Cataract Classification Systems: A Review

Abstract

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.

Zusammenfassung

Der Graue Star gehört weltweit zu einer der häufigsten Ursachen für Sehverschlechterung. Der Fortschritt der letzten Jahre in der Behandlung hat zu erheblich verbesserten Ergebnissen für die Patienten geführt, jedoch sind hierfür die richtigen diagnostischen Werkzeuge erforderlich. Diese Arbeit gibt einen Überblick über die verschiedenen Kataraktklassifikationssysteme, die in den letzten Jahrzehnten entwickelt wurden, und befasst sich mit deren Vor- und Nachteilen. Bis heute gilt das Lens Opacity Classification System III als Standard der Kataraktklassifikation. Dabei werden verschiedene Linsenmerkmale anhand von standardisierten Spaltlampenbildern eingestuft. Diese Methode wird sowohl bei Peer-Review-Publikationen zur Katarakt als auch im klinischen Alltag nur selten angewandt und ist aufgrund ihres subjektiven Charakters eingeschränkt. Die jüngsten Fortschritte in der Bildgebung, insbesondere die Scheimpflug-Tomografie sowie die optische Kohärenztomografie, haben die Möglichkeit einer objektiven Bewertung der Linsendichte eröffnet. Durch den Einsatz einer Software zur automatisierten Erkennung der Linsenanatomie konnten Forscher eine gute Korrelation zu funktionellen und chirurgischen Metriken wie Sehschärfe, Phakoemulsifikationsenergie und Operationszeit nachweisen. Die Entwicklung von Deep-Learning-Netzen hat die Leistungsfähigkeit dieser Klassifikationssysteme weiter erhöht, da diese die Interpretierbarkeit und die Robustheit bei normabweichenden Fällen verbessern. Diese Klassifikationssysteme, die sowohl für Screening als auch für präoperative Diagnostik eingesetzt werden können, könnten für gezielte prospektive Studien von großem Wert sein, bedürfen aber noch der Validierung und Implementierung im klinischen Alltag. Bislang fehlen jedoch kontrollierte Untersuchungen zur Spezifität und Sensitivität.

Key words

cataract - cataract grading - LOCS III - deep learning - Scheimpflug imaging - optical coherence tomography

Schlüsselwörter

Katarakt - Kataraktklassifikation - LOCS III - Deep Learning - optische Kohärenztomografie - Scheimpflug-Tomografie

Publication History

Received: 21 December 2022

Accepted: 06 November 2023

Article published online:
19 January 2024

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