Healthcare Technology Innovation Centre

We described a challenge named "Diabetic Retinopathy (DR)-Grading and Image Quality Estimation Challenge" in conjunction with ISBI 2020 to hold three sub-challenges and develop deep learning models for DR image assessment and grading. The scientific community responded positively to the challenge, with 34 submissions from 574 registrations. In the challenge, we provided the DeepDRiD dataset containing 2,000 regular DR images (500 patients) and 256 ultra-widefield images (128 patients), both having DR quality and grading annotations. We discussed details of the top 3 algorithms in each sub-challenges. The weighted kappa for DR grading ranged from 0.93 to 0.82, and the accuracy for image quality evaluation ranged from 0.70 to 0.65. The results showed that image quality assessment can be used as a further target for exploration. We also have released the DeepDRiD dataset on GitHub to help develop automatic systems and improve human judgment in DR screening and diagnosis.

Local pulse wave velocity (PWV) is evolving as one of the important determinants of arterial hemodynamics, localized vessel stiffening associated with several pathologies, and a host of other cardiovascular events. Although PWV was introduced over a century ago, only in recent decades, due to various technological advancements, has emphasis been directed toward its measurement from a single arterial section or from piecewise segments of a target arterial section. This emerging worldwide trend in the exploration of instrumental solutions for local PWV measurement has produced several invasive and noninvasive methods. As of yet, however, a univocal opinion on the ideal measurement method has not emerged. Neither have there been extensive comparative studies on the accuracy of the available methods. Recognizing this reality, makes apparent the need to establish guideline-recommended standards for the measurement methods and reference values, without which clinical application cannot be pursued. This paper enumerates all major local PWV measurement methods while pinpointing their salient methodological considerations and emphasizing the necessity of global standardization. Further, a summary of the advancements in measuring modalities and clinical applications is provided. Additionally, a detailed discussion on the minimally explored concept of incremental local PWV is presented along with suggestions of future research questions.

OBJECTIVE: Neonates and infants are patients who would benefit from less invasive vital sign sensing, especially from fewer cables and the avoidance of adhesive electrodes. Photoplethysmography imaging (PPGI) has been studied for medical applications in recent years: it is possible to assess various vital signs remotely, non-invasively, and without contact by using video cameras and light. However, studies on infants and especially on neonates in clinical settings are still rare. Hence, we conducted a single-center study to assess heart activity by estimating the pulse rate (PR) of 19 neonates. APPROACH: Time series were generated from tracked regions of interest (ROIs) and PR was estimated via a joint time-frequency analysis using a short-time Fourier transform. Artifacts, for example, induced by movement, were detected and flagged by applying a signal quality index in the frequency domain. MAIN RESULTS: The feasibility of PR estimation was demonstrated using visible light and near-infrared light at 850 nm and 940 nm, respectively: the estimated PR was as close as 3 heartbeats per minute in artifact-free time segments. Furthermore, an improvement could be shown when selecting the best performing ROI compared to the ROI containing the whole body. The main challenges are artifacts from motion, light sources, medical devices, and the detection and tracking of suitable regions for signal retrieval. Nonetheless, the PR extracted was found to be comparable to the contact-based photoplethysmography reference and is, therefore, a viable replacement if robust signal retrieval is ensured. SIGNIFICANCE: Neonates are seldom measured by PPGI and studies reporting measurements on darker skin tones are rare. In this work, not only a single camera was used, but a synchronized camera setup using multiple wavelengths. Various ROIs were used for signal extraction to examine the capabilities of PPGI. In addition, qualitative observations regarding camera parameters and noise sources were reported and discussed.

-vinylcaprolactam) (PNVCL) devoid of any classical fluorophore entity. PNVCL underwent a coil to globular conformational transition in an aqueous medium and appeared to be fluorescent above its lower critical solution temperature (LCST) near body temperature (38 °C). Eventually, this intriguing aspect enabled higher cellular uptake of PNVCL at the LCST boundary. By virtue of the AIE effect, the thermo-induced aggregation phenomenon has been ingeniously utilized to apply PNVCL as a novel fluorescent thermometer for intracellular temperature determination.

