Essam Sleiman, O. Mutlu, Saimourya Surabhi, Arman Husic, A. Kline, P. Washington, D. Wall
{"title":"Deep Learning-Based Autism Spectrum Disorder Detection Using Emotion Features From Video Recordings (Preprint)","authors":"Essam Sleiman, O. Mutlu, Saimourya Surabhi, Arman Husic, A. Kline, P. Washington, D. Wall","doi":"10.2196/39982","DOIUrl":"https://doi.org/10.2196/39982","url":null,"abstract":"","PeriodicalId":87288,"journal":{"name":"JMIR biomedical engineering","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-05-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44947339","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Md Tanzil Shahria, Md Samiul Haque Sunny, Md Ishrak Islam Zarif, Md Mahafuzur Rahaman Khan, Preet Parag Modi, Sheikh Iqbal Ahamed, Mohammad H Rahman
Background: Applications of robotics in daily life are becoming essential by creating new possibilities in different fields, especially in the collaborative environment. The potentials of collaborative robots are tremendous as they can work in the same workspace as humans. A framework employing a top-notch technology for collaborative robots will surely be worthwhile for further research.
Objective: This study aims to present the development of a novel framework for the collaborative robot using mixed reality.
Methods: The framework uses Unity and Unity Hub as a cross-platform gaming engine and project management tool to design the mixed reality interface and digital twin. It also uses the Windows Mixed Reality platform to show digital materials on holographic display and the Azure mixed reality services to capture and expose digital information. Eventually, it uses a holographic device (HoloLens 2) to execute the mixed reality-based collaborative system.
Results: A thorough experiment was conducted to validate the novel framework for mixed reality-based control of a collaborative robot. This framework was successfully applied to implement a collaborative system using a 5-degree of freedom robot (xArm-5) in a mixed reality environment. The framework was stable and worked smoothly throughout the collaborative session. Due to the distributed nature of cloud applications, there is a negligible latency between giving a command and the execution of the physical collaborative robot.
Conclusions: Opportunities for collaborative robots in telerehabilitation and teleoperation are vital as in any other field. The proposed framework was successfully applied in a collaborative session, and it can also be applied in other similar potential applications for robust and more promising performance.
{"title":"A Novel Framework for Mixed Reality-Based Control of Collaborative Robot: Development Study.","authors":"Md Tanzil Shahria, Md Samiul Haque Sunny, Md Ishrak Islam Zarif, Md Mahafuzur Rahaman Khan, Preet Parag Modi, Sheikh Iqbal Ahamed, Mohammad H Rahman","doi":"10.2196/36734","DOIUrl":"10.2196/36734","url":null,"abstract":"<p><strong>Background: </strong>Applications of robotics in daily life are becoming essential by creating new possibilities in different fields, especially in the collaborative environment. The potentials of collaborative robots are tremendous as they can work in the same workspace as humans. A framework employing a top-notch technology for collaborative robots will surely be worthwhile for further research.</p><p><strong>Objective: </strong>This study aims to present the development of a novel framework for the collaborative robot using mixed reality.</p><p><strong>Methods: </strong>The framework uses Unity and Unity Hub as a cross-platform gaming engine and project management tool to design the mixed reality interface and digital twin. It also uses the Windows Mixed Reality platform to show digital materials on holographic display and the Azure mixed reality services to capture and expose digital information. Eventually, it uses a holographic device (HoloLens 2) to execute the mixed reality-based collaborative system.</p><p><strong>Results: </strong>A thorough experiment was conducted to validate the novel framework for mixed reality-based control of a collaborative robot. This framework was successfully applied to implement a collaborative system using a 5-degree of freedom robot (xArm-5) in a mixed reality environment. The framework was stable and worked smoothly throughout the collaborative session. Due to the distributed nature of cloud applications, there is a negligible latency between giving a command and the execution of the physical collaborative robot.</p><p><strong>Conclusions: </strong>Opportunities for collaborative robots in telerehabilitation and teleoperation are vital as in any other field. The proposed framework was successfully applied in a collaborative session, and it can also be applied in other similar potential applications for robust and more promising performance.</p>","PeriodicalId":87288,"journal":{"name":"JMIR biomedical engineering","volume":"7 1","pages":"e36734"},"PeriodicalIF":0.0,"publicationDate":"2022-05-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11041473/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141322150","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Alexander T Adams, Ilan Mandel, Yixuan Gao, Bryan W Heckman, Rajalakshmi Nandakumar, Tanzeem Choudhury
Background: Many commodity pulse oximeters are insufficiently calibrated for patients with darker skin. We demonstrate a quantitative measurement of this disparity in peripheral blood oxygen saturation (SpO2) with a controlled experiment. To mitigate this, we present OptoBeat, an ultra-low-cost smartphone-based optical sensing system that captures SpO2 and heart rate while calibrating for differences in skin tone. Our sensing system can be constructed from commodity components and 3D-printed clips for approximately US $1. In our experiments, we demonstrate the efficacy of the OptoBeat system, which can measure SpO2 within 1% of the ground truth in levels as low as 75%.
