Optimization Algorithms for Integrating Advanced Facility-Level Healthcare Technologies into Personal Healthcare Devices

Healthcare is one of the most important services to preserve the quality of our daily lives, and it is capable of dealing with issues such as global aging, increase in the healthcare cost, and changes to the medical paradigm, i.e., from the in-facility cure to the prevention and cure outside the facility. Accordingly, there has been growing interest in the smart and personalized healthcare systems to diagnose and care themselves. Such systems are capable of providing facility-level diagnosis services by using smart devices (e.g., smartphones, smart watches, and smart glasses). However, in realizing the smart healthcare systems, it is very difficult, albeit impossible, to directly integrate high-precision healthcare technologies or scientific theories into the smart devices due to the stringent limitations in the computing power and battery lifetime, as well as environmental constraints. In this dissertation, we propose three optimization methods in the field of cell counting systems and gait-aid systems for Parkinson's disease patients that address the problems that arise when integrating a specialized healthcare system used in the facilities into mobile or wearable devices. First, we present an optimized cell counting algorithm based on heuristic optimization, which is a key building block for realizing the mobile point-of-care platforms. Second, we develop a learning-based cell counting algorithm that guarantees high performance and efficiency despite the existence of blurry cells due to out-focus and varying brightness of background caused by the limitation of lenses free in-line holographic apparatus. Finally, we propose smart gait-aid glasses for Parkinson’s disease patients based on mathematical optimization. ⓒ 2017 DGISTopenI. Introduction 1-- 1.1 Global Healthcare Trends 1-- 1.2 Smart Healthcare System 2-- 1.3 Benefits of Smart Healthcare System 3-- 1.4 Challenges of Smart Healthcare. 4-- 1.5 Optimization 6-- 1.6 Aims of the Dissertation 7-- 1.7 Dissertation Organization 8-- II.Optimization of a cell counting algorithm for mobile point-of-care testing platforms 9-- 2.1 Introduction 9-- 2.2 Materials and Methods. 13-- 2.2.1 Experimental Setup. 13-- 2.2.2 Overview of Cell Counting. 16-- 2.2.3 Cell Library Optimization. 18-- 2.2.4 NCC Approximation. 20-- 2.3 Results 21-- 2.3.1 Cell Library Optimization. 21-- 2.3.2 NCC Approximation. 23-- 2.3.3 Measurement Using an Android Device. 28-- 2.4 Summary 32-- III.Human-level Blood Cell Counting System using NCC-Deep learning algorithm on Lens-free Shadow Image. 33-- 3.1 Introduction 33-- 3.2 Cell Counting Architecture 36-- 3.3 Methods 37-- 3.3.1 Candidate Point Selection based on NCC. 37-- 3.3.2 Reliable Cell Counting using CNN. 40-- 3.4 Results 43-- 3.4.1 Subjects . 43-- 3.4.2 Evaluation for the cropped cell image 44-- 3.4.3 Evaluation on the blood sample image 46-- 3.4.4 Elapsed-time evaluation 50-- 3.5 Summary 50-- IV.Smart Gait-Aid Glasses for Parkinson’s Disease Patients 52-- 4.1 Introduction 52-- 4.2 Related Works 54-- 4.2.1 Existing FOG Detection Methods 54-- 4.2.2 Existing Gait-Aid Systems 56-- 4.3 Methods 57-- 4.3.1 Movement Recognition. 59-- 4.3.2 FOG Detection On Glasses. 62-- 4.3.3 Generation of Visual Patterns 66-- 4.4 Experiments . 67-- 4.5 Results 69-- 4.5.1 FOG Detection Performance. 69-- 4.5.2 Gait-Aid Performance. 71-- 4.6 Summary 73-- V. Conclusion 75-- Reference 77-- 요약문 89본 논문은 의료 관련 연구시설 및 병원 그리고 실험실 레벨에서 사용되는 전문적인 헬스케어 시스템을 개인의 일상생활 속에서 사용할 수 있는 스마트 헬스케어 시스템에 적용시키기 위한 최적화 문제에 대해 다룬다. 현대 사회에서 의료비용 증가 세계적인 고령화에 따라 의료 패러다임은 질병이 발생한 뒤 시설 내에서 치료 받는 방식에서 질병이나 건강관리에 관심있는 환자 혹은 일반인이 휴대할 수 있는 개인용 디바이스를 이용하여 의료 서비스에 접근하고, 이를 이용하여 질병을 미리 예방하는 방식으로 바뀌었다. 이에 따라 언제, 어디서나 스마트 디바이스(스마트폰, 스마트워치, 스마트안경 등)를 이용하여 병원 수준의 예방 및 진단을 실현하는 스마트 헬스케어가 주목 받고 있다. 하지만, 스마트 헬스케어 서비스 실현을 위하여 기존의 전문 헬스케어 장치 및 과학적 이론을 스마트 디바이스에 접목하는 데에는 스마트 디바이스의 제한적인 컴퓨팅 파워와 배터리, 그리고 연구소나 실험실에서 발생하지 않았던 환경적인 제약조건으로 인해 적용 할 수 없는 문제가 있다. 따라서 사용 환경에 맞춰 동작 가능하도록 최적화가 필요하다. 본 논문에서는 Cell counting 분야와 파킨슨 환자의 보행 보조 분야에서 전문 헬스케어 시스템을 스마트 헬스케어에 접목시키는데 발생하는 세 가지 문제를 제시하고 문제 해결을 위한 세 가지 최적화 알고리즘(Heuristic optimization, Learning-based optimization, Mathematical optimization) 및 이를 기반으로 하는 시스템을 제안한다.DoctordCollectio

