Najjar Raymond P

Umapathi Ruthra, Yu Marco, Cheng Ching-Yu, Najjar Raymond P, Husain Rahat, Ho Ching Lin et al.

This study identified three distinct angle closure subtypes using ASOCT. A spacious anterior chamber subtype was linked to stable PACS, while smaller dimensions were associated with glaucoma progression.

Finkelstein Maxwell Toan, Nongpiur Monisha Esther, Husain Rahat, Perera Shamira, Baskaran Mani, Wong Tina T et al.

Handheld chromatic pupillometry reliably detected glaucoma in highly myopic eyes, showing reduced pupillary responses. This offers a useful, objective tool for diagnosing glaucoma in high myopia.

Biousse Valérie, Najjar Raymond P, Tang Zhiqun, Lin Mung Yan, Wright David W, Keadey Matthew T et al.

A deep learning system accurately detected papilledema on ED fundus photos, showing strong potential as a diagnostic aid for non-ophthalmologists to improve critical early detection.

Tan Royston K Y, Panda Satish K, Braeu Fabian A, Muralidharan Arumugam R, Nongpiur Monisha E, Chan Anita S Y et al.

This study found porcine iris stroma is permeable and biomechanically distinct from the dilator muscle, offering insights into angle-closure glaucoma mechanisms.

Mehta Anjali, Man Ryan E K, Gan Alfred T, Najjar Raymond P, Nongpiur Monisha, Lamoureux Ecosse L et al.

This study found intermediate/high obstructive sleep apnea risk is associated with 50% higher glaucoma odds in Singaporean adults, particularly in Malays. This suggests OSA screening may be relevant for glaucoma risk assessment.

Leong Yuan-Yuh, Vasseneix Caroline, Finkelstein Maxwell Toan, Milea Dan, Najjar Raymond P

This review highlights AI advancements in neuro-ophthalmology for detecting optic nerve and eye movement disorders, promising faster, more accurate diagnosis and treatment for these complex conditions.

Najjar Raymond P, Rukmini A V, Finkelstein Maxwell T, Nusinovici Simon, Mani Baskaran, Nongpiur Monisha Esther et al.

Handheld chromatic pupillometry accurately and rapidly detects functional loss in glaucoma across all severities, offering a promising objective screening and diagnostic tool.

Chan Ebenezer Jia Jun, Najjar Raymond P, Tang Zhiqun, Milea Dan

This review highlights the need for deep learning-based retinal image quality assessment tailored for optic nerve head disorders, crucial for improving diagnostic accuracy and reducing unusable images in glaucoma management.

Najjar Raymond P, Sharma Sourabh, Atalay Eray, Rukmini Annadata V, Sun Christopher, Lock Jing Zhan et al.

Early-stage glaucoma patients show reduced pupillary responses to blue and red light, correlating with retinal nerve fiber layer thinning. This suggests early melanopsin cell dysfunction, potentially aiding early detection.

Najjar Raymond P, Sharma Sourabh, Drouet Morgane, Leruez Stephanie, Baskaran Mani, Nongpiur Monisha E et al.

Preperimetric glaucoma patients show altered, slower, and less accurate eye movements, suggesting early neurodegeneration or subtle visual impairment before standard field loss, potentially aiding early detection.