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Query: UMLS:C0086543 (cataract)
 29,165 document(s) hit in 31,850,051 MEDLINE articles (0.00 seconds)

We assessed vision before and after uncomplicated extracapsular cataract extraction and intraocular lens implantation in 72 symptomatic patients with acuity equal to or better than 20/80 and no other ocular abnormality. Contrast sensitivity was measured with the Pelli-Robson Letter Chart (Metropia Ltd, Cambridge, England) and disability glare was measured under daytime conditions with the Brightness Acuity Tester (Mentor O&O Inc, Norwell, Mass) and under nighttime conditions with a computer-controlled video display. Prior to surgery there was significant disability glare that was not correlated with acuity. There was also a loss in contrast sensitivity that was moderately correlated with acuity (r = -.43; P < .001). Following surgery, most patients' scores returned to normal on all tests. Improvement in disability glare and contrast sensitivity was independent of improvement in acuity. Furthermore, patients with the poorest preoperative vision were as likely to regain normal function after surgery as those with the best preoperative vision.
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PMID:Comparison of acuity, contrast sensitivity, and disability glare before and after cataract surgery. 842 25

Computer-assisted interventions (CAI) aim to increase the effectiveness, precision and repeatability of procedures to improve surgical outcomes. The presence and motion of surgical tools is a key information input for CAI surgical phase recognition algorithms. Vision-based tool detection and recognition approaches are an attractive solution and can be designed to take advantage of the powerful deep learning paradigm that is rapidly advancing image recognition and classification. The challenge for such algorithms is the availability and quality of labelled data used for training. In this Letter, surgical simulation is used to train tool detection and segmentation based on deep convolutional neural networks and generative adversarial networks. The authors experiment with two network architectures for image segmentation in tool classes commonly encountered during cataract surgery. A commercially-available simulator is used to create a simulated cataract dataset for training models prior to performing transfer learning on real surgical data. To the best of authors' knowledge, this is the first attempt to train deep learning models for surgical instrument detection on simulated data while demonstrating promising results to generalise on real data. Results indicate that simulated data does have some potential for training advanced classification methods for CAI systems.
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PMID:Can surgical simulation be used to train detection and classification of neural networks? 2918 68