Neuropathy Classification of Corneal Nerve Images Using Artificial Intelligence

Nerve variations in the human cornea have been associated with alterations in the neuropathy state of a patient suffering from chronic diseases. For some diseases, such as diabetes, detection of neuropathy prior to visible symptoms is important, whereas for others, such as multiple sclerosis, early prediction of disease worsening is crucial. As current methods fail to provide early diagnosis of neuropathy, in vivo corneal confocal microscopy enables very early insight into the nerve damage by illuminating and magnifying the human cornea. This non-invasive method captures a sequence of images from the corneal sub-basal nerve plexus. Current practices of manual nerve tracing and classification impede the advancement of medical research in this domain. Since corneal nerve analysis for neuropathy is in its initial stages, there is a dire need for process automation. To address this limitation, we seek to automate the two stages of this process: nerve segmentation and neuropathy classification of images. For nerve segmentation, we compare the performance of two existing solutions on multiple datasets to select the appropriate method and proceed to the classification stage. Consequently, we approach neuropathy classification of the images through artificial intelligence using Adaptive Neuro-Fuzzy Inference System, Support Vector Machines, Naïve Bayes and k-nearest neighbors. We further compare the performance of machine learning classifiers with deep learning. We ascertained that nerve segmentation using convolutional neural networks provided a significant improvement in sensitivity and false negative rate by at least 5% over the state-of-the-art software. For classification, ANFIS yielded the best classification accuracy of 93.7% compared to other classifiers. Furthermore, for this problem, machine learning approaches performed better in terms of classification accuracy than deep learning

Classification of Corneal Nerve Images Using Machine Learning Techniques

Recent research shows that small nerve fiber damage is an early detector of neuropathy. These small nerve fibers are present in the human cornea and can be visualized through the use of a corneal confocal microscope. A series of images can be acquired from the subbasal nerve plexus of the cornea. Before the images can be quantified for nerve loss, a human expert manually traces the nerves in the image and then classifies the image as having neuropathy or not. Some nerve tracing algorithms are available in the literature, but none of them are reported as being used in clinical practice. An alternate practice is to visually classify the image for neuropathy without quantification. In this paper, we evaluate the potential of various machine learning techniques for automating corneal nerve image classification. First, the images are down-sampled using discrete wavelet transform, filtering and a number of morphological operations. The resulting binary image is used for extracting characteristic features of the image. This is followed by training the classifier on the extracted features. The trained classifier is then used for predicting the state of the nerves in the images. Our experiments yield a classification accuracy of 0.91 reflecting the effectiveness of the proposed method

Virtual reality for ambulance simulation environment

Simulations are beneficial in evaluating clinicians’ empirical competencies through practical skills, prioritizing, and decision-making as part of patient care scenarios generally run in a full-scale physical context. However, such simulations require physical space, manufacturing, and replacement of damaged or used equipment. On the other hand, virtual reality (VR) computerized simulators are comparatively modern instruments for use in practical training. VR can be employed to simulate real-world situations without the actual need for physical devices. This work presents an ambulance patient compartment VR simulation that can be used by emergency medical services (EMS) staff to customize the configuration of the ambulance patient compartment according to their preference as well as for vehicle orientation or training purposes. The proposed simulation can be used repeatedly enabling the paramedics to access equipment in a fully immersive and safe environment. The user studies have demonstrated the usability and perceived effectiveness of the proposed simulation

Using virtual reality to allow paramedics to familiarise themselves with a new ambulance patient compartment design

Background: Virtual reality (VR) is still an evolving domain that presents a versatile medium to simulate various environments and scenarios that can be easily reset between users, which can be particularly useful for training purposes. In this pilot study, we recreated the interior of a modular ambulance patient compartment with elements that can be moved and also had access to the real physical ambulance with the same interior design and equipment. The primary objective of this study was to determine the usability of the VR patient compartment in terms of functionality and sense of presence. Methods: Paramedics were invited to take part in this pilot study which involved them attending a 15-minute presentation about ambulance safety and ergonomics, familiarise themselves with the VR equipment, position the modular elements of the ambulance patient compartment in the VR or real setting (and vice versa), and complete a questionnaire corresponding to the task completed and adapted from an existing tool. They were unknowingly timed during the activities inside the real and VR ambulance for comparative purposes. Results: Twenty-seven participants were recruited, 77.8% of whom had no prior VR experience. On the 7-point Likert scale questionnaire, the participants scored the various aspects of usability (ease of grabbing elements, ease of recognising fixed/movable elements, distinguishing close from far objects, ease of “playing” the game…) between 5.59 to 6.26 and their sense of presence as 6.11 (SD = 1.121). Participants were faster arranging the modular elements in the VR setting than in the real one (8.78 min, SD = 4.47 versus 13.05 min, SD = 5.04). Conclusion: VR technology and potential applications are still rapidly developing. This pilot study shows promising results in terms of ease of use and sense of presence for the paramedics. This demonstrates that VR can be used for interactive familiarisation with an environment such as an ambulance patient compartment and can be used to assist in their design

