Jeonbuk National University (JBNU) Animal Hospital veterinarians also excel in research achievements. At the recent Spring Conference of the Korean Society of Veterinary Clinical Medicine, three veterinarians attracted attention by receiving awards for outstanding academic presentations.

The awardees were Veterinarian Seung‑hun Jeong (Veterinary Surgery; Advisor: Professor Soo‑young Heo), Veterinarian Hae‑rin Im (Avian and Exotic Animal Outpatient Clinic; Advisor: Professor Jae‑ik Han), and Veterinarian Ye‑won Ji (Veterinary Diagnostic Imaging; Advisor: Professor Hak‑young Yoon).

They presented their research at the conference under the titles 'Postoperative prognosis evaluation of a dog with orthopaedic diseases using kitronix force-sensing resistor' (Seung‑hun Jeong), 'Diagnostic Value of Serum Amyloid A in Differentiating the Inflammatory Disorder in Bird' (Hae‑rin Im), and 'Deep learning model development for analysis of kidney volume in Computed tomographic images of dogs' (Ye‑won Ji), and were honored with awards.

Veterinarian Seung‑hun Jeong received favorable reviews for a study that evaluated changes in weight-bearing using pressure sensors in dogs with orthopedic disorders, thereby assessing changes in the load borne by each limb. This study is expected to be used in the future as an indicator for evaluating patient recovery.

Veterinarian Hae‑rin Im measured serum amyloid A (SAA), a representative acute‑phase protein in birds, in wild birds and evaluated its usefulness as a diagnostic marker for inflammatory diseases. The study confirmed that SAA can be applied to sensitively detect inflammatory diseases in wild birds.

Veterinarian Ye‑won Ji developed a deep learning model to automatically identify kidneys in CT images and measure their volume. The model can measure kidney volume automatically in a much shorter time than manual measurement using CT images, and it is expected to be used for more efficient clinical care in the future.

Through these studies by JBNU Animal Hospital veterinarians, the findings are expected to be widely applicable to patient diagnosis and prognosis assessment in the veterinary field.