Postprocedural Endophthalmitis or Postprocedural Intraocular Inflammation: A Diagnostic Conundrum

We experienced an atypical endophthalmitis occurring post consecutively performed in-office procedures; an intravitreal injection (IVI) of ranibizumab followed by an anterior chamber (AC) paracentesis performed twice in an eye with neovascular glaucoma (NVG). A 52-year-old diabetic male who was asymptomatic developed signs of endophthalmitis and decreased vision without pain in his left eye a few days post-IVI and AC paracentesis. The condition worsened after an initial vitreous tap and injection of antibiotics. Cultures of vitreous and aqueous samples were negative. Complete resolution occurred after a pars plana vitrectomy with IVI of antibiotics and steroid with removal of a dense “yellowish-brown” fibrinous plaque. The absence of pain, presence of a peculiar colored fibrin, mild-to-moderate vitritis without retinitis, negative cultures, and complete recovery despite the fulminant presentation; favor a diagnosis of inflammation over infection. We hypothesize that a micro-leak from a 26-gauge AC tap tract might have served as an entry port for 5% povidone-iodine from the ocular surface thus inciting inflammation. However, an exuberant inflammatory response that can be typically seen in NVG eyes after intraocular procedures cannot be excluded. Various causes of inflammation post-procedures, both toxic and nontoxic should be considered in atypical culture-negative fulminant endophthalmitis cases with good outcome posttreatment. Any minor ocular procedure may carry a risk of such complication. Patient counseling and care must be exercised in performing these procedures

Labelling imaging datasets on the basis of neuroradiology reports: a validation study

Natural language processing (NLP) shows promise as a means to automate the labelling of hospital-scale neuroradiology magnetic resonance imaging (MRI) datasets for computer vision applications. To date, however, there has been no thorough investigation into the validity of this approach, including determining the accuracy of report labels compared to image labels as well as examining the performance of non-specialist labellers. In this work, we draw on the experience of a team of neuroradiologists who labelled over 5000 MRI neuroradiology reports as part of a project to build a dedicated deep learning-based neuroradiology report classifier. We show that, in our experience, assigning binary labels (i.e. normal vs abnormal) to images from reports alone is highly accurate. In contrast to the binary labels, however, the accuracy of more granular labelling is dependent on the category, and we highlight reasons for this discrepancy. We also show that downstream model performance is reduced when labelling of training reports is performed by a non-specialist. To allow other researchers to accelerate their research, we make our refined abnormality definitions and labelling rules available, as well as our easy-to-use radiology report labelling app which helps streamline this process

Eye drop administration in patients attending and not attending a glaucoma education center

Background: To assess the technique of glaucoma eye drop instillation in patients who have and have not attended glaucoma education sessions. To compare this with their subjective perception of eye drop use and identify factors associated with improved performance. Patients and Methods: An observational study of 55 participants who instill their topical glaucoma medication for more than 1 year. Twenty-five patients attended (A) glaucoma teaching sessions >1 year before the study and were compared to thirty patients who never attended (NA). Patients completed a self-reporting questionnaire. They instilled their eye drop, and the technique was video-recorded digitally and later graded by two masked investigators. The results were analyzed using Fisher′s exact test and Chi-square test. Predictors were assessed using logistic regression models. Results: There was no significant difference in overall performance scores between the two groups. Good technique was observed in 16% of (A) group versus 23% (NA) group, (P = 0.498). There was a mismatch between patient′s subjective and actual performance. Female gender and higher educational level were found to be predictors of good performance of drop instillation on univariable logistic regression analysis. Conclusion: Glaucoma patients are challenged with eye drop instillation despite receiving education on drop administration. There is a discrepancy between patient′s perceptions and observed technique of drop administration

