Towards a smart medical home

Explainable federated learning scheme for secure healthcare data sharing

Artificial intelligence has immense potential for applications in smart healthcare. Nowadays, a large amount of medical data collected by wearable or implantable devices has been accumulated in Body Area Networks. Unlocking the value of this data can better explore the applications of artificial intelligence in the smart healthcare field. To utilize these dispersed data, this paper proposes an innovative Federated Learning scheme, focusing on the challenges of explainability and security in smart healthcare. In the proposed scheme, the federated modeling process and explainability analysis are independent of each other. By introducing post-hoc explanation techniques to analyze the global model, the scheme avoids the performance degradation caused by pursuing explainability while understanding the mechanism of the model. In terms of security, firstly, a fair and efficient client private gradient evaluation method is introduced for explainable evaluation of gradient contributions, quantifying client contributions in federated learning and filtering the impact of low-quality data. Secondly, to address the privacy issues of medical health data collected by wireless Body Area Networks, a multi-server model is proposed to solve the secure aggregation problem in federated learning. Furthermore, by employing homomorphic secret sharing and homomorphic hashing techniques, a non-interactive, verifiable secure aggregation protocol is proposed, ensuring that client data privacy is protected and the correctness of the aggregation results is maintained even in the presence of up to t colluding malicious servers. Experimental results demonstrate that the proposed scheme's explainability is consistent with that of centralized training scenarios and shows competitive performance in terms of security and efficiency.

Graphical abstract

Artificial intelligence in cardiology: a bibliometric study

OBJECTIVES

To perform a comprehensive bibliometric analysis of global publications on the applications of artificial intelligence (AI) in cardiology.

METHODS

Documents related to AI in cardiology published between 2002 and 2022 were retrieved from Web of Science Core Collection. R package "bibliometrix", VOSviewers and Microsoft Excel were applied to perform the bibliometric analysis.

RESULTS

A total of 4332 articles were included. United States topped the list of countries publishing articles, followed by China and United Kingdom. The Harvard University was the institution that contributed the most to this field, followed by University of California System and University of London. Disease risk prediction, diagnosis, treatment, disease detection, and prognosis assessment were the research hotspots for AI in cardiology.

CONCLUSIONS

Enhancing cooperation between different countries and institutions is a critical step in leading to breakthroughs in the application of AI in cardiology. It is foreseeable that the application of machine learning and deep learning in various areas of cardiology will be a research priority in the coming years.

Innovation in Digital Health Interventions for Frailty and Sarcopenia

Today, the challenges of an aging society are primarily seen in frailty, sarcopenia, and impaired functionality [...]

Consensus on Recommended Functions of a Smart Home System to Improve Self-Management Behaviors in People With Heart Failure: A Modified Delphi Approach

Background

Smart home systems could enhance clinical and self-management of chronic heart failure by supporting health monitoring and remote support, but evidence to guide the design of smart home system functionalities is lacking.

Objective

To identify consensus-based recommendations for functions of a smart home system that could augment clinical and self-management for people living with chronic heart failure in the community.

Methods

Healthcare professionals caring for people living with chronic heart failure participated in a two-round modified Delphi survey and a consensus workshop. Thirty survey items spanning eight chronic health failure categories were derived from international guidelines for the management of heart failure. In survey Round 1, participants rated the importance of all items using a 9-point Liket scale and suggested new functions to support people with chronic heart failure in their homes using a smart home system. The Likert scale scores ranged from 0 (not important) to 9 (very important) and scores were categorized into three groups: 1-3 = not important, 4-6 = important, and 7-9 = very important. Consensus agreement was defined a priori as ≥70% of respondents rating a score of ≥7 and ≤ 15% rating a score ≤ 3. In survey Round 2, panel members re-rated items where consensus was not reached, and rated the new items proposed in earlier round. Panel members were invited to an online consensus workshop to discuss items that had not reached consensus after Round 2 and agree on a set of recommendations for a smart home system.

