Deep Learning–Based Prediction of Glaucoma Severity and Progression Using Imo/TEMPO Screening Program - Kei Sano, MD, PhD; Euido Nishijima, MD, PhD; Shunsuke Sumi, MD, PhD; Takahiko Noro, MD, PhD; Shumpei Ogawa, MD, PhD; Yuka Igari, MD; Aiko Iwase, MD, PhD; Tadashi Nakano, MD, PhD (Ophthalmology Science)
Purpose: To develop DeepISP, a deep learning model that predicts the comprehensive visual field (VF) information of the Humphrey visual field analyzer (HFA) based on rapid screening perimetry (Imo/TEMPO screening program [ISP]). Design: A retrospective, cross-sectional, and longitudinal cohort database study. Participants: One hundred eighty-seven actual ISPs from 112 patients who underwent both ISP and HFA 24-2 on the same day at the Jikei University School of Medicine Affiliated Hospital and 3470 synthesized ISPs from 883 patients who underwent VF measurements using HFA 24-2 and HFA 10-2 at 4 hospitals affiliated with Jikei University School of Medicine. Methods: We developed 2 variants of multitask neural networks designed to predict both current VF parameters and VF progression parameters. We also evaluated the efficacy of data augmentation to synthesize ISP tests created by combining 20 points from HFA 24-2 and 8 points from HFA 10-2, with thresholding applied to these 28 points. Main Outcome Measures: Mean absolute error for mean deviation (MD), pattern standard deviation (PSD), and visual field index (VFI). Mean F1 score for total deviation (TD) and pattern deviation (PD) probability plot classification. Area under the curve (AUC) for MD progression (MD slope <−1.0 decibel/year) and VFI progression (VFI slope <−1.8%/year). Results: DeepISP could predict current VF status. Mean absolute errors for predicting MD, PSD, and VFI were 1.869 ± 0.114, 1.918 ± 0.082, and 5.146 ± 0.487, respectively. The mean F1 scores for pointwise classification of TD and PD probability plots were 0.761 ± 0.002 and 0.775 ± 0.002, respectively. The AUC for classifying glaucoma hemifield test was 0.920 ± 0.008. DeepISP was also capable of predicting VF progression, with AUCs of 0.828 ± 0.060 and 0.832 ± 0.062 for predicting MD and VFI progression, respectively. Conclusions: We demonstrated ISP's versatility and capability in predicting comprehensive VF information, including current severity and progression risk. Our DeepISP serves as an efficient tool for screening and prioritizing patients with glaucoma for clinical intervention using only a single rapid ISP test. Financial Disclosure(s): Proprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article.