Surgery is a particularly potent stressor and the detrimental effects of stress on people undergoing any surgery is indisputable. When left unchecked, the pre-surgery stress adversely impacts people's physical and psychological well-being, and may even evolve into severe pathological states. Therefore, it is essential to identify levels of preoperative stress in surgical patients. This paper focuses on developing an automatic pre-surgery stress detection scheme based on electrodermal activity (EDA). The measurement set up involves a wrist wearable that monitors EDA of a subject continuously in the most non-invasive and unobtrusive manner. Data were collected from 41 subjects [17 females and 24 males, age: 54.8 ± 16.8 years (mean ± SD)], who subsequently underwent different surgical procedures at the Sri Ramakrishna Hospital, Coimbatore, India. A supervised machine learning algorithm that detects motion artifacts in the recorded EDA data was developed. It yielded an accuracy of 97.83% on a new user dataset. The clean EDA data were further analyzed to determine low, moderate, and high levels of stress. A novel localized supervised learning scheme based on the adaptive partitioning of the dataset was adopted for stress detection. Consequently, the interindividual variability in the EDA due to person-specific factors such as the sweat gland density and skin thickness, which may lead to erroneous classification, could be eliminated. The scheme yielded a classification accuracy of 85.06% on a new user dataset and proved to be more effective than the general supervised classification model.

OBJECTIVE: We propose a calibration-free method and system for cuffless blood pressure (BP) measurement from superficial arteries. A prototype device with bi-modal probe arrangement was designed and developed to estimate carotid BP - an indicator of central aortic pressure. METHODS: Mathematical models relating BP parameters of an arterial segment to its dimensions and local pulse wave velocity (PWV) are introduced. A bi-modal probe utilizing ultrasound and photoplethysmograph sensors was developed and used to measure diameter values and local PWV from the carotid artery. Carotid BP was estimated using the measured physiological parameters without any subject- or population-specific calibration procedures. The proposed cuffless BP estimation method and system were tested for accuracy, usability, and for potential utility in hypertension screening, on a total of 83 subjects. RESULTS: The prototype device demonstrated its capability of detecting beat-by-beat arterial dimensions and local PWV simultaneously. Carotid diastolic BP (DBP) and systolic BP (SBP) were estimated over multiple cardiac cycles in real-time. The absolute error in carotid DBP was <10 mmHg in 82% cases, and root-mean-square-error = 8.3 mmHg. Consistent with the theory, estimated SBP at the carotid site was lower than the reference brachial SBP. ROC curves obtained for hypertension screening analysis revealed an area under the curve ≥0.8 for both carotid SBP and DBP values, illustrating the potential for using the developed method in hypertension screening. CONCLUSION: The feasibility of calibration-free, cuffless BP measurement at an arterial site of interest was demonstrated with a level of acceptable accuracy. The study also demonstrated the potential utility of the proposed method and system in hypertension screening and local evaluation of arterial stiffness indices. SIGNIFICANCE: Novel approach for calibration-free cuffless BP estimation; a potential tool for local BP measurement and hypertension screening.

OBJECTIVE: A novel photoplethysmograph probe employing dual photodiodes excited using a single infrared light source was developed for local pulse wave velocity (PWV) measurement. The potential use of the proposed system in cuffless blood pressure (BP) techniques was demonstrated. APPROACH: was investigated based on inter- and intra-subject models with best-case calibration. MAIN RESULTS: with population-specific models. More accurate estimates of arterial BP levels were achieved using local PWV via subject-specific models (root-mean-square error ⩽2.61 mmHg). SIGNIFICANCE: A reliable system for cuffless BP measurement and local estimation of arterial wall properties.

