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Toward Long-Term Visual Field Appearance Forecasting Using Artificial Intelligence for Ophthalmic Education and
Ye Tian1, Puja Bhavsar2, Mingyang Zang1
1Department of Biomedical Engineering, Columbia University, New York, New York.
Objective:
To provide state-of-the-art, post hoc-explainable visual field (VF) forecasts to aid in training ophthalmic residents to characterize glaucoma progression (GP), we train artificial intelligence (AI) to take as input VFs to detect GP and forecast VF appearance up to 10 years in the future. We conduct a user study to assess the clinical utility of forecasted VFs.
Design:
A retrospective study evaluating the impact of AI-assisted VF forecasts on the clinical decision-making of Columbia University Irving Medical Center ophthalmology residents.
Subjects:
For AI training, we utilize the largest available VF dataset to date including minority populations. We invited 8 ophthalmology residents to participate in the user study and 3 faculty to provide ground truth ratings.
Methods:
We implemented GenVF, a generative vision transformer for forecasting future VFs up to 10 years in the future. We showcase the promising utility of AI-forecasted VFs (AI-VF) by conducting a user study to evaluate the impact of AI information on the clinical decision-making process. In the user study, we provide residents with VFs generated by AI side-by-side with the actual first 3 VFs for a given patient to compare the outcome difference with versus without AI.
Main Outcome Measures:
We assess GP rating performance using mean absolute error (MAE), confidence levels, and reaction time with versus without AI guidance, analyzing all metrics with the nonparametric Wilcoxon signed-rank test.
Results:
Globally, MAE for GP ratings with and without AI forecasts show minor differences. Stratification by postgraduate year levels reveals the AI-VF condition demonstrates a uniform MAE, while VF-only demonstrates variability. Patient-specific analysis of resident performance reveals heterogeneous impacts of AI forecasts. Residents make more conservative decisions and report higher confidence levels with AI forecasts, while reaction times remain comparable with or without AI guidance.
Conclusions:
Our state-of-the-art, post hoc-explainable Al method using our GenVF model impacts performance, confidence, and time of GP detection, yielding more conservative decisions from user-study participants, which may help expedite glaucoma treatment for broader patient populations. Future work will address challenges in the variability of AI effectiveness across expertise levels and patient-specific contexts.
Financial Disclosures:
The authors have no proprietary or commercial interest in any materials discussed in this article.