Background Ischemic stroke is a time-sensitive disease, with improved outcomes associated with decreased time from onset to treatment. It was hypothesised that ambulance-based assessment of the National Institutes of Health Stroke Scale (NIHSS) using a Health Insurance Portability and Accountability Act (HIPAA)-compliant mobile platform immediately prior to arrival is feasible. Methods This is a proof-of-concept feasibility pilot study in two phases. The first phase consisted of an ambulance-equipped HIPAA-compliant video platform for remote NIHSS assessment of a simulated stroke patient. The second phase consisted of remote NIHSS assessment by a hospital-based neurologist of acute stroke patients en route to our facility. Five ambulances were equipped with a 4G/LTE-enabled tablet preloaded with a secure HIPAA-compliant telemedicine application. Secondary outcomes assessed satisfaction of staff with the remote platform. Results Phase one was successful in the assessment of three out of three simulated patients. Phase two was successful in the assessment of 10 out of 11 (91%) cases. One video attempt was unsuccessful because local LTE was turned off on the device. The video signal was dropped transiently due to weather, which delayed NIHSS assessment in one case. Average NIHSS assessment time was 7.6 minutes (range 3-9.8 minutes). Neurologists rated 83% of encounters as 'satisfied' to 'very satisfied', and the emergency medical service (EMS) rated 90% of encounters as 'satisfied' to 'very satisfied'. The one failed video attempt was associated with 'poor' EMS satisfaction. Conclusion This proof-of-concept pilot demonstrates that remote ambulance-based NIHSS assessment is feasible. This model could reduce door-to-needle times by conducting prehospital data collection.

A 39-year-old man with Candida glabrata endophthalmitis was successfully treated with a 28-day course of intravenous caspofungin. Presence of underlying renal insufficiency and infection with a drug-resistant strain precluded use of amphotericin B or fluconazole. Intravitreal administration of antifungals and vitrectomy were not required. The role of caspofungin in Candida endophthalmitis is discussed.

BackgroundMany artificial intelligence (AI) studies have focused on development of AI models, novel techniques, and reporting guidelines. However, little is understood about clinicians' perspectives of AI applications in medical fields including ophthalmology, particularly in light of recent regulatory guidelines. The aim for this study was to evaluate the perspectives of ophthalmologists regarding AI in 4 major eye conditions: diabetic retinopathy (DR), glaucoma, age-related macular degeneration (AMD) and cataract.MethodsThis was a multi-national survey of ophthalmologists between March 1st, 2020 to February 29th, 2021 disseminated via the major global ophthalmology societies. The survey was designed based on microsystem, mesosystem and macrosystem questions, and the software as a medical device (SaMD) regulatory framework chaired by the Food and Drug Administration (FDA). Factors associated with AI adoption for ophthalmology analyzed with multivariable logistic regression random forest machine learning.ResultsOne thousand one hundred seventy-six ophthalmologists from 70 countries participated with a response rate ranging from 78.8 to 85.8% per question. Ophthalmologists were more willing to use AI as clinical assistive tools (88.1%, n = 890/1,010) especially those with over 20 years' experience (OR 3.70, 95% CI: 1.10–12.5, p = 0.035), as compared to clinical decision support tools (78.8%, n = 796/1,010) or diagnostic tools (64.5%, n = 651). A majority of Ophthalmologists felt that AI is most relevant to DR (78.2%), followed by glaucoma (70.7%), AMD (66.8%), and cataract (51.4%) detection. Many participants were confident their roles will not be replaced (68.2%, n = 632/927), and felt COVID-19 catalyzed willingness to adopt AI (80.9%, n = 750/927). Common barriers to implementation include medical liability from errors (72.5%, n = 672/927) whereas enablers include improving access (94.5%, n = 876/927). Machine learning modeling predicted acceptance from participant demographics with moderate to high accuracy, and area under the receiver operating curves of 0.63–0.83.ConclusionOphthalmologists are receptive to adopting AI as assistive tools for DR, glaucoma, and AMD. Furthermore, ML is a useful method that can be applied to evaluate predictive factors on clinical qualitative questionnaires. This study outlines actionable insights for future research and facilitation interventions to drive adoption and operationalization of AI tools for Ophthalmology.