5,424 research outputs found
Accretion Disks Around Black Holes: Twenty Five Years Later
We study the progress of the theory of accretion disks around black holes in
last twenty five years and explain why advective disks are the best bet in
explaining varied stationary and non-stationary observations from black hole
candidates. We show also that the recently proposed advection dominated flows
are incorrect.Comment: 30 Latex pages including figures. Kluwer Style files included.
Appearing in `Observational Evidence for Black Holes in the Universe', ed.
Sandip K. Chakrabarti, Kluwer Academic Publishers (DORDRECHT: Holland
Capturing the Occult Central Retinal Artery Occlusion Using Optical Coherence Tomography
AIMS:
To report spectral-domain optical coherence tomography (OCT) findings in cases of impending or occult central retinal artery occlusion (CRAO) in which a diagnosis other than CRAO was made on initial presentation.
METHODS:
Retrospective, observational case series of patients diagnosed with CRAO for whom on initial presentation fundal examination and OCT findings were deemed unremarkable and/or a diagnosis other than CRAO was made. OCT images from the initial presentation were then reviewed for evidence of inner retinal ischaemia.
RESULTS:
In total, 214 cases of CRAO were identified. Eleven patients (5.14%) had been given an alternative initial diagnosis at their first presentation in casualty and were included. The age range was 20–84 years and 81% (9/11) were male. On review of initial OCT imaging performed in casualty, all cases had evidence of inner retinal ischaemia.
Conclusions: CRAO is an ophthalmic emergency which leads to vision loss which is often irreversible. Examination of the fundus may be normal early in the course of the disease and therefore a timely diagnosis may be missed. This case series reports the OCT findings of inner retinal ischaemia in patients with occult or impending CRAO which may aid in the early diagnosis and referral to stroke services
Clinician-driven artificial intelligence in ophthalmology: resources enabling democratization
PURPOSE OF REVIEW: This article aims to discuss the current state of resources enabling the democratization of artificial intelligence (AI) in ophthalmology. RECENT FINDINGS: Open datasets, efficient labeling techniques, code-free automated machine learning (AutoML) and cloud-based platforms for deployment are resources that enable clinicians with scarce resources to drive their own AI projects. SUMMARY: Clinicians are the use-case experts who are best suited to drive AI projects tackling patient-relevant outcome measures. Taken together, open datasets, efficient labeling techniques, code-free AutoML and cloud platforms break the barriers for clinician-driven AI. As AI becomes increasingly democratized through such tools, clinicians and patients stand to benefit greatly
Transcorneal electrical stimulation for the treatment of retinitis pigmentosa: results from the TESOLAUK trial
OBJECTIVE: To explore the impact of weekly transcorneal electrical stimulation (TES) over a 6-month period as a treatment for retinitis pigmentosa (RP). METHODS AND ANALYSIS: A prospective open-label observational trial was carried out assessing weekly TES in participants with RP for a period of 6 months followed by observation for a further 6 months. Clinical examination and investigations were carried out at 3 monthly intervals for a total of 12 months. The primary outcome measure explored safety through a descriptive analysis of adverse effects with secondary outcome measures evaluating structural and functional efficacy. RESULTS: Seven male and seven female participants with RP aged 18-80 years were recruited. TES was well tolerated with no serious adverse events reported. Two participants reported transient foreign body sensation and one participant had discomfort underneath the skin electrode. Following 6 months of TES, best-corrected visual acuity increased by 1.1±1.4 letters in the control arm and 0.93±1.4 letters in the treated arm. Central microperimetry threshold sensitivity rose by 0.02±0.5 decibels (dB) and 0.37±0.4 dB and Goldmann visual field volume by 0.16±0.09 steradians (sr) vs 0.22±0.12 sr for the control and treated eye, respectively. There was no statistical significance seen between eyes following the treatment or observation period. CONCLUSION:: This small open-label clinical trial showed that TES was safe and well tolerated in patients with RP. Visual function measurements at 6 months demonstrated no significant difference between the control and treated eyes. The results justify a larger clinical trial over a longer period of time in order to identify any treatment effect
Predicting Incremental and Future Visual Change in Neovascular Age-Related Macular Degeneration Using Deep Learning
