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Verfasst von:Müller, Simon [VerfasserIn]   i
 Jain, Mohit [VerfasserIn]   i
 Sachdeva, Bhuvan [VerfasserIn]   i
 Shah, Payal N. [VerfasserIn]   i
 Holz, Frank G. [VerfasserIn]   i
 Finger, Robert P. [VerfasserIn]   i
 Murali, Kaushik [VerfasserIn]   i
 Wintergerst, Maximilian W. M. [VerfasserIn]   i
 Schultz, Thomas [VerfasserIn]   i
Titel:Artificial intelligence in cataract surgery
Titelzusatz:a systematic review
Verf.angabe:Simon Müller, Mohit Jain, Bhuvan Sachdeva, Payal N. Shah, Frank G. Holz, Robert P. Finger, Kaushik Murali, Maximilian W. M. Wintergerst, Thomas Schultz
E-Jahr:2024
Jahr:April 2024
Umfang:15 S.
Illustrationen:Illustrationen
Fussnoten:Gesehen am 21.01.2025
Titel Quelle:Enthalten in: Translational Vision Science & Technology
Ort Quelle:Rockville, Md. : ARVO, 2012
Jahr Quelle:2024
Band/Heft Quelle:13(2024), 4 vom: Apr., Artikel-ID 20, Seite 1-15
ISSN Quelle:2164-2591
Abstract:The purpose of this study was to assess the current use and reliability of artificial intelligence (AI)-based algorithms for analyzing cataract surgery videos. A systematic review of the literature about intra-operative analysis of cataract surgery videos with machine learning techniques was performed. Cataract diagnosis and detection algorithms were excluded. Resulting algorithms were compared, descriptively analyzed, and metrics summarized or visually reported. The reproducibility and reliability of the methods and results were assessed using a modified version of the Medical Image Computing and Computer-Assisted (MICCAI) checklist. Thirty-eight of the 550 screened studies were included, 20 addressed the challenge of instrument detection or tracking, 9 focused on phase discrimination, and 8 predicted skill and complications. Instrument detection achieves an area under the receiver operator characteristic curve (ROC AUC) between 0.976 and 0.998, instrument tracking an mAP between 0.685 and 0.929, phase recognition an ROC AUC between 0.773 and 0.990, and complications or surgical skill performs with an ROC AUC between 0.570 and 0.970. The studies showed a wide variation in quality and pose a challenge regarding replication due to a small number of public datasets (none for manual small incision cataract surgery) and seldom published source code. There is no standard for reported outcome metrics and validation of the models on external datasets is rare making comparisons difficult. The data suggests that tracking of instruments and phase detection work well but surgical skill and complication recognition remains a challenge for deep learning. This overview of cataract surgery analysis with AI models provides translational value for improving training of the clinician by identifying successes and challenges.
DOI:doi:10.1167/tvst.13.4.20
URL:Bitte beachten Sie: Dies ist ein Bibliographieeintrag. Ein Volltextzugriff für Mitglieder der Universität besteht hier nur, falls für die entsprechende Zeitschrift/den entsprechenden Sammelband ein Abonnement besteht oder es sich um einen OpenAccess-Titel handelt.

kostenfrei: Volltext: https://doi.org/10.1167/tvst.13.4.20
 DOI: https://doi.org/10.1167/tvst.13.4.20
Datenträger:Online-Ressource
Sprache:eng
K10plus-PPN:1915215064
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