Results

In Round 1, 15 experts agreed 24/30 items were "very important", and suggested six new items. In Round 2, experts agreed 2/6 original items and 6/6 new items were "very important". During the consensus workshop, experts endorsed 2/4 remaining items. Finally, the expert panel recommended 34 items as "very important" for a smart home system including, healthy eating, body weight and fluid intake, physical activity and sedentary behavior, heart failure symptoms, tobacco cessation and alcohol reduction, medication adherence, physiological monitoring, interaction with healthcare professionals, and mental health among others.

Conclusion

A panel of healthcare professional experts recommended 34-item core functions in smart home systems designed to support people with chronic heart failure for self-management and clinical support. Results of this study will help researchers to co-design and protyping solutions with consumers and healthcare providers to achieve these core functions to improve self-management and clinical outcomes in people with chronic heart failure.

Efficacy of Moxibustion in the Treatment of Parkinson's Disease Based on Meta-Analysis under Intelligent Medical Treatment

Dementia in Parkinson's disease (PD) has become a major factor affecting the quality of life of patients with Parkinson's disease. Early detection and timely prevention can delay the progression of dementia, improve the quality of life of patients, and reduce the burden on society. This article is aimed at studying how to analyze the efficacy of moxibustion in the treatment of Parkinson's disease through meta-analysis on the basis of smart medicine. This article puts forward the related conceptual knowledge of smart medicine and meta-analysis and moxibustion treatment and proposes a deep learning method based on smart medicine to analyze the effects of moxibustion treatment on patients. The experiment in this article can be seen from the data in one of the figures that the highest curative effect of using a single moxibustion to treat Parkinson's disease is about 46%, while the curative effect of using a combination of moxibustion and Western medicine has reached 90%. It can be seen that a single moxibustion is not as effective as a combination of the two for Parkinson's disease. From the data in one of the tables, it can be seen that the proportion of Parkinson's disease in 2016 was 15%, showing an increase of 5%. By 2020, the proportion of Parkinson's disease was as high as 38%, and the growth rate reached 9%. It can be seen that the prevalence of this disease is getting higher and higher. Parkinson's disease has caused many undesirable effects on patients, such as slow movement, mental disorders, and a decline in mental state. Therefore, it is urgent to study the treatment of Parkinson's disease. Moxibustion can improve the patient's blood circulation and help the patient's local limbs to recover more easily and can help improve the patient's motor function.

Clinical Research on Gastrointestinal Surgery Based on Smart Medicine

Smart medical technology uses the medical information platform, with the help of current technical means, so that the information between medical staff and medical equipment can be shared. The combination of current technology and the medical field has become the norm. In the future, more artificial intelligence technologies will be incorporated in the medical field to promote the development of medical undertakings. At present, the information on the Internet is very large and complex, and general search engines often do not have knowledge in certain professional fields and can only perform shallow keyword searches, so it is difficult to meet people's medical diagnostic needs. Smart medical technology can solve the problem of these needs. Surgery, commonly known as operation, refers to the process of entering the body to change the condition of the disease through technical means and under the guidance of professionals. Earlier operations only performed cutting on the body surface. With the continuous maturity of surgical operations, operations can now be performed on any part, but manual operations are still the main one. In addition to manual surgery, there are many machine surgeries, such as laser surgeries. Clinical research takes the diagnosis and treatment of diseases as the main content, takes patients as the research object, and is a scientific research activity involving multiple personnel. This article aimed to study the clinical research of gastrointestinal surgery based on smart medicine and hoped to use smart medical technology to improve the clinical research level of gastrointestinal surgery and provide technical support for surgery. This study proposes to apply natural language processing technology to the medical field, build an intelligent diagnostic auxiliary system, and use the existing medical record data to realize the corresponding medical auxiliary function. The research measures and analyzes the basic information of medical students, the incidence of functional gastrointestinal disease, the incidence of common symptoms of functional gastrointestinal disease, and the differential distribution of various symptoms in different professions, genders, and ages. The experimental results of this article show that there are 27 cases of gastrointestinal bleeding, accounting for 18%; 10 cases of dysphagia, accounting for 7%; 78 cases of abdominal pain and bloating, accounting for 53%; and 19 cases of melena, accounting for 13%. Abdominal pain and bloating are the most common clinical manifestations of the gastrointestinal tract and require more attention.

