Healthy aims: developing new medical implants and diagnostic equipment

Healthy Aims is a €23-million, four-year project, funded under the EU’s Information Society Technology Sixth Framework program to develop intelligent medical implants and diagnostic systems (www.healthyaims.org). The project has 25 partners from 10 countries, including commercial, clinical, and research groups. This consortium represents a combination of disciplines to design and fabricate new medical devices and components as well as to test them in laboratories and subsequent clinical trials. The project focuses on medical implants for nerve stimulation and diagnostic equipment based on straingauge technology

Soft computing agents for e-health applied to the research and control of unknown diseases

This paper presents an Ontology-based Holonic Diagnostic System (OHDS) that combines the advantages of the holonic paradigm with multi-agent system technology and ontology design, for the organization of unstructured biomedical research into structured disease information. We use ontologies as 'brain' for the holonic diagnostic system to enhance its ability to structure information in a meaningful way and share information fast. To integrate dispersed heterogeneous knowledge available on the web we use a fuzzy mechanism ruled by intelligent agents, which automatically structures the information in the adequate ontology template. Our vision of how this system implementation should be backed by a solid security shield that ensures the privacy and safety of medical information concludes the paper

Maintaining the integrity of XML signatures by using the manifest element

One of the aims of providing 'security of data' in e-commerce transactions is making sure that the receiver receives the same data which the sender sends, that is the data has not been tampered in any way. To achieve this aim digital signatures are used. A digital signature helps in providing integrity, message authentication, and signer authentication for the signed data. An XML signature can contain or point to the data that is being signed. In this paper we discuss a possible solution of avoiding a signature from breaking when there is a change in the location of the document after it has been signed

Deep learning in ophthalmology: The technical and clinical considerations

The advent of computer graphic processing units, improvement in mathematical models and availability of big data has allowed artificial intelligence (AI) using machine learning (ML) and deep learning (DL) techniques to achieve robust performance for broad applications in social-media, the internet of things, the automotive industry and healthcare. DL systems in particular provide improved capability in image, speech and motion recognition as well as in natural language processing. In medicine, significant progress of AI and DL systems has been demonstrated in image-centric specialties such as radiology, dermatology, pathology and ophthalmology. New studies, including pre-registered prospective clinical trials, have shown DL systems are accurate and effective in detecting diabetic retinopathy (DR), glaucoma, age-related macular degeneration (AMD), retinopathy of prematurity, refractive error and in identifying cardiovascular risk factors and diseases, from digital fundus photographs. There is also increasing attention on the use of AI and DL systems in identifying disease features, progression and treatment response for retinal diseases such as neovascular AMD and diabetic macular edema using optical coherence tomography (OCT). Additionally, the application of ML to visual fields may be useful in detecting glaucoma progression. There are limited studies that incorporate clinical data including electronic health records, in AL and DL algorithms, and no prospective studies to demonstrate that AI and DL algorithms can predict the development of clinical eye disease. This article describes global eye disease burden, unmet needs and common conditions of public health importance for which AI and DL systems may be applicable. Technical and clinical aspects to build a DL system to address those needs, and the potential challenges for clinical adoption are discussed. AI, ML and DL will likely play a crucial role in clinical ophthalmology practice, with implications for screening, diagnosis and follow up of the major causes of vision impairment in the setting of ageing populations globally

Operations Research & Statistical Learning Methods to Monitor the Progression of Glaucoma and Chronic Diseases

This thesis focuses on advancing operations research and statistical learning methods for medical decision making to improve the care of patients diagnosed with chronic conditions. Because the National Center for Disease Prevention (2020) estimates chronic conditions affect approximately 60% of the US adult population, improving the care of patients with chronic conditions will improve the lives of most Americans. Patients diagnosed with chronic conditions face lifestyle changes, rising treatment costs, and frequently reductions in quality of life. To improve the way in which clinicians treat patients with chronic conditions, treatment decisions can be supplemented by evidenced-based, data driven algorithmic decision-making methods. This thesis provides data-driven methodologies of a general nature that are instantiated for several medical decision-making problems. In chapter two we proactively identify the time of a patient’s primary open angle glaucoma (POAG) progression under high measurement error conditions using a soft voting ensemble classification model. When medical tests have low residual variability (e.g., empirical difference between the patient's true and recorded value is small) they can effectively, without the use of sophisticated methods, identify the patient's current disease phase; however, when medical tests have moderate to high residual variability this may not be the case. We present a solution to the latter case. We find rapid progression disease phases can be proactively identified with the combination of denoising and supervised classification methods. In chapter three, we determine the optimal time to next follow-up appointment for patients with the chronic condition of ocular hypertension (OHTN). Patients with OHTN are at increased risk of developing glaucoma and should be observed over their lifetime. Follow-up appointment schedules that are chosen poorly can result in, at minimum, delay in the detection of a patient’s progression to glaucoma, and at worse, yield poor patient outcomes. To this end, we present a personalized decision support algorithm that uses the fitted Q-iteration reinforcement learning algorithm to recommend personalized time-to-next follow-up schedules that are based on a patient’s medical state. We find personalized follow-up appointments schedules produced by reinforcement learning methods are superior to both 1-year and 2-year fixed interval follow-up appointment schedules. In chapters four and five, we examine and compare several criteria for determining progression from OHTN to POAG and evaluate the use of a collective POAG conversion rule in predicting future occurrences of patients' POAG conversion. We find age, race, and sex are statistically significant determinants in progression for all compared criteria. However, there exists broad conversion discordance between the criteria, as demonstrated by statistically different survival curves and the limited overlap in eyes that progressed by multiple criteria. Ultimately, to permit machine learning models to predict conversion from OHTN to POAG, it is essential to have quantitative reference standards for POAG conversion for researchers to use. Additionally, using the collective POAG conversion rule, we find machine learning models can successfully predict future OHTN conversion events to POAG. This research was conducted in collaboration with clinical disease/domain experts. All the medical decision-making research herein addresses real world healthcare issues, that, if solved, have the potential to improve vision care if implemented. While these methodologies primarily focus on chronic conditions affecting the eyes (e.g., OHTN and POAG), it is important to note that much of the work produced offers methods applicable to other chronic diseases.PHDIndustrial & Operations EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/169926/1/isaacaj_1.pd

