Cataract classification systems

• Verfasst von: Mackenbrock, Lars [VerfasserIn]
• Verfasst von: Łabuz, Grzegorz [VerfasserIn]
• Verfasst von: Baur, Isabella [VerfasserIn]
• Verfasst von: Yildirim, Timur Mert [VerfasserIn]
• Verfasst von: Auffarth, Gerd U. [VerfasserIn]
• Verfasst von: Khoramnia, Ramin [VerfasserIn]
• Titel: Cataract classification systems
• Titelzusatz: a review
• Paralleltitel: Kataraktklassifikationssysteme : eine Übersicht
• Verf.angabe: Lars H.B. Mackenbrock, Grzegorz Labuz, Isabella D. Baur, Timur M. Yildirim, Gerd U. Auffarth, Ramin Khoramnia
• E-Jahr: 2024
• Jahr: 01/2024
• Umfang: 9 S.
• Fussnoten: Online verfügbar: 19. Januar 2024 ; Gesehen am 31.07.2024
• Titel Quelle: Enthalten in: Klinische Monatsblätter für Augenheilkunde
• Ort Quelle: Stuttgart : Thieme, 1980
• Jahr Quelle: 2024
• Band/Heft Quelle: 241(2024), 1, Seite 75-83
• ISSN Quelle: 1439-3999
• DOI: doi:10.1055/a-2003-2369
• Datenträger: Online-Ressource
• Sprache: eng
• K10plus-PPN: 1897212143

Abstract

<p>Cataract is among the leading causes of visual impairment worldwide. Innovations in treatment have drastically improved patient outcomes, but to be properly implemented, it is necessary to have the right diagnostic tools. This review explores the cataract grading systems developed by researchers in recent decades and provides insight into both merits and limitations. To this day, the gold standard for cataract classification is the Lens Opacity Classification System III. Different cataract features are graded according to standard photographs during slit lamp examination. Although widely used in research, its clinical application is rare, and it is limited by its subjective nature. Meanwhile, recent advancements in imaging technology, notably Scheimpflug imaging and optical coherence tomography, have opened the possibility of objective assessment of lens structure. With the use of automatic lens anatomy detection software, researchers demonstrated a good correlation to functional and surgical metrics such as visual acuity, phacoemulsification energy, and surgical time. The development of deep learning networks has further increased the capability of these grading systems by improving interpretability and increasing robustness when applied to norm-deviating cases. These classification systems, which can be used for both screening and preoperative diagnostics, are of value for targeted prospective studies, but still require implementation and validation in everyday clinical practice.</p>