Method for creating a prediction model for predicting the risk of glaucoma in a subject, method for determining the risk of glaucoma in a subject using such a prediction model, device for predicting the risk of glaucoma in a subject, computer program, and computer-readable medium

DE602020051552T2Active Publication Date: 2025-05-21GOSUDARSTVENNOE NAUCHNOE UCHREZHDENIE INST BIOORGANICHESKOY KHIMII NATSIONALNOY AKADI NAUK BELARUSI +1
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Patent Information

Application Number
DE602020051552
Authority / Receiving Office
DE · DE
Patent Type
Patents
Current Assignee / Owner
Filing Date
2020-04-09
Publication Date
2025-05-21
Estimated Expiration
2040-04-09

AI Technical Summary

Technical Problem

Current glaucoma diagnosis methods rely heavily on intraocular pressure assessment, which can lead to erroneous diagnoses, especially in cases with normal pressure, and are cumbersome, time-consuming, and prone to errors, necessitating a more effective and reliable method for predicting glaucoma risk.

Method used

A predictive model using machine learning algorithms to analyze 24-hour profiles of eyeball and cardiovascular system parameters, including correlations and additional features like age, corneal resistance, and hysteresis, to classify subjects as healthy or diseased, facilitating early risk assessment and personalized treatment.

Benefits of technology

The model provides an ultra-early and accurate glaucoma risk assessment, enabling personalized therapy and reducing the complexity of traditional diagnostic processes, thereby improving patient outcomes.

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Abstract

The invention relates to a method (100) for creating a predictive model for predicting glaucoma risk in a subject, the method comprising: a step of creating a diagnostic model comprising, for each one of a plurality of subjects: recording (s101a) a 24-hour profile of eyeball parameters; dividing (s102a) the recorded 24-hour profile of eyeball parameters at least into subperiods: an initial subperiod (START TP1); a subperiod preceding assuming a horizontal position for sleep (TP1 - SLEEP); a subperiod following assuming a horizontal position for sleep (SLEEP - TP2); a subperiod preceding assuming a vertical position after sleep (TP2 - WAKE); a subperiod following assuming a vertical position after sleep (WAKE -TP3) and a final subperiod (TP3-END); determining (s103a), in each subperiod, features describing a single subject in the form of at least one aggregating attribute; creating (s104) a record containing the determined features describing a single subject; assigning (s105) a label indicating a diagnosis (diseased / healthy) made by a physician to the created record. Furthermore, the method includes a step of creating a predictive model, based on a set of records created for the plurality of subjects, using supervised machine learning mechanisms based on one or more algorithms selected at least from regression algorithms, decision trees, Bayesian algorithms, ensemble algorithms and support vector-based algorithms. Furthermore, the invention relates to a method for determining glaucoma risk in a subject, the method comprising creating, for a patient to be examined, a record containing the same feature set as the one created in the step (s104) of the method (100) for creating a predictive model and determining an allocation of the subject to a group of diseased or healthy subjects with determined probability using the predictive model created according to the method for creating a predictive model. Furthermore, the invention relates to a device for predicting glaucoma in a subject, comprising means for performing methods according to the invention, and relates to a computer program comprising a program code for performing method steps according to the invention and to a computer readable medium on which the computer program is stored.
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