CATALYZE: a deep learning approach for cataract assessment and grading on SS-OCT images.

Journal: Journal of cataract and refractive surgery

PMID: 39680679

Abstract

PURPOSE: To assess a new objective deep learning model cataract grading method based on swept-source optical coherence tomography (SS-OCT) scans provided by the Anterion.

Authors

Christophe Panthier

Department of Ophthalmology, Rothschild Foundation, 25 Rue Manin, Paris 75019, France.
Pierre Zéboulon

Department of Ophthalmology, Rothschild Foundation, Paris, France. Electronic address: pierrezeboulon@gmail.com.
Hélène Rouger

Department of Ophthalmology, Rothschild Foundation, 25 Rue Manin, Paris 75019, France.
Jacques Bijon

Department of Ophthalmology, Rothschild Foundation, 25 Rue Manin, Paris 75019, France.
Damien Gatinel

Department of Ophthalmology, Rothschild Foundation, Paris, France; CEROC (Center of Expertise and Research in Optics for Clinicians), Paris, France.

Keywords

Adult Aged Aged, 80 and over Cataract Cross-Sectional Studies Deep Learning Female Humans Male Middle Aged Prospective Studies ROC Curve Tomography, Optical Coherence Young Adult

External Resources

View on PubMed Access via DOI PubMed (39680679)

CATALYZE: a deep learning approach for cataract assessment and grading on SS-OCT images.

Abstract

Authors

Keywords

External Resources

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