Genome-wide association study-based prediction of atrial fibrillation using artificial intelligence

OBJECTIVE

We previously reported early-onset atrial fibrillation (AF) associated genetic loci among a Korean population. We explored whether the AF-associated single-nucleotide polymorphisms (SNPs) selected from the Genome-Wide Association Study (GWAS) of an external large cohort has a prediction power for AF in Korean population through a convolutional neural network (CNN).

METHODS

This study included 6358 subjects (872 cases, 5486 controls) from the Korean population GWAS data. We extracted the lists of SNPs at each p value threshold of the association statistics from three different previously reported ethnical-specific GWASs. The Korean GWAS data were divided into training (64%), validation (16%) and test (20%) sets, and a stratified K-fold cross-validation was performed and repeated five times after data shuffling.

RESULTS

The CNN-GWAS predictive power for AF had an area under the curve (AUC) of 0.78±0.01 based on the Japanese GWAS, AUC of 0.79±0.01 based on the European GWAS, and AUC of 0.82±0.01 based on the multiethnic GWAS, respectively. Gradient-weighted class activation mapping assigned high saliency scores for AF associated SNPs, and the PITX2 obtained the highest saliency score. The CNN-GWAS did not show AF prediction power by SNPs with non-significant p value subset (AUC 0.56±0.01) despite larger numbers of SNPs. The CNN-GWAS had no prediction power for odd-even registration numbers (AUC 0.51±0.01).

CONCLUSIONS

AF can be predicted by genetic information alone with moderate accuracy. The CNN-GWAS can be a robust and useful tool for detecting polygenic diseases by capturing the cumulative effects and genetic interactions of moderately associated but statistically significant SNPs.

TRIAL REGISTRATION NUMBER

NCT02138695.

Seizure Diaries and Forecasting With Wearables: Epilepsy Monitoring Outside the Clinic

It is a major challenge in clinical epilepsy to diagnose and treat a disease characterized by infrequent seizures based on patient or caregiver reports and limited duration clinical testing. The poor reliability of self-reported seizure diaries for many people with epilepsy is well-established, but these records remain necessary in clinical care and therapeutic studies. A number of wearable devices have emerged, which may be capable of detecting seizures, recording seizure data, and alerting caregivers. Developments in non-invasive wearable sensors to measure accelerometry, photoplethysmography (PPG), electrodermal activity (EDA), electromyography (EMG), and other signals outside of the traditional clinical environment may be able to identify seizure-related changes. Non-invasive scalp electroencephalography (EEG) and minimally invasive subscalp EEG may allow direct measurement of seizure activity. However, significant network and computational infrastructure is needed for continuous, secure transmission of data. The large volume of data acquired by these devices necessitates computer-assisted review and detection to reduce the burden on human reviewers. Furthermore, user acceptability of such devices must be a paramount consideration to ensure adherence with long-term device use. Such devices can identify tonic–clonic seizures, but identification of other seizure semiologies with non-EEG wearables is an ongoing challenge. Identification of electrographic seizures with subscalp EEG systems has recently been demonstrated over long (>6 month) durations, and this shows promise for accurate, objective seizure records. While the ability to detect and forecast seizures from ambulatory intracranial EEG is established, invasive devices may not be acceptable for many individuals with epilepsy. Recent studies show promising results for probabilistic forecasts of seizure risk from long-term wearable devices and electronic diaries of self-reported seizures. There may also be predictive value in individuals' symptoms, mood, and cognitive performance. However, seizure forecasting requires perpetual use of a device for monitoring, increasing the importance of the system's acceptability to users. Furthermore, long-term studies with concurrent EEG confirmation are lacking currently. This review describes the current evidence and challenges in the use of minimally and non-invasive devices for long-term epilepsy monitoring, the essential components in remote monitoring systems, and explores the feasibility to detect and forecast impending seizures via long-term use of these systems.

