Accessibility-based reranking in multimedia search engines

Traditional multimedia search engines retrieve results based mostly on the query submitted by the user, or using a log of previous searches to provide personalized results, while not considering the accessibility of the results for users with vision or other types of impairments. In this paper, a novel approach is presented which incorporates the accessibility of images for users with various vision impairments, such as color blindness, cataract and glaucoma, in order to rerank the results of an image search engine. The accessibility of individual images is measured through the use of vision simulation filters. Multi-objective optimization techniques utilizing the image accessibility scores are used to handle users with multiple vision impairments, while the impairment profile of a specific user is used to select one from the Pareto-optimal solutions. The proposed approach has been tested with two image datasets, using both simulated and real impaired users, and the results verify its applicability. Although the proposed method has been used for vision accessibility-based reranking, it can also be extended for other types of personalization context

Corneae from body donors in anatomy department: valuable use for clinical transplantation and experimental research

Background: Explanted corneae are highly needed for the surgical management of patients with severe corneal diseases. The aim of this study was to determine whether the body donors from the Institute of Anatomy are a suitable source of donor corneae. Methods: At the Institute of Anatomy at Saarland University Medical Center in Homburg, corneae are prelevated from body donors who had consented to the removal of tissues for transplantation purposes during their lifetime. Following the report of death, the LIONS Eye Bank is informed and the contraindications of corneal explantation are clarified. Obtaining a blood sample within 24 h postmortem is mandatory. Results: The Institute of Anatomy had 150 body donors in the time period from January 2018 to June 2019. Out of these, 68 (45.3%) were reported to the Eye Bank. The age of the donors (median 82 years (range: 57–96)) is not critical since the quality of the corneae depends on the number of endothelial cells (mean: 2109 ± 67 cells/mm2 (range: 511–2944 cells/mm2)). Contraindications were present in 19 (12.6%) cases. The corneae were extracted from 49 (32.7%) body donors. Out of these 98 corneae, 46 (46.9%) were successfully transplanted. Of all non-transplanted corneae, 6 (6.1%) were microbiologically contaminated, 10 (10.2%) had a positive serology, 22 (22.5%) had an endothelial cell count < 2000 cells/mm2 and 6 (6.1%) are at time of this analysis still in culture medium. The non-transplanted tissues were used for research. Conclusions: Explanted corneae from the Institute of Anatomy are a valuable option in obtaining grafts for corneal transplantation, which is why we are working toward on expanding cooperation with this department

Improving Eye Care Delivery Through Data Sharing Technology

Preventable blindness has massive social, economic, and societal impacts around the world. The Armenian EyeCare Project (AECP) is addressing this through a network of regional and subspecialty ophthalmological clinics, but current data collection, storage and sharing methods are inadequate. With the organization’s input we conducted focused research to determine current state and best practices, and synthesized this information to develop recommendations and implementation plans for Electronic Medical Record and teleconsultation systems which would improve data sharing for better patient care

"Shoulder pain and limitation of motion in a young girl: think different"

Background Primary Synovial Chondromatosis (PSC) is a rare benign tumor of the synovial membrane in which cartilage metaplasia produces calcific loose bodies within the articular space. Only a few cases are reported in the pediatric population and its etiology remains unknown. This condition typically affects large weight-bearing joints with pain, swelling and decrease range of motion. Due to its slow progressions, delayed diagnosis is frequent and differential diagnosis should consider other chronic arthritis and malignancies. While arthroscopic removal of loose bodies is the current treatment up to now, the association of partial or complete synovectomy is debated. Case presentation We report about a 14-year-old girl with a long-lasting right shoulder pain, especially during movements or exercise, localized tenderness and hypotonia of the glenohumeral joint. No previous trauma was mentioned. Blood exams, Mantoux test and plain radiography of the right shoulder were unremarkable. Ultrasound imaging revealed echogenic and calcified bodies stretching the glenohumeral joint and dislocating the long head of biceps tendon. Magnetic resonance showed a "rice-grain" pattern of the right shoulder. From an arthroscopic surgery, multiple loose white bodies were removed within the synovial membrane, and synovial chondromatosis was confirmed by histological analysis. At one month follow up visit, the patient completely recovered without pain. Conclusion Synovial chondromatosis is a very uncommon cause of mono articular pain in children, especially when it affects shoulder. Pediatricians should keep in mind this condition to avoid delayed diagnosis and treatment, even in consideration of the low risk of malignant transformation. Through this case, we would highlight common diagnostic pitfalls and treatment of synovial chondromatosis

