Прогнозирование долгосрочных колебаний внутриглазного давления при открытоугольной глаукоме на основе машинного обучения

Machine Learning-Based Prediction of Long-Term Intraocular Pressure Fluctuations in Open-Angle Glaucoma.

Elevated intraocular pressure (IOP) is the primary modifiable risk factor for glaucoma progression, a leading cause of irreversible blindness worldwide.^1,2 Although the role of IOP fluctuations remains debated and requires further research, several studies have suggested an association with disease progression.³, ⁴, ⁵, ⁶, ⁷, ⁸, ⁹, ¹⁰, ¹¹, ¹², ¹³, ¹⁴, ¹⁵ Most of these fluctuations, ranging from instantaneous and nyctohemeral to short-term or long-term variations,^3,16, ¹⁷, ¹⁸, ¹⁹, ²⁰, ²¹, ²², ²³ are inherently challenging to characterize, as meaningful assessment requires high-frequency measurements. While nyctohemeral fluctuations can be evaluated using repeated Goldmann applanation tonometry over a 24-hour period,²¹ high-frequency long-term monitoring has only recently become feasible with the advent of implantable telemetric IOP sensors, such as the EyeMate-SC.^22,23