PLOS digital health最新文献

Manual surgical resection of soft tissue sarcoma tissue can involve many challenges, including the critical need for precise determination of tumor boundary with normal tissue and limitations of current surgical instrumentation, in addition to standard risks of infection or tissue healing difficulty. Substantial research has been conducted in the biomedical sensing landscape for development of non-human contact sensing devices. One such point-of-care platform, previously devised by our group, utilizes autofluorescence-based spectroscopic signatures to highlight important physiological differences in tumorous and healthy tissue. The following study builds on this work, implementing classification algorithms, including Artificial Neural Network, Support Vector Machine, Logistic Regression, and K-Nearest Neighbors, to diagnose freshly resected murine tissue as sarcoma or healthy. Classification accuracies of over 93% are achieved with Logistic Regression, and Area Under the Curve scores over 94% are achieved with Support Vector Machines, delineating a clear way to automate photonic diagnosis of ambiguous tissue in assistance of surgeons. These interpretable algorithms can also be linked to important physiological diagnostic indicators, unlike the black-box ANN architecture. This is the first known study to use machine learning to interpret data from a non-contact autofluorescence sensing device on sarcoma tissue, and has direct applications in rapid intraoperative sensing.

Background: Chronic Kidney Disease (CKD) is a long-term condition and a major health problem, which affects over 3.5 million adults in the UK. Use of digital technology has been proposed as a means of improving patient management. It is important to understand the factors that affect the acceptability of this technology to people living with chronic kidney disease. This study used the Technology Acceptance Model 3 (TAM) to investigate whether perceived ease of use and perceived usefulness could predict intention behaviour. It then investigated if intention to use digital technology predicted actual use.

Methodology: This was a cross-sectional study whereby the TAM3 questionnaire was sent online to people known to have chronic kidney disease via Kidney Care UK. The characteristics of the respondents (age, sex, CKD stage) were recorded.

Principal findings: The questionnaire was sent to 12,399 people, of which 229 (39% drop out) completed it. The respondents' age ranged from 24-90 years and 45% (n = 102) were male. Thirty-five percent of participants had advanced kidney care, 33% (n = 76) had kidney transplant and 22% (n = 51) had CKD. A multiple regression analysis showed a perceived ease of use and perceived usefulness of the technology predicted behaviour intention to use digital health technology. Behaviour intention did not significantly predict actual use behaviour.

Conclusion: Perceived usefulness and perceived ease of use are important factors in determining the intention of people with CKD to use digital healthcare. However, a gap exists between this intention and readiness to actually use the technology. This needs to be overcome if digital healthcare is to gain future traction in the clinical scenario.

Over the past 2 decades, exponential growth in data availability, computational power, and newly available modeling techniques has led to an expansion in interest, investment, and research in Artificial Intelligence (AI) applications. Ophthalmology is one of many fields that seek to benefit from AI given the advent of telemedicine screening programs and the use of ancillary imaging. However, before AI can be widely deployed, further work must be done to avoid the pitfalls within the AI lifecycle. This review article breaks down the AI lifecycle into seven steps-data collection; defining the model task; data preprocessing and labeling; model development; model evaluation and validation; deployment; and finally, post-deployment evaluation, monitoring, and system recalibration-and delves into the risks for harm at each step and strategies for mitigating them.

Digital transformation has led to an abundance of digital health technologies (DHTs) readily available for Physiotherapists. In July 2020, the Physiotherapy department at a London NHS Trust implemented a mobile health (mHealth) exercise application (app), Physitrack. This service evaluation aims to evaluate patient experience and identify any barriers to using Physitrack/PhysiApp in musculoskeletal (MSK) Physiotherapy. An online experience survey was sent to 10,287 MSK Physiotherapy patients who had appointments between January 17th and April 9th 2022.The survey received 1,447 responses (response rate: 14.07%), with 954 (65.93%) respondents previously provided PhysiApp as part of their Physiotherapy management. Most participants used PhysiApp (83.06%), found it easy to use (82.20%) and had positive perceptions on how it added value to their Physiotherapy treatment through its functionality. However, negative impacts on patient-centred care and practical exercise demonstration were apparent in the qualitative results. Key barriers to use included suboptimal explanation, digital exclusion, registration/ login issues and opinion that PhysiApp was superfluous to Physiotherapy treatment. These differed to the main barriers of why participants stopped using/ used PhysiApp less: if they were confident exercising without it, their condition improved/ resolved, loss of motivation, their exercise programme ended or if they found their exercise programme was unsuitable. Despite multiple interdependent factors influencing patient experience and barriers of using PhysiApp, the survey results revealed the significant influence that is exerted by MSK Physiotherapists. The patient-physiotherapist interaction can positively or negatively impact upon many barriers of use and the subsequent potential added value of PhysiApp to MSK Physiotherapy treatment. Future research should focus on those at most risk of digital exclusion and health inequalities, exploring their barriers to using mHealth apps and other DHTs.

