Optical Fibre-based Force Sensing Needle Driver for Minimally Invasive Surgery

Minimally invasive surgery has been limited from its inception by insufficient haptic feedback to surgeons. The loss of haptic information threatens patients safety and results in longer operation times. To address this problem, various force sensing systems have been developed to provide information about tool–tissue interaction forces. However, the provided results for axial and grasping forces have been inaccurate in most of these studies due to considerable amount of error and uncertainty in their force acquisition method. Furthermore, sterilizability of the sensorized instruments plays a pivotal role in accurate measurement of forces inside a patient\u27s body. Therefore, the objective of this thesis was to develop a sterilizable needle-driver type grasper using fibre Bragg gratings. In order to measure more accurate and reliable tool–tissue interaction forces, optical force sensors were integrated in the grasper jaw to measure axial and grasping forces directly at their exertion point on the tool tip. Two sets of sensor prototypes were developed to prove the feasibility of proposed concept. Implementation of this concept into a needle-driver instrument resulted in the final proposed model of the sensorized laparoscopic instrument. Fibre Bragg gratings were used for measuring forces due to their many advantages for this application such as small size, sterilizability and high sensitivity. Visual force feedback was provided for users based on the acquired real-time force data. Improvement and consideration points related to the current work were identified and potential areas to continue this project in the future are discussed

In-Vitro Classification of Saliva Samples of COPD Patients and Healthy Controls Using Machine Learning Tools

Chronic Obstructive Pulmonary Disease (COPD) is a life-threatening lung disease and a major cause of morbidity and mortality worldwide. Although a curative therapy has yet to be found, permanent monitoring of biomarkers that refiect the disease progression plays a pivotal role for the effective management of COPD. The accurate examination of respiratory tract fiuids like saliva is a promising approach for staging disease and predicting its upcoming exacerbations in a Point-of-Care (PoC) environment. However, the concurrent consideration of patients' demographic and medical parameters is necessary for achieving accurate outcomes. Therefore, Machine Learning (ML) tools can play an important role for analyzing patient data and providing comprehensive results for the recognition of COPD in a PoC setting. As a result, the objective of this research work was to implement ML tools on data acquired from characterizing saliva samples of COPD patients and healthy controls as well as their demographic information for PoC recognition of the disease. For this purpose, a permittivity biosensor was used to characterize dielectric properties of saliva samples and, subsequently, ML tools were applied on the acquired data for classification. The XGBoost gradient boosting algorithm provided a high classification accuracy and sensitivity of 91.25% and 100%, respectively, making it a promising model for COPD evaluation. Integration of this model on a neuromorphic chip, in the future, will enable the real-time assessment of COPD in PoC, with low cost, low energy consumption, and high patient privacy. In addition, constant monitoring of COPD in a near-patient setup will enable the better management of the disease exacerbations

Kafka-ML: Connecting the data stream with ML/AI frameworks

Machine Learning (ML) and Artificial Intelligence (AI) depend on data sources to train, improve, and make predictions through their algorithms. With the digital revolution and current paradigms like the Internet of Things, this information is turning from static data to continuous data streams. However, most of the ML/AI frameworks used nowadays are not fully prepared for this revolution. In this paper, we propose Kafka-ML, a novel and open-source framework that enables the management of ML/AI pipelines through data streams. Kafka-ML provides an accessible and user-friendly Web user interface where users can easily define ML models, to then train, evaluate, and deploy them for inferences. Kafka-ML itself and the components it deploys are fully managed through containerization technologies, which ensure their portability, easy distribution, and other features such as fault-tolerance and high availability. Finally, a novel approach has been introduced to manage and reuse data streams, which may eliminate the need for data storage or file systems.This work is funded by the Spanish projects RT2018-099777-B-100 (“rFOG: Improving Latency and Reliability of Offloaded Computation to the FOG for Critical Services”), PY20_00788 (“IntegraDos: Providing Real-Time Services for the Internet of Things through Cloud Sensor Integration”) and UMA18FEDERJA-215 (“Advanced Monitoring System Based on Deep Learning Services in Fog”). Cristian Martín was with a postdoc grant from the Spanish project TIC-1572 (“MIsTIca: Critical Infrastructures Monitoring based on Wireless Technologies”) and his research stay at IHP has been funded through a mobility grant from the University of Malaga and IHP funding. Funding for open access charge: Universidad de Malaga/CBUA . We are grateful for the work of all the reviewers who have greatly contributed to improving the quality of this article. We would like to express our gratitude to Kai Wähner for his inspiration and ideas through numerous articles and GitHub repositories on Kafka and its combination with TensorFlow

