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.

Identification of Tissue Types and Gene Mutations From Histopathology Images for Advancing Colorectal Cancer Biology.

Objective: Colorectal cancer (CRC) patients respond differently to treatments and are sub-classified by different approaches. We evaluated a deep learning model, which adopted endoscopic knowledge learnt from AI-doscopist, to characterise CRC patients by histopathological features. Results: Data of 461 patients were collected from TCGA-COAD database. The proposed framework was able to 1) differentiate tumour from normal tissues with an Area Under Receiver Operating Characteristic curve (AUROC) of 0.97; 2) identify certain gene mutations (MYH9, TP53) with an AUROC > 0.75; 3) classify CMS2 and CMS4 better than the other subtypes; and 4) demonstrate the generalizability of predicting KRAS mutants in an external cohort. Conclusions: Artificial intelligent can be used for on-site patient classification. Although KRAS mutants were commonly associated with therapeutic resistance and poor prognosis, subjects with predicted KRAS mutants in this study have a higher survival rate in 30 months after diagnoses.

Characterization of the vascular system using overnight wearable-based pulse arrival time and ambulatory blood pressure: A pilot study.

Pulse arrival time (PAT) has been broadly investigated for its potential for cuffless blood pressure (BP) estimation and ease of measurement by wearable devices. It is also of great significance to explore whether PAT conveys complementary information to BP for vascular health assessment. In this paper, the differences between the 24-h ambulatory BP and wearable-based PAT were compared among 12 young normotensives and 15 elderly hypertensives in terms of the mean and coefficients of variation (CoVs). The correlations of the nocturnal normalized PAT (n-PAT) and BP with two arterial stiffness-related parameters (i.e., the intrinsic elastic modulus E0 and the vascular modulation factor α) estimated by a proposed model-based method were also compared. The results showed that the inter-subject variances of the nocturnal average n-PAT were significantly different between the hypertensives and the normotensives (P < 0.001), and the intra-subject CoVs of the nocturnal n-PAT were also significantly different between the two groups (P < 0.05). However, these findings were not shown in the nocturnal BP. The correlation coefficient between the nocturnal average n-PAT and ln(E0) is larger than that with the nocturnal BP, i.e., 0.91 vs. 0.56. Furthermore, the result also revealed that all the hypertensives receiving antihypertensive medications did not achieve the optimal control of the nocturnal BP while presented diverse arterial stiffness indicated by the nocturnal average n-PAT and ln(E0). It is concluded that wearable-based PAT contains complementary information about the vascular system to the ambulatory BP, which may be useful for designing effective antihypertensive treatments.

Unobtrusive Blood Pressure Estimation using Personalized Autoregressive Models.

Cuffless and continuous blood pressure (BP) measurement using wearable devices is of great clinical value and health monitoring importance. Pulse arrival time (PAT) based technique was considered as one of the most promising methods for this purpose. Considering the dynamic and nonlinear relationship between BP, PAT and other cardiovascular variables, this paper proposes for the first time to use nonlinear autoregressive models with extra inputs (ARX) for BP estimation. The models were first trained by the baseline data of all 25 subjects to determine the model structure and then trained by individual data to obtain the personalized model parameters. To assess the effects of the dynamic and nonlinear factors, the data during water drinking and the first 5 minutes of recovery after drinking were used to validate the four models: linear regression, linear ARX, nonlinear regression and nonlinear ARX. The reference BP, which were measured by Finometer, were increased by 36.7±10.5 mmHg for SBP and 28.4 ±7.7 mmHg for DBP. This BP changes were best modelled by the nonlinear ARX, with Mean ± SD differences of 5.6 ± 8.8 mmHg for SBP and 3.8 ±5.8 mmHg for DBP. The study also showed that nonlinear factor significantly reduced the root mean square error (RSME) by about 50%, i.e., from 20.4 to 10.7 mmHg for SBP and 13.3 to 7.3 mmHg for DBP during drinking. While the effects of dynamic factors were not as significant as nonlinear factors, especially after introducing nonlinear factors.

AI-doscopist: a real-time deep-learning-based algorithm for localising polyps in colonoscopy videos with edge computing devices.