Microfluidic system for screening disease based on physical properties of blood

Introduction: A key feature of the 'One Health' concept pertains to the design of novel point of care systems for largescale screening of health of the population residing in resource-limited areas of low- and middle-income countries with a view to obtaining data at a community level as a rationale to achieve better public health outcomes. The physical properties of blood are different for different samples. Our study involved the development of an innovative system architecture based upon the physical properties of blood using automated classifiers to enable large-scale screening of the health of the population living in resource-limited settings. Methods: The proposed system consisted of a simple, robust and low-cost sensor with capabilities to sense and measure even the minute changes in the physical properties of blood samples. In this system, the viscosity of blood was derived from a power-law model coupled with the Rabinowitsch-Mooney correction for non-Newtonian shear rates developed in a steady laminar Poiseuille flow. Surface tension was measured by solving the Young-Laplace equation for pendant drop shape hanging on a vertical needle. An anticipated outcome of this study would be the development of a novel automated classifier based upon the rheological attributes of blood. This automated classifier would have potential application in evaluating the health status of a population at regional and global levels. Results: The proposed system was used to measure the physical properties of various samples like normal, tuberculous and anemic blood samples. The results showed that the physical properties of these samples were different as compared to normal blood samples. The major advantage of this system was low-cost, as well as its simplicity and portability. Conclusion: In this work, we proposed making a case for the validation of a low-cost version of a microfluidic system capable of scanning large populations for a variety of diseases as per the WHO mandate of "One Health"

Point of Care Diagnostics and Health Monitoring Devices

Existing disease screening methods mostly rely on symptom based diagnosis. This is mainly because of lack of accessibility and cost associated with the tests. Testing for the presence of the disease after the onset of symptoms has a negative impact on chances of survival and treatment costs. Miniaturized low cost diagnostic devices that can be used outside the hospital setting can provide continuous health monitoring and aid in early diagnosis. This thesis presents techniques to develop such disease screening and health monitoring devices. The techniques presented here focus on medical devices that can benefit from microfluidic devices, fluorescence imaging, and antibody testing

High-throughput Droplet Barcoding and Automated Image Analysis in Microfluidic Droplet Trapping Array

Molecularly-targeted therapeutics and personalized medicine have dramatically increased the median survival rate of patients suffering from cancer. However, cellular heterogeneity and the personalized nature of cancer have resulted in the limited success of single drug treatments which has led to the use of multiple therapeutic combinations. This has required the development of new analytical methods capable of multiplexed high-throughput screening (HTS) technologies necessary to identify is single or multi-agent therapies are effective in ex vivo samples like liquid biopsies. Droplet microfluidic devices have garnered significant interest to facilitate high-throughput, single cell analysis of heterogeneous populations. However, these devices are still limited in their ability to assess multiple input conditions such as combinations of multiple drugs or different doses of the same drug. Moreover, HTS approaches need to be coupled with automated image analysis metrics capable of rapidly processing raw data and quantifying it in an efficient manner. The goal of this work is to address these two areas of need by developing a new method to track different inputs in a droplet microfluidic trapping array coupled with automated image analysis of single cell behavior. The first part of this study highlights the use of rare-earth (RE)-doped luminescent nanoparticles (NP) as novel method to track input conditions in droplets in a microfluidic device. The second part of the work deals with the development of an algorithm called FluoroCellTrack to efficiently analyze single cell data from high-throughput experiments in the droplet microfluidic trapping array. The β-hexagonal NaYF4 nanoparticles used for droplet tracking were doped with a rare-earth emitter with unique spectral properties that do not overlap with established fluorophores like GFP and Rhodamine. In this study, we employed europium as the dopants which has a luminescence emission spectrum in the red region upon UV excitation. We demonstrated that the RE-doped nanoparticles are biologically inert and spectrally independent with common fluorophores and fluorescent stains. This work provided a foundation for future applications using the combination of NPs and microfluidics for multiplexed droplet tracking to quantify tumor heterogeneity and assess the effectiveness of combinatorial therapies. To perform HTS of single cells, a Python algorithm (FluoroCellTrack) was developed to: (i) automatically distinguish droplets from cells, (ii) count cells in each droplet, (iii) quantify cell viability, and (iv) identify input conditions using the RE-doped nanoparticles. The performance of FluoroCellTrack was compared to manual image analysis with a difference in intracellular quantification of ~2% coupled with a decrease in analysis time ofquantification, droplet barcoding and biomarker detection

