Scoping Review of the development of artificial eyes throughout the years

Losing an eye following trauma can lead to profound psychosocial difficulties making it imperative for the wearer to be fitted with an aesthetically pleasing custom-made artificial eye. Despite recent technological advancements, current design and manufacturing processes have remained unchanged in over 55 years. With the aim of portraying current knowledge regarding the development of artificial eyes in order to aid future development, a scooping review was conducted. Six online search engines were used: Scopus, PubMed, MedLine Complete, Web of Science, Science Direct and Google Scholar. Thirty-eight articles met the inclusion criteria and underwent numerical and thematic analysis with three thematic themes emerging. History and the current process of artificial eyes has been well documented, however, the impact of wearing artificial eyes is sparse. On-going research and development into the design and manufacturing processes of artificial eyes and the psychosocial impact of wearing an artificial eye is needed

Retinoblastoma: Identifying the Diagnostic Signs for Early Treatment

Retinoblastoma is a rare but significant cause of childhood eye cancer world-wide. The prognosis depends upon early diagnosis and treatment but also upon accurate classification of the tumours. Unilateral incidence is normally non-hereditary compared with bilateral incidence where secondary tumours are more common. Survivorship is much better for unilateral compared with bilateral and trilateral retinoblastoma. Early signs are important to detect and photography can assist in identifying no return of “red-eye” during flash photography and yellow appearance of the tumour. Treatment options are discussed together with new psycho-oncology approaches that address potential trauma in the survivor as well as in the family of the survivor

The importance of incorporating technological advancements into the artificial eye process: a perspective commentary

Application of technology into healthcare has typically been targeted to high demand illnesses and treatments. However, with an increasing need to meet patient’s expectations combined with increased accessibility and reduced costs, smaller healthcare fields are starting to investigate its function and usability. Services have historically been led by skills and expertise, and recent developments are being seen by ocularists in the field of prosthetic eyes who acknowledge the potential benefit from technological advancement. Utilising the technologies recently investigated in maxillofacial prosthesis can start the evolutionary process where products are continually re-designed and re-developed to achieve excellent patient outcome and satisfaction levels

Decomposability in formal conformance testing

We study the problem of deriving a specification for a third-party component, based on the specification of the system and the environment in which the component is supposed to reside. Particularly, we are interested in using component specifications for conformance testing of black-box components, using the theory of input-output conformance (ioco) testing. We propose and prove sufficient criteria for decompositionality, i.e., that components conforming to the derived specification will always compose to produce a correct system with respect to the system specification. We also study the criteria for strong decomposability, by which we can ensure that only those components conforming to the derived specification can lead to a correct system

Cross Pixel Optical Flow Similarity for Self-Supervised Learning

We propose a novel method for learning convolutional neural image representations without manual supervision. We use motion cues in the form of optical flow, to supervise representations of static images. The obvious approach of training a network to predict flow from a single image can be needlessly difficult due to intrinsic ambiguities in this prediction task. We instead propose a much simpler learning goal: embed pixels such that the similarity between their embeddings matches that between their optical flow vectors. At test time, the learned deep network can be used without access to video or flow information and transferred to tasks such as image classification, detection, and segmentation. Our method, which significantly simplifies previous attempts at using motion for self-supervision, achieves state-of-the-art results in self-supervision using motion cues, competitive results for self-supervision in general, and is overall state of the art in self-supervised pretraining for semantic image segmentation, as demonstrated on standard benchmarks

Monitoring the kinematics of Walking and Running Gait after total knee replacement using a generation of Kinematic Retaining prosthetic knee implant

Gait analysis has its role in rehabilitation medicine, orthopaedics, kinesiology, sports science, and other related fields of human locomotion. Use of gait analysis in the evaluation of the efficacy of joint replacement has increased over the last two decades due to the advancement of computer technology and the requirements of more quantitative data which can allow for better and more referenceable assessment of the performance of in service knees. This study was designed to investigate and monitor the kinematics of running and walking gait after a total unilateral or bilateral knee implant operation using the new-generation high-performance kinematic retaining prosthesis “Lima Corp Italy”. This type of post operation for running gait analysis had never been performed previously. It is designed to identify further kinematic data about the knee that may not be possible to observe using walking gait analysis alone. The kinematics of running gait in a group of 12 patients were monitored and results are presented here. The cost and resources required to do this was also questioned and the possibility of a more controlled image capture using cheaper mobile devices was examined

Effect of the “Orem Self Care Model”-based Educational-Supportive Intervention on the Anxiety of Primigravidae

