The present study aimed to conduct a real-time automatic analysis of two important surgical phases, which are continuous curvilinear capsulorrhexis (CCC), nuclear extraction, and three other surgical phases of cataract surgery using artificial intell...
Wrong-site surgeries can occur due to the absence of an appropriate surgical time-out. However, during a time-out, surgical participants are unable to review the patient's charts due to their aseptic hands. To improve the conditions in surgical time-...
International journal of computer assisted radiology and surgery
32040704
PURPOSE: Annotation of surgical videos is a time-consuming task which requires specific knowledge. In this paper, we present and evaluate a deep learning-based method that includes pre-annotation of the phases and steps in surgical videos and user as...
Asia-Pacific journal of ophthalmology (Philadelphia, Pa.)
32349116
The rising popularity of artificial intelligence (AI) in ophthalmology is fuelled by the ever-increasing clinical "big data" that can be used for algorithm development. Cataract is one of the leading causes of visual impairment worldwide. However, co...
PURPOSE OF REVIEW: The subject of artificial intelligence has recently been responsible for the advancement of many industries including aspects of medicine and many of its subspecialties. Within ophthalmology, artificial intelligence technology has ...
In order to increase the probability of having a successful cataract post-surgery, the customisation of the haptic design of the intraocular lens (IOL) according to the characteristics of the patient is recommended. In this study, we present two pred...
IEEE journal of biomedical and health informatics
32750919
Cataracts are the leading cause of visual impairment worldwide. Examination of the retina through cataracts using a fundus camera is challenging and error-prone due to degraded image quality. We sought to develop an algorithm to dehaze such images to...
PURPOSE: To build and evaluate deep learning models for recognizing cataract surgical steps from whole-length surgical videos with minimal preprocessing, including identification of routine and complex steps.
PURPOSE: To develop deep learning models to perform automated diagnosis and quantitative classification of age-related cataract from anterior segment photographs.
This study assessed the performance of automated machine learning (AutoML) in classifying cataract surgery phases from surgical videos. Two ophthalmology trainees without coding experience designed a deep learning model in Google Cloud AutoML Video C...