2022 BCLA Asia In Taipei In Conjunction With Myopia Update Symposium In Taiwan

各位先進與同仁: 近視病已經帶來嚴重的併發症後果,疫情時期學童視力更趨惡化,因此,近視病防治刻不容緩,需要眼科醫師全體關注與參與。台灣近視病新知研討會將由高雄長庚紀念醫院眼科部近視防治中心與英國隱形眼鏡協會(BCLA)於8月13-14日假台北張榮發基金會國際會議中心擴大舉辦,主題為”Myopia Control, Must do it “ !

延續先前MUSIT 的議程安排,今年更特別增加二個重點課程。第一 ,與全球性的國際近視機構International Myopia Institute (IMI)合作分享近視白皮書中的”近視臨床管理方式”並特別邀請四位專家針對目前現有四種近視控制方式做案例討論及現場觀眾投票表達意見,預計有精彩應答。第二,特別與出版Contact Lens and Anterior Eye 期刊(SCI)的英國隱形眼鏡協會(BCLA)合作,針對用隱形眼鏡控制近視做深度討論。

今年不僅有多位國內醫師與專家的參與,邀請的國際近視大師包括休士頓洪立方、德布勒森Noemi Szell、溫州周翔天、上海陳志,香港Pauline Cho、伯明翰James Wolffsohn 、坎培拉Ian Morgan ,舊金山Maria Liu(劉悅),舊金山Meng C Lin (林孟青),雪梨 Padmaja Sankaridurg 等。

因新冠肺炎疫情影響,受邀之國際講師將以視訊形式參加,並提供視訊問答。但因疫情變化與配合防疫政策,若有必要,將改為全視訊會議,最終決定將於會議前用電郵通知所有與會者。

藉由本次研討會與國際學者與與國內學者們,一起對於近視病防治作更進一步的探討,回顧相關近視疾病領域之論文、分享年度新知或研究成果,以期提升近視病防治之共識,並以「提供眼科醫師最新的實證知識,提供患者最好最適切的治療」為使命,誠摯歡迎您報名參加。

Speakers


林孟青 醫師


Speaker

林孟青醫師


Institution

University of California, Berkeley, School of Optometry Clinical Research Center

UC Berkeley Dry Eye Clinic

 

Position

Director, UC Berkeley Clinical Research Center (CRC)

Co-Chief, Dry Eye Clinic


Presentation Title

Artificial Intelligence Provides Deeper Understanding of Meibomian Gland Features

 

Abstract

This presentation discusses how Artificial Intelligence (AI) can be leveraged as a critical tool to go deeper into data for uncovering novel findings.  A deep learning approach for Meibomian gland features is applied for gland phenotyping and classification.  Results from the recent studies conducted at the UC Berkeley Clinical Research Center are presented to shed lights about what clinical signs and symptoms can be predicted by machine learning-driven Meibomian gland features and how AI approach predicts demographic characteristics from Meibomian gland images.  Knowledge gained from these studies may be relevant to the pediatric group for providing guidance to healthy ocular surface necessary for longevity of successful contact lens wear. Finally, a potential of meibography as biometric identifier and the capabilities of AI to disclose subject characteristics from de-identified medical imaging are also discussed.

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