We have designed a deep-learning model, an "Artificial Intelligent Endoscopist (a.k.a. AI-doscopist)", to localise colonic neoplasia during colonoscopy. This study aims to evaluate the agreement between endoscopists and AI-doscopist for colorectal neoplasm localisation. AI-doscopist was pre-trained by 1.2 million non-medical images and fine-tuned by 291,090 colonoscopy and non-medical images. The colonoscopy images were obtained from six databases, where the colonoscopy images were classified into 13 categories and the polyps' locations were marked image-by-image by the smallest bounding boxes. Seven categories of non-medical images, which were believed to share some common features with colorectal polyps, were downloaded from an online search engine. Written informed consent were obtained from 144 patients who underwent colonoscopy and their full colonoscopy videos were prospectively recorded for evaluation. A total of 128 suspicious lesions were resected or biopsied for histological confirmation. When evaluated image-by-image on the 144 full colonoscopies, the specificity of AI-doscopist was 93.3%. AI-doscopist were able to localise 124 out of 128 polyps (polyp-based sensitivity = 96.9%). Furthermore, after reviewing the suspected regions highlighted by AI-doscopist in a 102-patient cohort, an endoscopist has high confidence in recognizing four missed polyps in three patients who were not diagnosed with any lesion during their original colonoscopies. In summary, AI-doscopist can localise 96.9% of the polyps resected by the endoscopists. If AI-doscopist were to be used in real-time, it can potentially assist endoscopists in detecting one more patient with polyp in every 20-33 colonoscopies.

Using a Multi-Task Recurrent Neural Network With Attention Mechanisms to Predict Hospital Mortality of Patients.

Estimating hospital mortality of patients is important in assisting clinicians to make decisions and hospital providers to allocate resources. This paper proposed a multi-task recurrent neural network with attention mechanisms to predict patients' hospital mortality, using reconstruction of patients' physiological time series as an auxiliary task. Experiments were conducted on a large public electronic health record database, i.e., MIMIC-III. Fifteen physiological measurements during the first 24 h of critical care were used to predict death before hospital discharge. Compared with the conventional simplified acute physiology score (SAPS-II), the proposed multi-task learning model achieved better sensitivity (0.503 ± 0.020 versus 0.365 ± 0.021), when predictions were made based on the same 24-h observation period. The multi-task learning model is recommended to be updated daily with at least a 6-h observation period, in order for it to perform similarly or better than the SAPS-II. In the future, the need for intervention can be considered as another task to further optimize the performance of the multi-task learning model.

Localisation of Colorectal Polyps by Convolutional Neural Network Features Learnt from White Light and Narrow Band Endoscopic Images of Multiple Databases.

Algorithms for localising colorectal polyps have been studied extensively; however, they were often trained and tested using the same database. In this study, we present a new application of a unified and real-time object detector based on You-Only-Look-Once (YOLO) convolutional neural network (CNN) for localizing polyps with bounding boxes in endoscopic images. The model was first pre-trained with non-medical images and then fine-tuned with colonoscopic images from three different databases, including an image set we collected from 106 patients using narrow-band (NB) imaging endoscopy. YOLO was tested on 196 white light (WL) images of an independent public database. YOLO achieved a precision of 79.3% and sensitivity of 68.3% with time efficiency of 0.06 sec/frame in the localization task when trained by augmented images from multiple WL databases. In conclusion, YOLO has great potential to be used to assist endoscopists in localising colorectal polyps during endoscopy. CNN features of WL and NB endoscopic images are different and should be considered separately. A large-scale database that covers different scenarios, imaging modalities and scales is lacking but crucial in order to bring this research into reality.

Polyp detection during colonoscopy using a regression-based convolutional neural network with a tracker.

A computer-aided detection (CAD) tool for locating and detecting polyps can help reduce the chance of missing polyps during colonoscopy. Nevertheless, state-of-the-art algorithms were either computationally complex or suffered from low sensitivity and therefore unsuitable to be used in real clinical setting. In this paper, a novel regression-based Convolutional Neural Network (CNN) pipeline is presented for polyp detection during colonoscopy. The proposed pipeline was constructed in two parts: 1) to learn the spatial features of colorectal polyps, a fast object detection algorithm named ResYOLO was pre-trained with a large non-medical image database and further fine-tuned with colonoscopic images extracted from videos; and 2) temporal information was incorporated via a tracker named Efficient Convolution Operators (ECO) for refining the detection results given by ResYOLO. Evaluated on 17,574 frames extracted from 18 endoscopic videos of the AsuMayoDB, the proposed method was able to detect frames with polyps with a precision of 88.6%, recall of 71.6% and processing speed of 6.5 frames per second, i.e. the method can accurately locate polyps in more frames and at a faster speed compared to existing methods. In conclusion, the proposed method has great potential to be used to assist endoscopists in tracking polyps during colonoscopy.

Automatic Detection and Classification of Colorectal Polyps by Transferring Low-Level CNN Features From Nonmedical Domain.