PURPOSE: To evaluate the predictive utility of quantitative imaging biomarkers, acquired automatically from optical coherence tomography (OCT) scans, of cross-sectional and future visual outcomes of patients with neovascular age-related macular degeneration (AMD) starting anti-vascular endothelial growth factor (VEGF) therapy. DESIGN: Retrospective cohort study. PARTICIPANTS: Treatment-naïve, first-treated eyes of patients with neovascular AMD between 2007 and 2017 at Moorfields Eye Hospital (a large, UK single-centre) undergoing anti-VEGF therapy METHODS: Automatic segmentation was carried out by applying a deep learning segmentation algorithm to 137,379 OCT scans from 6467 eyes of 3261 patients with neovascular AMD. After applying selection criteria 926 eyes of 926 patients were taken forward for analysis. MAIN OUTCOME MEASURES: Correlation coefficients (R2) and mean absolute error (MAE) between quantitative OCT (qOCT) parameters and cross-sectional visual-function. The predictive value of these parameters for short-term visual change i.e. incremental visual acuity [VA] resulting from an individual injection, as well as, VA at distant timepoints (up to 12 months post-baseline). RESULTS: VA at distant timepoints could be predicted: R2 0.80 (MAE 5.0 ETDRS letters) and R2 0.7 (MAE 7.2) post-injection 3 and at 12 months post-baseline (both p < 0.001), respectively. Best performing models included both baseline qOCT parameters and treatment-response. Furthermore, we present proof-of-principle evidence that the incremental change in VA from an injection can be predicted: R2 0.14 (MAE 5.6) for injection 2 and R2 0.11 (MAE 5.0) for injection 3 (both p < 0.001). CONCLUSIONS: Automatic segmentation enables rapid acquisition of quantitative and reproducible OCT biomarkers with potential to inform treatment decisions in the care of neovascular AMD. This furthers development of point-of-care decision-aid systems for personalized medicine
Safety and disease monitoring biomarkers in Duchenne muscular dystrophy: results from a Phase II trial
Aim: Evaluate the utility of glutamate dehydrogenase (GLDH) and cardiac troponin I as safety biomarkers, and creatine kinase and muscle injury panel as muscle health biomarkers in Duchenne muscular dystrophy. Patients & methods: Data were collected during a Phase II trial of domagrozumab. Results: GLDH was a more specific biomarker for liver injury than alanine aminotransferase. Cardiac troponin I elevations were variable and not sustained, limiting its applicability as a biomarker. Muscle injury panel biomarkers were no more informative than creatine kinase as a muscle health biomarker. Conclusion: Results support the use of GLDH as a specific biomarker for liver injury in patients with Duchenne muscular dystrophy
Temporal trends in mode, site and stage of presentation with the introduction of colorectal cancer screening: a decade of experience from the West of Scotland
background:Â Â Population colorectal cancer screening programmes have been introduced to reduce cancer-specific mortality through the detection of early-stage disease. The present study aimed to examine the impact of screening introduction in the West of Scotland.
methods:Â Â Data on all patients with a diagnosis of colorectal cancer between January 2003 and December 2012 were extracted from a prospectively maintained regional audit database. Changes in mode, site and stage of presentation before, during and after screening introduction were examined.
results:  In a population of 2.4 million, over a 10-year period, 14 487 incident cases of colorectal cancer were noted. Of these, 7827 (54%) were males and 7727 (53%) were socioeconomically deprived. In the postscreening era, 18% were diagnosed via the screening programme. There was a reduction in both emergency presentation (20% prescreening vs 13% postscreening, P0.001) and the proportion of rectal cancers (34% prescreening vs 31% pos-screening, P0.001) over the timeframe. Within non-metastatic disease, an increase in the proportion of stage I tumours at diagnosis was noted (17% prescreening vs 28% postscreening, P0.001).
conclusions:Â Â Within non-metastatic disease, a shift towards earlier stage at diagnosis has accompanied the introduction of a national screening programme. Such a change should lead to improved outcomes in patients with colorectal cancer
Code-free deep learning for multi-modality medical image classification
© 2021, The Author(s). A number of large technology companies have created code-free cloud-based platforms that allow researchers and clinicians without coding experience to create deep learning algorithms. In this study, we comprehensively analyse the performance and featureset of six platforms, using four representative cross-sectional and en-face medical imaging datasets to create image classification models. The mean (s.d.) F1 scores across platforms for all model–dataset pairs were as follows: Amazon, 93.9 (5.4); Apple, 72.0 (13.6); Clarifai, 74.2 (7.1); Google, 92.0 (5.4); MedicMind, 90.7 (9.6); Microsoft, 88.6 (5.3). The platforms demonstrated uniformly higher classification performance with the optical coherence tomography modality. Potential use cases given proper validation include research dataset curation, mobile ‘edge models’ for regions without internet access, and baseline models against which to compare and iterate bespoke deep learning approaches
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