Romana Turk
Papers
1
Total Citations
19
H-Index
1
About
Romana Turk is a pioneering veterinary researcher whose work sits at the intersection of artificial intelligence and clinical pathology. Her primary research areas include computer vision, deep learning, and their application to veterinary hematology and diagnostic imaging. Turk’s most notable contribution is her groundbreaking 2018 study, “Using Convolutional Neural Networks for Determining Reticulocyte Percentage in Cats,” which has garnered 19 citations and stands as a landmark in the field. This work demonstrated how deep learning algorithms could automate the traditionally manual and time-consuming process of counting reticulocytes—immature red blood cells—in feline blood samples, offering a faster, more objective diagnostic tool for anemia. By bridging the gap between advanced AI techniques and everyday veterinary practice, Turk has opened new avenues for precision medicine in animal health. Her research not only showcases the power of convolutional neural networks in non-human medical contexts but also inspires further exploration into AI-driven diagnostics for companion animals. Turk’s innovative approach continues to influence both veterinary pathologists and computer scientists, highlighting the transformative potential of interdisciplinary collaboration.
Research Focus
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Top Papers
- 1