Assessment of <i>CFH</i> and <i>HTRA1</i> polymorphisms in age-related macular degeneration using classic and machine-learning approaches

Martínez-Velasco, Antonieta; Perez-Ortiz, Andric; Antonio-Aguirre, Bani; Martínez-Villaseñor, Lourdes; Lira-Romero, Esmeralda; Palacio-Pastrana, Claudia; Ruíz, Juan-Carlos; Ramírez, Israel; Zepeda-Palacio, Claudia; Mendoza-Velásquez, Cristina; Camacho-Ordóñez, Azyadeh; Bibriesca, Daniela Michelle Ortiz; Estrada-Mena, Francisco Javier

doi:10.1080/13816810.2020.1804945

Cited by 2 publications

(1 citation statement)

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“…In ophthalmology, genomics-led AI identified novel loci for increased cup-disc-ratio (CDR) [91 ▪ ,92 ▪ ] and AMD [93 ▪ ,94 ▪ ], generated risk scores for glaucoma [92 ▪ ], predicted survival outcomes in uveal melanoma [95 ▪ ], and progression to late AMD [96 ▪ ]. These prediction models enhance the development of precision medicine.…”

Section: Text Of Reviewmentioning

confidence: 99%

Novel technical and privacy-preserving technology for artificial intelligence in ophthalmology

Lim

Hong²,

Lam

et al. 2022

Current Opinion in Ophthalmology

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Purpose of reviewThe application of artificial intelligence (AI) in medicine and ophthalmology has experienced exponential breakthroughs in recent years in diagnosis, prognosis, and aiding clinical decision-making. The use of digital data has also heralded the need for privacy-preserving technology to protect patient confidentiality and to guard against threats such as adversarial attacks. Hence, this review aims to outline novel AI-based systems for ophthalmology use, privacy-preserving measures, potential challenges, and future directions of each. Recent findingsSeveral key AI algorithms used to improve disease detection and outcomes include: Data-driven, imagedriven, natural language processing (NLP)-driven, genomics-driven, and multimodality algorithms. However, deep learning systems are susceptible to adversarial attacks, and use of data for training models is associated with privacy concerns. Several data protection methods address these concerns in the form of blockchain technology, federated learning, and generative adversarial networks. SummaryAI-applications have vast potential to meet many eyecare needs, consequently reducing burden on scarce healthcare resources. A pertinent challenge would be to maintain data privacy and confidentiality while supporting AI endeavors, where data protection methods would need to rapidly evolve with AI technology needs. Ultimately, for AI to succeed in medicine and ophthalmology, a balance would need to be found between innovation and privacy.

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