39 research outputs found

    A Review of Systemic Biologics and Local Immunosuppressive Medications in Uveitis

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    Uveitis is one of the most common causes of vision loss and blindness worldwide. Local and/or systemic immunosuppression is often required to treat ocular inflammation in noninfectious uveitis. An understanding of safety and efficacy of these medications is required to individualize treatment to each patient to ensure compliance and achieve the best outcome. In this article, we reviewed the effectiveness of systemic biologic response modifiers and local treatments commonly used in the management of patients with noninfectious uveitis

    Medication-induced Uveitis: An Update

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    Drug-induced uveitis is an uncommon but important cause of ocular inflammation. Uveitis can be seen in association with various systemic, topical, and intraocular medications. In this article, we review common medications associated with uveitis. Most cases of drug-induced uveitis resolve with termination of the suspected medication with or without administration of topical or systemic steroids. It is important for clinicians to readily identify medications that may cause uveitis in order to provide rapid treatment, avoid consequences of longstanding inflammation, and prevent costly and excessive laboratory testing

    Brain abscess as a manifestation of spinal dermal sinus

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    Dermal sinuses have been associated with a wide spectrum of clinical manifestations ranging from asymptomatic to drainage of purulent material from the sinus tract, inclusion tumors, meningitis, and spinal abscess. To date, there has been no documented report of brain abscess as a complication of spinal dermal sinus. Here, we report an 8-month-old girl who was presented initially with a brain abscess at early infancy but lumbar dermal sinus and associated spinal abscess were discovered afterwards. The probable mechanisms of this rare association have been discussed

    Performance of Automated Machine Learning in Predicting Outcomes of Pneumatic Retinopexy

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    PURPOSE: Automated machine learning (AutoML) has emerged as a novel tool for medical professionals lacking coding experience, enabling them to develop predictive models for treatment outcomes. This study evaluated the performance of AutoML tools in developing models predicting the success of pneumatic retinopexy (PR) in treatment of rhegmatogenous retinal detachment (RRD). These models were then compared with custom models created by machine learning (ML) experts. DESIGN: Retrospective multicenter study. PARTICIPANTS: Five hundred and thirty nine consecutive patients with primary RRD that underwent PR by a vitreoretinal fellow at 6 training hospitals between 2002 and 2022. METHODS: We used 2 AutoML platforms: MATLAB Classification Learner and Google Cloud AutoML. Additional models were developed by computer scientists. We included patient demographics and baseline characteristics, including lens and macula status, RRD size, number and location of breaks, presence of vitreous hemorrhage and lattice degeneration, and physicians\u27 experience. The dataset was split into a training (n = 483) and test set (n = 56). The training set, with a 2:1 success-to-failure ratio, was used to train the MATLAB models. Because Google Cloud AutoML requires a minimum of 1000 samples, the training set was tripled to create a new set with 1449 datapoints. Additionally, balanced datasets with a 1:1 success-to-failure ratio were created using Python. MAIN OUTCOME MEASURES: Single-procedure anatomic success rate, as predicted by the ML models. F2 scores and area under the receiver operating curve (AUROC) were used as primary metrics to compare models. RESULTS: The best performing AutoML model (F2 score: 0.85; AUROC: 0.90; MATLAB), showed comparable performance to the custom model (0.92, 0.86) when trained on the balanced datasets. However, training the AutoML model with imbalanced data yielded misleadingly high AUROC (0.81) despite low F2-score (0.2) and sensitivity (0.17). CONCLUSIONS: We demonstrated the feasibility of using AutoML as an accessible tool for medical professionals to develop models from clinical data. Such models can ultimately aid in the clinical decision-making, contributing to better patient outcomes. However, outcomes can be misleading or unreliable if used naively. Limitations exist, particularly if datasets contain missing variables or are highly imbalanced. Proper model selection and data preprocessing can improve the reliability of AutoML tools. FINANCIAL DISCLOSURES: Proprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article
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