Objective: The objective of this work is to test the following hypotheses and implement an ultra-low-cost smartphone adapter to measure SpO2: skin tone has a significant effect on pulse oximeter measurements (hypothesis 1), images of skin tone can be used to calibrate pulse oximeter error (hypothesis 2), and SpO2 can be measured with a smartphone camera using the screen as a light source (hypothesis 3).
Methods: Synthetic skin with the same optical properties as human skin was used in ex vivo experiments. A skin tone scale was placed in images for calibration and ground truth. To achieve a wide range of SpO2 for measurement, we reoxygenated sheep blood and pumped it through synthetic arteries. A custom optical system was connected from the smartphone screen (flashing red and blue) to the analyte and into the phone's camera for measurement.
Results: The 3 skin tones were accurately classified according to the Fitzpatrick scale as types 2, 3, and 5. Classification was performed using the Euclidean distance between the measured red, green, and blue values. Traditional pulse oximeter measurements (n=2000) showed significant differences between skin tones in both alternating current and direct current measurements using ANOVA (direct current: F2,5997=3.1170 × 105, P<.01; alternating current: F2,5997=8.07 × 106, P<.01). Continuous SpO2 measurements (n=400; 10-second samples, 67 minutes total) from 95% to 75% were captured using OptoBeat in an ex vivo experiment. The accuracy was measured to be within 1% of the ground truth via quadratic support vector machine regression and 10-fold cross-validation (R2=0.97, root mean square error=0.7, mean square error=0.49, and mean absolute error=0.5). In the human-participant proof-of-concept experiment (N=3; samples=3 × N, duration=20-30 seconds per sample), SpO2 measurements were accurate to within 0.5% of the ground truth, and pulse rate measurements were accurate to within 1.7% of the ground truth.
Conclusions: In this work, we demonstrate that skin tone has a significant effect on SpO2
{"title":"Equity-Driven Sensing System for Measuring Skin Tone-Calibrated Peripheral Blood Oxygen Saturation (OptoBeat): Development, Design, and Evaluation Study.","authors":"Alexander T Adams, Ilan Mandel, Yixuan Gao, Bryan W Heckman, Rajalakshmi Nandakumar, Tanzeem Choudhury","doi":"10.2196/34934","DOIUrl":"10.2196/34934","url":null,"abstract":"<p><strong>Background: </strong>Many commodity pulse oximeters are insufficiently calibrated for patients with darker skin. We demonstrate a quantitative measurement of this disparity in peripheral blood oxygen saturation (SpO<sub>2</sub>) with a controlled experiment. To mitigate this, we present OptoBeat, an ultra-low-cost smartphone-based optical sensing system that captures SpO<sub>2</sub> and heart rate while calibrating for differences in skin tone. Our sensing system can be constructed from commodity components and 3D-printed clips for approximately US $1. In our experiments, we demonstrate the efficacy of the OptoBeat system, which can measure SpO<sub>2</sub> within 1% of the ground truth in levels as low as 75%.</p><p><strong>Objective: </strong>The objective of this work is to test the following hypotheses and implement an ultra-low-cost smartphone adapter to measure SpO<sub>2</sub>: skin tone has a significant effect on pulse oximeter measurements (hypothesis 1), images of skin tone can be used to calibrate pulse oximeter error (hypothesis 2), and SpO<sub>2</sub> can be measured with a smartphone camera using the screen as a light source (hypothesis 3).</p><p><strong>Methods: </strong>Synthetic skin with the same optical properties as human skin was used in ex vivo experiments. A skin tone scale was placed in images for calibration and ground truth. To achieve a wide range of SpO<sub>2</sub> for measurement, we reoxygenated sheep blood and pumped it through synthetic arteries. A custom optical system was connected from the smartphone screen (flashing red and blue) to the analyte and into the phone's camera for measurement.