Association between a genetic variant of type-1 cannabinoid receptor and inflammatory neurodegeneration in multiple sclerosis

Genetic ablation of type-1 cannabinoid receptors (CB1Rs) exacerbates the neurodegenerative damage of experimental autoimmune encephalomyelitis, the rodent model of multiple sclerosis (MS). To address the role on CB1Rs in the pathophysiology of human MS, we first investigated the impact of AAT trinucleotide short tandem repeat polymorphism of CNR1 gene on CB1R cell expression, and secondly on the inflammatory neurodegeneration process responsible for irreversible disability in MS patients. We found that MS patients with long AAT repeats within the CNR1 gene (≥12 in both alleles) had more pronounced neuronal degeneration in response to inflammatory white matter damage both in the optic nerve and in the cortex. Optical Coherence Tomography (OCT), in fact, showed more severe alterations of the retinal nerve fiber layer (RNFL) thickness and of the macular volume (MV) after an episode of optic neuritis in MS patients carrying the long AAT genotype of CNR1. MS patients with long AAT repeats also had magnetic resonance imaging (MRI) evidence of increased gray matter damage in response to inflammatory lesions of the white matter, especially in areas with a major role in cognition. In parallel, visual abilities evaluated at the low contrast acuity test, and cognitive performances were negatively influenced by the long AAT CNR1 genotype in our sample of MS patients. Our results demonstrate the biological relevance of the (AAT)n CNR1 repeats in the inflammatory neurodegenerative damage of MS

DIAGNOSE EYES DISEASES USING VARIOUS FEATURES EXTRACTION APPROACHES AND MACHINE LEARNING ALGORITHMS

Ophthalmic diseases like glaucoma, diabetic retinopathy, and cataracts are the main cause of visual impairment worldwide. With the use of the fundus images, it could be difficult for a clinician to detect eye diseases early enough. By other hand, the diagnoses of eye disease are prone to errors, challenging and labor-intensive. Thus, for the purpose of identifying various eye problems with the use of the fundus images, a system of automated ocular disease detection with computer-assisted tools is needed. Due to machine learning (ML) algorithms' advanced skills for image classification, this kind of system is feasible. An essential area of artificial intelligence)AI (is machine learning. Ophthalmologists will soon be able to deliver accurate diagnoses and support individualized healthcare thanks to the general capacity of machine learning to automatically identify, find, and grade pathological aspects in ocular disorders. This work presents a ML-based method for targeted ocular detection. The Ocular Disease Intelligent Recognition (ODIR) dataset, which includes 5,000 images of 8 different fundus types, was classified using machine learning methods. Various ocular diseases are represented by these classes. In this study, the dataset was divided into 70% training data and 30% test data, and preprocessing operations were performed on all images starting from color image conversion to grayscale, histogram equalization, BLUR, and resizing operation. The feature extraction represents the next phase in this study ,two algorithms are applied to perform the extraction of features which includes: SIFT(Scale-invariant feature transform) and GLCM(Gray Level Co-occurrence Matrix), ODIR dataset is then subjected to the classification techniques Naïve Bayes, Decision Tree, Random Forest, and K-nearest Neighbor. This study achieved the highest accuracy for binary classification (abnormal and normal) which is 75% (NB algorithm), 62% (RF algorithm), 53% (KNN algorithm), 51% (DT algorithm) and achieved the highest accuracy for multiclass classification (types of eye diseases) which is 88% (RF algorithm), 61% (KNN algorithm) 42% (NB algorithm), and 39% (DT algorithm)

Repeated freezing procedures preserve structural and functional properties of amniotic membrane for application in ophthalmology

