Dogs

Accuracy of Artificial Intelligence Software for the Detection of Confirmed Pleural Effusion in Thoracic Radiographs in Dogs

Abstract

The use of artificial intelligence (AI) algorithms in diagnostic radiology is a developing area in veterinary medicine and may provide substantial benefit in many clinical settings. These range from timely image interpretation in the emergency setting when no boarded radiologist is available to allowing boarded radiologists to focus on more challenging cases that require complex medical decision making. Testing the performance of artificial intelligence (AI) software in veterinary medicine is at its early stages, and only a scant number of reports of validation of AI software have been published. 

The purpose of this study was to investigate the performance of an AI algorithm (Vetology AI®) in the detection of pleural effusion in thoracic radiographs of dogs. 

  • In this retrospective, diagnostic case–controlled study, 62 canine patients were recruited.
    • A control group of 21 dogs with normal thoracic radiographs
    • and a sample group of 41 dogs with confirmed pleural effusion were selected from the electronic medical records at the Cummings School of Veterinary Medicine.
  • The images were cropped to include only the area of interest (i.e., thorax).
  • The software then classified images into those with pleural effusion and those without.
  • The AI algorithm was able to determine the presence of pleural effusion with 88.7% accuracy (P < 0.05). The sensitivity and specificity were 90.2% and 81.8%, respectively (positive predictive value, 92.5%; negative predictive value, 81.8%).

The application of this technology in the diagnostic interpretation of thoracic radiographs in veterinary medicine appears to be of value and warrants further investigation and testing.

KEEP READING

Click one of the buttons below to continue reading. If you have a subscription to Wiley Online, you can access the article there; otherwise, you click to load the PDF.

Clinical Review: AI vs. Veterinary Radiologist on Canine Cardiogenic Pulmonary Edema

Comparison of Artificial Intelligence to the Veterinary Radiologist's Diagnosis of Canine Cardiogenic Pulmonary Edema

Abstract

Application of artificial intelligence (AI) to improve clinical diagnosis is a burgeoning field in human and veterinary medicine. The objective of this prospective, diagnostic accuracy study was to determine the accuracy, sensitivity, and specificity of an AI-based software for diagnosing canine cardiogenic pulmonary edema from thoracic radiographs, using an American College of Veterinary Radiology-certified veterinary radiologist’s interpretation as the reference standard.

  • Five hundred consecutive canine thoracic radiographs made after-hours by a veterinary Emergency Department were retrieved.
  • A total of 481 of 500 cases were technically analyzable.
  • Based on the radiologist’s assessment:
    • 46 (10.4%) of these 481 dogs were diagnosed with cardiogenic pulmonary edema (CPE+).
    • Of these cases, the AI software designated 42 of 46 as CPE+ and four of 46 as cardiogenic pulmonary edema negative (CPE−).
  • Accuracy, sensitivity, and specificity of the AI-based software compared to radiologist diagnosis were:
    • 92.3%, 91.3%, and 92.4%, respectively
    • (positive predictive value, 56%; negative predictive value, 99%).

Findings supported using AI software screening for thoracic radiographs of dogs with suspected cardiogenic pulmonary edema to assist with short-term decision-making when a radiologist is unavailable.

KEEP READING

Click one of the buttons below to continue reading. If you have a subscription to Wiley Online, you can access the article there; otherwise, you click to load the PDF.

Artificial Intelligence in Clinical Radiology

Artificial Intelligence in Clinical Radiology

Join the clever minds behind the Veterinary Innovation Podcast – Shawn Wilkie, CEO of Talkatoo and Dr. Ivan Zak, CEO of Veterinary Integration Solutions – as we discuss veterinary radiology with our own Dr. Seth Wallack, DACVR.

One of the largest issues in veterinary radiology today is an incredibly high caseload. Radiologists in North America consult on 2.5 million cases per year, and that number is projected to more than double within the next three years. With fewer educational opportunities available in radiology, how can this vital specialization keep up with the demand?

This week on the Veterinary Innovation Podcast, Shawn and Ivan speak with Dr. Seth Wallack, the founder and CEO of Vetology, about how artificial intelligence can improve the workflow of clinical radiologists, whether we’re too late in adopting it, and how the best veterinarians are those who are most eager to learn.

Topics Covered in the Conversation

  • The shrinking number of Radiologists and how AI can fill those gaps.
  • The adoption of new technologies in clinical radiology.
  • Higher amounts of specialization in veterinary medicine.
$

Read the Full Article on Today's Veterinary Business

This article was originally published on October 1, 2022

Innovations in The Use of AI Veterinary Radiology Products

Innovations in The Use of AI Veterinary Radiology Products

As technology rapidly advances, AI veterinary radiology products are reshaping the field of veterinary medicine. These innovations offer new opportunities for veterinary practices to enhance diagnostics and care. In this article you will learn how:
  • AI enhances veterinary radiology for quicker, precise imaging assessments.
  • Wearable devices will revolutionize pet health monitoring and care.
  • Combining AI with human expertise ensures better patient outcomes.
  • AI-driven consultations reduce wait times and improve diagnostic accuracy.
  • Developments AI in veterinary medicine are paving the way for personalized medicine and genomics.