Neuro-fuzzy classifier for corneal nerve images

Alterations in the corneal nerves have been associated with changes in the neuropathy condition of a patient suffering from chronic diseases. A corneal confocal microscope provides a non-invasive way to capture a series of images from the corneal sub-basal nerve plexus. These images undergo a tedious process of manual analysis before the classification of the state of nerves is determined as normal or abnormal. To address this limitation, we introduce a pioneering technique for automating corneal nerve image classification using Adaptive Neuro-Fuzzy Inference System. Prior to image classification, the images are preprocessed using discrete wavelet transform, filtering and morphological operations. The resulting segmented image is used to produce a feature set representative of the image. This is followed by training the neuro fuzzy classifier on the extracted features. The trained classifier is then used for predicting the state of the nerves in the images. Initial experiments yield a classification accuracy of 0.86 reflecting the effectiveness of the proposed technique.Scopu

Cyber Physical Systems and Smart Homes in Healthcare: Current State and Challenges

Cyber Physical Systems is an emerging paradigm which has gained particular attention in research and development. CPS has transformed the way we interact with the physical world by introducing smart communication between the physical world and its cyber components. As the requirements of today are increasing, a diverse range of applications has made its way in the healthcare domain. This paper provides a survey of noteworthy applications in the healthcare area, particularly smart homes, including state-of-the-art applications for medication intake systems and medical status monitoring. The success of every system is hindered by challenges that need to be addressed. Some of these challenges for CPS include security, patient information privacy, heterogeneous data management, real time patient monitoring, interoperability between different systems, system usability and energy consumption. 2020 IEEE.Scopu

Computer Based E-Healthcare Clinical Systems: A Comprehensive Survey

Over the years, interactive computer-based systems have provided crucial support to clinics, hospitals and other health-based centers. These systems have continued to influence the manner in which clinical tasks are organized and fulfilled in terms of performing tests, diagnosis procedures, treatment methods, as well as storing, analyzing and accessing patient and staff information. At the present time, the computer-based systems used in healthcare settings of high standards are the result of joint efforts of clinicians, software developers and clinical informaticians hence triggering the outcome of the desired system to outdo that of existing applications. Major concerns arise in designing clinical application including data privacy, minimal bias offered by a system (i.e. in terms of searching and decision-making), a user friendly GUI and an efficient integration of the new system with the existing standard applications at the health based setting being considered. In this paper, we provide a comprehensive survey on the existing research work on computer based E-Healthcare applications for clinicians highlighting both the challenges and benefits of such applications which would be of value to both patients and clinicians

A Virtual Reality Nutrition Awareness Learning System for Children

Taking daily breakfast with healthy food is highly important for children to provide their body with indispensable nutritional elements after a lengthy rest. Skipping the morning breakfast, makes the majority of them feel tired and unfocused in the classroom. In addition, eating junk food may result in regression in children performance and generate important diseases like overweight or teeth decay. Several initiatives have been proposed to incite children to take their daily breakfast before going to school. However, we still need more efficient and attractive approaches to motivate the children eat healthily. In this research, we propose a virtual reality immersive system that allows the children to prepare their breakfast through direct interaction with food items. They can move into the virtual environment and select the food they like and receive immediate feedback about them. This approach enhanced the learning skills of children and kept them engaged for a longer time. An animated multimedia based tutorial is presented first to the children within a family setting to teach them about the importance of daily breakfast and the available items. They are then asked to use the virtual reality system to select healthy elements for their breakfast using a handy controller. They can see the items in an immersive environment and hold them virtually and place them on their plates. We compare the proposed VR approach with the traditional approaches of teaching. A group of 29 school children of 7-9 years old have been assessed using the different approaches. Our findings show clearly that the VR approach is a promising approach to learn. The children enjoyed learning through the VR and become more engaged as they interacted directly with the food items in a fully immersive environment.Scopu