Aims: The health of mother and fetus might be affected by anxiety during pregnancy. Training interventions can prevent the anxiety disorders. The aim of the study was to investigate the effectiveness of the supportive-educational intervention based on Orem self-care model on the anxiety of primigravidae. Materials & Methods: In the single-blind clinical trial study, sixty 28- to 34-week pregnant women in their first pregnancy, referred to the health centers of Mashhad, were studied in 2015. The subjects, selected via purposeful cluster sampling method, were randomly divided into two 30-person groups including experimental and control groups. Data was collected by demographic and pregnancy questionnaires and Spielberger anxiety scale. Four 60-minute supportive-educational program sessions were conducted based on Orem self-care model in experimental group. The manifest anxiety was measured in both groups at the beginning of the conduction of the intervention and one week after the end of the intervention. Data was analyzed by SPSS 18 software using Chi-square, Mann-Whitney, independent T, and paired T tests. Findings: Before the intervention, the mean scores of manifest anxiety in the groups were not significantly different (p=0.793). Nevertheless, after the intervention, the scores were significantly different (p=0.0001). In addition, the mean scores of manifest anxiety were significantly different before and after the intervention in experimental group (p=0.006). However, the difference was not significant in control group (p=0.086). Conclusion: Supportive-educational intervention based on Orem self-care model reduces anxiety in 3-month primigravidae

Unsupervised Learning of Video Representations via Dense Trajectory Clustering

This paper addresses the task of unsupervised learning of representations for action recognition in videos. Previous works proposed to utilize future prediction, or other domain-specific objectives to train a network, but achieved only limited success. In contrast, in the relevant field of image representation learning, simpler, discrimination-based methods have recently bridged the gap to fully-supervised performance. We first propose to adapt two top performing objectives in this class - instance recognition and local aggregation, to the video domain. In particular, the latter approach iterates between clustering the videos in the feature space of a network and updating it to respect the cluster with a non-parametric classification loss. We observe promising performance, but qualitative analysis shows that the learned representations fail to capture motion patterns, grouping the videos based on appearance. To mitigate this issue, we turn to the heuristic-based IDT descriptors, that were manually designed to encode motion patterns in videos. We form the clusters in the IDT space, using these descriptors as a an unsupervised prior in the iterative local aggregation algorithm. Our experiments demonstrates that this approach outperform prior work on UCF101 and HMDB51 action recognition benchmarks. We also qualitatively analyze the learned representations and show that they successfully capture video dynamics

Classifying the unknown: discovering novel gravitational-wave detector glitches using similarity learning

The observation of gravitational waves from compact binary coalescences by LIGO and Virgo has begun a new era in astronomy. A critical challenge in making detections is determining whether loud transient features in the data are caused by gravitational waves or by instrumental or environmental sources. The citizen-science project \emph{Gravity Spy} has been demonstrated as an efficient infrastructure for classifying known types of noise transients (glitches) through a combination of data analysis performed by both citizen volunteers and machine learning. We present the next iteration of this project, using similarity indices to empower citizen scientists to create large data sets of unknown transients, which can then be used to facilitate supervised machine-learning characterization. This new evolution aims to alleviate a persistent challenge that plagues both citizen-science and instrumental detector work: the ability to build large samples of relatively rare events. Using two families of transient noise that appeared unexpectedly during LIGO's second observing run (O2), we demonstrate the impact that the similarity indices could have had on finding these new glitch types in the Gravity Spy program

Questionnaire study to gain an insight into the manufacturing and fitting process of artificial eyes in children: an ocularist perspective

Purpose To gain an insight into the manufacturing and fitting of artificial eyes in children and potential improvements to the process. Method An online qualitative survey was distributed to 39 ocularists/prosthetists in Europe and Canada. Participants were recruited through purposive sampling, specifically maximum variation sampling from the researcher’s contacts and an online search. Results The findings highlighted the current impression technique as being the most difficult yet most important part of the current process for both the ocularist and child patient. Negatively affecting obtaining a good impression, the child patients distress can be reduced by their parents by providing encouragement, reassurance, practicing the insertion and removal of the artificial eye and being matter of fact. Whilst improvements to the current process provided mixed views, the incorporation of current technology was perceived as not being able to meet the requirements to produce aesthetically pleasing artificial eyes. Conclusion The current artificial eye process can be seen as an interaction with its success being dependent on the child patient’s acceptance and adjustment which is dependent on the factors associated to the process. Investigation into the needs of the patient and whether technology can improve the process are the next steps in its advancement