Big Data: Potential as an Ocular Epidemiology and Public Health Tool

Refractive error is a significant cause of vision impairment both through the limited access to correction in some areas and the associated ocular diseases for which refractive errors are risk factors. Having timely, regular access to population level estimates of refractive error and vision impairment is necessary to adequately plan public health resources and resource appropriate interventions. A lack of access to current and regularly updated refractive error and vision impairment prevalence data has been identified as a significant limitation in predicting future population trends with many countries lacking any prevalence data or available data being outdated. This project addresses this gap by utilising the untapped potential of Big Data in the form of spectacle lens sales data and optometric electronic medical record data and assesses the potential of these data sources as a public health tool. Chapter 5 contains a review of the application of Big Data and Artificial Intelligence to the field of eyecare and describes the revolutionary potential these new technologies may hold. Chapter 6 describes the data used in this project and the steps taken to acquire and clean the data. Chapter 7 and 8 compare the prevalence of refractive error found using spectacle lens sales data and optometric electronic medical record data to a large population survey of refractive error and demonstrate that with careful analysis an accurate estimation of population distribution of refractive error can be obtained from both types of data. Chapter 8 also estimates the likely level of vision impairment by age 75 given the distribution of myopia in the spectacle lens sales data. Chapter 9 analyses visual acuity data within the optometric electronic medical records which allowed the optimum recall interval and visual acuity threshold for driving licence renewal to be determined. Chapter 10 provides a summary and conclusion on the work, and contains recommendations for future research

Integrating artificial intelligence into an ophthalmologist’s workflow: obstacles and opportunities

Introduction: Demand in clinical services within the field of ophthalmology is predicted to rise over the future years. Artificial intelligence, in particular, machine learning-based systems, have demonstrated significant potential in optimizing medical diagnostics, predictive analysis, and management of clinical conditions. Ophthalmology has been at the forefront of this digital revolution, setting precedents for integration of these systems into clinical workflows. Areas covered: This review discusses integration of machine learning tools within ophthalmology clinical practices. We discuss key issues around ethical consideration, regulation, and clinical governance. We also highlight challenges associated with clinical adoption, sustainability, and discuss the importance of interoperability. Expert opinion: Clinical integration is considered one of the most challenging stages within the implementation process. Successful integration necessitates a collaborative approach from multiple stakeholders around a structured governance framework, with emphasis on standardization across healthcare providers and equipment and software developers

Automatic extraction of retinal features from colour retinal images for glaucoma diagnosis: a review

Glaucoma is a group of eye diseases that have common traits such as, high eye pressure, damage to the Optic Nerve Head and gradual vision loss. It affects peripheral vision and eventually leads to blindness if left untreated. The current common methods of pre-diagnosis of Glaucoma include measurement of Intra-Ocular Pressure (IOP) using Tonometer, Pachymetry, Gonioscopy; which are performed manually by the clinicians. These tests are usually followed by Optic Nerve Head (ONH) Appearance examination for the confirmed diagnosis of Glaucoma. The diagnoses require regular monitoring, which is costly and time consuming. The accuracy and reliability of diagnosis is limited by the domain knowledge of different ophthalmologists. Therefore automatic diagnosis of Glaucoma attracts a lot of attention.This paper surveys the state-of-the-art of automatic extraction of anatomical features from retinal images to assist early diagnosis of the Glaucoma. We have conducted critical evaluation of the existing automatic extraction methods based on features including Optic Cup to Disc Ratio (CDR), Retinal Nerve Fibre Layer (RNFL), Peripapillary Atrophy (PPA), Neuroretinal Rim Notching, Vasculature Shift, etc., which adds value on efficient feature extraction related to Glaucoma diagnosis. © 2013 Elsevier Ltd