Colorectal cancer (CRC) is a leading cause of cancer deaths worldwide. Although polypectomy at early stage reduces CRC incidence, 90% of the polyps are small and diminutive, where removal of them poses risks to patients that may outweigh the benefits. Correctly detecting and predicting polyp type during colonoscopy allows endoscopists to resect and discard the tissue without submitting it for histology, saving time, and costs. Nevertheless, human visual observation of early stage polyps varies. Therefore, this paper aims at developing a fully automatic algorithm to detect and classify hyperplastic and adenomatous colorectal polyps. Adenomatous polyps should be removed, whereas distal diminutive hyperplastic polyps are considered clinically insignificant and may be left in situ . A novel transfer learning application is proposed utilizing features learned from big nonmedical datasets with 1.4-2.5 million images using deep convolutional neural network. The endoscopic images we collected for experiment were taken under random lighting conditions, zooming and optical magnification, including 1104 endoscopic nonpolyp images taken under both white-light and narrowband imaging (NBI) endoscopy and 826 NBI endoscopic polyp images, of which 263 images were hyperplasia and 563 were adenoma as confirmed by histology. The proposed method identified polyp images from nonpolyp images in the beginning followed by predicting the polyp histology. When compared with visual inspection by endoscopists, the results of this study show that the proposed method has similar precision (87.3% versus 86.4%) but a higher recall rate (87.6% versus 77.0%) and a higher accuracy (85.9% versus 74.3%). In conclusion, automatic algorithms can assist endoscopists in identifying polyps that are adenomatous but have been incorrectly judged as hyperplasia and, therefore, enable timely resection of these polyps at an early stage before they develop into invasive cancer.

A Therapeutic Wireless Capsule for Treatment of Gastrointestinal Haemorrhage by Balloon Tamponade Effect.

OBJECTIVE: Wireless capsule endoscope (WCE) is a revolutionary approach to diagnose small bowel pathologies. Currently available WCEs are mostly passive devices with image capturing function only, while on-going efforts have been placed on robotizing WCEs or to enhance them with therapeutic functions. In this paper, the authors present a novel inflatable WCE for haemostasis in the gastrointestinal (GI) tracts by balloon tamponade effect. METHODS: The proposed wireless capsule consists of a balloon that can be inflated using the endothermic reaction of acid and base. When the balloon reached a precalculated pressure level, it is able to stop at a bleeding site in the bowel, and achieve haemostasis by tamponade effect. The prototype is 14 mm in diameter, with three sections of 13, 35, and 12 mm in length, respectively. The three sections are linked together with flexible joints and enclosed in a silicone balloon. The prototypes were tested in ex vivo porcine intestine models. RESULTS: In the ten ex vivo trials conducted, the inflatable wireless capsule achieved average balloon pressure of 46.0 mmHg and withstood average maximum longitudinal pulling force at 1.46 N. An in vivo study was carried out as a proof-of-concept for treating bleeding in a porcine model. The proposed inflatable WCE succeeded in the animal test by controlling haemostasis within 5 min. No rebleeding was observed in the next 20 min. CONCLUSION: The results suggested that the inflatable capsule with a real-time bleeding detection algorithm can be implemented. Moreover, the proposed inflatable WCE prototype can achieve haemorrhage control in the lower GI. SIGNIFICANCE: To our best knowledge, this is the first study that demonstrated the potential to treat GI haemorrhage by an inflatable WCE. The proposed capsule enables the development of a closed-loop system based on a body sensor network to provide early treatment of GI bleeding for p-medicine.

Motion compensated controller for a tendon-sheath-driven flexible endoscopic robot.

BACKGROUND: A tendon-sheath system (TSS) has the advantages of being relatively compact in size, flexible and low cost, and therefore is favoured in building flexible endoscopic robots to pass through long and tortuous human lumen. TSS, however, is prone to nonlinear behaviors such as backlash, hysteresis and direction dependent properties. A compensation technique is required to improve its positioning performance. METHODS: Tension and elongation models of TSS are analyzed. A feedforward motion compensation controller is designed to compensate the asymmetric backlash behavior of the TSS in real time. RESULTS: Motion tracking experiments were conducted on a TSS driven two DOFs continuum manipulator. The results showed that using the proposed compensation methods, tracking error can be reduced by 74%. CONCLUSIONS: The proposed compensation method is useful for controlling flexible continuum robots, which are anticipated to have emerging roles in assisting surgeons to perform the increasingly technically challenging endoscopic procedures. Copyright © 2016 John Wiley & Sons, Ltd.

Pulse Arrival Time Based Cuff-Less and 24-H Wearable Blood Pressure Monitoring and its Diagnostic Value in Hypertension.