</p><p><strong>Results: </strong>The 3 skin tones were accurately classified according to the Fitzpatrick scale as types 2, 3, and 5. Classification was performed using the Euclidean distance between the measured red, green, and blue values. Traditional pulse oximeter measurements (n=2000) showed significant differences between skin tones in both alternating current and direct current measurements using ANOVA (direct current: F<sub>2,5997</sub>=3.1170 × 10<sup>5</sup>, P<.01; alternating current: F<sub>2,5997</sub>=8.07 × 10<sup>6</sup>, P<.01). Continuous SpO<sub>2</sub> measurements (n=400; 10-second samples, 67 minutes total) from 95% to 75% were captured using OptoBeat in an ex vivo experiment. The accuracy was measured to be within 1% of the ground truth via quadratic support vector machine regression and 10-fold cross-validation (R<sup>2</sup>=0.97, root mean square error=0.7, mean square error=0.49, and mean absolute error=0.5). In the human-participant proof-of-concept experiment (N=3; samples=3 × N, duration=20-30 seconds per sample), SpO<sub>2</sub> measurements were accurate to within 0.5% of the ground truth, and pulse rate measurements were accurate to within 1.7% of the ground truth.</p><p><strong>Conclusions: </strong>In this work, we demonstrate that skin tone has a significant effect on SpO<sub>2</sub","PeriodicalId":87288,"journal":{"name":"JMIR biomedical engineering","volume":" ","pages":"e34934"},"PeriodicalIF":0.0,"publicationDate":"2022-04-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11041433/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44570162","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Les Bogdanowicz, Onur Fidaner, Donato Ceres, Alexander Grycuk, Martina Guidetti, David Demos
Background: Lung cancer is the world's leading cause of cancer deaths, and diagnosis remains challenging. Lung cancer starts as small nodules; early and accurate diagnosis allows timely surgical resection of malignant nodules while avoiding unnecessary surgery in patients with benign nodules.
Objective: The Cole relaxation frequency (CRF) is a derived electrical bioimpedance signature, which may be utilized to distinguish cancerous tissues from normal tissues.
Methods: Human testing ex vivo was conducted with NoduleScan in freshly resected lung tissue from 30 volunteer patients undergoing resection for nonsmall cell lung cancer. The CRF of the tumor and the distant normal lung tissue relative to the tumor were compared to histopathology specimens to establish a potential algorithm for point-of-care diagnosis. For animal testing in vivo, 20 mice were implanted with xenograft human lung cancer tumor cells injected subcutaneously into the right flank of each mouse. Spectral impedance measurements were taken on the tumors on live animals transcutaneously and on the tumors after euthanasia. These CRF measurements were compared to healthy mouse lung tissue. For porcine lung testing ex vivo, porcine lungs were received with the trachea. After removal of the vocal box, a ventilator was attached to pressurize the lung and simulate breathing. At different locations of the lobes, the lung's surface was cut to produce a pocket that could accommodate tumors obtained from in vivo animal testing. The tumors were placed in the subsurface of the lung, and the electrode was placed on top of the lung surface directly over the tumor but with lung tissue between the tumor and the electrode. Spectral impedance measurements were taken when the lungs were in the deflated state, inflated state, and also during the inflation-deflation process to simulate breathing.
Results: Among 60 specimens evaluated in 30 patients, NoduleScan allowed ready discrimination in patients with clear separation of CRF in tumor and distant normal tissue with a high degree of sensitivity (97%) and specificity (87%). In the 25 xenograft small animal model specimens measured, the CRF aligns with the separation observed in the human in vivo measurements. The CRF was successfully measured of tumors implanted into ex vivo porcine lungs, and CRF measurements aligned with previous tests for pressurized and unpressurized lungs.
