Policy-Driven, Multimodal Deep Learning for Predicting Visual Fields from the Optic Disc and OCT Imaging.
Policy-Driven, Multimodal Deep Learning for Predicting Visual Fields from the Optic Disc and OCT Imaging.
复制标题
DOI：
10.1016/j.ophtha.2022.02.017
复制
发表时间：
2022-07
期刊：
影响因子：
13.7
通讯作者：
Lee, Aaron Y.
中科院分区：
文献类型：
--
作者：
Kihara, Yuka;Montesano, Giovanni;Chen, Andrew;Amerasinghe, Nishani;Dimitriou, Chrysostomos;Jacob, Aby;Chabi, Almira;Crabb, David P.;Lee, Aaron Y.
To develop and validate a deep learning (DL) system for predicting each point on visual fields (VF) from disc and optical coherence tomography (OCT) imaging and derive a structure-function mapping. Retrospective, cross-sectional database study 6437 patients undergoing routine care for glaucoma in three clinical sites in the UK. OCT and infrared reflectance (IR) optic disc imaging was paired with the closest VF within 7 days. Efficient-Net B2 was used to train two single modality DL models to predict each of the 52 sensitivity points on the 24-2 VF pattern. A policy DL model was designed and trained to fuse the two model predictions. Pointwise Mean Absolute Error (PMAE) A total of 5078 imaging to VF pairs were used as a held-out test set to measure the final performance. The improvement in PMAE with the policy model was 0.485 [0.438, 0.533] dB compared to the IR image of the disc alone and 0.060 [0.047, 0.073] dB compared to the OCT alone. The improvement with the policy fusion model was statistically significant (p < 0.0001). Occlusion masking shows that the DL models learned the correct structure function mapping in a data-driven, feature agnostic fashion. The multimodal, policy DL model performed the best; it provided explainable maps of its confidence in fusing data from single modalities and provides a pathway for probing the structure-function relationship in glaucoma. We used a large, real-world dataset to train a multimodal interpretable deep learning method to predict visual field sensitivity values in patients with glaucoma.
登录
查看更多内容
影响因子：
4.4
作者：
Guo Z;Kwon YH;Lee K;Wang K;Wahle A;Alward WLM;Fingert JH;Bettis DI;Johnson CA;Garvin MK;Sonka M;Abràmoff MD
通讯作者：
Abràmoff MD
影响因子：
13.7
作者：
Christopher M;Bowd C;Belghith A;Goldbaum MH;Weinreb RN;Fazio MA;Girkin CA;Liebmann JM;Zangwill LM
通讯作者：
Zangwill LM
影响因子：
3
作者：
Denniss, Jonathan;McKendrick, Allison M.;Turpin, Andrew
通讯作者：
Turpin, Andrew
影响因子：
1.8
作者：
Marin-Franch, Ivan;Swanson, William H.
通讯作者：
Swanson, William H.
影响因子：
4.4
作者：
Denniss, Jonathan;McKendrick, Allison M.;Turpin, Andrew
通讯作者：
Turpin, Andrew