Ambulatory blood pressure monitoring (ABPM) has become an essential tool in the diagnosis and management of hypertension. Current standard ABPM devices use an oscillometric cuff-based method which can cause physical discomfort to the patients with repeated inflations and deflations, especially during nighttime leading to sleep disturbance. The ability to measure ambulatory BP accurately and comfortably without a cuff would be attractive. This study validated the accuracy of a cuff-less approach for ABPM using pulse arrival time (PAT) measurements on both healthy and hypertensive subjects for potential use in hypertensive management, which is the first of its kind. The wearable cuff-less device was evaluated against a standard cuff-based device on 24 subjects of which 15 have known hypertension. BP measurements were taken from each subject over a 24-h period by the cuff-less and cuff-based devices every 15 to 30 minutes during daily activities. Mean BP of each subject during daytime, nighttime and over 24-h were calculated. Agreement between mean nighttime systolic BP (SBP) and diastolic (DBP) measured by the two devices evaluated using Bland-Altman plot were -1.4 ± 6.6 and 0.4 ± 6.7 mmHg, respectively. Receiver operator characteristics (ROC) statistics was used to assess the diagnostic accuracy of the cuff-less approach in the detection of BP above the hypertension threshold during nighttime (>120/70 mmHg). The area under ROC curves were 0.975/0.79 for nighttime. The results suggest that PAT-based approach is accurate and promising for ABPM without the issue of sleep disturbances associated with cuff-based devices.

Design of wormlike automated robotic endoscope: dynamic interaction between endoscopic balloon and surrounding tissues.

BACKGROUND: The current design of capsule endoscope is limited by the inability to control the motion within gastrointestinal tract. The rising incidence of gastrointestinal cancers urged improvement in the method of screening endoscopy. OBJECTIVES: This preclinical study aimed to design and develop a novel locomotive module for capsule endoscope. We investigated the feasibility and physical properties of this newly designed caterpillar-like capsule endoscope with a view to enhancing screening endoscopy. DESIGN: This study consisted of preclinical design and experimental testing on the feasibility of automated locomotion for a prototype caterpillar endoscope. The movement was examined first in the PVC tube and then in porcine intestine. The image captured was transmitted to handheld device to confirm the control of movement. The balloon pressure and volume as well as the contact force between the balloon and surroundings were measured when the balloon was inflated inside (1) a hard PVC tube, (2) a soft PVC tube, (3) muscular sites of porcine colons and (4) less muscular sites of porcine colons. RESULTS: The prototype caterpillar endoscope was able to move inward and backward within the PVC tubing and porcine intestine. Images were able to be captured from the capsule endoscope attached and being observed with a handheld device. Using the onset of a contact force as indication of the buildup of the gripping force between the balloon and the lumen walls, it is concluded from the results of this study that the rate of change in balloon pressure and volume is two good estimators to optimize the inflation of the balloon. CONCLUSION: The results of this study will facilitate further refinement in the design of caterpillar robotic endoscope to move inside the GI tract.

Enhancing proficiency in performing endoscopic submucosal dissection (ESD) by using a prototype robotic endoscope.

BACKGROUND AND STUDY AIMS: One of the challenges in performing endoscopic submucosal dissection (ESD) is the lack of counter traction during submucosal dissection. MASTER (Master and Slave Transluminal Endoscopic Robot) was designed to allow performance of complex endoluminal procedures using two arms with excellent control. This study aimed to compare the performance of ESD between endoscopists and novices using MASTER. METHODS: This is a prospective study comparing the differences in performing ESD using MASTER in an ex vivo porcine stomach model among individuals with or without experience in surgery and endoscopy. Multiple standardized lesions of 20 mm(2) were pre-marked on an ex vivo porcine stomach. Each participant received basic training in controlling MASTER before the ESD procedure. The operative time and size of specimen obtained by each participant were noted. RESULTS: Nine individuals (three ESD expert endoscopists, three ESD non-expert endoscopists, and three non-clinician novices) participated in the experiment. All participants completed the ESD procedure for en bloc resection of standardized lesions using EndoMASTER without perforation. The mean times (mean ± SD) taken by ESD expert endoscopists, ESD non-expert endoscopists, and novices to complete Robotic ESD were 122 ±â€Š58 s, 203 ±â€Š150 s, and 561 ±â€Š496 s, respectively. There was no statistically significant difference in the mean operative time to complete the ESD between the three groups (P = 0.242). When the performance of the six endoscopists was compared to that of the three novices, the endoscopists took an average of 162 ±â€Š111 s to complete the ESD, while the non-clinicians required an average of 561 ±â€Š496 s (P = 0.085). CONCLUSIONS: There was a trend to shorter operative time when comparing endoscopists to non-endoscopists in performing ESD using MASTER. The use of MASTER enabled the novice without endoscopy experience to complete the ESD procedure.

