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[Use of artificial intelligence for recognition of biomarkers in intermediate age-related macular degeneration]. / Einsatz von künstlicher Intelligenz zur Erkennung von Biomarkern bei der intermediären altersabhängigen Makuladegeneration.
von der Emde, Leon; Künzel, Sandrine H; Pfau, Maximilian; Morelle, Olivier; Liermann, Yannick; Chang, Petrus; Pfau, Kristina; Thiele, Sarah; Holz, Frank G.
Afiliación
  • von der Emde L; Augenklinik, Universitätsklinikum Bonn, Venusberg-Campus 1, 53127, Bonn, Deutschland. leon.von_der_emde@ukbonn.de.
  • Künzel SH; Augenklinik, Universitätsklinikum Bonn, Venusberg-Campus 1, 53127, Bonn, Deutschland.
  • Pfau M; Institut für Molekulare und Klinische Ophthalmologie Basel, Basel, Schweiz.
  • Morelle O; Institut für Informatik 2, visual computing, Universität Bonn, Bonn, Deutschland.
  • Liermann Y; Augenklinik, Universitätsklinikum Bonn, Venusberg-Campus 1, 53127, Bonn, Deutschland.
  • Chang P; Augenklinik, Universitätsklinikum Bonn, Venusberg-Campus 1, 53127, Bonn, Deutschland.
  • Pfau K; Institut für Molekulare und Klinische Ophthalmologie Basel, Basel, Schweiz.
  • Thiele S; Augenklinik, Universitätsklinikum Bonn, Venusberg-Campus 1, 53127, Bonn, Deutschland.
  • Holz FG; Klinik für Augenheilkunde, Universitätsklinikum Hamburg-Eppendorf, Hamburg, Deutschland.
Ophthalmologie ; 121(8): 609-615, 2024 Aug.
Article en De | MEDLINE | ID: mdl-39083095
ABSTRACT
Advances in imaging and artificial intelligence (AI) have revolutionized the detection, quantification and monitoring for the clinical assessment of intermediate age-related macular degeneration (iAMD). The iAMD incorporates a broad spectrum of manifestations, which range from individual small drusen, hyperpigmentation, hypopigmentation up to early stages of geographical atrophy. Current high-resolution imaging technologies enable an accurate detection and description of anatomical features, such as drusen volumes, hyperreflexive foci and photoreceptor degeneration, which are risk factors that are decisive for prediction of the course of the disease; however, the manual annotation of these features in complex optical coherence tomography (OCT) scans is impractical for the routine clinical practice and research. In this context AI provides a solution by fully automatic segmentation and therefore delivers exact, reproducible and quantitative analyses of AMD-related biomarkers. Furthermore, the application of AI in iAMD facilitates the risk assessment and the development of structural endpoints for new forms of treatment. For example, the quantitative analysis of drusen volume and hyperreflective foci with AI algorithms has shown a correlation with the progression of the disease. These technological advances therefore improve not only the diagnostic precision but also support future targeted treatment strategies and contribute to the prioritized target of personalized medicine in the diagnostics and treatment of AMD.
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Inteligencia Artificial / Biomarcadores / Tomografía de Coherencia Óptica / Degeneración Macular Límite: Humans Idioma: De Revista: Ophthalmologie Año: 2024 Tipo del documento: Article Pais de publicación: Alemania

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Inteligencia Artificial / Biomarcadores / Tomografía de Coherencia Óptica / Degeneración Macular Límite: Humans Idioma: De Revista: Ophthalmologie Año: 2024 Tipo del documento: Article Pais de publicación: Alemania