Digital Twin Models for Personalised and Predictive Medicine in Ophthalmology

This article explores the integration of Digital Twins in Systems and Predictive Medicine, focusing on eye diagnosis. By utilizing the Digital Twin models, the proposed framework can support early diagnosis and predict evolution after treatment by providing customized simulation scenarios. Furthermo...

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Bibliographic Details
Published in:Technologies (Basel) 2024-04, Vol.12 (4), p.55
Main Authors: Iliuţă, Miruna-Elena, Moisescu, Mihnea-Alexandru, Caramihai, Simona-Iuliana, Cernian, Alexandra, Pop, Eugen, Chiş, Daniel-Ioan, Mitulescu, Traian-Costin
Format: Article
Language:English
Subjects:
Biomarkers
Cloud computing
Customization
Deep learning
Diagnosis
digital twin
Digital twins
Eye diseases
genetic eye diseases
Glaucoma
Human body
Machine learning
medical informatics
Medicine
Mutation
Neural networks
Ophthalmology
Parameter identification
Patients
predictive medicine
Statistical analysis
systems medicine
Twins
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Summary:This article explores the integration of Digital Twins in Systems and Predictive Medicine, focusing on eye diagnosis. By utilizing the Digital Twin models, the proposed framework can support early diagnosis and predict evolution after treatment by providing customized simulation scenarios. Furthermore, a structured architectural framework comprising five levels has been proposed, integrating Digital Twin, Systems Medicine, and Predictive Medicine for managing eye diseases. Based on demographic parameters, statistics were performed to identify potential correlations that may contribute to predispositions to glaucoma. With the aid of a dataset, a neural network was trained with the goal of identifying glaucoma. This comprehensive approach, based on statistical analysis and Machine Learning, is a promising method to enhance diagnostic accuracy and provide personalized treatment approaches.
ISSN:2227-7080
2227-7080
DOI:10.3390/technologies12040055