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Levin JM, Lorentz SG, Hurley ET, Lee J, Throckmorton TW, Garrigues GE, MacDonald P, Anakwenze O, Schoch BS, Klifto C. Artificial intelligence in shoulder and elbow surgery: overview of current and future applications. J Shoulder Elbow Surg 2024; 33:1633-1641. [PMID: 38430978 DOI: 10.1016/j.jse.2024.01.033] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/22/2023] [Revised: 01/10/2024] [Accepted: 01/14/2024] [Indexed: 03/05/2024]
Abstract
Artificial intelligence (AI) is amongst the most rapidly growing technologies in orthopedic surgery. With the exponential growth in healthcare data, computing power, and complex predictive algorithms, this technology is poised to aid providers in data processing and clinical decision support throughout the continuum of orthopedic care. Understanding the utility and limitations of this technology is vital to practicing orthopedic surgeons, as these applications will become more common place in everyday practice. AI has already demonstrated its utility in shoulder and elbow surgery for imaging-based diagnosis, predictive modeling of clinical outcomes, implant identification, and automated image segmentation. The future integration of AI and robotic surgery represents the largest potential application of AI in shoulder and elbow surgery with the potential for significant clinical and financial impact. This editorial's purpose is to summarize common AI terms, provide a framework to understand and interpret AI model results, and discuss current applications and future directions within shoulder and elbow surgery.
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Affiliation(s)
- Jay M Levin
- Department of Orthopaedic Surgery, Duke University Medical Center, Durham, NC, USA.
| | - Samuel G Lorentz
- Department of Orthopaedic Surgery, Duke University Medical Center, Durham, NC, USA
| | - Eoghan T Hurley
- Department of Orthopaedic Surgery, Duke University Medical Center, Durham, NC, USA
| | - Julia Lee
- Department of Orthopedic Surgery, Sierra Pacific Orthopedics, Fresno, CA, USA
| | - Thomas W Throckmorton
- Department of Orthopaedic Surgery, University of Tennessee-Campbell Clinic, Germantown, TN, USA
| | | | - Peter MacDonald
- Section of Orthopaedic Surgery & The Pan Am Clinic, University of Manitoba, Winnipeg, MB, Canada
| | - Oke Anakwenze
- Department of Orthopaedic Surgery, Duke University Medical Center, Durham, NC, USA
| | - Bradley S Schoch
- Department of Orthopedic Surgery, Mayo Clinic, Jacksonville, FL, USA
| | - Christopher Klifto
- Department of Orthopaedic Surgery, Duke University Medical Center, Durham, NC, USA
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Macias Alonso AK, Hirt J, Woelfle T, Janiaud P, Hemkens LG. Definitions of digital biomarkers: a systematic mapping of the biomedical literature. BMJ Health Care Inform 2024; 31:e100914. [PMID: 38589213 PMCID: PMC11015196 DOI: 10.1136/bmjhci-2023-100914] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/27/2023] [Accepted: 03/06/2024] [Indexed: 04/10/2024] Open
Abstract
BACKGROUND Technological devices such as smartphones, wearables and virtual assistants enable health data collection, serving as digital alternatives to conventional biomarkers. We aimed to provide a systematic overview of emerging literature on 'digital biomarkers,' covering definitions, features and citations in biomedical research. METHODS We analysed all articles in PubMed that used 'digital biomarker(s)' in title or abstract, considering any study involving humans and any review, editorial, perspective or opinion-based articles up to 8 March 2023. We systematically extracted characteristics of publications and research studies, and any definitions and features of 'digital biomarkers' mentioned. We described the most influential literature on digital biomarkers and their definitions using thematic categorisations of definitions considering the Food and Drug Administration Biomarkers, EndpointS and other Tools framework (ie, data type, data collection method, purpose of biomarker), analysing structural similarity of definitions by performing text and citation analyses. RESULTS We identified 415 articles using 'digital biomarker' between 2014 and 2023 (median 2021). The majority (283 articles; 68%) were primary research. Notably, 287 articles (69%) did not provide a definition of digital biomarkers. Among the 128 articles with definitions, there were 127 different ones. Of these, 78 considered data collection, 56 data type, 50 purpose and 23 included all three components. Those 128 articles with a definition had a median of 6 citations, with the top 10 each presenting distinct definitions. CONCLUSIONS The definitions of digital biomarkers vary significantly, indicating a lack of consensus in this emerging field. Our overview highlights key defining characteristics, which could guide the development of a more harmonised accepted definition.
