http://chineseinput.net/에서 pinyin(병음)방식으로 중국어를 변환할 수 있습니다.
변환된 중국어를 복사하여 사용하시면 됩니다.
- 中文 을 입력하시려면 zhongwen을 입력하시고 space를누르시면됩니다.
- 北京 을 입력하시려면 beijing을 입력하시고 space를 누르시면 됩니다.
RISS 인기검색어
- 검색키워드
- 저자 : Richul Oh(Richul Oh) (검색결과 1 건)
- 무료
- 기관 내 무료
- 유료
-
Deep Learning-based Prediction of Axial Length Using Ultra-widefield Fundus Photography
Richul Oh(Richul Oh),Eun Kyoung Lee(Eun Kyoung Lee),Kunho Bae(Kunho Bae),Un Chul Park(Un Chul Park),Hyeong Gon Yu(Hyeong Gon Yu),Chang Ki Yoon(Chang Ki Yoon) 대한안과학회 2023 Korean Journal of Ophthalmology Vol.37 No.2
Purpose: To develop a deep learning model that can predict the axial lengths of eyes using ultra-widefield (UWF) fundus photography. Methods: We retrospectively enrolled patients who visited the ophthalmology clinic at the Seoul National University Hospitalbetween September 2018 and December 2021. Patients with axial length measurements and UWF images taken within 3months of axial length measurement were included in the study. The dataset was divided into a development set and a testset at an 8:2 ratio while maintaining an equal distribution of axial lengths (stratified splitting with binning). We used transferlearning-based on EfficientNet B3 to develop the model. We evaluated the model’s performance using mean absolute error(MAE), R-squared (R2), and 95% confidence intervals (CIs). We used vanilla gradient saliency maps to illustrate the regions predominantlyused by convolutional neural network. Results: In total, 8,657 UWF retinal fundus images from 3,829 patients (mean age, 63.98 ±15.25 years) were included in thestudy. The deep learning model predicted the axial lengths of the test dataset with MAE and R2 values of 0.744 mm (95% CI,0.709-0.779 mm) and 0.815 (95% CI, 0.785-0.840), respectively. The model’s accuracy was 73.7%, 95.9%, and 99.2% in prediction,with error margins of ±1.0, ±2.0, and ±3.0 mm, respectively. Conclusions: We developed a deep learning-based model for predicting the axial length from UWF images with good performance.
연관 검색어 추천
이 검색어로 많이 본 자료
활용도 높은 자료
