Training and validation of a novel non-invasive imaging system for ruling out malignancy in canine subcutaneous and cutaneous masses using machine learning in 664 masses - Dank, Gillian; Buber, Tali; Rice, Anna; Kraicer, Noa; Hanael, Erez; Shasha, Tamir; Aviram, Gal; Yehudayoff, Amir; Kent, Michael S. (Frontiers in Veterinary Science, 2023)
Objective. To train and validate the use of a novel artificial intelligence-based thermal imaging system as a screening tool to rule out malignancy in cutaneous and subcutaneous masses in dogs. Animals. Training study: 147 client-owned dogs with 233 masses. Validation Study: 299 client-owned dogs with 525 masses. Cytology was non-diagnostic in ninety-four masses, resulting in 431 masses from 248 dogs with diagnostic samples. Procedures. The prospective studies were conducted between June 2020 and July 2022. During the scan, each mass and its adjacent healthy tissue was heated by a high-power Light-Emitting Diode. The tissue temperature was recorded by the device and consequently analyzed using a supervised machine learning algorithm to determine whether the mass required further investigation. The first study was performed to collect data to train the algorithm. The second study validated the algorithm, as the real-time device predictions were compared to the cytology and/or biopsy results. Results. The results for the validation study were that the device correctly classified 45 out of 53 malignant masses and 253 out of 378 benign masses (sensitivity = 85% and specificity=67%). The negative predictive value of the system (i.e., percent of benign masses identified as benign) was 97%. Clinical Relevance. The results demonstrate that this novel system could be used as a decision-support tool at the point of care, enabling clinicians to differentiate between benign lesions and those requiring further diagnostics.