Glaucoma Home Monitoring Using a Tablet-Based Visual Field Test (Eyecatcher): An Assessment of Accuracy and Adherence Over 6 Months

Purpose

To assess accuracy and adherence of visual field (VF) home monitoring in a pilot sample of patients with glaucoma.

Design

Prospective longitudinal feasibility and reliability study.

Methods

Twenty adults (median 71 years) with an established diagnosis of glaucoma were issued a tablet perimeter (Eyecatcher) and were asked to perform 1 VF home assessment per eye, per month, for 6 months (12 tests total). Before and after home monitoring, 2 VF assessments were performed in clinic using standard automated perimetry (4 tests total, per eye).

Results

All 20 participants could perform monthly home monitoring, though 1 participant stopped after 4 months (adherence: 98% of tests). There was good concordance between VFs measured at home and in the clinic (r = 0.94, P < .001). In 21 of 236 tests (9%), mean deviation deviated by more than ±3 dB from the median. Many of these anomalous tests could be identified by applying machine learning techniques to recordings from the tablets' front-facing camera (area under the receiver operating characteristic curve = 0.78). Adding home-monitoring data to 2 standard automated perimetry tests made 6 months apart reduced measurement error (between-test measurement variability) in 97% of eyes, with mean absolute error more than halving in 90% of eyes. Median test duration was 4.5 minutes (quartiles: 3.9-5.2 minutes). Substantial variations in ambient illumination had no observable effect on VF measurements (r = 0.07, P = .320).

Conclusions

Home monitoring of VFs is viable for some patients and may provide clinically useful data.

Deep learning-based ambient assisted living for self-management of cardiovascular conditions

According to the World Health Organization, cardiovascular diseases contribute to 17.7 million deaths per year and are rising with a growing ageing population. In order to handle these challenges, the evolved countries are now evolving workable solutions based on new communication technologies such as ambient assisted living. In these solutions, the most well-known solutions are wearable devices for patient monitoring, telemedicine and mHealth systems. This systematic literature review presents the detailed literature on ambient assisted living solutions and helps to understand how ambient assisted living helps and motivates patients with cardiovascular diseases for self-management to reduce associated morbidity and mortalities. Preferred reporting items for systematic reviews and meta-analyses technique are used to answer the research questions. The paper is divided into four main themes, including self-monitoring wearable systems, ambient assisted living in aged populations, clinician management systems and deep learning-based systems for cardiovascular diagnosis. For each theme, a detailed investigation shows (1) how these new technologies are nowadays integrated into diagnostic systems and (2) how new technologies like IoT sensors, cloud models, machine and deep learning strategies can be used to improve the medical services. This study helps to identify the strengths and weaknesses of novel ambient assisted living environments for medical applications. Besides, this review assists in reducing the dependence on caregivers and the healthcare systems.

Artificial intelligence in cardiology

This review examines the current state and application of artificial intelligence (AI) and machine learning (ML) in cardiovascular medicine. AI is changing the clinical practice of medicine in other specialties. With progress continuing in this emerging technology, the impact for cardiovascular medicine is highlighted to provide insight for the practicing clinician and to identify potential patient benefits.

Machine learning and wearable devices of the future

Machine learning (ML) is increasingly recognized as a useful tool in healthcare applications, including epilepsy. One of the most important applications of ML in epilepsy is seizure detection and prediction, using wearable devices (WDs). However, not all currently available algorithms implemented in WDs are using ML. In this review, we summarize the state of the art of using WDs and ML in epilepsy, and we outline future development in these domains. There is published evidence for reliable detection of epileptic seizures using implanted electroencephalography (EEG) electrodes and wearable, non-EEG devices. Application of ML using the data recorded with WDs from a large number of patients could change radically the way we diagnose and manage patients with epilepsy.

Mobile Health Monitoring of Cardiac Status

Cardiovascular diseases (CVDs) are responsible for more deaths than any other cause, with coronary heart disease and stroke accounting for two-thirds of those deaths. Morbidity and mortality due to CVD are largely preventable, through either primary prevention of disease or secondary prevention of cardiac events. Monitoring cardiac status in healthy and diseased cardiovascular systems has the potential to dramatically reduce cardiac illness and injury. Smart technology in concert with mobile health platforms is creating an environment where timely prevention of and response to cardiac events are becoming a reality.