Neuromorphic on-chip recognition of saliva samples of COPD and healthy controls using memristive devices

Chronic Obstructive Pulmonary Disease (COPD) is a life-threatening lung disease, affecting millions of people worldwide. Implementation of Machine Learning (ML) techniques is crucial for the effective management of COPD in home-care environments. However, shortcomings of cloud-based ML tools in terms of data safety and energy efficiency limit their integration with low-power medical devices. To address this, energy efficient neuromorphic platforms can be used for the hardware-based implementation of ML methods. Therefore, a memristive neuromorphic platform is presented in this paper for the on-chip recognition of saliva samples of COPD patients and healthy controls. Results of its performance evaluations showed that the digital neuromorphic chip is capable of recognizing unseen COPD samples with accuracy and sensitivity values of 89% and 86%, respectively. Integration of this technology into personalized healthcare devices will enable the better management of chronic diseases such as COPD. © 2020, The Author(s)

Permittivitäts-Biosensor für die Charakterisierung von Speichelproben von COPD-Patienten mit neuromorphischem maschinellem Lernen

Chronic Obstructive Pulmonary Disease (COPD) is an inflammatory lung disease, causing breathing difficulties in patients due to obstructed airflow in lungs. COPD is one of the main leading causes of death worldwide with an annual mortality rate of three million people. Despite the absence of an effective treatment for COPD, an early-stage diagnosis plays a crucial role for the effective management of the disease. However, majority of patients with objective COPD go undiagnosed until late stages of their disease due to the lack of a reliable technology for the recognition and monitoring of COPD in Point-of-Care (PoC). Alternative diagnostic approaches such as the accurate examination of respiratory tract fluids like saliva can address this issue using a portable biosensor in a home-care environment. Nonetheless, the accurate diagnosis of COPD based on this approach is only possible by concurrent consideration of patients demographic--medical parameters. Therefore, Machine Learning (ML) tools are necessary for the comprehensive recognition of COPD in a PoC setting. On the other hand, drawbacks of cloud-based ML techniques for medical applications such as data safety, immerse energy consumption, and enormous computation requirements need to be addressed for this application. Therefore, the objective of this thesis was to develop a ML-equipped system for the management of COPD in a PoC setup. A portable permittivity biosensor was developed in this work and its in-vitro performance was evaluated throughout clinical experiments. ML techniques were applied on biosensor results, demonstrating the significant role of these algorithms for the recognition of COPD. Moreover, developed ML models were deployed on a neuromorphic platform for addressing the shortcomings of cloud-based approaches.Die chronisch obstruktive Lungenerkrankung (COPD) ist eine entzündliche Lungenerkrankung, die bei Patienten Atembeschwerden aufgrund eines behinderten Luftstroms in der Lunge verursacht. COPD ist eine der häufigsten Todesursachen weltweit mit einer jährlichen Sterblichkeitsrate von drei Millionen Menschen. Obwohl es keine wirksame Behandlung für COPD gibt, spielt die Diagnose im Frühstadium eine entscheidende Rolle für die effektive Behandlung der Krankheit. Die Mehrheit der Patienten mit objektiver COPD bleibt jedoch bis in späte Stadien ihrer Erkrankung unerkannt, da es keine zuverlässige Technologie zur Erkennung und Überwachung von COPD am Point-of-Care (PoC) gibt. Alternative diagnostische Ansätze wie die genaue Untersuchung von Atemwegsflüssigkeiten wie Speichel können dieses Problem mit Hilfe eines tragbaren Biosensors in einer Home-Care-Umgebung beheben. Die genaue Diagnose von COPD auf Basis dieses Ansatzes ist jedoch nur bei gleichzeitiger Berücksichtigung der demographisch-medizinischen Parameter des Patienten möglich. Daher sind Werkzeuge des maschinellen Lernens (ML) für die umfassende Erkennung von COPD in einer PoC-Umgebung notwendig. Auf der anderen Seite müssen die Nachteile von Cloud-basierten ML-Techniken für medizinische Anwendungen, wie z.B. die Datensicherheit, der immersive Energieverbrauch und der enorme Rechenaufwand, für diese Anwendung adressiert werden. Daher war das Ziel dieser Arbeit, ein ML-ausgerüstetes System für das Management von COPD in einem PoC-Setup zu entwickeln. In dieser Arbeit wurde ein tragbarer Permittivitäts-Biosensor entwickelt und seine in-vitro Leistung wurde in klinischen Experimenten evaluiert. ML-Techniken wurden auf die Ergebnisse des Biosensors angewandt, was die bedeutende Rolle dieser Algorithmen für die Erkennung von COPD demonstrierte. Darüber hinaus wurden die entwickelten ML-Modelle auf einer neuromorphen Plattform eingesetzt, um die Unzulänglichkeiten von Cloud-basierten Ansätzen zu beheben