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Affiliation(s)
- Ana Karen Macias Alonso
- Department of Applied Natural Sciences, Technische Hochschule Lübeck, Lübeck, Germany
- Pragmatic Evidence Lab, Research Center for Clinical Neuroimmunology and Neuroscience Basel (RC2NB), University Hospital Basel and University of Basel, Basel, Switzerland
| | - Julian Hirt
- Pragmatic Evidence Lab, Research Center for Clinical Neuroimmunology and Neuroscience Basel (RC2NB), University Hospital Basel and University of Basel, Basel, Switzerland
- Department of Clinical Research, University Hospital Basel and University of Basel, Basel, Switzerland
- Department of Health, Eastern Switzerland University of Applied Sciences, St.Gallen, Switzerland
| | - Tim Woelfle
- Pragmatic Evidence Lab, Research Center for Clinical Neuroimmunology and Neuroscience Basel (RC2NB), University Hospital Basel and University of Basel, Basel, Switzerland
- Department of Neurology and MS Center, University Hospital Basel and University of Basel, Basel, Switzerland
| | - Perrine Janiaud
- Pragmatic Evidence Lab, Research Center for Clinical Neuroimmunology and Neuroscience Basel (RC2NB), University Hospital Basel and University of Basel, Basel, Switzerland
- Department of Clinical Research, University Hospital Basel and University of Basel, Basel, Switzerland
| | - Lars G Hemkens
- Pragmatic Evidence Lab, Research Center for Clinical Neuroimmunology and Neuroscience Basel (RC2NB), University Hospital Basel and University of Basel, Basel, Switzerland
- Department of Clinical Research, University Hospital Basel and University of Basel, Basel, Switzerland
- Meta-Research Innovation Center at Stanford (METRICS), Stanford University, Stanford, California, USA
- Meta-Research Innovation Center Berlin (METRIC-B), Berlin Institute of Health, Berlin, Germany
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Bi CL, Kurland DB, Ber R, Kondziolka D, Lau D, Pacione D, Frempong-Boadu A, Laufer I, Oermann EK. Digital Biomarkers and the Evolution of Spine Care Outcomes Measures: Smartphones and Wearables. Neurosurgery 2023; 93:745-754. [PMID: 37246874 DOI: 10.1227/neu.0000000000002519] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/19/2022] [Accepted: 03/19/2023] [Indexed: 05/30/2023] Open
Abstract
Over the past generation, outcome measures in spine care have evolved from a reliance on clinician-reported assessment toward recognizing the importance of the patient's perspective and the wide incorporation of patient-reported outcomes (PROs). While patient-reported outcomes are now considered an integral component of outcomes assessments, they cannot wholly capture the state of a patient's functionality. There is a clear need for quantitative and objective patient-centered outcome measures. The pervasiveness of smartphones and wearable devices in modern society, which passively collect data related to health, has ushered in a new era of spine care outcome measurement. The patterns emerging from these data, so-called "digital biomarkers," can accurately describe characteristics of a patient's health, disease, or recovery state. Broadly, the spine care community has thus far concentrated on digital biomarkers related to mobility, although the researcher's toolkit is anticipated to expand in concert with advancements in technology. In this review of the nascent literature, we describe the evolution of spine care outcome measurements, outline how digital biomarkers can supplement current clinician-driven and patient-driven measures, appraise the present and future of the field in the modern era, as well as discuss present limitations and areas for further study, with a focus on smartphones (see Supplemental Digital Content , http://links.lww.com/NEU/D809 , for a similar appraisal of wearable devices).
