American journal of ophthalmology · 2026
An Artificial Intelligence-Based Prognostic Model for Prediction of Functional Glaucoma Progression From Clinical and Structural Data.
СтатьяСравнительное исследованиеПоддержка NIH: внешняя программаНазвание и аннотация: Перевод в очереди
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Mohammadzadeh Vahid, Wu Sean, Besharati Sajad, Rafiee Mahshad, Banaei Yasamin, Martinyan Arthur, Zou Jane, Kung Evelyn, Edalati Kiumars, Morales Esteban, Scalzo Fabien, Caprioli Joseph, Nouri-Mahdavi Kouros
Аннотация
PURPOSE: Integration of various sources of information for prediction of disease progression is an unmet need in glaucoma diagnostics. We designed a deep learning-based prognostic model incorporating clinical and structural data for forecasting functional glaucoma progression and compared its performance to clinicians.
DESIGN: Retrospective, comparative cohort study of prognostic accuracy.
SUBJECTS: We included 1599 eyes (908 patients) with definite or suspected glaucoma with ≥5 24-2 visual fields (VF) and 3 or more years of follow-up.
METHODS: VF mean deviation (MD) rates of change were estimated with linear regression. Sequential MD rates of change were estimated with each series spanning only 5 years of follow-up. VF progression was declared when four sequential statistically significant negative MD slopes were observed, and slope for the entire follow-up was significant. A convolutional neural network pretrained on ImageNet was designed to predict VF progression using baseline clinical and demographic data, disc photographs, and optical coherence tomography-derived global and sectoral retinal nerve fiber layer and macular thickness measurements. In addition, average intraocular pressure and treatment information during follow-up were put into the model. The same data for a subset of patients was provided to two clinicians to independently predict future progression. The model was validated on a separate cohort of eyes in which optical coherence tomography imaging was done with a different device (291 eyes).
MAIN OUTCOME MEASURES: Model's area under receiver operating characteristic curves (AUC), accuracy, and area under the precision and recall curves.
RESULTS: Average (SD) baseline MD and number of VF exams were -3.5 (4.9) dB and 10.1 (4.7). 399 eyes (25%) deteriorated. The best-performing model incorporated baseline disc photographs, and retinal nerve fiber layer and macular thickness: AUC, 0.839 (0.771-0.906), accuracy, 76.0% (62.0%-85.0%), and area under the precision and recall curves, 0.558 (0.385-0.733). Deep learning model significantly outperformed clinical graders (AUC : 0.629 [0.531-0738], P < .001 and 0.680 [0.584-0.776], P = .001, for grader one and two, respectively). Model performance was similar on the validation cohort (AUC: 0.754 [0.671-0.837], and accuracy: 77% [71%-82%], respectively, P = .122). The model performed well when predicting fast-progression, defined as MD rate <-1.0 dB/y (AUC: 0.869 [0.792-0.947]).
CONCLUSIONS: Our newly designed deep learning model can combine baseline demographic and clinical data with widely available structural measurements and provide clinically relevant information for the prediction of glaucoma progression.
DESIGN: Retrospective, comparative cohort study of prognostic accuracy.
SUBJECTS: We included 1599 eyes (908 patients) with definite or suspected glaucoma with ≥5 24-2 visual fields (VF) and 3 or more years of follow-up.
METHODS: VF mean deviation (MD) rates of change were estimated with linear regression. Sequential MD rates of change were estimated with each series spanning only 5 years of follow-up. VF progression was declared when four sequential statistically significant negative MD slopes were observed, and slope for the entire follow-up was significant. A convolutional neural network pretrained on ImageNet was designed to predict VF progression using baseline clinical and demographic data, disc photographs, and optical coherence tomography-derived global and sectoral retinal nerve fiber layer and macular thickness measurements. In addition, average intraocular pressure and treatment information during follow-up were put into the model. The same data for a subset of patients was provided to two clinicians to independently predict future progression. The model was validated on a separate cohort of eyes in which optical coherence tomography imaging was done with a different device (291 eyes).
MAIN OUTCOME MEASURES: Model's area under receiver operating characteristic curves (AUC), accuracy, and area under the precision and recall curves.
RESULTS: Average (SD) baseline MD and number of VF exams were -3.5 (4.9) dB and 10.1 (4.7). 399 eyes (25%) deteriorated. The best-performing model incorporated baseline disc photographs, and retinal nerve fiber layer and macular thickness: AUC, 0.839 (0.771-0.906), accuracy, 76.0% (62.0%-85.0%), and area under the precision and recall curves, 0.558 (0.385-0.733). Deep learning model significantly outperformed clinical graders (AUC : 0.629 [0.531-0738], P < .001 and 0.680 [0.584-0.776], P = .001, for grader one and two, respectively). Model performance was similar on the validation cohort (AUC: 0.754 [0.671-0.837], and accuracy: 77% [71%-82%], respectively, P = .122). The model performed well when predicting fast-progression, defined as MD rate <-1.0 dB/y (AUC: 0.869 [0.792-0.947]).
CONCLUSIONS: Our newly designed deep learning model can combine baseline demographic and clinical data with widely available structural measurements and provide clinically relevant information for the prediction of glaucoma progression.