Abstract
We developed a hybrid deep learning model (HDLM) algorithm that quantitatively predicts macular ganglion cell-inner plexiform layer (mGCIPL) thickness from red-free retinal nerve fiber layer photographs (RNFLPs). A total of 789 pairs of RNFLPs and spectral domain-optical coherence tomography (SD-OCT) scans for 431 eyes of 259 participants (183 eyes of 114 healthy controls, 68 eyes of 46 glaucoma suspects, and 180 eyes of 99 glaucoma patients) were enrolled. An HDLM was built by combining a pre-trained deep learning network and support vector machine. The correlation coefficient and mean absolute error (MAE) between the predicted and measured mGCIPL thicknesses were calculated. The measured (OCT-based) and predicted (HDLM-based) average mGCIPL thicknesses were 73.96 ± 8.81 µm and 73.92 ± 7.36 µm, respectively (P = 0.844). The predicted mGCIPL thickness showed a strong correlation and good agreement with the measured mGCIPL thickness (Correlation coefficient r = 0.739; P < 0.001; MAE = 4.76 µm). Even when the peripapillary area (diameter: 1.5 disc diameters) was masked, the correlation (r = 0.713; P < 0.001) and agreement (MAE = 4.87 µm) were not changed significantly (P = 0.378 and 0.724, respectively). The trained HDLM algorithm showed a great capability for mGCIPL thickness prediction from RNFLPs.
Highlights
Spectral domain-optical coherence tomography (SD-OCT) is widely utilized for detection and quantitative assessment of glaucomatous structural loss of retinal nerve fiber layer (RNFL) and macular ganglion cell-inner plexiform layer[8,9,10,11]
This study developed and validated a novel hybrid deep learning model (HDLM) to quantify macular ganglion cell-inner plexiform layer (mGCIPL) thickness from red-free RNFL photographs (RNFLPs) so as to determine if HDLM-based RNFLP analysis could be an effective alternative to macular spectral domain-optical coherence tomography (SD-OCT) at clinics or glaucoma-screening centers where SD-OCTs are unavailable
A novel deep learning model for quantification of mGCIPL thickness from RNFLPs was validated in this study
Summary
Spectral domain-optical coherence tomography (SD-OCT) is widely utilized for detection and quantitative assessment of glaucomatous structural loss of retinal nerve fiber layer (RNFL) and macular ganglion cell-inner plexiform layer (mGCIPL)[8,9,10,11]. It is useful for diagnosing glaucoma and for monitoring glaucoma progression even before apparent visual field (VF) change[12,13]. They showed the potential of deep learning models to provide quantitative information on the extent of neural damage from qualitative data (optic disc photographs) These studies are enlightening, mounting evidence from the investigation of glaucomatous damage implicates early macular involvement[10,16,17]. This study developed and validated a novel hybrid deep learning model (HDLM) to quantify mGCIPL thickness from red-free RNFL photographs (RNFLPs) so as to determine if HDLM-based RNFLP analysis could be an effective alternative to macular SD-OCT at clinics or glaucoma-screening centers where SD-OCTs are unavailable
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