The HT Vista paper demonstrates the use of thermal imaging and artificial intelligence (AI) for accurate screening between benign masses and those requiring further investigation.
The paper revealed the device has a 97 percent negative predictive value and a sensitivity of 85 percent helping clinicians to ensure potentially malignant masses in dogs are not missed.
Liron Levy-Hirsch, managing director of HT Vista’s UK subsidiary comments, “we are thrilled to have scientific research validating the success of the HT Vista device.
“The veterinary teams who have already adopted the device into their practice are having great success with it, and with the backing of this newly published paper we hope to reach more practices and ultimately save more dog’s lives.”
The study evaluated over 660 masses between 2020 and 2022 across two phases, the first to train the algorithm and the second to validate it.
Liron continues, “it is exciting to see the algorithm improve with every scan. We are very pleased with the results we have and continue to look to the future where we are sure the sensitivity will increase further.”
Tali Buber, veterinary medical director at HT Vet, says, “I’m excited to be a significant part of bringing innovation to the veterinary profession.
“The HT Vista development has been extremely satisfying – scanning the cases, comparing the results to the lab reports, enriching the database and working hand in hand with the AI team to improve the algorithm.”
The study, conducted on a diverse canine population with cutaneous and subcutaneous masses, yielded impressive results.
HT Vista’s combination of thermal imaging and AI led to a 97 percent negative predictive value, affirming its ability to confidently screen benign masses. Notably, the system exhibited a high sensitivity of 85 percent, ensuring reliable detection of potential malignant masses and indicating the need for further investigation in these cases.
The HT Vista device combines thermal imaging technology with artificial intelligence.
Unlike traditional methods, HT Vista measures heat transfer rate differences between masses and adjacent normal tissues, providing a more accurate analysis.
The data is processed using advanced machine learning algorithms, enabling rapid and precise classification of masses.
This study has shown that this novel system could be used as a screening tool and decision support tool for the everyday diagnosis of dermal and subcutaneous masses in general practice, enabling clinicians to differentiate between benign lesions and those requiring additional diagnostics.