Conclusions: As previously shown in breast tissue, CRF in the range of 1kHz-10MHz was able to distinguish nonsmall cell lung cancer versus normal tissue. Further, as evidenced by in vivo small animal studies, perfused tumors have the same CRF signature as shown in breast tissue and human ex vivo testing. Inflation and deflation of the lung have no effect on the CRF signature. With additional development, CRF derived from spectral impedance measurements may permit point
{"title":"The Cole Relaxation Frequency as a Parameter to Identify Cancer in Lung Tissue: Preliminary Animal and Ex Vivo Patient Studies.","authors":"Les Bogdanowicz, Onur Fidaner, Donato Ceres, Alexander Grycuk, Martina Guidetti, David Demos","doi":"10.2196/35346","DOIUrl":"10.2196/35346","url":null,"abstract":"<p><strong>Background: </strong>Lung cancer is the world's leading cause of cancer deaths, and diagnosis remains challenging. Lung cancer starts as small nodules; early and accurate diagnosis allows timely surgical resection of malignant nodules while avoiding unnecessary surgery in patients with benign nodules.</p><p><strong>Objective: </strong>The Cole relaxation frequency (CRF) is a derived electrical bioimpedance signature, which may be utilized to distinguish cancerous tissues from normal tissues.</p><p><strong>Methods: </strong>Human testing ex vivo was conducted with NoduleScan in freshly resected lung tissue from 30 volunteer patients undergoing resection for nonsmall cell lung cancer. The CRF of the tumor and the distant normal lung tissue relative to the tumor were compared to histopathology specimens to establish a potential algorithm for point-of-care diagnosis. For animal testing in vivo, 20 mice were implanted with xenograft human lung cancer tumor cells injected subcutaneously into the right flank of each mouse. Spectral impedance measurements were taken on the tumors on live animals transcutaneously and on the tumors after euthanasia. These CRF measurements were compared to healthy mouse lung tissue. For porcine lung testing ex vivo, porcine lungs were received with the trachea. After removal of the vocal box, a ventilator was attached to pressurize the lung and simulate breathing. At different locations of the lobes, the lung's surface was cut to produce a pocket that could accommodate tumors obtained from in vivo animal testing. The tumors were placed in the subsurface of the lung, and the electrode was placed on top of the lung surface directly over the tumor but with lung tissue between the tumor and the electrode. Spectral impedance measurements were taken when the lungs were in the deflated state, inflated state, and also during the inflation-deflation process to simulate breathing.</p><p><strong>Results: </strong>Among 60 specimens evaluated in 30 patients, NoduleScan allowed ready discrimination in patients with clear separation of CRF in tumor and distant normal tissue with a high degree of sensitivity (97%) and specificity (87%). In the 25 xenograft small animal model specimens measured, the CRF aligns with the separation observed in the human in vivo measurements. The CRF was successfully measured of tumors implanted into ex vivo porcine lungs, and CRF measurements aligned with previous tests for pressurized and unpressurized lungs.</p><p><strong>Conclusions: </strong>As previously shown in breast tissue, CRF in the range of 1kHz-10MHz was able to distinguish nonsmall cell lung cancer versus normal tissue. Further, as evidenced by in vivo small animal studies, perfused tumors have the same CRF signature as shown in breast tissue and human ex vivo testing. Inflation and deflation of the lung have no effect on the CRF signature. With additional development, CRF derived from spectral impedance measurements may permit point","PeriodicalId":87288,"journal":{"name":"JMIR biomedical engineering","volume":"7 1","pages":"e35346"},"PeriodicalIF":0.0,"publicationDate":"2022-02-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11041438/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141322152","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Daniel Felipe Bohorquez Vargas, Henry Humberto Leon Ariza, Luis Mauricio Agudelo-Otalora, D. B. Botero Rosas, William Daniel Moscoso Barrera
{"title":"Portable system for the acquisition of the cardiac electrical signal and the calculation of heart rate variability metrics in real time: Statistical validation (Preprint)","authors":"Daniel Felipe Bohorquez Vargas, Henry Humberto Leon Ariza, Luis Mauricio Agudelo-Otalora, D. B. Botero Rosas, William Daniel Moscoso Barrera","doi":"10.2196/37453","DOIUrl":"https://doi.org/10.2196/37453","url":null,"abstract":"","PeriodicalId":87288,"journal":{"name":"JMIR biomedical engineering","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-02-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46745561","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Advances in mobile phone technologies coupled with the availability of modern wireless networks are beginning to have a marked impact on digital health through the growing array of apps and connected devices. That said, limited deployment outside of developed nations will require additional approaches to collectively reach the 8 billion people on earth. Another consideration for development of digital health centered around mobile devices lies in the need for pairing steps, firmware updates, and a variety of user inputs, which can increase friction for the patient. An alternate, so-called Beyond the Mobile approach where medicaments, devices, and health services communicate directly to the cloud offers an attractive means to expand and fully realize our connected health utopia. In addition to offering highly personalized experiences, such approaches could address cost, security, and convenience concerns associated with smartphone-based systems, translating to improved engagement and adherence rates among patients. Furthermore, connecting these Internet of Medical Things instruments through next-generation networks offers the potential to reach patients with acute needs in nonurban regions of developing nations. Herein, we outline how deployment of Beyond the Mobile technologies through low-power wide-area networks could offer a scalable means to democratize digital health and contribute to improved patient outcomes globally.
