Work

Application of machine learning techniques in GlaucomAI system for glaucoma diagnosis and collaborative research support

Year: 2025

Type: article

Abstract: This paper proposes an architecture of the system that provides support for collaborative research focused on analysis of data acquired using Triggerfish contact lens sensor and devices for continuous monitoring of cardiovascular system properties. The system enables application of machine learning (ML) models for glaucoma diagnosis without direct intraocular pressure measurement and independently of complex imaging techniques used in clinical practice. We describe development of ML models based on sensor data and measurements of corneal biomechanical properties. Application scenarios involve collection, sharing and analysis of multi-sensor data. We give a view of issues concerning interpretability and evaluation of ML model predictions. We also refer to the problems related to personalized medicine and transdisciplinary research. The system can be a base for community-wide initiative including ophthalmologists, data scientists and machine learning experts that has the potential to leverage data acquired by the devices to understand glaucoma risk factors and the processes related to progression of the disease.

Source: Scientific Reports

DOI: 10.1038/s41598-025-89893-2

Authors: Hubert Świerczyński, Juliusz Pukacki, Szymon Szczęsny, Cezary Mazurek, Robert Wasilewicz

Institutions: Poznan Supercomputing and Networking Center, Poznań University of Technology

Language: English

Topic: Glaucoma and retinal disorders

Subfield: Ophthalmology

Field: Medicine

Domain: Health Sciences

SDG: Good health and well-being

Open Access status: gold

Estimated APC paid: $2,690