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Affiliation(s)
- Christina L Bi
- Department of Neurological Surgery, New York University, New York , New York , USA
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Cheng N, Lou B, Wang H. Discovering the digital biomarker of hepatocellular carcinoma in serum with SERS-based biosensors and intelligence vision. Colloids Surf B Biointerfaces 2023; 226:113315. [PMID: 37086688 DOI: 10.1016/j.colsurfb.2023.113315] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/10/2023] [Revised: 03/31/2023] [Accepted: 04/11/2023] [Indexed: 04/24/2023]
Abstract
By its many virtues, non-biomarker-reliant molecular detection has recently shown bright prospects for cancer screening but its clinical application is hindered by the shortage of measurable criteria that are analogous to biomarkers. Here, we report a digital biomarker, as a new-concept serum biomarker, of hepatocellular carcinoma (HCC) found with SERS-based biosensors and a deep neural network "digital retina" for visualizing and explicitly defining spectral fingerprints. We validate the discovered digital biomarker (a collection of 10 characteristic peaks in the serum SERS spectra) with unsupervised clustering of spectra from an independent sample batch comprised normal individuals and HCC cases; the validation results show clustering accuracies of 95.71% and 100.00%, respectively. Furthermore, we find that the digital biomarker of HCC shares a few common peaks with three clinically applied serum biomarkers, which means it could convey essential biomolecular information similar to these biomarkers. Accordingly, we present an intelligent method for early HCC detection that leverages the digital biomarker with similar traits as biomarkers. Employing the digital biomarker, we could accurately stratify HCC, hepatitis B, and normal populations with linear classifiers, exhibiting accuracies over 92% and area under the receiver operating curve values above 0.93. It is anticipated that this non-biomarker-reliant molecular detection method will facilitate mass cancer screening.
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Affiliation(s)
- Ningtao Cheng
- School of Medicine, Zhejiang University, Hangzhou, Zhejiang 310058, China.
| | - Bin Lou
- Department of Laboratory Medicine, the First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang 310003, China
| | - Hongyang Wang
- International Cooperation Laboratory on Signal Transduction, Eastern Hepatobiliary Surgery Hospital, Shanghai 200438, China; National Center for Liver Cancer, Shanghai 201805, China.
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D'Amico RS, White TG, Shah HA, Langer DJ. I Asked a ChatGPT to Write an Editorial About How We Can Incorporate Chatbots Into Neurosurgical Research and Patient Care…. Neurosurgery 2023; 92:663-664. [PMID: 36757199 DOI: 10.1227/neu.0000000000002414] [Citation(s) in RCA: 30] [Impact Index Per Article: 30.0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/06/2023] [Accepted: 01/11/2023] [Indexed: 02/10/2023] Open
Affiliation(s)
- Randy S D'Amico
- Department of Neurological Surgery, Lenox Hill Hospital/Donald and Barbara Zucker School of Medicine at Hofstra/Northwell, New York, New York, USA
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Martín-Noguerol T, Oñate Miranda M, Amrhein TJ, Paulano-Godino F, Xiberta P, Vilanova JC, Luna A. The role of Artificial intelligence in the assessment of the spine and spinal cord. Eur J Radiol 2023; 161:110726. [PMID: 36758280 DOI: 10.1016/j.ejrad.2023.110726] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/19/2022] [Revised: 01/13/2023] [Accepted: 01/31/2023] [Indexed: 02/05/2023]
Abstract
Artificial intelligence (AI) application development is underway in all areas of radiology where many promising tools are focused on the spine and spinal cord. In the past decade, multiple spine AI algorithms have been created based on radiographs, computed tomography, and magnetic resonance imaging. These algorithms have wide-ranging purposes including automatic labeling of vertebral levels, automated description of disc degenerative changes, detection and classification of spine trauma, identification of osseous lesions, and the assessment of cord pathology. The overarching goals for these algorithms include improved patient throughput, reducing radiologist workload burden, and improving diagnostic accuracy. There are several pre-requisite tasks required in order to achieve these goals, such as automatic image segmentation, facilitating image acquisition and postprocessing. In this narrative review, we discuss some of the important imaging AI solutions that have been developed for the assessment of the spine and spinal cord. We focus on their practical applications and briefly discuss some key requirements for the successful integration of these tools into practice. The potential impact of AI in the imaging assessment of the spine and cord is vast and promises to provide broad reaching improvements for clinicians, radiologists, and patients alike.