移动电话技术的进步，加上现代无线网络的可用性，正开始通过越来越多的应用程序和连接设备对数字健康产生显著影响。也就是说，在发达国家之外的有限部署将需要额外的方法来共同覆盖地球上的80亿人口。以移动设备为中心开发数字医疗的另一个考虑因素是需要配对步骤、固件更新和各种用户输入，这可能会增加患者的摩擦。另一种被称为“超越移动”(Beyond the Mobile)的方法是药物、设备和健康服务直接与云通信，这为扩展和充分实现我们的互联健康乌托邦提供了一种有吸引力的方式。除了提供高度个性化的体验外，这种方法还可以解决与基于智能手机的系统相关的成本、安全性和便利性问题，从而提高患者的参与度和依从性。此外，通过下一代网络将这些医疗物联网设备连接起来，为发展中国家非城市地区有迫切需求的患者提供了可能。在此，我们概述了通过低功耗广域网部署超越移动技术如何提供可扩展的手段，使数字健康民主化，并有助于改善全球患者的治疗效果。
{"title":"Democratizing Global Health Care Through Scalable Emergent (Beyond the Mobile) Wireless Technologies.","authors":"Graham B Jones, Andrew Bryant, Justin Wright","doi":"10.2196/31079","DOIUrl":"10.2196/31079","url":null,"abstract":"<p><p>Advances in mobile phone technologies coupled with the availability of modern wireless networks are beginning to have a marked impact on digital health through the growing array of apps and connected devices. That said, limited deployment outside of developed nations will require additional approaches to collectively reach the 8 billion people on earth. Another consideration for development of digital health centered around mobile devices lies in the need for pairing steps, firmware updates, and a variety of user inputs, which can increase friction for the patient. An alternate, so-called Beyond the Mobile approach where medicaments, devices, and health services communicate directly to the cloud offers an attractive means to expand and fully realize our connected health utopia. In addition to offering highly personalized experiences, such approaches could address cost, security, and convenience concerns associated with smartphone-based systems, translating to improved engagement and adherence rates among patients. Furthermore, connecting these Internet of Medical Things instruments through next-generation networks offers the potential to reach patients with acute needs in nonurban regions of developing nations. Herein, we outline how deployment of Beyond the Mobile technologies through low-power wide-area networks could offer a scalable means to democratize digital health and contribute to improved patient outcomes globally.</p>","PeriodicalId":87288,"journal":{"name":"JMIR biomedical engineering","volume":" ","pages":"e31079"},"PeriodicalIF":0.0,"publicationDate":"2022-02-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11041427/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48440059","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The COVID-19 pandemic has illuminated multiple challenges within the health care system and is unique to those living with chronic conditions. Recent advances in digital health technologies (eHealth) present opportunities to improve quality of care, self-management, and decision-making support to reduce treatment burden and the risk of chronic condition management burnout. There are limited available eHealth models that can adequately describe how this can be carried out. In this paper, we define treatment burden and the related risk of affective burnout; assess how an eHealth enhanced Chronic Care Model can help prioritize digital health solutions; and describe an emerging machine learning model as one example aimed to alleviate treatment burden and burnout risk. We propose that eHealth-driven machine learning models can be a disruptive change to optimally support persons living with chronic conditions.
