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

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

SEVEN: Deep Semi-supervised Verification Networks

Verification determines whether two samples belong to the same class or not, and has important applications such as face and fingerprint verification, where thousands or millions of categories are present but each category has scarce labeled examples, presenting two major challenges for existing deep learning models. We propose a deep semi-supervised model named SEmi-supervised VErification Network (SEVEN) to address these challenges. The model consists of two complementary components. The generative component addresses the lack of supervision within each category by learning general salient structures from a large amount of data across categories. The discriminative component exploits the learned general features to mitigate the lack of supervision within categories, and also directs the generative component to find more informative structures of the whole data manifold. The two components are tied together in SEVEN to allow an end-to-end training of the two components. Extensive experiments on four verification tasks demonstrate that SEVEN significantly outperforms other state-of-the-art deep semi-supervised techniques when labeled data are in short supply. Furthermore, SEVEN is competitive with fully supervised baselines trained with a larger amount of labeled data. It indicates the importance of the generative component in SEVEN.Comment: 7 pages, 2 figures, accepted to the 2017 International Joint Conference on Artificial Intelligence (IJCAI-17

Applying Ensemble Neural Networks to an Inverse Problem Solution to Prosthetic Socket Pressure Measurement

Ensemble neural networks are commonly used as a method to boost performance of artificial intelligence applications. By collating the response of multiple networks with differences in composition or training and hence a range of estimation error, an overall improvement in the appraisal of new problem data can be made. In this work, artificial neural networks are used as an inverse-problem solver to calculate the internal distribution of pressures on a lower limb prosthetic socket using information on the deformation of the external surface of the device. Investigation into the impact of noise injection was studied by changing the maximum noise alteration parameter and the differences in network composition by altering the variance around this maximum noise value. Results indicate that use of ensembles of networks provides a meaningful improvement in overall performance. RMS error expressed as a percentage of the total applied load was 3.86% for the best performing ensemble, compared to 5.32% for the mean performance of the networks making up that ensemble. Although noise injection resulted in an improvement in typical network estimates of load distribution, ensembles performed better with low noise and low variance between network training patterns. These results mean that ensembles have been implemented in the research tool under developmen

Monitoring the suitability of the fit of a lower-limb prosthetic socket using artificial neural network in commonly encountered walking conditions

Prosthetic sockets are still routinely designed without the aid of quantitative measurement, relying instead on the experience and skill of clinicians. Sockets remain the most common cause for complaint regarding the suitability of a prosthesis, and poor pressure distribution is implicated in many forms of unacceptable care outcomes. Monitoring pressure distribution has been effectively restricted to laboratory settings, and only limited work has examined conditions other than flat walking. In this work, a transtibial amputee completed static and dynamic tasks on flat ground, on slopes and with changes to prosthetic materials and alignment. This was achieved using a set of wireless measurement nodes and custom LabView and MATLAB code, using external strain measurements and a neural network to understand the internal pressure distribution. Future work will focus on modifying the software to be more user-friendly for a clinical operator, and in simplifying the required hardware. Although the system in its current form facilitated the desired measurements effectively, it required engineering support to function accurately. Improving the reliability and stability of the system will be necessary before routine use is possible

Unsupervised Holistic Image Generation from Key Local Patches

We introduce a new problem of generating an image based on a small number of key local patches without any geometric prior. In this work, key local patches are defined as informative regions of the target object or scene. This is a challenging problem since it requires generating realistic images and predicting locations of parts at the same time. We construct adversarial networks to tackle this problem. A generator network generates a fake image as well as a mask based on the encoder-decoder framework. On the other hand, a discriminator network aims to detect fake images. The network is trained with three losses to consider spatial, appearance, and adversarial information. The spatial loss determines whether the locations of predicted parts are correct. Input patches are restored in the output image without much modification due to the appearance loss. The adversarial loss ensures output images are realistic. The proposed network is trained without supervisory signals since no labels of key parts are required. Experimental results on six datasets demonstrate that the proposed algorithm performs favorably on challenging objects and scenes.Comment: 16 page