How point-of-care HbA1c testing changes the behaviour of people with diabetes and clinicians - a qualitative study

AIM

To explore adults with diabetes and clinician views of point-of-care HbA_1c testing.

METHODS

Adults with diabetes and HbA_1c ≥ 58 mmol/mol (7.5%) receiving HbA_1c point-of-care testing in primary care were invited to individual interviews. Participants were interviewed twice, once prior to point-of-care testing and once after 6 months follow-up. Clinicians were interviewed once. A thematic framework based on an a priori framework was used to analyse the data.

RESULTS

Fifteen participants (eight women, age range 30-70 years, two Asians, 13 white Europeans) were interviewed. They liked point-of-care testing and found the single appointment more convenient than usual care. Receiving the test result at the appointment helped some people understand how some lifestyle behaviours affected their control of diabetes and motivated them to change behaviours. Receiving an immediate test result reduced the anxiety some people experience when waiting for a result. People thought there was little value in using point-of-care testing for their annual review. Clinicians liked the point-of-care testing but expressed concerns about costs.

CONCLUSIONS

This work suggests that several features of point-of-care testing may encourage behavioural change. It helped some people to link their HbA_1c result to recent lifestyle behaviours, thereby motivating behavioural change and reinforcing healthy lifestyle choices.

Self-health monitoring and wearable neurotechnologies

Brain-computer interfaces and wearable neurotechnologies are now used to measure real-time neural and physiologic signals from the human body and hold immense potential for advancements in medical diagnostics, prevention, and intervention. Given the future role that wearable neurotechnologies will likely serve in the health sector, a critical state-of-the-art assessment is necessary to gain a better understanding of their current strengths and limitations. In this chapter we present wearable electroencephalography systems that reflect groundbreaking innovations and improvements in real-time data collection and health monitoring. We focus on specifications reflecting technical advantages and disadvantages, discuss their use in fundamental and clinical research, their current applications, limitations, and future directions. While many methodological and ethical challenges remain, these systems host the potential to facilitate large-scale data collection far beyond the reach of traditional research laboratory settings.

Artificial Intelligence in Cardiology

Artificial intelligence and machine learning are poised to influence nearly every aspect of the human condition, and cardiology is not an exception to this trend. This paper provides a guide for clinicians on relevant aspects of artificial intelligence and machine learning, reviews selected applications of these methods in cardiology to date, and identifies how cardiovascular medicine could incorporate artificial intelligence in the future. In particular, the paper first reviews predictive modeling concepts relevant to cardiology such as feature selection and frequent pitfalls such as improper dichotomization. Second, it discusses common algorithms used in supervised learning and reviews selected applications in cardiology and related disciplines. Third, it describes the advent of deep learning and related methods collectively called unsupervised learning, provides contextual examples both in general medicine and in cardiovascular medicine, and then explains how these methods could be applied to enable precision cardiology and improve patient outcomes.

Feasibility and performance of a device for automatic self-detection of symptomatic acute coronary artery occlusion in outpatients with coronary artery disease: a multicentre observational study

BACKGROUND

Time delay between onset of symptoms and seeking medical attention is a major determinant of mortality and morbidity in patients with acute coronary artery occlusion. Response time might be reduced by reliable self-detection. We aimed to formally assess the proof-of-concept and accuracy of self-detection of acute coronary artery occlusion by patients during daily life situations and during the very early stages of acute coronary artery occlusion.

METHODS

In this multicentre, observational study, we tested the operational feasibility, specificity, and sensitivity of our RELF method, a three-lead detection system with an automatic algorithm built into a mobile handheld device, for detection of acute coronary artery occlusion. Patients were recruited continuously by physician referrals from three Belgian hospitals until the desired sample size was achieved, had been discharged with planned elective percutaneous coronary intervention, and were able to use a smartphone; they were asked to perform random ambulatory self-recordings for at least 1 week. A similar self-recording was made before percutaneous coronary intervention and at 60 s of balloon occlusion. Patients were clinically followed up until 1 month after discharge. We quantitatively assessed the operational feasibility with an automated dichotomous quality check of self-recordings. Performance was assessed by analysing the receiver operator characteristics of the ST difference vector magnitude. This trial is registered with ClinicalTrials.gov, number NCT02983396.