{"title":"Reducing Treatment Burden Among People With Chronic Conditions Using Machine Learning: Viewpoint.","authors":"Harpreet Nagra, Aradhana Goel, Dan Goldner","doi":"10.2196/29499","DOIUrl":"10.2196/29499","url":null,"abstract":"<p><p>The COVID-19 pandemic has illuminated multiple challenges within the health care system and is unique to those living with chronic conditions. Recent advances in digital health technologies (eHealth) present opportunities to improve quality of care, self-management, and decision-making support to reduce treatment burden and the risk of chronic condition management burnout. There are limited available eHealth models that can adequately describe how this can be carried out. In this paper, we define treatment burden and the related risk of affective burnout; assess how an eHealth enhanced Chronic Care Model can help prioritize digital health solutions; and describe an emerging machine learning model as one example aimed to alleviate treatment burden and burnout risk. We propose that eHealth-driven machine learning models can be a disruptive change to optimally support persons living with chronic conditions.</p>","PeriodicalId":87288,"journal":{"name":"JMIR biomedical engineering","volume":"7 1","pages":"e29499"},"PeriodicalIF":0.0,"publicationDate":"2022-02-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11041463/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141322151","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
UNSTRUCTURED Pain is a subjective phenomenon caused/perceived centrally and modified by physical, physiological, or social influences. Currently, the most commonly used approaches for pain measurement rely on self-reporting of pain level on a discrete rating scale. This provides a subjective and only semi-quantitative indicator of pain. This paper presents an approach that combines self-reported pain with pain-related biomarkers to be obtained from biosensors (in development) and possibly other sources of evidence to provide more dependable estimates of experienced pain, a clinical decision support system. We illustrate the approach using a Bayes network, but also describe other artificial intelligence (AI) methods that provide other ways to combine evidence. We also propose an optimization approach for tuning the AI method parameters (opaque to clinicians) so as to best approximate the kinds of outputs most useful to medical practitioners. We present some data from a sample of 379 patients that illustrate several evidence patterns we may expect in real healthcare situations. The majority (79.7%) of our patients show consistent evidence suggesting this biomarker approach may be reasonable. We also found five patterns of inconsistent evidence. These suggest a direction for further exploration. Finally, we sketch out an approach for collecting medical experts’ guidance as to the way the combined evidence might be presented so as to provide the most useful guidance (also needed for any optimization approach). We recognize that one possible outcome may be that all this approach may be able to provide is a quantified measure of the extent to which the evidence is consistent or not, leaving the final decision to the clinicians (where it must reside). Pointers to additional sources of evidence might also be possible in some situations.
{"title":"A Bayesian Network Concept for Pain Assessment (Preprint)","authors":"O. Sadik","doi":"10.2196/preprints.35711","DOIUrl":"https://doi.org/10.2196/preprints.35711","url":null,"abstract":"\u0000 UNSTRUCTURED\u0000 Pain is a subjective phenomenon caused/perceived centrally and modified by physical, physiological, or social influences. Currently, the most commonly used approaches for pain measurement rely on self-reporting of pain level on a discrete rating scale. This provides a subjective and only semi-quantitative indicator of pain. \u0000\u0000This paper presents an approach that combines self-reported pain with pain-related biomarkers to be obtained from biosensors (in development) and possibly other sources of evidence to provide more dependable estimates of experienced pain, a clinical decision support system. We illustrate the approach using a Bayes network, but also describe other artificial intelligence (AI) methods that provide other ways to combine evidence. We also propose an optimization approach for tuning the AI method parameters (opaque to clinicians) so as to best approximate the kinds of outputs most useful to medical practitioners.\u0000\u0000We present some data from a sample of 379 patients that illustrate several evidence patterns we may expect in real healthcare situations. The majority (79.7%) of our patients show consistent evidence suggesting this biomarker approach may be reasonable. We also found five patterns of inconsistent evidence. These suggest a direction for further exploration. Finally, we sketch out an approach for collecting medical experts’ guidance as to the way the combined evidence might be presented so as to provide the most useful guidance (also needed for any optimization approach). We recognize that one possible outcome may be that all this approach may be able to provide is a quantified measure of the extent to which the evidence is consistent or not, leaving the final decision to the clinicians (where it must reside). Pointers to additional sources of evidence might also be possible in some situations.\u0000","PeriodicalId":87288,"journal":{"name":"JMIR biomedical engineering","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-12-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42073654","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Hari Bhimaraju, Nitish Nag, Vaibhav Pandey, Ramesh Jain
Background: Modern environmental health research extensively focuses on outdoor air pollutants and their effects on public health. However, research on monitoring and enhancing individual indoor air quality is lacking. The field of exposomics encompasses the totality of human environmental exposures and its effects on health. A subset of this exposome deals with atmospheric exposure, termed the "atmosome." The atmosome plays a pivotal role in health and has significant effects on DNA, metabolism, skin integrity, and lung health.