Reading hand and foot X-rays in rheumatoid arthritis patients is difficult and time-consuming. In research, physicians use the modified Sharp van der Heijde Sharp (mvdH) score by reading of hand and foot radiographs. The aim of this study was to create a new method of determining the mvdH via eye tracking and to study its concordance with the mvdH score. We created a new method of quantifying the mvdH score based on reading time of a reader monitored via eye tracking (Tobii Pro Lab software) after training with the aid of a metronome. Radiographs were read twice by the trained eye-tracking reader and once by an experienced reference radiologist. A total of 440 joints were selected; 416 could be interpreted for erosion, and 396 could be interpreted for joint space narrowing (JSN) when read by eye tracking (eye tracking could not measure the time spent when two pathological joints were too close together). The agreement between eye tracking mvdH Sharp score and classical mvdH Sharp score yes (at least one erosion or JSN) versus no (no erosion or no JSN) was excellent for both erosions (kappa 0.97; 95% CI: 0.96-0.99) and JSN (kappa: 0.95; 95% CI: 0.93-0.097). This agreement by class (0 to 10) remained excellent for both erosions (kappa 0.82; 95% CI: 0.79-0.0.85) and JSN (kappa: 0.68; 95% CI: 0.65-0.0.71). To conclude, eye-tracking reading correlates strongly with classical mvdH-Sharp and is useful for assessing severity, segmenting joints and establishing a rapid score for lesions.

Delays in diagnosis and detection of skin neglected tropical diseases (NTDs) pose obstacles to prompt treatment, which is crucial in preventing disability. Recent developments in digital health have given rise to approaches that could increase access to diagnosis in resource-poor areas affected by skin NTDs. This scoping review provides an overview of current digital health approaches that aim to aid in the diagnosis of skin NTDs and provides an insight into the diverse functionalities of current digital health tools, their feasibility, usability, and the current gaps in research around these digital health approaches. This scoping review included a comprehensive literature search on PubMed, EMBASE and SCOPUS, following the PRISMA guidelines. Eleven studies were included in the review and were analysed using a descriptive thematic approach. Most digital tools were found to be mobile-phone based, such as mobile Health (mHealth) apps, store-and-forward tele-dermatology, and Short Messaging Service (SMS) text-messaging. Other digital approaches were based on computer software, such as tele-dermatopathology, computer-based telemedicine, and real-time tele-dermatology. Digital health tools commonly facilitated provider-provider interactions, which helped support diagnoses of skin NTDs at the community level. Articles which focused on end-user user experience reported that users appreciated the usefulness and convenience of these digital tools. However, the results emphasized the existing lack of data regarding the diagnostic precision of these tools, and highlighted various hurdles to their effective implementation, including insufficient infrastructure, data security issues and low adherence to the routine use of digital health tools. Digital health tools can help ascertain diagnosis of skin NTDs through remote review or consultations with patients, and support health providers in the diagnostic process. However, further research is required to address the data security issues associated with digital health tools. Developers should consider adapting digital health tools to diverse socio-cultural and technical environments, where skin NTDs are endemic. Researchers are encouraged to assess the diagnostic accuracy of digital health tools and conduct further qualitative studies to inform end-user experience. Overall, future studies should consider expanding the geographical and disease scope of research on digital health tools which aid the diagnosis of skin NTDs.

Alzheimer's disease (AD), as the most common form of dementia and leading cause for disability and death in old age, represents a major burden to healthcare systems worldwide. For the development of disease-modifying interventions and treatments, the detection of cognitive changes at the earliest disease stages is crucial. Recent advancements in mobile consumer technologies provide new opportunities to collect multi-dimensional data in real-life settings to identify and monitor at-risk individuals. Based on evidence showing that deficits in spatial navigation are a common hallmark of dementia, we assessed whether a memory clinic sample of patients with subjective cognitive decline (SCD) who still scored normally on neuropsychological assessments show differences in smartphone-assisted wayfinding behavior compared with cognitively healthy older and younger adults. Guided by a mobile application, participants had to find locations along a short route on the medical campus of the Magdeburg university. We show that performance measures that were extracted from GPS and user input data distinguish between the groups. In particular, the number of orientation stops was predictive of the SCD status in older participants. Our data suggest that subtle cognitive changes in patients with SCD, whose risk to develop dementia in the future is elevated, can be inferred from smartphone data, collected during a brief wayfinding task in the real world.