Automated image analysis systems to quantify physical and behavioral attributes of biological entities

All life forms in nature have physical and behavioral attributes which help them survive and thrive in their environment. Technologies, both within the areas of hardware systems and data processing algorithms, have been developed to extract relevant information about these attributes. Understanding the complex interplay of physical and behavioral attributes is proving important towards identifying the phenotypic traits displayed by organisms. This thesis attempts to leverage the unique advantages of portable/mobile hardware systems and data processing algorithms for applications in three areas of bioengineering: skin cancer diagnostics, plant parasitic nematology, and neglected tropical disease. Chapter 1 discusses the challenges in developing image processing systems that meet the requirements of low cost, portability, high-throughput, and accuracy. The research motivation is inspired by these challenges within the areas of bioengineering that are still elusive to the technological advancements in hardware electronics and data processing algorithms. A literature review is provided on existing image analysis systems that highlight the limitations of current methods and provide scope for improvement. Chapter 2 is related to the area of skin cancer diagnostics where a novel smartphone-based method is presented for the early detection of melanoma in the comfort of a home setting. A smartphone application is developed along with imaging accessories to capture images of skin lesions and classify them as benign or cancerous. Information is extracted about the physical attributes of a skin lesion such as asymmetry, border irregularity, number of colors, and diameter. Machine learning is employed to train the smartphone application using both dermoscopic and digital lesion images. Chapter 3 is related to the area of plant parasitic nematology where automated methods are presented to provide the nematode egg count from soil samples. A new lensless imaging system is built to record holographic videos of soil particles flowing through microscale flow assays. Software algorithms are written to automatically identify the nematode eggs from low resolution holographic videos or images captured from a scanner. Deep learning algorithm was incorporated to improve the learning process and train the software model. Chapter 4 is related to the area of neglected tropical diseases where new worm tracking systems have been developed to characterize the phenotypic traits of Brugia malayi adult male worms and their microfilaria. The worm tracking algorithm recognizes behavioral attributes of these parasites by extracting a number of features related to their movement and body posture. An imaging platform is optimized to capture high-resolution videos with appropriate field of view of B. malayi. The relevance of each behavioral feature was evaluated through drug screening using three common antifilarial compounds. The abovementioned image analysis systems provide unique advantages to the current experimental methods. For example, the smartphone-based software application is a low-cost alternative to skin cancer diagnostics compared to standard dermoscopy available in skin clinics. The lensless imaging system is a low-cost and high-throughput alternative for obtaining egg count densities of plant parasitic nematodes compared with visual counting under a microscope by trained personnel. The B. malayi worm tracking system provides an alternative to available C. elegans tracking software with options to extract multiple parameters related to its body skeleton and posture

Overview of Sepsis and Sepsis Biomarker Detection

Sepsis being a fatal physiological state due to an imbalance in the immune system caused by infection, and one of the most common cause for millions of deaths in the non-coronary intensive care unit worldwide requires special attention in its diagnostic methods and cure. Therefore an understanding of literature related to sepsis is of utmost importance. With the advent of inter-disciplinary research, the study and diagnosis of sepsis problem are not limited to the medical field, rather it requires interventions and active participation of other fields of science and technology. However, often subject matter from interdisciplinary research is expounded in an abstruse manner and hence it becomes elusive for a researcher from different research domain to understand it, leading to loss of quality and efficiency in research. In this survey report, the material is presented in a form that facilitates easy comprehension for the non-medical researchers and has been focused on introducing sepsis, it\u27s causes, extent, comparison of diagnosis techniques: conventional labeled and label-free detections; with special emphasis on sepsis biomarkers to help researchers from multi-disciplinary domain to develop and fabricate devices and ideas to compliment the existing sepsis diagnosis system present in the medical field. A future direction of sepsis diagnosis along with the implementation of novel techniques for sepsis biomarker quantification is also reported