Epileptic Seizure Detection Using a Neuromorphic-Compatible Deep Spiking Neural Network

International audienc

Dielectrophoretic Immobilization of Yeast Cells Using CMOS Integrated Microfluidics

This paper presents a dielectrophoretic system for the immobilization and separation of live and dead cells. Dielectrophoresis (DEP) is a promising and efficient investigation technique for the development of novel lab-on-a-chip devices, which characterizes cells or particles based on their intrinsic and physical properties. Using this method, specific cells can be isolated from their medium carrier or the mixture of cell suspensions (e.g., separation of viable cells from non-viable cells). Main advantages of this method, which makes it favorable for disease (blood) analysis and diagnostic applications are, the preservation of the cell properties during measurements, label-free cell identification, and low set up cost. In this study, we validated the capability of complementary metal-oxide-semiconductor (CMOS) integrated microfluidic devices for the manipulation and characterization of live and dead yeast cells using dielectrophoretic forces. This approach successfully trapped live yeast cells and purified them from dead cells. Numerical simulations based on a two-layer model for yeast cells flowing in the channel were used to predict the trajectories of the cells with respect to their dielectric properties, varying excitation voltage, and frequency

Development of a Portable Dielectric Biosensor for Rapid Detection of Viscosity Variations and Its In Vitro Evaluations Using Saliva Samples of COPD Patients and Healthy Control

Chronic Obstructive Pulmonary Disease (COPD) is a life-threatening lung disease affecting millions of people worldwide. Although the majority of patients with objective COPD go undiagnosed until the late stages of their disease, recent studies suggest that the regular screening of sputum viscosity could provide important information on the disease detection. Since the viscosity of sputum is mainly defined by its mucin–protein and water contents, dielectric biosensors can be used for detection of viscosity variations by screening changes in sputum’s contents. Therefore, the objective of this work was to develop a portable dielectric biosensor for rapid detection of viscosity changes and to evaluate its clinical performance in characterizing viscosity differences of saliva samples collected from COPD patients and Healthy Control (HC). For this purpose, a portable dielectric biosensor, capable of providing real-time measurements, was developed. The sensor performance for dielectric characterization of mediums with high water content, such as saliva, was evaluated using isopropanol–water mixtures. Subsequently, saliva samples, collected from COPD patients and HC, were investigated for clinical assessments. The radio frequency biosensor provided high repeatability of 1.1% throughout experiments. High repeatability, ease of cleaning, low-cost, and portability of the biosensor made it a suitable technology for point-of-care applications

Design and Fabrication of a BiCMOS Dielectric Sensor for Viscosity Measurements: A Possible Solution for Early Detection of COPD

The viscosity variation of sputum is a common symptom of the progression of Chronic Obstructive Pulmonary Disease (COPD). Since the hydration of the sputum defines its viscosity level, dielectric sensors could be used for the characterization of sputum samples collected from patients for early diagnosis of COPD. In this work, a CMOS-based dielectric sensor for the real-time monitoring of sputum viscosity was designed and fabricated. A proper packaging for the ESD-protection and short-circuit prevention of the sensor was developed. The performance evaluation results show that the radio frequency sensor is capable of measuring dielectric constant of biofluids with an accuracy of 4.17%. Integration of this sensor into a portable system will result in a hand-held device capable of measuring viscosity of sputum samples of COPD-patients for diagnostic purposes

Development of a 2-DOF Sensorized Surgical Grasper for Grasping and Axial Force Measurements

Minimally invasive surgery (MIS) has been limited from its inception by insufficient haptic feedback to surgeons. To address this, various force sensing methods have been developed that can provide information about tool-tissue interaction forces. However, the results have been unreliable in most of these studies, since there are many sources of error and uncertainty in the force acquisition method. Furthermore, none of the force sensing devices described in the literature can be fully cleaned or sterilized for use in surgery. To address this, a sterilizable needle-driver type grasper is presented in this paper that incorporates fiber Bragg gratings to measure axial and grasping forces. The results of its performance evaluation show that the two-degrees of freedom grasper is capable of measuring grasping and axial forces with an accuracy of 0.27 and 0.3 N, respectively. Integration of this device into a needle driver instrument will result in a sterilizable instrument capable of measuring tool-tissue interaction forces during MIS