For decades, the unique regenerative properties of the human amniotic membrane (hAM) have been successfully utilized in ophthalmology. As a directly applied biomaterial, the hAM should be available in a ready to use manner in clinical settings. However, an extended period of time is obligatory for performing quality and safety tests. Hence, the low temperature storage of the hAM is a virtually inevitable step in the chain from donor retrieval to patient application. At the same time, the impact of subzero temperatures carries an increased risk of irreversible alterations of the structure and composition of biological objects. In the present study, we performed a comprehensive analysis of the hAM as a medicinal product; this is intended for a novel strategy of application in ophthalmology requiring a GMP production protocol including double freezing– thawing cycles. We compared clinically relevant parameters, such as levels of growth factors and extracellular matrix proteins content, morphology, ultrastructure and mechanical properties, before and after one and two freezing cycles. It was found that epidermal growth factor (EGF), transforming growth factor beta 1 (TGF-ß1), hepatocyte growth factor (HGF), basic fibroblast growth factor (bFGF), hyaluronic acid, and laminin could be detected in all studied conditions without significant differences. Additionally, histological and ultrastructure analysis, as well as transparency and mechanical tests, demonstrated that properties of the hAM required to support therapeutic efficacy in ophthalmology are not impaired by dual freezing. © 2020 by the authors. Licensee MDPI, Basel, Switzerland

Posterior Interhemispheric Transfalcine Transprecuneus Approach for Microsurgical Resection of Peri-Atrial Lesions: Indications, Technique, and Outcomes

OBJECT Surgical exposure of the peritrigonal or periatrial region has been challenging due to the depth of the region and overlying important functional cortices and white matter tracts. The authors demonstrate the operative feasibility of a contralateral posterior interhemispheric transfalcine transprecuneus approach (PITTA) to this region and present a series of patients treated via this operative route. METHODS Fourteen consecutive patients underwent the PITTA and were included in this study. Pre- and postoperative clinical and radiological data points were retrospectively collected. Complications and extent of resection were reviewed. RESULTS The mean age of patients at the time of surgery was 39 years (range 11–64 years). Six of the 14 patients were female. The mean duration of follow-up was 4.6 months (range 0.5–19.6 months). Pathology included 6 arteriovenous malformations, 4 gliomas, 2 meningiomas, 1 metastatic lesion, and 1 gray matter heterotopia. Based on the results shown on postoperative MRI, 1 lesion (7%) was intentionally subtotally resected, but ≥ 95% resection was achieved in all others (93%) and gross-total resection was accomplished in 7 (54%) of 13. One patient (7%) experienced a temporary approach-related complication. At last follow-up, 1 patient (7%) had died due to complications of his underlying malignancy unrelated to his cranial surgery, 2 (14%) demonstrated a Glasgow Outcome Scale (GOS) score of 4, and 11 (79%) manifested a GOS score of 5. CONCLUSIONS Based on this patient series, the contralateral PITTA potentially offers numerous advantages, including a wider, safer operative corridor, minimal need for ipsilateral brain manipulation, and better intraoperative navigation and working angles

Towards Learning Representations in Visual Computing Tasks

abstract: The performance of most of the visual computing tasks depends on the quality of the features extracted from the raw data. Insightful feature representation increases the performance of many learning algorithms by exposing the underlying explanatory factors of the output for the unobserved input. A good representation should also handle anomalies in the data such as missing samples and noisy input caused by the undesired, external factors of variation. It should also reduce the data redundancy. Over the years, many feature extraction processes have been invented to produce good representations of raw images and videos. The feature extraction processes can be categorized into three groups. The first group contains processes that are hand-crafted for a specific task. Hand-engineering features requires the knowledge of domain experts and manual labor. However, the feature extraction process is interpretable and explainable. Next group contains the latent-feature extraction processes. While the original feature lies in a high-dimensional space, the relevant factors for a task often lie on a lower dimensional manifold. The latent-feature extraction employs hidden variables to expose the underlying data properties that cannot be directly measured from the input. Latent features seek a specific structure such as sparsity or low-rank into the derived representation through sophisticated optimization techniques. The last category is that of deep features. These are obtained by passing raw input data with minimal pre-processing through a deep network. Its parameters are computed by iteratively minimizing a task-based loss. In this dissertation, I present four pieces of work where I create and learn suitable data representations. The first task employs hand-crafted features to perform clinically-relevant retrieval of diabetic retinopathy images. The second task uses latent features to perform content-adaptive image enhancement. The third task ranks a pair of images based on their aestheticism. The goal of the last task is to capture localized image artifacts in small datasets with patch-level labels. For both these tasks, I propose novel deep architectures and show significant improvement over the previous state-of-art approaches. A suitable combination of feature representations augmented with an appropriate learning approach can increase performance for most visual computing tasks.Dissertation/ThesisDoctoral Dissertation Computer Science 201