Objective: The aim of this work is to develop a low-cost, comprehensive measurement system for collecting and analyzing atmosomic factors. The research explores the significance of the atmosome in personalized and preventive care for public health.
Methods: An internet of things microcontroller-based system is introduced and demonstrated. The system collects real-time indoor air quality data and posts it to the cloud for immediate access.
Results: The experimental results yield air quality measurements with an accuracy of 90% when compared with precalibrated commercial devices and demonstrate a direct correlation between lifestyle and air quality.
Conclusions: Quantifying the individual atmosome is a monumental step in advancing personalized health, medical research, and epidemiological research. The 2 main goals in this work are to present the atmosome as a measurable concept and to demonstrate how to implement it using low-cost electronics. By enabling atmosome measurements at a communal scale, this work also opens up potential new directions for public health research. Researchers will now have the data to model the impact of indoor air pollutants on the health of individuals, communities, and specific demographics, leading to novel approaches for predicting and preventing diseases.
{"title":"Understanding \"Atmosome\", the Personal Atmospheric Exposome: Comprehensive Approach.","authors":"Hari Bhimaraju, Nitish Nag, Vaibhav Pandey, Ramesh Jain","doi":"10.2196/28920","DOIUrl":"10.2196/28920","url":null,"abstract":"<p><strong>Background: </strong>Modern environmental health research extensively focuses on outdoor air pollutants and their effects on public health. However, research on monitoring and enhancing individual indoor air quality is lacking. The field of exposomics encompasses the totality of human environmental exposures and its effects on health. A subset of this exposome deals with atmospheric exposure, termed the \"atmosome.\" The atmosome plays a pivotal role in health and has significant effects on DNA, metabolism, skin integrity, and lung health.</p><p><strong>Objective: </strong>The aim of this work is to develop a low-cost, comprehensive measurement system for collecting and analyzing atmosomic factors. The research explores the significance of the atmosome in personalized and preventive care for public health.</p><p><strong>Methods: </strong>An internet of things microcontroller-based system is introduced and demonstrated. The system collects real-time indoor air quality data and posts it to the cloud for immediate access.</p><p><strong>Results: </strong>The experimental results yield air quality measurements with an accuracy of 90% when compared with precalibrated commercial devices and demonstrate a direct correlation between lifestyle and air quality.</p><p><strong>Conclusions: </strong>Quantifying the individual atmosome is a monumental step in advancing personalized health, medical research, and epidemiological research. The 2 main goals in this work are to present the atmosome as a measurable concept and to demonstrate how to implement it using low-cost electronics. By enabling atmosome measurements at a communal scale, this work also opens up potential new directions for public health research. Researchers will now have the data to model the impact of indoor air pollutants on the health of individuals, communities, and specific demographics, leading to novel approaches for predicting and preventing diseases.</p>","PeriodicalId":87288,"journal":{"name":"JMIR biomedical engineering","volume":"6 4","pages":"e28920"},"PeriodicalIF":0.0,"publicationDate":"2021-11-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11041466/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141441213","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Meshari F Alwashmi, Gerald Mugford, Brett Vokey, Waseem Abu-Ashour, John Hawboldt
Background: The majority of medications used in treating asthma and chronic obstructive pulmonary disease (COPD) are taken through metered-dose inhalers (MDIs). Studies have reported that most patients demonstrate poor inhaler technique, which has resulted in poor disease control. Digital Health applications have the potential to improve the technique and adherence of inhaled medications.
