Deep learning based highly accurate transplanted bioengineered corneal equivalent thickness measurement using optical coherence tomography
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 co...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2024-11-01
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| Series: | npj Digital Medicine |
| Online Access: | https://doi.org/10.1038/s41746-024-01305-3 |
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| author | Daewoon Seong Euimin Lee Yoonseok Kim Che Gyem Yae JeongMun Choi Hong Kyun Kim Mansik Jeon Jeehyun Kim |
| author_facet | Daewoon Seong Euimin Lee Yoonseok Kim Che Gyem Yae JeongMun Choi Hong Kyun Kim Mansik Jeon Jeehyun Kim |
| author_sort | Daewoon Seong |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-066c86984e60482e83c3a5981d7e1083 |
| institution | Kabale University |
| issn | 2398-6352 |
| language | English |
| publishDate | 2024-11-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | npj Digital Medicine |
| spelling | doaj-art-066c86984e60482e83c3a5981d7e10832024-11-10T12:43:36ZengNature Portfolionpj Digital Medicine2398-63522024-11-017111310.1038/s41746-024-01305-3Deep learning based highly accurate transplanted bioengineered corneal equivalent thickness measurement using optical coherence tomographyDaewoon Seong0Euimin Lee1Yoonseok Kim2Che Gyem Yae3JeongMun Choi4Hong Kyun Kim5Mansik Jeon6Jeehyun Kim7School of Electronic and Electrical Engineering, College of IT engineering, Kyungpook National UniversitySchool of Electronic and Electrical Engineering, College of IT engineering, Kyungpook National UniversitySchool of Electronic and Electrical Engineering, College of IT engineering, Kyungpook National UniversityBio-Medical Institute, Kyungpook National University HospitalBio-Medical Institute, Kyungpook National University HospitalBio-Medical Institute, Kyungpook National University HospitalSchool of Electronic and Electrical Engineering, College of IT engineering, Kyungpook National UniversitySchool of Electronic and Electrical Engineering, College of IT engineering, Kyungpook National UniversityAbstract 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.https://doi.org/10.1038/s41746-024-01305-3 |
| spellingShingle | Daewoon Seong Euimin Lee Yoonseok Kim Che Gyem Yae JeongMun Choi Hong Kyun Kim Mansik Jeon Jeehyun Kim Deep learning based highly accurate transplanted bioengineered corneal equivalent thickness measurement using optical coherence tomography npj Digital Medicine |
| title | Deep learning based highly accurate transplanted bioengineered corneal equivalent thickness measurement using optical coherence tomography |
| title_full | Deep learning based highly accurate transplanted bioengineered corneal equivalent thickness measurement using optical coherence tomography |
| title_fullStr | Deep learning based highly accurate transplanted bioengineered corneal equivalent thickness measurement using optical coherence tomography |
| title_full_unstemmed | Deep learning based highly accurate transplanted bioengineered corneal equivalent thickness measurement using optical coherence tomography |
| title_short | Deep learning based highly accurate transplanted bioengineered corneal equivalent thickness measurement using optical coherence tomography |
| title_sort | deep learning based highly accurate transplanted bioengineered corneal equivalent thickness measurement using optical coherence tomography |
| url | https://doi.org/10.1038/s41746-024-01305-3 |
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