The implementation of artificial intelligence in the field of veterinary radiology allows doctors to receive crucial diagnostic information almost immediately.

The world of medicine is constantly changing, and the veterinary medical field is no different. Technology is advancing rapidly, and the rule is to adapt to these changes or succumb to the consequences. The use of artificial intelligence (AI) in veterinary radiology is a relatively new area. Leaders in this field are ethically responsible for providing correct product knowledge to the veterinary community, and should follow the principles of transparency, honesty, and integrity. At the same time, veterinary professionals eager to use this new technology must understand that the field is ever-evolving. As such, offered products will be in different development stages. Good Machine Learning Practices (GMLP) should be adhered to and documented.1

AI in veterinary medicine – potential areas of impact

Implementing an artificial intelligence strategy is a must for veterinary practices moving into the future. Access to specialists is not always available to hospitals or clients, and AI offers an attractive solution. However, experts believe that depending solely on AI can be detrimental. A strategic combination of both human competency and AI technology is important to drive the best care.

Some of the products related to AI in veterinary practice have already been in the marketplace for years. These are expected to gain immense popularity in the near future. The market for global wearable devices for the remote monitoring of pet health and activity is forecast to reach over $8 billion by year 2025.

When it comes to the human health market, similar products have gained in popularity. These are known for measuring simple parameters such as movement, heart rate, and body temperature. In addition, these devices keep tabs on food intake. They will make recommendations based on appropriate behavioral responses. For instance, these devices will inform diabetic patients about requirements for glucose or insulin.

New devices with the capability of measuring other important parameters will soon be available. In the veterinary field, cattle can be fitted with movement sensors to identify the onset of estrus. Similar technology will no doubt become available for other species, including pigs. At present, these sensors are quite expensive, especially for routine usage. Special efforts are being made to produce low-cost versions of the technology that would make them more accessible to farming operations.

This process will emulate the rapid decrease in the cost of genome analysis, resulting in possible developments of custom-made medicines in animals and human patients. Apart from identifying individuals with genotypes that may make them more or less vulnerable to the effects of specific drugs, professionals will also carry out genomic analyses on microbiome samples from the skin, gut, and other sites to evaluate which disease-causing organisms may exist in the patient’s body. There have been speculations on whether these approaches will ultimately lead to a reduction in the demand for antibiotics.

Advancements in monitoring technologies will similarly lead to challenges in processing and applying the available data. It is believed that doctors will have access to 200 times more data than the human mind can process. Consequently, another priority is the development of artificial intelligence software that analyzes this mass of information and draws precise conclusions about its meaning.

Google and Apple have special teams working on these issues. Significant development has already been seen; a team has produced diagnostic software that can easily identify human patients with early indications of diabetes. It is based on constant measurements of heart rate variation. This technology and others will change the way clinical practice operates.

Current applications

1. Medical imaging processing and assessment

The use of AI in this area includes quick yet precise and sensitive interpretations of radiographs, MRI images, CT scans, ultrasound images, and cytology assessments. At present, all aspects of AI are progressing exponentially, from computer processing power, speed, and affordability, to the development of machine vision reference directories. Hence, it is expected that most standard tasks involving clinical interpretation in veterinary practice will be allotted to AI. This will help veterinarians obtain quick, accurate, and detailed reports as well as consistency in interpretation, a factor that currently depends on the experience and skill level of the individual practitioner.

2. First-line primary consultations

This is another area where AI shows enormous potential. For human patients, smart kiosks are available that cut down on wait times, a major source of dissatisfaction among patients and stress for physicians. However, a combination of AI and detailed AR (augmented reality) instructions ensures a consistent, accurate, and detailed collection of crucial patient history and physical examination data. This is provided and collected by the patients themselves while they are guided through the entire procedure. After collecting the relevant data (usually completed in under 15 minutes), the doctor is sent a detailed patient work-up that includes a proper breakdown of predicted illnesses and treatment options. The medical practitioner then conducts a video consultation in order to confirm the authenticity of the AI-collected information, verify the diagnosis, and approve or alter the treatment plan. AI will also help maintain detailed and accurate healthcare records, and will automatically follow up with patients within a few days of the consultation.

Special efforts are being made to roll out veterinary-related versions of this system. Experts in the field believe that this technology will significantly augment the experience of clients and their pets. Implementation of this technology will also improve the professional lives of veterinarians, who face similar issues of stress and overload as their human physician counterparts.

Using artificial intelligence software provides an attractive option for all doctors, allowing them to receive crucial diagnostic information almost immediately.

Author disclosure statement: Eric Goldman is President of Vetology AI, a company that designs and delivers service innovation for the veterinary industry. He has a financial interest in Vetology Innovations LLC. For more information visit vetology.ai.

 

_______________________________

 

1American Association of Veterinary Radiologists guidelines, submission and review process for Veterinary Radiology Artificial Intelligence (AI) AAVR GMLP SaMD Product Certification— American Association of Veterinary Radiologists.

Pin It on Pinterest