MACHINE LEARNING TO ASSESS THE CLINICAL SIGNIFICANCE OF VITREOUS FLOATERS

Particular embodiments disclosed herein provide a method for training a machine learning model to estimate the clinical significance of floaters in a patient's eye. One or more images, such as SLO images or en face retinal OCT images, are evaluated to identify shaded regions corresponding to fl...

Ausführliche Beschreibung

Bibliographische Detailangaben

Hauptverfasser:	BOR, Zsolt, MALEK TABRIZI, Alireza
Format:	Patent
Sprache:	eng ; fre
Schlagworte:	CALCULATING COMPUTING COUNTING DIAGNOSIS HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATIONTECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING ORPROCESSING OF MEDICAL OR HEALTHCARE DATA HUMAN NECESSITIES HYGIENE IDENTIFICATION IMAGE DATA PROCESSING OR GENERATION, IN GENERAL INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTEDFOR SPECIFIC APPLICATION FIELDS MEDICAL OR VETERINARY SCIENCE PHYSICS SURGERY
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