한빛사논문
Daewoon Seong1,4, Euimin Lee1,4, Yoonseok Kim1, Che Gyem Yae2,3, JeongMun Choi2,3, Hong Kyun Kim2,3, Mansik Jeon1 & Jeehyun Kim1
1School of Electronic and Electrical Engineering, College of IT engineering, Kyungpook National University, Daegu, Republic of Korea.
2Bio-Medical Institute, Kyungpook National University Hospital, Daegu, Korea.
3Department of Ophthalmology, School of Medicine, Kyungpook National University, Daegu, Korea.
4These authors contributed equally: Daewoon Seong, Euimin Lee.
Corresponding authors
Correspondence to Hong Kyun Kim or Mansik Jeon.
Abstract
Corneal transplantation is the primary treatment for irreversible corneal diseases, but due to limited donor availability, bioengineered corneal equivalents are being developed as a solution, with biocompatibility, structural integrity, and physical function considered key factors. Since conventional evaluation methods may not fully capture the complex properties of the cornea, there is a need for advanced imaging and assessment techniques. In this study, we proposed a deep learning-based automatic segmentation method for transplanted bioengineered corneal equivalents using optical coherence tomography to achieve a highly accurate evaluation of graft integrity and biocompatibility. Our method provides quantitative individual thickness values, detailed maps, and volume measurements of the bioengineered corneal equivalents, and has been validated through 14 days of monitoring. Based on the results, it is expected to have high clinical utility as a quantitative assessment method for human keratoplasties, including automatic opacity area segmentation and implanted graft part extraction, beyond animal studies.
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