Notes

Erdheim-Chester illness: a rare non-Langerhans histiocytosis.
Intra- and intergrader agreement for cone density is high in CHM. CNN performance increased when it was trained on CHM images in comparison to normal, but had lower agreement than manual grading.

Manual cone identifications and cone density measurements are repeatable and reliable for images of CHM. CNNs show promise for automated cone selections, although additional improvements are needed to equal the accuracy of manual measurements.

These results are important for designing and interpreting longitudinal studies of cone mosaic metrics in disease progression or treatment intervention in CHM.
These results are important for designing and interpreting longitudinal studies of cone mosaic metrics in disease progression or treatment intervention in CHM.
To implement the classification of fundus diseases using deep convolutional neural networks (CNN), which is trained end-to-end from fundus images directly, the only input are pixels and disease labels, and the output is a probability distribution of a fundus image belonging to 18 fundus diseases.

Automated classification of fundus diseases using images is a challenging task owing to the fine-grained variability in the appearance of fundus lesions. #link# Deep CNNs show potential for general and highly variable tasks across many fine-grained object categories. Deep CNNs need large amounts of labeled samples, yet the available fundus images, especially labeled samples, are limited, which cannot satisfy the training requirement. So image augmentations such as rotation, scaling, and noising are implemented to enlarge the training dataset. We fine-tune the ResNet CNN architecture with 120,100 fundus images consisting of 18 different diseases and use it to classify the fundus images into corresponding diseases.

The performance is tested against two board-certified ophthalmologists. The CNN achieves performance on par with the experts for the classification accuracy.

Deep CNN is capable of predicting fundus diseases given fundus images as input, which can enhance the efficiency of diagnosis process and promote better visual outcomes. Outfitted with deep neural networks, mobile devices can potentially extend the reach of ophthalmologists outside of the clinic and provide low-cost universal access to vital diagnostic care.

This article implemented automatic prediction of fundus diseases that was done by ophthalmologists previously.
This article implemented automatic prediction of fundus diseases that was done by ophthalmologists previously.
Optical coherence tomography angiography (OCT-A) permits visualization of the changes to the retinal circulation due to diabetic retinopathy (DR), a microvascular complication of diabetes. We demonstrate accurate segmentation of the vascular morphology for the superficial capillary plexus (SCP) and deep vascular complex (DVC) using a convolutional neural network (CNN) for quantitative analysis.

The main CNN training dataset consisted of retinal OCT-A with a 6 × 6-mm field of view (FOV), acquired using a Zeiss PlexElite. Multiple-volume acquisition and averaging enhanced the vasculature contrast used for constructing the ground truth for neural network training. We used transfer learning from a CNN trained on smaller FOVs of the SCP acquired using different OCT instruments. Quantitative analysis of perfusion was performed on the resulting automated vasculature segmentations in representative patients with DR.

The automated segmentations of the OCT-A images maintained the distinct morphologies of the SCP and DVC. The network segmented the SCP with an accuracy and Dice index of 0.8599 and 0.8618, respectively, and 0.7986 and 0.8139, respectively, for the DVC. The inter-rater comparisons for the SCP had an accuracy and Dice index of 0.8300 and 0.6700, respectively, and 0.6874 and 0.7416, respectively, for the DVC.

Transfer learning reduces the amount of manually annotated images required while producing high-quality automatic segmentations of the SCP and DVC that exceed inter-rater comparisons. The resulting intercapillary area quantification provides a tool for in-depth clinical analysis of retinal perfusion.

Accurate retinal microvasculature segmentation with the CNN results in improved perfusion analysis in diabetic retinopathy.
Accurate retinal microvasculature segmentation with the CNN results in improved perfusion analysis in diabetic retinopathy.
The purpose of this study was to explore the use of fluorescein angiography (FA) images in a convolutional neural network (CNN) in the management of retinopathy of prematurity (ROP).

The dataset involved a total of 835 FA images of 149 eyes (90 patients), where each eye was associated with a binary outcome (57 "untreated" eyes and 92 "treated"; 308 "untreated" images, 527 "treated"). The resolution of the images was 1600 and 1200 px in 20% of cases, whereas the remaining 80% had a resolution of 640 and 480 px. All the images were resized to 640 and 480 px before training and no other preprocessing was applied. A CNN with four convolutional layers was trained on 90% of the images (
= 752) randomly chosen. The accuracy of the prediction was assessed on the remaining 10% of images (
= 83). Keras version 2.2.0 for R with Tensorflow backend version 1.11.0 was used for the analysis.

The validation accuracy after 100 epochs was 0.88, whereas training accuracy was 0.97. The receiver operating characteristic (ROC) presented an area under the curve (AUC) of 0.91.

Our study showed, we believe for the first time, the applicability of artificial intelligence (CNN) technology in the ROP management driven by FA. Further studies are needed to exploit different fields of applications of this technology.

