Key takeaways:

* Researchers used machine learning to predict glaucoma progression based on dozens of biomarkers, in

Machine Learning Models Predict Glaucoma Progression

Researchers have developed machine learning models that can predict the progression of primary open-angle glaucoma, potentially allowing for more personalized patient management. The study,published recently,utilized data from individuals with different stages of primary open-angle glaucoma for at least 36 months, then used the data to build predictive machine learning models using ranked partial least squares discriminant analysis.

Two models were trained: one for early stage glaucoma trained on 27 variables and one for moderate/advanced stage glaucoma trained on 20 variables. Both showed “high prognostic accuracy” for identifying slow, moderate and rapid glaucoma progression, the researchers wrote.

For early stage primary open-angle glaucoma, the most crucial variable in the prediction model was retinal nerve fiber layer thickness in the inferotemporal sector.

“This finding emphasizes the vulnerability of the inferotemporal [retinal nerve fiber layer] region in early glaucomatous damage, likely reflecting the anatomical susceptibility of nerve fibers entering the optic disc from this sector,” the researchers wrote.

And for more advanced disease, the most important predictor was ganglion cell complex thickness.

“This reflects the critical importance of macular ganglion cell integrity assessment in advanced disease, where residual functional capacity directly correlates with remaining retinal ganglion cells,” the researchers wrote.

the findings “emphasize that vascular factors remain significant predictors of glaucoma progression at both early and advanced disease stages,” Kurysheva and colleagues wrote.

The researchers wrote that their machine learning approach could benefit clinical practices by allowing them to fine-tune follow-up intervals and selectively target preventive measures.

“Rather than relying on a few key metrics, our optimized models for early and advanced cohorts incorporate all measured predictors …to capture the full spectrum of disease heterogeneity,” they wrote. “This extensive approach achieves high prognostic accuracy (AUC 0.90) and accommodates the continuous and multifaceted nature of glaucoma.”