Development and testing of a multi-lingual Natural Language Processing-based deep learning system in 10 languages for COVID-19 pandemic crisis: A multi-center study.

Purpose: The COVID-19 pandemic has drastically disrupted global healthcare systems. With the higher demand for healthcare and misinformation related to COVID-19, there is a need to explore alternative models to improve communication. Artificial Intelligence (AI) and Natural Language Processing (NLP) have emerged as promising solutions to improve healthcare delivery.

Centration and Stability of Small-Aperture Intraocular Lens in Aberrated Eyes.

Purpose: To investigate the visual outcomes and postoperative stability of IC-8 intraocular lens (AcuFocus, Inc) implantation following femtosecond laser-assisted cataract surgery.

Methods: In this prospective study, the IC-8 IOL was implanted in the non-dominant eye of 12 patients. Centration and uncorrected and corrected distance, intermediate, and near visual acuities (UDVA, UIVA, UNVA, CDVA, CIVA, CNVA) were evaluated up to postoperative month 3 (POM3).

Corneal neuromediator profiles following laser refractive surgery.

Laser refractive surgery is one of the most commonly performed procedures worldwide. In laser refractive surgery, Femtosecond Laser in Situ Keratomileusis and Refractive Lenticule Extraction have emerged as promising alternatives to microkeratome Laser in Situ Keratomileusis and Photorefractive Keratectomy. Following laser refractive surgery, the corneal nerves, epithelial and stromal cells release neuromediators, including neurotrophins, neuropeptides and neurotransmitters.

Cross-sectional Study on Corneal Denervation in Contralateral Eyes Following SMILE Versus LASIK.

Purpose: To compare long-term corneal nerve status following small incision lenticule extraction (SMILE) versus laser in situ keratomileusis (LASIK).

Methods: Twenty-four patients were randomized to receive SMILE in one eye and LASIK in the other eye. In vivo confocal microscopy examination and dry eye assessments were performed at a mean of 4.

Virtual care to increase military medical centre capacity in the primary health care setting: A prospective self-controlled pilot study of symptoms collection and telemedicine.

Introduction: The Singapore Armed Forces (SAF) recognises the potential benefits and looks to harnessing telemedicine for primary health care services. In this prospective self-controlled pilot study, we aimed to evaluate the safety, efficiency and user satisfaction outcomes of virtual care (VC) at a military medical centre.

Methods: Out of 320 patients seen during the study period, 28 were enrolled in this study and underwent on-premises VC, comprising digital symptoms collection and telemedicine in addition to the usual in-person physician consultation.

Results of a colorectal enhanced recovery after surgery (ERAS) programme and a qualitative analysis of healthcare workers' perspectives.

Background: Enhanced recovery after surgery (ERAS) is a structured programme using a multimodal, evidence-based approach to improve post-operative outcomes. Successful implementation of ERAS can be challenging. We aimed to evaluate our initial experience with colorectal ERAS and explore the perspectives of specialist doctors and nurses.

Validation of the Use of Automated and Manual Quantitative Analysis of Corneal Nerve Plexus Following Refractive Surgery.

Following refractive surgery, the cornea is denervated and re-innervated, hence a reproducible tool to objectively quantify this change is warranted. This study aimed to determine the repeatability and reproducibility of corneal nerve quantification between automated (ACCMetrics) and manual software (CCMetrics) following refractive surgery. A total of 1007 in vivo confocal microscopy images from 20 post-small incision lenticule extraction (SMILE) or post-laser-assisted in situ keratomileusis (LASIK) patients were evaluated by two independent observers using CCMetrics for corneal nerve fibre density (CNFD), corneal nerve branch density (CNBD), and corneal nerve fibre length (CNFL).

Artificial intelligence for anterior segment diseases: Emerging applications in ophthalmology.

With the advancement of computational power, refinement of learning algorithms and architectures, and availability of big data, artificial intelligence (AI) technology, particularly with machine learning and deep learning, is paving the way for 'intelligent' healthcare systems. AI-related research in ophthalmology previously focused on the screening and diagnosis of posterior segment diseases, particularly diabetic retinopathy, age-related macular degeneration and glaucoma. There is now emerging evidence demonstrating the application of AI to the diagnosis and management of a variety of anterior segment conditions.