Big data for health.

This paper provides an overview of recent developments in big data in the context of biomedical and health informatics. It outlines the key characteristics of big data and how medical and health informatics, translational bioinformatics, sensor informatics, and imaging informatics will benefit from an integrated approach of piecing together different aspects of personalized information from a diverse range of data sources, both structured and unstructured, covering genomics, proteomics, metabolomics, as well as imaging, clinical diagnosis, and long-term continuous physiological sensing of an individual. It is expected that recent advances in big data will expand our knowledge for testing new hypotheses about disease management from diagnosis to prevention to personalized treatment. The rise of big data, however, also raises challenges in terms of privacy, security, data ownership, data stewardship, and governance. This paper discusses some of the existing activities and future opportunities related to big data for health, outlining some of the key underlying issues that need to be tackled.

Body Sensor Networks: In the Era of Big Data and Beyond.

Body sensor networks (BSN) have emerged as an active field of research to connect and operate sensors within, on or at close proximity to the human body. BSN have unique roles in health applications, particularly to support real-time decision making and therapeutic treatments. Nevertheless, challenges remain in designing BSN nodes with antennas that operate efficiently around, ingested or implanted inside the human body, as well as new methods to process the heterogeneous and growing amount of data on-node and in a distributed system for optimized performance and power consumption. As the battery operating time and sensor size are two important factors in determining the usability of BSN nodes, ultralow power transceivers, energy-aware network protocol, data compression, on-node processing, and energy-harvesting techniques are highly demanded to ultimately achieve a self-powered BSN.

Noninvasive Characterization of Vascular Tone by Model-Based System Identification in Healthy and Heart Failure Patients.

Current markers for heart failure (HF) diagnosis and prognosis are mainly for the evaluation of cardiac functions. Since previous studies have reported that HF patients demonstrated abnormal vascular responses to external stimuli, it is speculated that vascular tone, a measure of activation level of vascular wall, may be able to reflect these abnormalities to assist HF detection. Nevertheless, vascular tone is difficult to be objectively quantified using existing tools. In this study, a vascular tone index was estimated from noninvasive blood pressure and pulse transit time measurements using system identification technique. This method was evaluated in 35 subjects (10 healthy, 13 with HF risk factors and 12 HF patients) in a regular maximal exercise test. It was found that the vascular tone index significantly increased by 24.4 ± 26.6% (p < 0.01) during maximal exercise in the healthy subjects. Moreover, the response was gradually attenuated in the risk-factor and HF groups (15.8 ± 36.5 and 0.9 ± 17.9%, respectively). The results reveal the association between the vascular tone response to maximal exercise and HF disease or risks. To conclude, the proposed method provides a quantitative characterization of vascular tone which may be a useful indicator of the pathological changes of the arteries or the heart.

An armband wearable device for overnight and cuff-less blood pressure measurement.

24-h blood pressure (BP) has significant prognostic value for cardiovascular risk screening, but the present BP devices are mainly cuff-based, which are unsuitable for long-term BP measurement, especially during nighttime. In this paper, we developed an armband wearable pulse transit time (PTT) system for 24-h cuff-less BP measurement and evaluated it in an unattended out-of-laboratory setting. Ten healthy young subjects participated in this ambulatory study, where PTT was measured at 30-min interval by this wearable system and the reference BP was measured by a standard oscillometric ambulatory BP monitor. Due to the misalignment of BP and PTT on their recording time caused by the different measurement principles of the two BP devices, a new estimation method has been proposed: transients in PTT were removed from the raw data by defined criteria, and then evenly interpolated, low-pass filtered, and resampled to synchronize at the time when BP was recorded. The results show that with the proposed method, the correlation between PTT and systolic BP (SBP) during nighttime with dynamic range of 21.8 ± 14.2 mmHg has improved from -0.50 ± 0.24 to -0.62 ± 0.20 , and the difference between the estimated and reference SBP has improved from 0.7 ± 10.7 to 2.8 ± 8.2 mmHg with root mean square error reduced from 10.7 to 8.7 mmHg. In addition, the correlation between a very low frequency component of SBP and PTT obtained from the proposed method during nighttime is -0.80 ± 0.10 and the difference is 2.4 ± 5.7 mmHg for a dynamic BP range of 13.5 ± 8.0 mmHg. It is therefore concluded from this study that the proposed wearable system has great potential to be used for overnight SBP monitoring, especially to measure the averaged SBP over a long period.