FINDINGS

From Nov 18, 2016, to April 25, 2018, we enrolled 64 patients into the study, of whom 59 (92%) were eligible for self-applications. 58 (91%) of 64 (95% CI 81·0-95·6) patients were able to perform ambulatory self-recordings. Of all 5011 self-recordings, 4567 (91%) were automatically classified as successful within 1 min. In 65 balloon occlusions, 63 index tests at 60 s of occlusion in 55 patients were available. The mean specificity of daily life recordings was 0·96 (0·95-0·97). The mean false positive rate during daily life conditions was 4·19% (95% CI 3·29-5·10). The sensitivity for the target conditions was 0·87 (55 of 63; 95% CI 0·77-0·93) for acute coronary artery occlusion, 0·95 (54 of 57; 0·86-0·98) for acute coronary artery occlusion with electrocardiogram (ECG) changes, and 1·00 (35 of 35) for acute coronary artery occlusion with ECG changes and ST-segment elevation myocardial infarction criteria (STEMI). The index test was more sensitive to detect a 60 s balloon occlusion than the STEMI criteria on 12-lead ECG (87% vs 56%; p<0·0001). The proportion of total variation in study estimates due to heterogeneity between patients (I²) was low (12·6%). The area under the receiver operator characteristics curve was 0·973 (95% CI 0·956-0·990) for acute coronary artery occlusion at different cutoff values of the magnitude of the ST difference vector. No patients died during the study.

INTERPRETATION

Self-recording with our RELF device is feasible for most patients with coronary artery disease. The sensitivity and specificity for automatic detection of the earliest phase of acute coronary artery occlusion support the concept of our RELF device for patient empowerment to reduce delay and increase Survival without overloading emergency services.

FUNDING

Ghent University, Industrial Research Fund.

The next generation of precision medicine: observational studies, electronic health records, biobanks and continuous monitoring

Precision medicine can utilize new techniques in order to more effectively translate research findings into clinical practice. In this article, we first explore the limitations of traditional study designs, which stem from (to name a few): massive cost for the assembly of large patient cohorts; non-representative patient data; and the astounding complexity of human biology. Second, we propose that harnessing electronic health records and mobile device biometrics coupled to longitudinal data may prove to be a solution to many of these problems by capturing a 'real world' phenotype. We envision that future biomedical research utilizing more precise approaches to patient care will utilize continuous and longitudinal data sources.

Integration of an in-home monitoring system into home care nurses' workflow: A case study

BACKGROUND

The healthcare system faces a major challenge in caring for an increasingly ageing population as this task requires more resources than are currently available. Adopting monitoring technologies could enable more efficient care practices and support ageing in place.

OBJECTIVES

To investigate how the use of an in-home motion monitoring system can be integrated into home care nurses' workflows and to uncover the factors behind system adoption.

DESIGN

A single case study adopting a qualitative approach.

SETTING

A home care unit serving older adults living in independent living residences within an apartment complex.

METHOD

Multiple data collection methods were used including individual and group interviews, a questionnaire with open-ended questions, evaluation probes, and system log data. The qualitative material was analysed using a stepwise-deductive inductive approach.

RESULTS

A central factor behind system adoption was the perceived usefulness of gaining information about older adults' night-time activities. In particular, monitoring older adults suffering from memory disorders was considered advantageous. The information that the system provided supported nurses in health assessments and assisted in adjusting care decisions. Previous negative experiences with similar technologies initially influenced the time for adoption. Further, although nurses were closely involved in the system design process, they took some time to get acquainted with the system and to integrate its use into daily practice. System reliability and accuracy issues influenced nurses' trust in the sensory data.

