Category Archives for "AI Software"

Vetology In the News

Vetology In the News

The American Medical Veterinary Association interviewed Dr. Seth Wallack, Founder of Vetology and Eric Goldman, President of Vetology. We discussed with the AVMA the challenges of not having enough radiologists to meet the demand. This is not just a problem for our teleradiology practice, but a global problem that impacts general practice managers, specialty and emergency hospitals. Turn-around times for obtaining a radiology over-read by a boarded radiologist can take upwards of 8 hours to 5 days. A 2018 study predicted that, by the end of 2022, 66 percent of the North American veterinary teleradiology caseload will not be met.

Vetology recognized the power of AI technology to automatically read images and interpret results. In 2017, Vetology developed software to use visual object resolution to help diagnose diseases. The techniques were first used internally to support our own radiologists to be more efficient and meet demand. In 2018, the capabilities were expanded and we were the first to market to offer an augmented interpretation of radiographs using artificial intelligence and machine learning. This allowed for a complete radiograph interpretation in 5 minutes or less and on par to that of a human veterinary radiologist.

The AVMA identified that artificial intelligence has the ability to speed up radiology interpretations and provides a substantial benefit to both the patient health and the DVM. Faster, more reliable results mean faster diagnoses, better treatment, and healthier pets.

To see more details read the full article the AVMA

Artificial intelligence & veterinary medicine

Can machine learning live up to expectations?

Radiology Technician

How Intelligent Is Artificial Intelligence?

How Intelligent Is Artificial Intelligence?

AI For Human And Veterinary Radiology

Radiology Artificial Intelligence, commonly referred to as AI, is in full development, and the FDA is actively testing AI in human radiology. In veterinary radiology we’re not far behind and are quickly catching up. Veterinary medicine AI product development’s greatest strength is also its greatest weakness. That is, oversight, or more specifically, the lack of oversight by a governing body.

In human radiology, AI products require FDA oversight and approval prior to coming to market (see the American Association of Veterinary Radiologists GMLP write-up here to learn more). Some veterinary products do fall under FDA oversight BUT veterinary radiology AI isn’t one of them. This means a veterinary radiology AI company can develop quickly but also have no formal obligation to demonstrate their product actually works.

If a company doesn’t provide clinical test results, it is up to you, the veterinarian, to determine the worthiness of the product. A true 'caveat emptor.'


Assessment Is Critical

So how should a veterinarian assess a veterinary radiology AI product? The same way the entire medical community evaluates any diagnostic test for a condition, by measuring clinical performance.

The two standard measures of clinical performance are SENSITIVITY and SPECIFICITY. To better understand how these measures can assist us to asses clinical performance, we will briefly revisit a couple of formulas from that old favorite - statistics class.


Sensitivity And Specificity

SENSITIVITY is the probability that a test will identify a patient who HAS a condition (true positive). It is calculated by the following formula:

Sensitivity = True Positives / (True Positives + False Negatives)


SPECIFICITY is the probability a test will correctly identify a patient who DOES NOT have a condition (true negative). It is calculated by the following formula:

Specificity = True Negatives / (True Negatives + False Positives)


The Confusion Matrix

These two standard measures of clinical performance lead us to the four outcomes possible for each patient:

True Positive, False Positive, True Negative, False Negative


The four outcomes are typically reported in a 2 x 2 table called a confusion matrix showing the total numbers of true and false positives and negatives. A generic example is shown here next.

A Typical Confusion Matrix

Name of Condition or Disease

Total Number of Cases Measured

% Sensitivity

% Specificity


Radiologist Positive

Radiologist Negative

AI Positive

# of cases

# of cases

AI Negative

# of cases

# of cases


Vetology's AI Testing

Vetology’s AI testing evaluates AI results against veterinary radiologist reports as a reference standard. The results and confusion matrix tables are displayed just below on this page.


The Truth Is In The Confusion Matrix

To help you evaluate a product’s performance, always ask an AI vendor for their confusion matrix tables.

Also, be aware that an AI product MUST be 100% autonomous to have a valid result. If a human intervenes during any part of the result creation, it’s not artificial intelligence, it’s human intelligence.

Next we show the confusion matrix for several diseases among 75 random cases:

Cardiomegaly

47 Cases

90% Sensitivity

76% Specificity


Radiologist Positive

Radiologist Negative

AI Positive

17

19

AI Negative

3

8

Heart Failure

39 Cases

100% Sensitivity

89% Specificity


Radiologist Positive

Radiologist Negative

AI Positive

3

4

AI Negative

0

32

Dynamic Airway Pattern

30 Cases

100% Sensitivity

85% Specificity


Radiologist Positive

Radiologist Negative

AI Positive

3

4

AI Negative

0

23

Dynamic Airway Collapse

30 Cases

67% Sensitivity

93% Specificity


Radiologist Positive

Radiologist Negative

AI Positive

2

2

AI Negative

1

25

Buyers Beware

As we said earlier in this article, AI brings to the forefront of your purchase decision-making, the phrase 'caveat emptor,' or buyer beware. Make sure you review the provider's confusion matrix tables (if they have them at all), and make sure their AI is fully autonomous, else you'll just be buying expensive human intelligence.

At Vetology, we assertively and proactively test ourselves and continually train and improve the AI for everyone's benefit. We are as transparent as possible. We have the data and are willing to publish it.

If you have any questions about our veterinary radiology software or services, we encourage you to reach out to us via email here.

AI Machine Learning

Veterinary Radiology Artificial Intelligence Software Made Simple: A Step-By-Step Guide

Veterinary Radiology Artificial Intelligence Software Made Simple: A Step-By-Step Guide

A Brief History

Need a brief history of AI software in general here.


Teleradiology

Need a history of the current state of veterinary teleradiology here.


Image Match

Need to discuss image matching; its strengths and drawbacks.


AI In Medicine Is Growing Very Fast



AI For Veterinary Radiology

This is where we would pivot to the specific application of AI and Machine Learning applied to veterinary radiology


Sensitivity

Need to explain what sensitivity means.


Specificity

Need to explain what specificity means.


Confusion Matrix

Need to explain our confusion matrix and enough to give confidence.


Vetology's AI Guardian Software

Need to give overview of our software.


Intended Use

This is where we would provide intended use


Post-Market Monitoring Plan

What are our post-market ongoing monitoring, reporting and corrective plans? This should consider latest data and new data sources.