Cardiac arrhythmias are presently diagnosed by manual interpretation of Electrocardiography (ECG) signals. Automated ECG interpretation is required to perform efficient screening of arrhythmia from long term ECG data. Existing automated ECG interpretation tools however require extensive preprocessing and knowledge to determine relevant features. Thus there is a need for a comprehensive feature extractor and classifier to analyze ECG signals. In this paper, we propose three robust deep neural network (DNN) architectures to perform feature extraction and classification of a given two second ECG signal. The first network is a Convolutional Neural Network (CNN) with multiple kernel sizes, the second network is a Long Short Term Memory (LSTM) network and the third network is a combination of CNN and LSTM based feature extractor, CLSTM network. The proposed networks are end to end networks which can be directly trained without any preprocessing. The networks were trained and tested with the MITDB ECG dataset on three classes Normal (N), Premature Ventricular Contraction (PVC) and Premature Atrial Contraction (PAC). The best model CLSTM gave an accuracy of 97.6%. Further, transfer learning is showcased on the best performing network for use with multiple ECG datasets requiring training only on the final three layers. The results showcase the potential of the network as feature extractor for ECG datasets. Our results outperform the state-of-the art works on ECG classification on several metrics.

Vascular stiffness is an indicator of cardiovascular health, with carotid artery stiffness having established correlation to coronary heart disease and utility in cardiovascular diagnosis and screening. State of art equipment for stiffness evaluation are expensive, require expertise to operate and not amenable for field deployment. In this context, we developed ARTerial Stiffness Evaluation for Noninvasive Screening (ARTSENS), a device for image free, noninvasive, automated evaluation of vascular stiffness amenable for field use. ARTSENS has a frugal hardware design, utilizing a single ultrasound transducer to interrogate the carotid artery, integrated with robust algorithms that extract arterial dimensions and compute clinically accepted measures of arterial stiffness. The ability of ARTSENS to measure vascular stiffness in vivo was validated by performing measurements on 125 subjects. The accuracy of results was verified with the state-of-the-art ultrasound imaging-based echo-tracking system. The relation between arterial stiffness measurements performed in sitting posture for ARTSENS measurement and sitting/supine postures for imaging system was also investigated to examine feasibility of performing ARTSENS measurements in the sitting posture for field deployment. This paper verified the feasibility of the novel ARTSENS device in performing accurate in vivo measurements of arterial stiffness. As a portable device that performs automated measurement of carotid artery stiffness with minimal operator input, ARTSENS has strong potential for use in large-scale screening.

to the 2012 Olympic Stadium and right in the heart of London's vibrant and ethnically diverse East End. I am Director of the Healthcare Innovation and Policy Unit at Queen Mary. Innovation: an idea, a technology, a research discovery, a way of working-anything that is perceived as new and which requires a change of hearts and minds and structures and systems to become business as usual. 1 Policy: perhaps best defined as 'the authoritative exposition of values'.

A new chelator and ICT donor based visible light excitable Zn²⁺sensor was designed and developed by integrating quinoline and 2-hydroxy-3-(hydroxymethyl)-5-methylbenzaldehyde.

in solution, whereas complexes 1(a-c) are able to selectively detect pyrophosphate (PPi) in aqueous medium. The structure of the pyrophosphate (PPi) complex has been proposed using DFT calculations and the selectivity is due to the unique ability of this anion to simultaneously coordinate to both the Zn metal centers. The anticancer activity of complexes 1(a-c) was also explored.

An ultra-low power ECG platform for continuous and minimally intrusive monitoring for systems with minimal processing capabilities, is presented in this paper. The platform is capable of detecting abnormalities in the ECG signal by extracting and analyzing features related to various cardiac trends. The platform is built to continuously operate on any of the 12 leads and the presented work includes a single lead implementation that works on lead I or II. A single lead, wearable ECG patch that can detect rhythm based arrhythmias and continuously monitor beat-to-beat heart rate and respiratory rate has been developed. In addition, the device stores raw ECG waveform locally and is designed to run for 10 days on a single charge. The ECG patch works in conjunction with a front end device or tablet and updates the results on the tablet interface. Upon detection of an abnormality or an arrhythmia the device switches to an ECG visualization mode enabling manual analysis on the acquired signal. The front end device also functions as a gateway for remote monitoring. The functionality and processing capabilities of the platform along with the validation tests carried out in a controlled setting are presented.