Micro- and Nanofluidics for Bionanoparticle Analysis

Bionanoparticles such as microorganisms and exosomes are recoganized as important targets for clinical applications, food safety, and environmental monitoring. Other nanoscale biological particles, includeing liposomes, micelles, and functionalized polymeric particles are widely used in nanomedicines. The recent deveopment of microfluidic and nanofluidic technologies has enabled the separation and anslysis of these species in a lab-on-a-chip platform, while there are still many challenges to address before these analytical tools can be adopted in practice. For example, the complex matrices within which these species reside in create a high background for their detection. Their small dimension and often low concentration demand creative strategies to amplify the sensing signal and enhance the detection speed. This Special Issue aims to recruit recent discoveries and developments of micro- and nanofluidic strategies for the processing and analysis of biological nanoparticles. The collection of papers will hopefully bring out more innovative ideas and fundamental insights to overcome the hurdles faced in the separation and detection of bionanoparticles

Bioimprinting technologies for removal of myeloblasts from peripheral blood

Acute Myeloid Leukaemia (AML) is a malignancy occurring in the bone marrow and blood whereby immature and defective blast cells are overproduced. As a genetic condition, no cure is available. The condition is traditionally managed by treatment reliant on non-specific cytotoxic chemotherapy and bone marrow transplantation. Treatment is associated with causing discomfort and mortality and is ultimately ineffective; relapse is common and survival rates are poor.Bioimprinting is a technology whereby the size, shape and morphology of biological templates are recreated in polymer matrices. Studies aim to mimic and exploit specific binding reliant on complementary size and shape interactions as seen in a number of biological processes. The field has developed from the templating of rudimentary macromolecules to whole cells with extracellular features accurate on a nanometre scale.This study aimed to fabricate AML specific bioimprints able to discriminate neoplastic cells from patient aspirate. Myeloblasts provide an ideal target due to their inherent size difference and morphological irregularity. Bioimprints incorporated into a high throughput device could provide a vehicle for selectivity of myeloblasts, yielding an alternate treatment pathway in reducing the leukaemic burden in AML sufferers.Herein, methods were devised and evaluated to reliably fabricate high quality bioimprints, representative of the templated cell. Key in the protocol design is the control over the proportion of the cell surface exposed to the curing polymer matrix which dictates the size of the cavities produced and in-turn the ability of uptake of target cells to the bioimprint substrates. This method should be compatible with roll-to-roll nanoimprint lithography which has been highlighted as a viable method to upscale the imprints in order to deplete very high myeloblast cell populations in AML sufferers. Bioimprints of various cell types and polymer particles of similar size were made and further used to produce positive imprints and subsequent negative replica imprints. Ultimately, a methodology was devised and bioimprints of an AML in vitro cultured cell line were fabricated and reproduced into an area of hundreds of square metres.The success of bioimprinting technology was evaluated with high resolution microscopy and surface profiling; characterising bioimprinted cavities in comparison with the template cell type. Surface modifications were trialled in order to incur an attraction between substrates and incubated cell populations. A coating of weak cationic surface charge was introduced on the bioimprint surface, to attract the negative charges of extracellular groups. This interaction is amplified by an increased surface area contact, allowing binding of cells fitting flush into cavities. Cells unable to fit into cavities did not receive this attraction and remained unbound. With the intended use in mind, a method using materials approved for clinical use was found.Once produced and functionalised, the retention of incubated cell populations was examined under flow conditions. In doing, a bespoke microfluidic device was designed in order to control the hydrodynamics experienced by the bioimprint allowing for a comparison of retention per surface modification parameters. Retention of target cells to bioimprints made using the same cell type was measured as a function of incubated cell suspension concentration; analysis confirmed cells were retained and localised to the bioimprinted cavities. This was compared to cells incubated on bioimprints produced from microparticles of the same size distribution. Significantly poorer retention was observed, indicating the importance of cell shape and cellular surface properties in bioimprint capture.The preference of the bioimprints to the target cell type was assessed by exposure to binary cell mixtures of myeloblasts and PBMCs. Cell populations were characterised on account of size and shape and separately fluorescently labelled for identification and automated enumeration. Bioimprint selectivity towards the targeted cells (myeloblasts) was compared by the proportions of each cell type retained to the bioimprints. In each instance the bioimprint showed a preference for capture of the target cell type over the healthy control. It is anticipated that by reapplying or recirculating patient aspirate, myeloblasts can be completely depleted from samples due to the higher affinity. This effect was confirmed by comparison of the bioimprint path length on selectivity; using larger areas of bioimprint at fixed cell concentration to represent a recirculated population