CONCLUSION

The findings suggests that in a home care setting, focusing on motion pattern monitoring for older adults with memory disorders can provide significant benefits and therefore also facilitate system adoption among nurses. Involving nurses in the design of the technology and providing opportunities to trial the system in real practice also appear to be important in achieving system adoption.

Digital Health for Geriatric Oncology

In this review, we describe state-of-the-art digital health solutions for geriatric oncology and explore the potential application of emerging remote health-monitoring technologies in the context of cancer care. We also discuss the benefits and motivations behind adopting technology for symptom monitoring of older adults with cancer. We provide an overview of common symptoms and of the digital solutions–designed remote symptom assessment. We describe state-of-the-art systems for this purpose and highlight the limitations and challenges for the full-scale adoption of such solutions in geriatric oncology. With rapid advances in Internet-of-things technologies, many remote assessment systems have been developed in recent years. Despite showing potential in several health care domains and reliable functionality, few of these solutions have been designed for or tested in older patients with cancer. As a result, the geriatric oncology community lacks a consensus understanding of a possible correlation between remote digital assessments and health-related outcomes. Although the recent development of digital health solutions has been shown to be reliable and effective in many health-related applications, there exists an unmet need for development of systems and clinical trials specifically designed for remote cancer management of older adults with cancer, including developing advanced remote technologies for cancer-related symptom assessment and psychological behavior monitoring at home and developing outcome-oriented study protocols for accurate evaluation of existing or emerging systems. We conclude that perhaps the clearest path to future large-scale use of remote digital health technologies in cancer research is designing and conducting collaborative studies involving computer scientists, oncologists, and patient advocates.

Reflecting health: smart mirrors for personalized medicine

Inexpensive embedded computing and the related Internet of Things technologies enable the recent development of smart products that can respond to human needs and improve everyday tasks in an attempt to make traditional environments more “intelligent”. Several projects have augmented mirrors for a range of smarter applications in automobiles and homes. The opportunity to apply smart mirror technology to healthcare to predict and to monitor aspects of health and disease is a natural but mostly underdeveloped idea. We envision that smart mirrors comprising a combination of intelligent hardware and software could identify subtle, yet clinically relevant changes in physique and appearance. Similarly, a smart mirror could record and evaluate body position and motion to identify posture and movement issues, as well as offer feedback for corrective actions. Successful development and implementation of smart mirrors for healthcare applications will require overcoming new challenges in engineering, machine learning, computer vision, and biomedical research. This paper examines the potential uses of smart mirrors in healthcare and explores how this technology might benefit users in various medical environments. We also provide a brief description of the state-of-the-art, including a functional prototype concept developed by our group, and highlight the directions to make this device more mainstream in health-related applications.

Assistive technologies to overcome sarcopenia in ageing

Sarcopenia is an age-related decline in skeletal muscle mass and function that results in disability and loss of independence. It affects up to 30% of older adults. Exercise (particularly progressive resistance training) and nutrition are key strategies in preventing and reversing declines in muscle mass, strength and power during ageing, but many sarcopenic older adults fail to meet recommended levels of both physical activity and dietary nutrient intake. Assistive technology (AT) describes devices or systems used to maintain or improve physical functioning. These may help sarcopenic older adults to maintain independence, and also to achieve adequate physical activity and nutrition. There is a paucity of research exploring the use of AT in sarcopenic patients, but there is evidence that AT, including walking aids, may reduce functional decline in other populations with disability. Newer technologies, such as interactive and virtual reality games, as well as wearable devices and smartphone applications, smart homes, 3D printed foods, exoskeletons and robotics, and neuromuscular electrical stimulation also hold promise for improving engagement in physical activity and nutrition behaviours to prevent further functional declines. While AT may be beneficial for sarcopenic patients, clinicians should be aware of its potential limitations. In particular, there are high rates of patient abandonment of AT, which may be minimised by appropriate training and monitoring of use. Clinicians should preferentially prescribe AT devices which promote physical activity. Further research is required in sarcopenic populations to identify strategies for effective use of current and emerging AT devices.