While the development of health information technology, particularly electronic health records (EHR), is a triumph for the advancement of healthcare, non-interoperable clinical data systems lead to fragmented communication and incomplete records. If interoperable HIT systems could be achieved integrated HIT could be leveraged to lessen medical errors, improve patient care and optimize epidemiological research. To understand the barriers to interoperability or health information exchange (HIE), we reviewed the literature on HIT and barriers to HIE. Our search yielded 492 articles, 25 meeting our inclusion criteria. In general, we found that the predominant barriers to HIE are need for standards, security concerns, economic loss to competitors, and federated systems. Research on interoperability is limited because most HIE programs are still in formative stages. More research is needed to fully understand interoperability of HIT, how to overcome the barriers to interoperability, and how to design HIT to better facilitate HIE.

BACKGROUND: Increasing health costs, demand and patient multimorbidity challenge the sustainability of healthcare systems. These challenges persist and have been amplified by the global pandemic. OBJECTIVES: We aimed to develop an understanding of how the sustainable performance of healthcare systems (SPHS) has been conceptualised, defined and measured. DESIGN: Scoping review of peer-reviewed articles and editorials published from database inception to February 2021. DATA SOURCES: PubMed and Ovid Medline, and snowballing techniques. ELIGIBILITY CRITERIA: We included articles that discussed key focus concepts of SPHS: (1) definitions, (2) measurement, (3) identified challenges, (4) identified solutions for improvement and (5) scaling successful solutions to maintain SPHS. DATA EXTRACTION AND SYNTHESIS: After title/abstract screening, full-text articles were reviewed, and relevant information extracted and synthesised under the five focus concepts. RESULTS: Of 142 included articles, 38 (27%) provided a definition of SPHS. Definitions were based mainly on financial sustainability, however, SPHS was also more broadly conceptualised and included acceptability to patients and workforce, resilience through adaptation, and rapid absorption of evidence and innovations. Measures of SPHS were also predominantly financial, but recent articles proposed composite measures that accounted for financial, social and health outcomes. Challenges to achieving SPHS included the increasingly complex patient populations, limited integration because of entrenched fragmented systems and siloed professional groups, and the ongoing translational gaps in evidence-to-practice and policy-to-practice. Improvement strategies for SPHS included developing appropriate workplace cultures, direct community and consumer involvement, and adoption of evidence-based practice and technologies. There was also a strong identified need for long-term monitoring and evaluations to support adaptation of healthcare systems and to anticipate changing needs where possible. CONCLUSIONS: To implement lasting change and to respond to new challenges, we need context-relevant definitions and frameworks, and robust, flexible, and feasible measures to support the long-term sustainability and performance of healthcare systems.

(Raman) vibrations of the other two. The increase in DNA, protein and lipid content with malignancy was more clearly elucidated by examining both spectra. Principal component analysis (PCA)-linear discriminant analysis (LDA) with 10-fold cross validation of the FTIR and Raman spectral data sets showed efficient discrimination between normal and pathological conditions while overlapping was seen between the two pathologies. The PCA-LDA model of the dual spectra yielded a classification accuracy of 98% in comparison with either FTIR (85%) or Raman (82%) in a spectrum-wise comparison. In the patient-wise approach (mean of all spectra from a patient), the overall classification efficiency was 73%, 80% and 87% for FTIR, Raman and integrated spectral approaches respectively. Moreover, the efficiency of the integrated FTIR-Raman PCA-LDA model as a prediction tool was tested to screen susceptible individuals (11 cigarette smokers) using the dual spectra acquired from these individuals. The study presents proof-of-concept for adopting a large-scale, follow-up trial of the approach for mass screening purposes.