Patient portals allowing access to electronic health care records and services can inform and empower but may widen existing sociodemographic inequities. We aimed to describe associations between activation of a paediatric patient portal and patient race/ethnicity, socioeconomic status and markers of previous engagement with health care. A retrospective single site cross-sectional study was undertaken to examine patient portal adoption amongst families of children receiving care for chronic or complex disorders within the United Kingdom. Descriptive and multivariable regression analysis was undertaken to describe associations between predictors (Race/Ethnicity, age, socio-economic deprivation status based on family residence, and previous non-attendance to outpatient consultations) and outcome. A sample of 3687 children, representative of the diverse 'real world' patient population, was identified. Of these 37% (1364) were from a White British background, 71% (2631) had English as the primary family spoken language (PSL), 14% (532) lived in areas of high deprivation, and 17% (643) had high (>33%) rates of non-attendance. The families of 73% (2682) had activated the portal. In adjusted analyses, English as a PSL (adjusted odds ratio [aOR] 1.58, 95% confidence interval 1.29-1.95) and multi-morbidity (aOR 1.26, 1.22-1.30) was positively associated with portal activation, whilst families from British Black African backgrounds (aOR 0.68, 0.50-0.93), and those with high rates of non-attendance (aOR 0.48, 0.40-0.58) were less likely to use the portal. Family race/ethnicity and previous low engagement with health care services are potentially key drivers of widening inequity in access to health care following the implementation of patient portals, a digital health innovation intended to inform and empower. Health care providers should be aware that innovative human-driven engagement approaches, targeted towards previously underserved communities, are needed to ensure equitable access to high quality patient-centred care.

This paper introduces a novel approach to address the challenges associated with transferring blood pressure (BP) data obtained from oscillometric devices used in self-measured BP monitoring systems to integrate this data into medical health records or a proxy database accessible by clinicians, particularly in low literacy populations. To this end, we developed an automated image transcription technique to effectively transcribe readings from BP devices, ultimately enhancing the accessibility and usability of BP data for monitoring and managing BP during pregnancy and the postpartum period, particularly in low-resource settings and low-literate populations. In the designed study, the photos of the BP devices were captured as part of perinatal mobile health (mHealth) monitoring programs, conducted in four studies across two countries. The Guatemala Set 1 and Guatemala Set 2 datasets include the data captured by a cohort of 49 lay midwives from 1697 and 584 pregnant women carrying singletons in the second and third trimesters in rural Guatemala during routine screening. Additionally, we designed an mHealth system in Georgia for postpartum women to monitor and report their BP at home with 23 and 49 African American participants contributing to the Georgia I3 and Georgia IMPROVE projects, respectively. We developed a deep learning-based model which operates in two steps: LCD localization using the You Only Look Once (YOLO) object detection model and digit recognition using a convolutional neural network-based model capable of recognizing multiple digits. We applied color correction and thresholding techniques to minimize the impact of reflection and artifacts. Three experiments were conducted based on the devices used for training the digit recognition model. Overall, our results demonstrate that the device-specific model with transfer learning and the device independent model outperformed the device-specific model without transfer learning. The mean absolute error (MAE) of image transcription on held-out test datasets using the device-independent digit recognition were 1.2 and 0.8 mmHg for systolic and diastolic BP in the Georgia IMPROVE and 0.9 and 0.5 mmHg in Guatemala Set 2 datasets. The MAE, far below the FDA recommendation of 5 mmHg, makes the proposed automatic image transcription model suitable for general use when used with appropriate low-error BP devices.

A number of challenges hinder artificial intelligence (AI) models from effective clinical translation. Foremost among these challenges is the lack of generalizability, which is defined as the ability of a model to perform well on datasets that have different characteristics from the training data. We recently investigated the development of an AI pipeline on digital images of the cervix, utilizing a multi-heterogeneous dataset of 9,462 women (17,013 images) and a multi-stage model selection and optimization approach, to generate a diagnostic classifier able to classify images of the cervix into "normal", "indeterminate" and "precancer/cancer" (denoted as "precancer+") categories. In this work, we investigate the performance of this multiclass classifier on external data not utilized in training and internal validation, to assess the generalizability of the classifier when moving to new settings. We assessed both the classification performance and repeatability of our classifier model across the two axes of heterogeneity present in our dataset: image capture device and geography, utilizing both out-of-the-box inference and retraining with external data. Our results demonstrate that device-level heterogeneity affects our model performance more than geography-level heterogeneity. Classification performance of our model is strong on images from a new geography without retraining, while incremental retraining with inclusion of images from a new device progressively improves classification performance on that device up to a point of saturation. Repeatability of our model is relatively unaffected by data heterogeneity and remains strong throughout. Our work supports the need for optimized retraining approaches that address data heterogeneity (e.g., when moving to a new device) to facilitate effective use of AI models in new settings.