994 research outputs found
Human-centred artificial intelligence for mobile health sensing:challenges and opportunities
Advances in wearable sensing and mobile computing have enabled the collection of health and well-being data outside of traditional laboratory and hospital settings, paving the way for a new era of mobile health. Meanwhile, artificial intelligence (AI) has made significant strides in various domains, demonstrating its potential to revolutionize healthcare. Devices can now diagnose diseases, predict heart irregularities and unlock the full potential of human cognition. However, the application of machine learning (ML) to mobile health sensing poses unique challenges due to noisy sensor measurements, high-dimensional data, sparse and irregular time series, heterogeneity in data, privacy concerns and resource constraints. Despite the recognition of the value of mobile sensing, leveraging these datasets has lagged behind other areas of ML. Furthermore, obtaining quality annotations and ground truth for such data is often expensive or impractical. While recent large-scale longitudinal studies have shown promise in leveraging wearable sensor data for health monitoring and prediction, they also introduce new challenges for data modelling. This paper explores the challenges and opportunities of human-centred AI for mobile health, focusing on key sensing modalities such as audio, location and activity tracking. We discuss the limitations of current approaches and propose potential solutions
Developments in Transduction, Connectivity and AI/Machine Learning for Point-of-Care Testing
We review some emerging trends in transduction, connectivity and data analytics for Point-of-Care Testing (POCT) of infectious and non-communicable diseases. The patient need for POCT is described along with developments in portable diagnostics, specifically in respect of Lab-on-chip and microfluidic systems. We describe some novel electrochemical and photonic systems and the use of mobile phones in terms of hardware components and device connectivity for POCT. Developments in data analytics that are applicable for POCT are described with an overview of data structures and recent AI/Machine learning trends. The most important methodologies of machine learning, including deep learning methods, are summarised. The potential value of trends within POCT systems for clinical diagnostics within Lower Middle Income Countries (LMICs) and the Least Developed Countries (LDCs) are highlighted
On the Impact of Voice Anonymization on Speech-Based COVID-19 Detection
With advances seen in deep learning, voice-based applications are burgeoning,
ranging from personal assistants, affective computing, to remote disease
diagnostics. As the voice contains both linguistic and paralinguistic
information (e.g., vocal pitch, intonation, speech rate, loudness), there is
growing interest in voice anonymization to preserve speaker privacy and
identity. Voice privacy challenges have emerged over the last few years and
focus has been placed on removing speaker identity while keeping linguistic
content intact. For affective computing and disease monitoring applications,
however, the paralinguistic content may be more critical. Unfortunately, the
effects that anonymization may have on these systems are still largely unknown.
In this paper, we fill this gap and focus on one particular health monitoring
application: speech-based COVID-19 diagnosis. We test two popular anonymization
methods and their impact on five different state-of-the-art COVID-19 diagnostic
systems using three public datasets. We validate the effectiveness of the
anonymization methods, compare their computational complexity, and quantify the
impact across different testing scenarios for both within- and across-dataset
conditions. Lastly, we show the benefits of anonymization as a data
augmentation tool to help recover some of the COVID-19 diagnostic accuracy loss
seen with anonymized data.Comment: 11 pages, 10 figure
Towards a tricorder: clinical, health economic, and ethical investigation of point-of-care artificial intelligence electrocardiogram for heart failure
Heart failure (HF) is an international public health priority and a focus of the NHS Long Term
Plan. There is a particular need in primary care for screening and early detection of heart failure with
reduced ejection fraction (HFrEF) – the most common and serious HF subtype, and the only one
with an abundant evidence base for effective therapies. Digital health technologies (DHTs) integrating
artificial intelligence (AI) could improve diagnosis of HFrEF. Specifically, through a convergence of
DHTs and AI, a single-lead electrocardiogram (ECG) can be recorded by a smart stethoscope and
interrogated by AI (AI-ECG) to potentially serve as a point-of-care HFrEF test. However, there are
concerning evidence gaps for such DHTs applying AI; across intersecting clinical, health economic,
and ethical considerations. My thesis therefore investigates hypotheses that AI-ECG is 1.) Reliable,
accurate, unbiased, and can be patient self-administered, 2.) Of justifiable health economic impact for
primary care deployment, and 3.) Appropriate across ethical domains for deployment as a tool for
patient self-administered screening. The theoretical basis for this work is presented in the Introduction (Chapter 1). Chapter 2 describes the first large-scale, multi-centre independent external validation study of AI-ECG, prospectively recruiting 1,050 patients and highlighting impressive performance: area under the curve, sensitivity, and specificity up to 0·91 (95% confidence interval: 0·88–0·95), 91·9% (78·1–98·3), and
80·2% (75·5–84·3) respectively; and absence of bias by age, sex, and ethnicity. Performance was
independent of operator, and usability of the tool extended to patients being able to self-examine.
Chapter 3 presents a clinical and health economic outcomes analysis using a contemporary digital
repository of 2.5 million NHS patient records. A propensity-matched cohort was derived using all
patients diagnosed with HF from 2015-2020 (n = 34,208). Novel findings included the unacceptable
reality that 70% of index HF diagnoses are made through hospitalisation; where index diagnosis through primary care conferred a medium-term survival advantage and long-term cost saving (£2,500
per patient). This underpins a health economic model for the deployment of AI-ECG across primary
care. Chapter 4 approaches a normative ethical analysis focusing on equity, agency, data rights, and
responsibility for safe, effective, and trustworthy implementation of an unprecedented at-home patient
self-administered AI-ECG screening programme. I propose approaches to mitigating any potential
harms, towards preserving and promoting trust, patient engagement, and public health.
Collectively, this thesis marks novel work highlighting AI-ECG as tool with the potential to
address major cardiovascular public health priorities. Scrutiny through complimentary clinical, health
economic, and ethical considerations can directly serve patients and health systems by blueprinting
best-practice for the evaluation and implementation of DHTs integrating AI – building the conviction
needed to realise the full potential of such technologies.Open Acces
The 2023 wearable photoplethysmography roadmap
Photoplethysmography is a key sensing technology which is used in wearable devices such as smartwatches and fitness trackers. Currently, photoplethysmography sensors are used to monitor physiological parameters including heart rate and heart rhythm, and to track activities like sleep and exercise. Yet, wearable photoplethysmography has potential to provide much more information on health and wellbeing, which could inform clinical decision making. This Roadmap outlines directions for research and development to realise the full potential of wearable photoplethysmography. Experts discuss key topics within the areas of sensor design, signal processing, clinical applications, and research directions. Their perspectives provide valuable guidance to researchers developing wearable photoplethysmography technology
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