This algorithm is the basis for a system that could be applied to both ROP as well as experimental oxygen induced retinopathy.
This algorithm is the basis for a system that could be applied to both ROP as well as experimental oxygen induced retinopathy.
To test the feasibility of using deep learning for optical coherence tomography angiography (OCTA) detection of diabetic retinopathy.

A deep-learning convolutional neural network (CNN) architecture, VGG16, was employed for this study. A transfer learning process was implemented to retrain the CNN for robust OCTA classification. One dataset, consisting of images of 32 healthy eyes, 75 eyes with diabetic retinopathy (DR), and 24 eyes with diabetes but no DR (NoDR), was used for training and cross-validation. A second dataset consisting of 20 NoDR and 26 DR eyes was used for external validation. To demonstrate the feasibility of using artificial intelligence (AI) screening of DR in clinical environments, the CNN was incorporated into a graphical user interface (GUI) platform.

With the last nine layers retrained, the CNN architecture achieved the best performance for automated OCTA classification. link2 The cross-validation accuracy of the retrained classifier for differentiating among healthy, NoDR, and DR eyes was 87.27%, with 83.76% sensitivity and 90.82% specificity. link3 The AUC metrics for binary classification of healthy, NoDR, and DR eyes were 0.97, 0.98, and 0.97, respectively. The GUI platform enabled easy validation of the method for AI screening of DR in a clinical environment.

With a transfer learning process for retraining, a CNN can be used for robust OCTA classification of healthy, NoDR, and DR eyes. The AI-based OCTA classification platform may provide a practical solution to reducing the burden of experienced ophthalmologists with regard to mass screening of DR patients.

Deep-learning-based OCTA classification can alleviate the need for manual graders and improve DR screening efficiency.
Deep-learning-based OCTA classification can alleviate the need for manual graders and improve DR screening efficiency.The novel coronavirus disease 2019 (COVID-19) has become a global pandemic with more than 4 million confirmed cases and over 280,000 confirmed deaths worldwide. Evidence exists on the influence of temperature and humidity on the transmission of related infectious respiratory diseases, such as influenza and severe acute respiratory syndrome (SARS). This study therefore explored the effects of daily temperature and humidity on COVID-19 transmission and mortality in Lagos state, the epicenter of COVID-19 in Nigeria. Correlation analysis was performed using incidence data on COVID-19 and meteorological data for the corresponding periods from 9th March to 12th May, 2020. Our results showed that atmospheric temperature has a significant weak negative correlation with COVID-19 transmission in Lagos. Also, a significant weak negative correlation was found to exist between temperature and cumulative mortality. The strength of the relationship between temperature and the disease incidence increased when 1 week and 2 weeks' predetection delays were put into consideration. However, ARS-853 was found between atmospheric humidity and COVID-19 transmission or mortality in Lagos. This study contributes more knowledge on COVID-19 and will benefit efforts and decision-making geared towards its control.Renal cell carcinoma (RCC) is associated with a variety of different histopathologic subtypes in which each subtype may be further subclassified. These entities carry with them unique prognoses and necessitate treatment with specific immunotherapy agents should advanced disease be uncovered. Meanwhile, aberrant physiologic processes may lead to unique histologic findings within these subtypes, further complicating management and prognostication. Heterotopic ossification within RCC is one of these rare occurrences and was once thought to have favorable prognostic implications. We report a case of a young female with papillary type 2 RCC with heterotopic ossification.Priapism is a rare urological emergency. It is rarely a telltale sign of myeloid leukemia. We report two cases of acute myeloid leukemia in a child and chronic myeloid leukemia in a young adult presenting with priapism. Puncture irrigation of the corpora cavernosa followed by systemic treatment to lower the hyperviscosity of the blood due to leukemia provided optimal outcome. Prompt emergency management is required to lower the complication of erectile dysfunction.Kratom is a synthetic opioid that is federally unregulated and thus available for purchase through online retail and smoke shops in most states. Due to its availability, there is concern for misuse in the pediatric population. There is existing literature describing toxicity of kratom in adults; yet, to the best of our knowledge, there are no cases describing kratom toxicity in the pediatric population. Thus, we present the case of kratom overdose in a pediatric patient.
Two elderly males presented with traumatic shoulder dislocation and bony Bankart fracture consisting of greater than 25% of the glenoid width. Due to several concomitant factors such as polytrauma, activity level, rotator cuff pathology, optimization of comorbidities, risk of complications, and potential for revision surgery, the patients were treated with reverse shoulder arthroplasty (RSA).

RSA may be a satisfactory treatment option for isolated, large glenoid fractures associated with anterior glenohumeral instability in the elderly. These patients are susceptible to rapid deconditioning with prolonged immobilization and may not be medically suited to undergo the prolonged recovery period associated with open reduction internal fixation or potentially undergo revision operations.
RSA may be a satisfactory treatment option for isolated, large glenoid fractures associated with anterior glenohumeral instability in the elderly. These patients are susceptible to rapid deconditioning with prolonged immobilization and may not be medically suited to undergo the prolonged recovery period associated with open reduction internal fixation or potentially undergo revision operations.
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