In American journal of ophthalmology ; h5-index 67.0
PURPOSE : To estimate central 10° visual field (VF) map from spectral-domain optical coherence tomography (SD-OCT) retinal nerve fiber layer thickness (RNFL) measurements in glaucoma with artificial intelligence.
DESIGN : Artificial intelligence (convolutional neural networks) study.
METHODS : This study included 5352 SD-OCT and 10-2 VF pairs from 1365 eyes of 724 healthy, glaucoma suspect and glaucoma patients. Convolutional neural networks (CNN) were developed to estimate the 68 individual sensitivity thresholds of 10-2 VF map using all-sectors (CNNA) and temporal-sectors (CNNT) RNFL thickness information of the SD-OCT circle scan (768 thickness points). 10-2 indices including pointwise total deviation (TD) values, mean deviation (MD) and pattern standard deviation (PSD) were generated using the CNN-estimated sensitivity thresholds at individual test locations. Linear regression (LR) models with the same input were used for comparison.
RESULTS : The CNNA model achieved an average pointwise mean absolute error (MAE) of 4.04 dB (95%CI: 3.76,4.35) and correlation coefficient (r) of 0.59 (95%CI: 0.52,0.64) over 10-2 map and the MAE and r of 2.88 dB (95%CI: 2.63, 3.15) and 0.74 (95%CI: 0.67, 0.80) for MD and 2.31 dB (95%CI: 2.03, 2.61) and 0.59 (95%CI: 0.51, 0.65) for PSD estimations, respectively, significantly outperforming the LRA model.
CONCLUSIONS : The proposed CNNA model improved the estimation of 10-2 VF map based on circumpapillary SD-OCT RNFL thickness measurements. These artificial intelligence methods using SD-OCT structural data show promise to individualize the frequency of central VF assessment in glaucoma patients and would enable reallocation of resources from those patients at lowest risk to those at highest risk of central VF damage.
Kamalipour Alireza, Moghimi Sasan, Khosravi Pooya, Jazayeri Mohammad Sadegh, Nishida Takashi, Mahmoudinezhad Golnoush, Li Elizabeth H, Christopher Mark, Liebmann Jeffrey M, Fazio Massimo A, Girkin Christopher A, Zangwill Linda, Weinreb Robert N
2022-Oct-31
10-2, Artificial Intelligence, Deep Learning, Glaucoma, Optical Coherence Tomography, Visual Field
