| 대한안과학회 학술대회 발표 연제 초록 |
| 녹 F-025 |
| 딥러닝을 이용하여 안구광학단층촬영으로 부터 시야검사 추론 |
부산대학교 의과대학 안과학교실 |
| 박건형, 이지웅 |
목적 : To develop a novel deep learning architecture to predict visual field using optical coherence tomography (OCT) imaging and to evaluate its performance. 방법 : Two OCT images, macular ganglion cell-inner plexiform layer (mGCIPL) and peripapillary retinal nerve fibre layer (pRNFL) thicknesses, were acquired and combined. A convolutional neural network architecture was constructed to predict visual field using this combined OCT image. The root mean square error (RMSE) between the actual and predicted visual fields was calculated to evaluate the performance. 결과 : Globally (the entire visual field area), the RMSE for all patients was 4.79 ± 2.56 dB, with 3.27 dB and 5.27 dB for the normal and glaucoma groups, respectively. The RMSE of the macular region (4.40 dB) was higher than that of the peripheral region (4.29 dB) for all subjects. In normal subjects, the RMSE of the macular region (2.45 dB) was significantly lower than that of the peripheral region (3.11 dB), whereas in glaucoma subjects, the RMSE was higher (5.62 dB versus 5.03 dB, respectively). 결론 : The deep learning method effectively predicted the visual field 24-2 using the combined OCT image. This method may help clinicians determine visual fields, particularly for patients who are unable to undergo a physical visual field exam. |