Deep Learning–Based Prediction of Glaucoma Severity and Progression Using Imo/TEMPO Screening Program
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 datab...
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Elsevier
2025-11-01
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| Series: | Ophthalmology Science |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2666914525001034 |
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| author | 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 |
| author_facet | 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 |
| author_sort | Kei Sano, MD, PhD |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-cd7e2929bcb646fb8fb601e666bbbe11 |
| institution | DOAJ |
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| language | English |
| publishDate | 2025-11-01 |
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| series | Ophthalmology Science |
| spelling | doaj-art-cd7e2929bcb646fb8fb601e666bbbe112025-08-20T03:13:11ZengElsevierOphthalmology Science2666-91452025-11-015610080510.1016/j.xops.2025.100805Deep Learning–Based Prediction of Glaucoma Severity and Progression Using Imo/TEMPO Screening ProgramKei Sano, MD, PhD0Euido Nishijima, MD, PhD1Shunsuke Sumi, MD, PhD2Takahiko Noro, MD, PhD3Shumpei Ogawa, MD, PhD4Yuka Igari, MD5Aiko Iwase, MD, PhD6Tadashi Nakano, MD, PhD7Department of Ophthalmology, The Jikei University School of Medicine, Tokyo, JapanDepartment of Ophthalmology, The Jikei University School of Medicine, Tokyo, Japan; Correspondence: Euido Nishijima, MD, PhD, Department of Ophthalmology, The Jikei University School of Medicine, 3-25-8, Nishi-Shimbashi, Minato-ku, Tokyo 105-8461, Japan.Institute for Quantitative Biosciences (IQB), University of Tokyo, Tokyo, Japan; Graduate School of Medicine, Kyoto University, Kyoto, Japan; Shunsuke Sumi, MD, PhD, Institute for Quantitative Biosciences (IQB), University of Tokyo, 1-1-1 Yayoi, Bunkyo-ku, Tokyo 113-0032, Japan.Department of Ophthalmology, The Jikei University School of Medicine, Tokyo, JapanDepartment of Ophthalmology, The Jikei University School of Medicine, Tokyo, JapanDepartment of Ophthalmology, The Jikei University School of Medicine, Tokyo, JapanTajimi Iwase Eye Clinic, Tajimi, JapanDepartment of Ophthalmology, The Jikei University School of Medicine, Tokyo, Japan; Tadashi Nakano, MD, PhD, Department of Ophthalmology, The Jikei University School of Medicine, 3-25-8, Nishi-Shimbashi, Minato-ku, Tokyo 105-8461, Japan.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.http://www.sciencedirect.com/science/article/pii/S2666914525001034GlaucomaVisual fieldPerimetryScreeningDeep learning |
| spellingShingle | 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 Deep Learning–Based Prediction of Glaucoma Severity and Progression Using Imo/TEMPO Screening Program Ophthalmology Science Glaucoma Visual field Perimetry Screening Deep learning |
| title | Deep Learning–Based Prediction of Glaucoma Severity and Progression Using Imo/TEMPO Screening Program |
| title_full | Deep Learning–Based Prediction of Glaucoma Severity and Progression Using Imo/TEMPO Screening Program |
| title_fullStr | Deep Learning–Based Prediction of Glaucoma Severity and Progression Using Imo/TEMPO Screening Program |
| title_full_unstemmed | Deep Learning–Based Prediction of Glaucoma Severity and Progression Using Imo/TEMPO Screening Program |
| title_short | Deep Learning–Based Prediction of Glaucoma Severity and Progression Using Imo/TEMPO Screening Program |
| title_sort | deep learning based prediction of glaucoma severity and progression using imo tempo screening program |
| topic | Glaucoma Visual field Perimetry Screening Deep learning |
| url | http://www.sciencedirect.com/science/article/pii/S2666914525001034 |
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