Artificial Intelligence to Detect Papilledema from Ocular Fundus Photographs.

Background: Nonophthalmologist physicians do not confidently perform direct ophthalmoscopy. The use of artificial intelligence to detect papilledema and other optic-disk abnormalities from fundus photographs has not been well studied.

Methods: We trained, validated, and externally tested a deep-learning system to classify optic disks as being normal or having papilledema or other abnormalities from 15,846 retrospectively collected ocular fundus photographs that had been obtained with pharmacologic pupillary dilation and various digital cameras in persons from multiple ethnic populations.

[Clinical polymorphism of myasthenia gravis beginning with isolated ocular symptoms; a five years retrospective analysis].

Introduction: myasthenia gravis is a neuromuscular junction disorder that can jeopardize the patient's life and has a high clinical polymorphism that makes it difficult to diagnose.

Patients And Methods: after reviewing the disease physiology, its clinical symptoms, and the different means to diagnose and treat it, we present a 15-patient series that we cared for at the Rothschild ophthalmologic foundation from 2002 to 2007 for myasthenia gravis that began with isolated ocular symptoms, so as to highlight the clinical diversity of this pathology.

Results: when the disease was diagnosed, 11 patients out of 15 had a ptosis with diplopia, two had an isolated ptosis, and two had isolated diplopia.