Triamcinolone injection in the treatment of lid retraction for thyroid-associated ophthalmopathy: A systematic review.

INTRODUCTION AND OBJECTIVES: Lid retraction is one of the most common symptoms of Thyroid-Associated Ophthalmopathy (TAO), which potentially precipitates various complications, such as dry eyes, exposure keratopathy, and cosmetic concerns. Local corticosteroid injections, such as triamcinolone, have been proposed as a choice of treatment for TAO. This approach may be a favorable alternative for patients intolerant to the systemic effects of high-dose methylprednisolone. However, the efficacy of this intervention remains unestablished. Hence, our review aims to evaluate the efficacy of triamcinolone injection in reducing lid retraction. METHODS: This systematic review followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guideline and was conducted in three databases (PubMed, Science Direct, and ProQuest). This review included studies that use local triamcinolone injections for patients with thyroid-associated ophthalmopathy. The outcome of interest in this review is lid retraction parameters. RESULTS: From six studies, a total of 392 patients were included. All studies showed significant improvement in lid retraction in the patient who received triamcinolone (all p < 0.05) as shown by ΔMRD (-0.93 mm in 1 month and -1.38 mm in 3 months), ΔMLD (-1.98 mm at 6 months), and Δpalpebral fissure height (-1.68 in 1 month). The majority of studies showed rapid improvement in lid retraction in the first month of therapy. CONCLUSION: Triamcinolone injection is an effective therapy for lid retraction related to thyroid-associated ophthalmopathy.

The use of artificial intelligence in detecting papilledema from fundus photographs.

Papilledema is an optic disc swelling with increased intracranial pressure as the underlying cause. Diagnosis of papilledema is made based on ophthalmoscopy findings. Although important, ophthalmoscopy can be challenging for general physicians and nonophthalmic specialists. Meanwhile, artificial intelligence (AI) has the potential to be a useful tool for the detection of fundus abnormalities, including papilledema. Even more, AI might also be useful in grading papilledema. We aim to review the latest advancement in the diagnosis of papilledema using AI and explore its potential. This review was conducted following the Preferred Reporting Items for Systematic Reviews and Meta-analyses guidelines. A systematic literature search was performed on four databases (PubMed, Cochrane, ProQuest, and Google Scholar) using the Keywords "AI" and "papilledema" including their synonyms. The literature search identified 372 articles, of which six met the eligibility criteria. Of the six articles included in this review, three articles assessed the use of AI for detecting papilledema, one article evaluated the use of AI for papilledema grading using Frisèn criteria, and two articles assessed the use of AI for both detection and grading. The models for both papilledema detection and grading had shown good diagnostic value, with high sensitivity (83.1%-99.82%), specificity (82.6%-98.65%), and accuracy (85.89%-99.89%). Even though studies regarding the use of AI in papilledema are still limited, AI has shown promising potential for papilledema detection and grading. Further studies will help provide more evidence to support the use of AI in clinical practice.