Deep Learning–Based Prediction of Glaucoma Severity and Progression Using Imo/TEMPO Screening Program

IF 3.2 Q1 OPHTHALMOLOGY

Ophthalmology science Pub Date : 2025-04-28 DOI:10.1016/j.xops.2025.100805

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

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引用次数: 0

Abstract

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.

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使用Imo/TEMPO筛选程序深度学习预测青光眼严重程度和进展

目的开发基于快速筛选视野法（Imo/TEMPO筛选程序[ISP]）预测Humphrey视野分析仪（HFA）综合视野（VF）信息的深度学习模型DeepISP。设计一项回顾性、横断面和纵向队列数据库研究。参与者：同一天在纪庆大学医学院附属医院接受ISP和HFA 24-2治疗的112名患者的187个实际ISP，以及在纪庆大学医学院附属4家医院使用HFA 24-2和HFA 10-2进行VF测量的883名患者的3470个合成ISP。方法开发了两种多任务神经网络变体，用于预测当前VF参数和VF进展参数。我们还评估了数据增强的有效性，以综合由HFA 24-2中的20点和HFA 10-2中的8点组成的ISP测试，并对这28点应用阈值。主要观察指标：平均偏差（MD）、模式标准差（PSD）和视野指数（VFI）的平均绝对误差。总偏差（TD）和模式偏差（PD）概率图分类的平均F1分数。MD进展（MD斜率<；−1.0分贝/年）和VFI进展（VFI斜率<；−1.8%/年）的曲线下面积（AUC）。结果deepisp可以预测当前VF状态。预测MD、PSD和VFI的平均绝对误差分别为1.869±0.114、1.918±0.082和5.146±0.487。TD和PD概率图逐点分类F1平均得分分别为0.761±0.002和0.775±0.002。青光眼半视野试验分型的AUC为0.920±0.008。DeepISP还能够预测VF进展，预测MD和VFI进展的auc分别为0.828±0.060和0.832±0.062。我们证明了ISP的通用性和预测VF综合信息的能力，包括当前严重程度和进展风险。我们的DeepISP是筛选和优先考虑青光眼患者进行临床干预的有效工具，仅使用一次快速ISP测试。财务披露专有或商业披露可在本文末尾的脚注和披露中找到。

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