Veterinary Technology Students Collaborate with Vetology on AI Software

Veterinary Technology Students Collaborate with Vetology on AI Software

by | Mar 26, 2024 | News, reviews2

FOR IMMEDIATE RELEASE

Massasoit Community College Veterinary Technology Students Collaborate with Vetology on AI Software

BROCKTON, Mass (March 26, 2024) – This spring, the Massasoit Community College Veterinary Technology Program has partnered with Vetology to offer students access to a new veterinary artificial intelligence software: Vetology.ai. Massasoit is the first community college to receive access to this technology.

Vetology introduced their AI software in 2018 as a solution to the increasing demand for veterinary radiologists amidst the limited number of graduating certified radiologists. Utilizing state-of-the-art technology, Vetology’s AI can provide virtual reports, with 90% accuracy, within minutes while patients are still on location.

“We at Vetology.ai are honored to be in a position to extend access to our software to Veterinary Technician students at Massasoit Community College,” said Eric Goldman, Vetology President. “We firmly believe in the synergy of humans and AI, and providing early exposure to our cutting-edge tool equips the next generation with essential skills for their careers while empowering them to shape the future of patient care.”

With Vetology, Massasoit students learn from the software in real time and gain more experience. In return Vetology gains insight from the students on how the program is working and how humans and AI can work better together.

 “The generous partnership with Vetology enables students in the Massasoit Veterinary Technology Program to learn cutting edge technology that is the future of veterinary medicine,” said Roda Motta, Director of the Veterinary Technology Program. “Training better veterinary technicians with better tools for better patient outcomes.”

Massasoit’s Veterinary Technology Program is an AVMA CVTEA accredited program. Students who graduate from this program are eligible to take the Veterinary Technician National Exam (VTNE). In Massachusetts, students are required to graduate from an AVMA CVTEA accredited program and obtain a passing score on the VTNE before they can apply for credentialing through the Massachusetts Veterinary Technician Association.

Massasoit students currently in the Veterinary Technology Imaging course are the first class to have access to the Vetology database. With the program they are learning to improve their positioning skills and reduce the number of radiographs taken.

“The software is very helpful for our field,” said Jenna Hodgson, Vet Tech student, Class of 2025. “The feedback is quick and helps technicians learn what a good radiograph looks like. This is very helpful for students who are learning. It will be very helpful having this experience going into practice.”

Interested in learning more about the Massasoit Veterinary Technology program? Visit our Veterinary Technology landing page.

For more information on how Vetology’s AI-driven radiology reports and boarded radiologist services can support your practice, visit Vetology.ai to contact us.

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ABOUT MASSASOIT COMMUNITY COLLEGE

Founded in 1966, Massasoit Community College offers students from southeastern Massachusetts and beyond access to more than 60 associate degree and certificate programs across arts, sciences, applied sciences, healthcare, and technology at locations in Brockton, Canton, Middleborough,​ and online. More than 8,000 students enroll for credit at Massasoit each year; another 1,500 students annually enroll in non-credit community education or workforce development courses. Typically, more than 800 students graduate from Massasoit with an associate degree or certificate each year. Massasoit students are given access to wraparound services provided on campus to support their success inside and outside the classroom. For more information, visit massasoit.edu.

ABOUT VETOLOGY

Vetology is a leading innovator in veterinary artificial intelligence and teleradiology, dedicated to enhancing diagnostic accuracy and efficiency. Our AI-powered tools and teleradiology services support imaging diagnostics, streamlined workflows, and enables veterinarians to make informed treatment decisions swiftly. Vetology combines advanced AI solutions with strong professional relationships to help veterinary teams deliver exceptional care.

FOR MEDIA INQUIRIES, PLEASE CONTACT US 

Veterinary AI Technology New Integrations

Veterinary AI Technology New Integrations

by | Apr 1, 2023 | News

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

by | Apr 22, 2022 | Peer Reviews

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

STUDY AUTHORS

Thiago Rinaldi Müller, Mauricio Solano, Mirian Harumi Tsunemi

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.

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

by | Jan 19, 2022 | Peer Reviews

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

STUDY AUTHORS

Eunbee Kim, Anthony J. Fischetti, Pratheev Sreetharan, Joel G. Weltman, Philip R. Fox

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.

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