Women ophthalmologists are authoring publications in increasing numbers that match their prevalence in the academic and overall workforce. However, all editors are men. This discrepancy relates to the relatively younger generation of female ophthalmologists or selection bias, a subject that requires further investigation.
AimTo describe an extensive scleral excision technique to treat uveal effusion in nanophthalmic eyes.MethodsThis prospective, interventional series of eight eyes of five consecutive patients with nanophthalmos underwent scleral window surgeries. Ninety per cent of the scleral thickness, extending from immediately behind the extraocular muscle insertions to the vortex veins for 3 and 1/4 quadrants, was removed. The main outcome measure was resolution of the uveal effusions.ResultsEight eyes of five patients (one female and four male) with a mean age of 46 years were studied. The mean (range) axial length was 16.1 mm (14.6–17.6 mm), and the mean refractive error was +13.6 dioptres (+10.75 to +16.00 dioptres). Following scleral excision surgery, all uveal effusions resolved within an average (±SD) of 13.9 (±8.7) days. The uveal effusion recurred in only one eye that had a vasoproliferative retinal tumour. The mean best corrected visual acuity improved from 0.69 logarithm of the minimum angle of resolution (logMAR) (Snellen equivalent: 20/97) at baseline to 0.51 logMAR (Snellen equivalent: 20/64; Wilcoxon paired t-test: p=0.016) after a mean follow-up of 35.6 months.ConclusionThe circumferential scleral window technique produces rapid resolution of uveal effusion in nanophthalmic eyes. No adverse effects were noted after surgery and the clinical effect was durable through 1 year.Trial registration numberNCT03748732.
To report a case of bilateral acute angle closure glaucoma (AACG) that occurred after cervical spine surgery with the use of glycopyrolate. A 59-year-old male who presented with severe bilateral bifrontal headache and eye pain that started 12 h postextubation from a cervical spine surgery. Neostigmine 0.05 mg/kg (4.5 mg) and glycopyrrolate 0.01 mg/kg (0.9 mg) were used as muscle relaxant reversals at the end of the surgery. Ophthalmic examination revealed he had bilateral AACG with plateau iris syndrome that was treated medically along with laser iridotomies. Thorough examination of anterior chamber should be performed preoperatively on all patients undergoing surgeries in the prone position and receiving mydriatic agents under general anesthesia.
Background
In the last decade, a lot of attention has been given to develop artificial intelligence (AI) solutions for mental health using machine learning. To build trust in AI applications, it is crucial for AI systems to provide for practitioners and patients the reasons behind the AI decisions. This is referred to as Explainable AI. While there has been significant progress in developing stress prediction models, little work has been done to develop explainable AI for mental health.
Methods
In this work, we address this gap by designing an explanatory AI report for stress prediction from wearable sensors. Because medical practitioners and patients are likely to be familiar with blood test reports, we modeled the look and feel of the explanatory AI on those of a standard blood test report. The report includes stress prediction and the physiological signals related to stressful episodes. In addition to the new design for explaining AI in mental health, the work includes the following contributions: Methods to automatically generate different components of the report, an approach for evaluating and validating the accuracies of the explanations, and a collection of ground truth of relationships between physiological measurements and stress prediction.
Results
Test results showed that the explanations were consistent with ground truth. The reference intervals for stress versus non-stress were quite distinctive with little variation. In addition to the quantitative evaluations, a qualitative survey, conducted by three expert psychiatrists confirmed the usefulness of the explanation report in understanding the different aspects of the AI system.
Conclusion
In this work, we have provided a new design for explainable AI used in stress prediction based on physiological measurements. Based on the report, users and medical practitioners can determine what biological features have the most impact on the prediction of stress in addition to any health-related abnormalities. The effectiveness of the explainable AI report was evaluated using a quantitative and a qualitative assessment. The stress prediction accuracy was shown to be comparable to state-of-the-art. The contributions of each physiological signal to the stress prediction was shown to correlate with ground truth. In addition to these quantitative evaluations, a qualitative survey with psychiatrists confirmed the confidence and effectiveness of the explanation report in the stress made by the AI system. Future work includes the addition of more explanatory features related to other emotional states of the patient, such as sadness, relaxation, anxiousness, or happiness.
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