Compliance of amblyopic patients with occlusion therapy: A pilot study

Background: Increasing evidence shows that good compliance with occlusion therapy is paramount for successful amblyopia therapy. Purpose: To study the degree of compliance and explore factors affecting compliance in patients undergoing occlusion therapy for amblyopia in our practice. Design: Nonrandomized clinical intervention study. Materials and Methods: A total of 31 families with a child (aged 2-12 years), undergoing unilateral amblyopia treatment at the pediatric ophthalmology clinic of Sultan Qaboos University Hospital, Oman, were recruited for this one month study. Parents were interviewed and completed a closed-ended questionnaire. Clinical data including, visual acuity, refraction, diagnosis and treatment, for each patient was collected from the hospital chart and was entered in a data collection sheet. Compliance with occlusion therapy was assessed by self-report accounts of parents and was graded into good, partial, or poor. Association between various factors and degree of compliance was studied using logistic regression modeling. Results: Only 14 (45%) patients showed good compliance to occlusion therapy. 17 (55%) patients were noncompliant. Improvement in visual acuity strongly correlated with compliance to patching (P = 0.008). Other variables that were studied included, age at onset of therapy; gender; degree of amblyopia; type of amblyopia; use of glasses; and compliance with glasses. These did not emerge as significant predictors of compliance. All but one family with poor compliance stated that the main challenge in following the recommendation to patch for requisite hours was in getting their child to cooperate. Only in one instance, the family cited nonavailability of patches as the main hindrance to compliance. 10/31 (32%) families expressed a desire for more information and 18/31 (58%) parents did not understand that amblyopia meant decreased vision. Conclusion: Poor compliance is a barrier to successful amblyopia therapy in our practice. Improvement in visual acuity is associated with better compliance with patching. Parents find it difficult to comprehend and retain verbal explanations of various components regarding occlusion therapy for amblyopia. Future study with a larger sample of patients is recommended to investigate the factors affecting compliance with amblyopia therapy and determine predictors for poor compliance

Overview of Assault-Induced Trauma Presenting to a Trauma Centre in Oman

Objectives: Assault-induced trauma (AIT) is a public health concern that must be addressed and acknowledged. This study aimed to characterise cases of AIT presenting to Sultan Qaboos University Hospital (SQUH), Muscat, Oman. Methods: This retrospective descriptive study included patients presenting with AIT to the emergency department of SQUH from January 2007 to December 2018. The data obtained included incidence, patients’ demographics, mode of assault, triaging, management and hospital stay. The data were collected using the hospital’s information system and subsequently analysed. Results: A total of 268 cases of AIT were identified and 239 fulfilled the study criteria. The highest incidence recorded was in 2018, accounting for 72 cases. The mean incidence of AIT was 20 ± 19 per year. The sample was predominantly comprised of males (82.4%) and Omani citizens (65.3%). Most patients (66.9%) were between the ages of 20 and 39. The most common mode of assault was the use of bodily force (34.7%). Additionally, 18.4% were triaged as red cases. In terms of management, 84.5% of the cohort were treated non-surgically. No incidence of in-patient mortality was recorded. Conclusion: This study found that the rate of AIT averaged at 20 per year with most of the victims being young males. This was the first study that examined AIT in Oman and its results will aid future research and the estimation of the magnitude of this problem in the community. Keywords: Trauma; Physical Violence; Demography; Oman

Factors affecting the labelling accuracy of brain MRI studies relevant for deep learning abnormality detection

Unlocking the vast potential of deep learning-based computer vision classification systems necessitates large data sets for model training. Natural Language Processing (NLP)—involving automation of dataset labelling—represents a potential avenue to achieve this. However, many aspects of NLP for dataset labelling remain unvalidated. Expert radiologists manually labelled over 5,000 MRI head reports in order to develop a deep learning-based neuroradiology NLP report classifier. Our results demonstrate that binary labels (normal vs. abnormal) showed high rates of accuracy, even when only two MRI sequences (T2-weighted and those based on diffusion weighted imaging) were employed as opposed to all sequences in an examination. Meanwhile, the accuracy of more specific labelling for multiple disease categories was variable and dependent on the category. Finally, resultant model performance was shown to be dependent on the expertise of the original labeller, with worse performance seen with non-expert vs. expert labellers

Automated Labelling using an Attention model for Radiology reports of MRI scans (ALARM)

Labelling large datasets for training high-capacity neural networks is a major obstacle to the development of deep learning-based medical imaging applications. Here we present a transformer-based network for magnetic resonance imaging (MRI) radiology report classification which automates this task by assigning image labels on the basis of free-text expert radiology reports. Our model's performance is comparable to that of an expert radiologist, and better than that of an expert physician, demonstrating the feasibility of this approach. We make code available online for researchers to label their own MRI datasets for medical imaging applications