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An Update: Uveitis in Children.

SERI and the Asia-ARVO meeting-The only translational ophthalmology meeting in Asia.

Interview with the Expert: Prof Wong Tien Yin.

Partnering for Success by Mr Johnson Chen, Clearbridge BioMedics

Endofotonics: From Technology Innovation to Start-up Venture by Associate Prof. Hwang Zhi-Wei and Prof. Ho Khek-Yu

Generating the Pipeline of Biotech Start-ups by Ms Susan Kheng

AYOXXA with A Clear Vision towards Ophthalmology by Dr. Marion Lammertz

Sharper Focus on Vision: Interview with Dr. Tina Wong

Current and Emerging Diagnostic and Therapeutic Developments in Age-Related Macular Degeneration (AMD).

International Collaborative Research Program focusing on Aging.

Anterior Segment Optical Coherence Tomography Angiography (OCTA).

Clinical Trials in our Real World.

Ophthalmology Workforce Planning and Projection – A New Integrated Approach.

Robots, A Potential Staple in Eye Surgery.

Interviews at Commonwealth Science Conference 2017.

Precision Medicine for Cancer Patients: Interview with Dr Allen Lai.

The following topics are under this section:

• Lower your risk of chronic diseases with good nutrition
• Empowering sustainability through innovation
• Meeting clinical needs in Asia with HealthTech
• Showcasing Innovations and Technologies in the Food Industry

The following topics are under this section:

• ASIA-PACIFIC — Collaborative research effort reveals chemical compound that kills drug-resistant bacteria
• ASIA-PACIFIC — Big Data Analytics used for Personalized Assessment Tool for Cancer Diagnosis
• ASIA-PACIFIC — Identifying the Molecular Mechanisms of Creating a Memory
• ASIA-PACIFIC — Funding for New Stem Cell Research provides Hope for Premature Born Babies
• ASIA-PACIFIC — Human Cortical Organoids used to Identify Mechanisms behind Epilepsy in Angelman Syndrome
• ASIA-PACIFIC — The Future of Contact Lenses: Self-Moisturising Technology
• ASIA-PACIFIC — Glycosylation of Protein Structure on Virus provide Insight to Molecular Understanding
• ASIA-PACIFIC — Comprehensive Mapping of the Human Brain using Synchrotron X-rays
• REST OF THE WORLD — Uncovering New Tricks for Old Drugs
• REST OF THE WORLD — Combination of Prenatal Smoking and Drinking adds on to Risk of SIDS
• REST OF THE WORLD — Blood Pressure Readings Could Provide Valuable Information on Early Warning Signs of Heart Disease
• REST OF THE WORLD — Study finds Households in the United States Wastes nearly a Third of the Food they Buy

In the area of ophthalmology, glaucoma affects an increasing number of people. It is a major cause of blindness. Early detection avoids severe ocular complications such as glaucoma, cystoid macular edema, or diabetic proliferative retinopathy. Intelligent artificial intelligence has been confirmed beneficial for glaucoma assessment. In this paper, we describe an approach to automate glaucoma diagnosis using funds images. The setup of the proposed framework is in order: The Bi-dimensional Empirical Mode Decomposition (BEMD) algorithm is applied to decompose the Regions of Interest (ROI) to components (BIMFs+residue). CNN architecture VGG19 is implemented to extract features from decomposed BEMD components. Then, we fuse the features of the same ROI in a bag of features. These last very long; therefore, Principal Component Analysis (PCA) are used to reduce features dimensions. The bags of features obtained are the input parameters of the implemented classifier based on the Support Vector Machine (SVM). To train the built models, we have used two public datasets, which are ACRIMA and REFUGE. For testing our models, we have used a part of ACRIMA and REFUGE plus four other public datasets, which are RIM-ONE, ORIGA-light, Drishti-GS1, and sjchoi86-HRF. The overall precision of 98.31%, 98.61%, 96.43%, 96.67%, 95.24%, and 98.60% is obtained on ACRIMA, REFUGE, RIM-ONE, ORIGA-light, Drishti-GS1, and sjchoi86-HRF datasets, respectively, by using the model trained on REFUGE. Again an accuracy of 98.92%, 99.06%, 98.27%, 97.10%, 96.97%, and 96.36% is obtained in the ACRIMA, REFUGE, RIM-ONE, ORIGA-light, Drishti-GS1, and sjchoi86-HRF datasets, respectively, using the model training on ACRIMA. The experimental results obtained from different datasets demonstrate the efficiency and robustness of the proposed approach. A comparison with some recent previous work in the literature has shown a significant advancement in our proposal.

To accurately guide surgical instruments during ophthalmic procedures, some necessary intraoperative depth perception is required, which standard surgical microscopes supply limitedly. Intraoperative optical coherence tomography (iOCT), combining optical coherence tomography (OCT) technology and surgical microscope, enables noninvasive, real-time and high-resolution cross-sectional imaging. Currently, though iOCT enables structural imaging, little research has been done on intraoperative angiography. In this work, we presented a swept-source intraoperative OCT angiography (SS-iOCTA) system based on a standard surgical microscope, which provides both structural and angiographic images. The feasibility of the proposed SS-iOCTA was confirmed through deep anterior lamellar keratoplasty (DALK) of ex vivo porcine eyes and blood perfusion imaging of in vivo rat cortex. High-resolution intraoperative feedback, including sub-surface structure and angiogram of biological tissue, can be visualized simultaneously with the SS-iOCTA system, which expand the surgeon’s capabilities and could be widely used in clinical surgery.

Eye tracking, or oculography, provides insight into where a person is looking. Recent advances in camera technology and machine learning have enabled prevalent devices like smart-phones to track gaze and visuo-motor behavior at near clinical-quality resolution. A critical gap in using oculography to diagnose visuo-motor dysfunction on a large scale is in the design of visual task paradigms, algorithms for diagnosis, and sufficiently large datasets. In this study, we used a 500 Hz infrared oculography dataset in healthy controls and patients with various neurological diseases causing visuo-motor abnormality due to eye movement disorder or vision loss. We used novel visuo-motor tasks involving rapid reading of 40 single-digit numbers per page and developed a machine learning algorithm for predicting disease state. We show that oculography data acquired while a person reads one page of 40 single-digit numbers (15-30 seconds duration) is predictive of of visuo-motor dysfunction (ROC-AUC = 0:973). Remarkably, we also find that short recordings of about 2.5 seconds (6-12× reduction in time) are sufficient for disease detection (ROC-AUC = 0:831). We identify which tasks are most informative for identifying visuo-motor dysfunction (those with the most visual crowding), and more specifically, which aspects of the task are most predictive (the recording segments where gaze moves vertically across lines). In addition to segregating disease and controls, our novel visuo-motor paradigms can discriminate among diseases impacting eye movement, diseases associated with vision loss, and healthy controls (81% accuracy compared with baseline of 33%).