A pilot study for a non-invasive system for detection of malignancy in canine subcutaneous and cutaneous masses using machine learning
Early diagnosis of cancer enhances treatment planning and improves prognosis. Many masses presenting to veterinary clinics are difficult to diagnose without using invasive, time-consuming, and costly tests. Our objective was to perform a preliminary proof-of-concept for the HT Vista device, a novel...
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Published in | Frontiers in veterinary science Vol. 10; p. 1109188 |
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Main Authors | , , , , , , |
Format | Journal Article |
Language | English |
Published |
Switzerland
Frontiers Media S.A
26.01.2023
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Subjects | |
Online Access | Get full text |
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Summary: | Early diagnosis of cancer enhances treatment planning and improves prognosis. Many masses presenting to veterinary clinics are difficult to diagnose without using invasive, time-consuming, and costly tests. Our objective was to perform a preliminary proof-of-concept for the HT Vista device, a novel artificial intelligence-based thermal imaging system, developed and designed to differentiate benign from malignant, cutaneous and subcutaneous masses in dogs.
Forty-five dogs with a total of 69 masses were recruited. Each mass was clipped and heated by the HT Vista device. The heat emitted by the mass and its adjacent healthy tissue was automatically recorded using a built-in thermal camera. The thermal data from both areas were subsequently analyzed using an Artificial Intelligence algorithm. Cytology and/or biopsy results were later compared to the results obtained from the HT Vista system and used to train the algorithm. Validation was done using a "Leave One Out" cross-validation to determine the algorithm's performance.
The accuracy, sensitivity, specificity, positive predictive value, and negative predictive value of the system were 90%, 93%, 88%, 83%, and 95%, respectively for all masses.
We propose that this novel system, with further development, could be used to provide a decision-support tool enabling clinicians to differentiate between benign lesions and those requiring additional diagnostics. Our study also provides a proof-of-concept for ongoing prospective trials for cancer diagnosis using advanced thermodynamics and machine learning procedures in companion dogs. |
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Bibliography: | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23 Edited by: Carlos Eduardo Fonseca-Alves, Paulista University, Brazil Reviewed by: Stanislaw Dzimira, Wrocław University of Environmental and Life Sciences, Poland; Cord Brundage, University of Wisconsin–La Crosse, United States This article was submitted to Oncology in Veterinary Medicine, a section of the journal Frontiers in Veterinary Science Present address: Gillian Dank, Onco Pet, Kfar Saba, Israel |
ISSN: | 2297-1769 2297-1769 |
DOI: | 10.3389/fvets.2023.1109188 |