medRxiv - Ophthalmology最新文献_第10页

Development of the Advised Protocol for OCT Study Terminology and Elements Anterior Segment OCT extension reporting guidelines: APOSTEL-AS OCT研究术语和要素建议方案的发展前段OCT扩展报告指南:APOSTEL-AS

medRxiv - Ophthalmology Pub Date : 2023-11-22 DOI: 10.1101/2023.11.21.23298845

Ameenat L Solebo, Edmund Tsui, Alastair K Denniston, Axel Petzold, Alex S Huang

引用次数: 0

Clinical applicability of the WaveFront Phase Imaging Sensor in the Keratoconus Disease 波前相位成像传感器在圆锥角膜疾病中的临床应用

medRxiv - Ophthalmology Pub Date : 2023-11-20 DOI: 10.1101/2023.11.20.23298756

Carolina Belda-Para, Gonzalo Velarde-Rodríguez, Miriam Velasco-Ocaña, Juan M. Trujillo-Sevilla, Iván Rodríguez-Méndez, Javier Rodríguez-Martin, Nicolas Alejandre-Alba, Silvia Rodríguez-García, José M. Rodríguez-Ramos

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The impact of pre-existing eye donation awareness of the next of kin on donation rate after grief counseling - A cross-sectional study. 哀伤辅导后近亲已有捐眼意识对捐眼率影响之横断面研究。

medRxiv - Ophthalmology Pub Date : 2023-11-16 DOI: 10.1101/2023.11.15.23298568

Oshin Puri, Neeti Gupta, Sanjeev Mittal

{"title":"The impact of pre-existing eye donation awareness of the next of kin on donation rate after grief counseling - A cross-sectional study.","authors":"Oshin Puri, Neeti Gupta, Sanjeev Mittal","doi":"10.1101/2023.11.15.23298568","DOIUrl":"https://doi.org/10.1101/2023.11.15.23298568","url":null,"abstract":"This cross-sectional study was conducted to assess the impact of pre-existing eye donation awareness of the next of kin on the donation rate after grief counseling with due ethical approval. The N=164 most stable next-of-kin, mostly the Brother (20.7%) or Father (20.1%) of the deceased, were approached by the Eye Donation Counselor (EDC). After assessing their awareness about eye donation through the Awareness and Perception on Eye Donation questionnaire (Ronanki, V.R, et. al), the EDC grief counseled the next of kin. 84.8% of all participants were aware of eye donation. The mean awareness, knowledge, and perception scores of the study population were 2.46 +/- 1.34 out of 4, 2.73 +/- 2.37 out of 6, and 1.79 +/- 1.72 out of 4 respectively. Eye care professionals (N=105 (64%)) and mass media (N=61 (37.2%)) were identified as the most common sources of information. While 52.4% expressed willingness to donate, only 7.3% donated and there was 1 voluntary donation. Counseling and the belief is eye donation being a noble deed were identified as the major motivators, and objections by other family members, and religious beliefs were the major barriers. There is no significant association between eye donation and the pre-existing awareness of the next of kin regarding eye donation. Although awareness is associated with the increased willingness to donate eyes.","PeriodicalId":501390,"journal":{"name":"medRxiv - Ophthalmology","volume":"25 3","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-11-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138525832","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Self-supervised contrastive learning improves machine learning discrimination of full thickness macular holes from epiretinal membranes in retinal OCT scans 自监督对比学习改进了机器学习在视网膜OCT扫描中对视网膜前膜全层黄斑孔的识别

medRxiv - Ophthalmology Pub Date : 2023-11-15 DOI: 10.1101/2023.11.14.23298513

Tim Wheeler, Kaitlyn Hunter, Patricia Anne Garcia, Henry Li, Andrew Thomson, Allan Hunter, Courosh Mehanian

{"title":"Self-supervised contrastive learning improves machine learning discrimination of full thickness macular holes from epiretinal membranes in retinal OCT scans","authors":"Tim Wheeler, Kaitlyn Hunter, Patricia Anne Garcia, Henry Li, Andrew Thomson, Allan Hunter, Courosh Mehanian","doi":"10.1101/2023.11.14.23298513","DOIUrl":"https://doi.org/10.1101/2023.11.14.23298513","url":null,"abstract":"There is a growing interest in using computer-assisted models for the detection of macular conditions using optical coherence tomography (OCT) data. As the quantity of clinical scan data of specific conditions is limited, these models are typically developed by fine-tuning a generalized network to classify specific macular conditions of interest. Full thickness macular holes (FTMH) present a condition requiring timely surgical intervention to prevent permanent vision loss. Other works on automated FTMH classification have tended to use supervised ImageNet pre-trained networks with good results but leave room for improvement. In this paper, we develop a model for FTMH classification using OCT slices around the central foveal region to pre-train a naïve network using contrastive self-supervised learning. We found that self-supervised pre-trained networks outperform ImageNet pre-trained networks despite a small training set size (284 eyes total, 51 FTMH+ eyes, 3 slices from each eye). 3D spatial contrast pre-training yields a model with an F1- score of 1.0 on holdout data (50 eyes total, 10 FTMH+), compared ImageNet pre-trained models, respectively. These results demonstrate that even limited data may be applied toward self-supervised pre- training to substantially improve performance for FTMH classification, indicating applicability toward other OCT-based problems.","PeriodicalId":501390,"journal":{"name":"medRxiv - Ophthalmology","volume":"58 9","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-11-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138525833","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0