Peer-reviewed veterinary case report
Non-invasive AI system detects cancer in dog skin lumps
By Gillian Dank et al.·Published in Frontiers in Veterinary Science·2023·Koret School of Veterinary Medicine, Hebrew University, Rehovot, Israel, CH·View original on DOAJ →
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Original publication title: A pilot study for a non-invasive system for detection of malignancy in canine subcutaneous and cutaneous masses using machine learning
- Species:
- dog
Plain-English summary
A group of 45 dogs with skin lumps were tested using a new thermal imaging device designed to help tell if these lumps were benign or cancerous without needing invasive procedures. The device measured the heat from the lumps and surrounding tissue, and an artificial intelligence system analyzed the data. The results showed that the device was quite accurate, correctly identifying cancerous masses 90% of the time. While this technology is still in development, it could eventually help veterinarians make quicker and more accurate decisions about whether a lump needs further testing.
People also search for: dog skin lump cancer detection · non-invasive cancer test for dogs · thermal imaging for dog tumors
Abstract
IntroductionEarly 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.MethodsForty-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.ResultsThe accuracy, sensitivity, specificity, positive predictive value, and negative predictive value of the system were 90%, 93%, 88%, 83%, and 95%, respectively for all masses.ConclusionWe 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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Search related cases →Original publication on DOAJ: https://doi.org/10.3389/fvets.2023.1109188