Objective: This study aimed to validate the effectiveness of the BreatheSuite MDI device in assessing the technique of taking a dose via an MDI.
Methods: The study was a validation study. Thirty participants who self-reported a diagnosis of asthma or COPD were recruited from community pharmacies in Newfoundland and Labrador, Canada. Participants used a BreatheSuite MDI device attached to a placebo MDI and resembled taking 3 doses. Pharmacists used a scoring sheet to evaluate the technique of using the MDI. An independent researcher compared the results of the pharmacist's scoring sheet with the results of the BreatheSuite device.
Results: This study found that the BreatheSuite MDI can objectively detect several errors in the MDI technique. The data recorded by the BreatheSuite MDI device showed that all participants performed at least one error in using the MDI. The BreatheSuite device captured approximately 40% (143/360) more errors compared to observation alone. The distribution of participants who performed errors in MDI steps as recorded by BreatheSuite compared to errors reported by observation alone were as follows: shaking before actuation, 33.3% (30/90) versus 25.5% (23/90); upright orientation of the inhaler during actuation, 66.7% (60/90) versus 18.87% (17/90); coordination (actuating after the start of inhalation), 76.6% (69/90) versus 35.5% (32/90); and duration of inspiration, 96.7% (87/90) versus 34.4% (31/90).
Conclusions: The BreatheSuite MDI can objectively detect several errors in the MDI technique, which were missed by observation alone. It has the potential to enhance treatment outcomes among patients with chronic lung diseases.
{"title":"Effectiveness of the BreatheSuite Device in Assessing the Technique of Metered-Dose Inhalers: Validation Study.","authors":"Meshari F Alwashmi, Gerald Mugford, Brett Vokey, Waseem Abu-Ashour, John Hawboldt","doi":"10.2196/26556","DOIUrl":"10.2196/26556","url":null,"abstract":"<p><strong>Background: </strong>The majority of medications used in treating asthma and chronic obstructive pulmonary disease (COPD) are taken through metered-dose inhalers (MDIs). Studies have reported that most patients demonstrate poor inhaler technique, which has resulted in poor disease control. Digital Health applications have the potential to improve the technique and adherence of inhaled medications.</p><p><strong>Objective: </strong>This study aimed to validate the effectiveness of the BreatheSuite MDI device in assessing the technique of taking a dose via an MDI.</p><p><strong>Methods: </strong>The study was a validation study. Thirty participants who self-reported a diagnosis of asthma or COPD were recruited from community pharmacies in Newfoundland and Labrador, Canada. Participants used a BreatheSuite MDI device attached to a placebo MDI and resembled taking 3 doses. Pharmacists used a scoring sheet to evaluate the technique of using the MDI. An independent researcher compared the results of the pharmacist's scoring sheet with the results of the BreatheSuite device.</p><p><strong>Results: </strong>This study found that the BreatheSuite MDI can objectively detect several errors in the MDI technique. The data recorded by the BreatheSuite MDI device showed that all participants performed at least one error in using the MDI. The BreatheSuite device captured approximately 40% (143/360) more errors compared to observation alone. The distribution of participants who performed errors in MDI steps as recorded by BreatheSuite compared to errors reported by observation alone were as follows: shaking before actuation, 33.3% (30/90) versus 25.5% (23/90); upright orientation of the inhaler during actuation, 66.7% (60/90) versus 18.87% (17/90); coordination (actuating after the start of inhalation), 76.6% (69/90) versus 35.5% (32/90); and duration of inspiration, 96.7% (87/90) versus 34.4% (31/90).</p><p><strong>Conclusions: </strong>The BreatheSuite MDI can objectively detect several errors in the MDI technique, which were missed by observation alone. It has the potential to enhance treatment outcomes among patients with chronic lung diseases.</p>","PeriodicalId":87288,"journal":{"name":"JMIR biomedical engineering","volume":" ","pages":"e26556"},"PeriodicalIF":0.0,"publicationDate":"2021-11-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11041462/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45302248","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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