Facile synthesis of water soluble fluorescent N-doped graphene nanosheets for multifunctional applications in photocatalysis and sensing.

Abstract Objective : Work stress is identified as the ‘health epidemic of 21st century’ by WHO because, when left unchecked, it wreaks havoc on human mind and body by accelerating the onset and progression of several health disorders. Hence, the evolution of strategies for early detection of mental stress is pivotal. The study presented here is one step towards the goal of developing a physiological parameter based psychological stress detection scheme which can further be incorporated into a wearable vital signs monitor. Approach : A group of 34 subjects (14 females and 20 males, age: 21.4 ± 1.7 years; mean ± SD) volunteered to participate in a pilot laboratory intervention that emulated real-life job stress scenarios by incorporating stress factors like mental workload, time pressure, performance pressure and social evaluative threat. Electrodermal Activity (EDA), Electrocardiogram (ECG), and Skin Temperature (ST) were monitored throughout the experiment to capture sympathetic activation during stress. Stress response elicitation was validated using salivary cortisol levels. A total of 61 features were extracted from these signals and four classifiers were investigated regarding their ability to detect ‘stress’ using single and multimodal schemes. A fusion framework that combined the benefits of feature fusion and decision fusion was employed to generate classifier ensembles for multimodal stress detection schemes. As the generated datasets exhibited a class imbalance issue, three separate schemes for class imbalance rectification viz., undersampling, oversampling and SMOTE were investigated concerning their ability to yield the best classification performance. While ECG based performance analysis was restricted to data segments of 300 s duration to conform to international guidelines for short-term HRV analysis, non-overlapping EDA and ST data segments of durations 300 s, 180 s, 60 s, and 30 s were examined to determine the optimum data length that can generate best results. Main Results : EDA gave a superior performance for 60 s windows while ST performed best with data segments of duration 30 s. A comparative study was performed with 25%, 50%, 75% and 90% overlapping data segments as well. However, overlapping did not enhance the performance of the classifiers significantly.While EDA emerged as the best single modality, the highest stress recognition accuracy of 97.13% was yielded by a combination of EDA and ST with data segments of 60 s duration. Furthermore, the differential effect of ‘physical’ and ‘psychological’ stressors on EDA and ST was analyzed. Significance : The results clearly suggest that these physiological parameters can not only reliably detect psychological stress but can also discriminate it from physical stress.

Water soluble fluorescent carbon nano onions (wsCNO) cross the blood brain barrier (BBB) in the CADASIL murine model as well as in GBM induced mice.

Post-implantation failure associated with insufficient host tissue integration at the bone-implant interface and aseptic loosening is a major concern in orthopaedics as well as in dentistry. To overcome the failure in early stages of implantation, prosthetic design combining the mechanisms of porosity guided bone ingrowth along with topographic manipulation of osteogenic cells over bacterial colonization would be an ideal choice, although achieving such a goal is highly challenging. In this study, facile rapid hydrothermal synthesis of nanostructures with simultaneous deposition of hydroxyapatite on the titanium alloy surface was demonstrated by using an aqueous sodium tripolyphosphate and calcium hydroxide mixture. Nanostructures with wire-like morphology exhibited significantly higher osteogenic related gene expression (COL I, OPN, and OCN) through differentiation of adipose derived mesenchymal stem cells as well as the bactericidal response against S. aureus and E. coli as compared to other nanotopographic features. The same also exhibited elongated cell morphology with the highest expression of paxillin towards cell boundaries as compared to the polished surface with flattened cell morphology and localized expression of paxillin around the nucleus. Implantation of treated porous Ti6Al4V samples representing a multiscalar hierarchy with wire-like nanostructures accelerated osteochondral healing in rabbits without any major signs of infection. Also, significantly higher bone formation was observed within the defects implanted with treated porous Ti6Al4V (44.0%) as compared to that of untreated porous samples (36.9%) as well as empty defects (19.6%).