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Affiliation(s)
| | - Marta Oñate Miranda
- Department of Radiology, Centre Hospitalier Universitaire de Sherbrooke, Sherbrooke, Quebec, Canada.
| | - Timothy J Amrhein
- Department of Radiology, Duke University Medical Center, Durham, USA.
| | | | - Pau Xiberta
- Graphics and Imaging Laboratory (GILAB), University of Girona, 17003 Girona, Spain.
| | - Joan C Vilanova
- Department of Radiology. Clinica Girona, Diagnostic Imaging Institute (IDI), University of Girona, 17002 Girona, Spain.
| | - Antonio Luna
- MRI unit, Radiology department. HT medica, Carmelo Torres n°2, 23007 Jaén, Spain.
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Cui Y, Zhu J, Duan Z, Liao Z, Wang S, Liu W. Artificial Intelligence in Spinal Imaging: Current Status and Future Directions. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2022; 19:11708. [PMID: 36141981 PMCID: PMC9517575 DOI: 10.3390/ijerph191811708] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/12/2022] [Revised: 09/14/2022] [Accepted: 09/15/2022] [Indexed: 06/16/2023]
Abstract
Spinal maladies are among the most common causes of pain and disability worldwide. Imaging represents an important diagnostic procedure in spinal care. Imaging investigations can provide information and insights that are not visible through ordinary visual inspection. Multiscale in vivo interrogation has the potential to improve the assessment and monitoring of pathologies thanks to the convergence of imaging, artificial intelligence (AI), and radiomic techniques. AI is revolutionizing computer vision, autonomous driving, natural language processing, and speech recognition. These revolutionary technologies are already impacting radiology, diagnostics, and other fields, where automated solutions can increase precision and reproducibility. In the first section of this narrative review, we provide a brief explanation of the many approaches currently being developed, with a particular emphasis on those employed in spinal imaging studies. The previously documented uses of AI for challenges involving spinal imaging, including imaging appropriateness and protocoling, image acquisition and reconstruction, image presentation, image interpretation, and quantitative image analysis, are then detailed. Finally, the future applications of AI to imaging of the spine are discussed. AI has the potential to significantly affect every step in spinal imaging. AI can make images of the spine more useful to patients and doctors by improving image quality, imaging efficiency, and diagnostic accuracy.
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Affiliation(s)
- Yangyang Cui
- Tsinghua Shenzhen International Graduate School, Tsinghua University, Shenzhen 518055, China
- Department of Mechanical Engineering, Tsinghua University, Beijing 100084, China
- Biomechanics and Biotechnology Lab, Research Institute of Tsinghua University in Shenzhen, Shenzhen 518057, China
| | - Jia Zhu
- Tsinghua Shenzhen International Graduate School, Tsinghua University, Shenzhen 518055, China
- Department of Mechanical Engineering, Tsinghua University, Beijing 100084, China
- Biomechanics and Biotechnology Lab, Research Institute of Tsinghua University in Shenzhen, Shenzhen 518057, China
| | - Zhili Duan
- Tsinghua Shenzhen International Graduate School, Tsinghua University, Shenzhen 518055, China
- Department of Mechanical Engineering, Tsinghua University, Beijing 100084, China
- Biomechanics and Biotechnology Lab, Research Institute of Tsinghua University in Shenzhen, Shenzhen 518057, China
| | - Zhenhua Liao
- Biomechanics and Biotechnology Lab, Research Institute of Tsinghua University in Shenzhen, Shenzhen 518057, China
| | - Song Wang
- Biomechanics and Biotechnology Lab, Research Institute of Tsinghua University in Shenzhen, Shenzhen 518057, China
| | - Weiqiang Liu
- Tsinghua Shenzhen International Graduate School, Tsinghua University, Shenzhen 518055, China
- Department of Mechanical Engineering, Tsinghua University, Beijing 100084, China
- Biomechanics and Biotechnology Lab, Research Institute of Tsinghua University in Shenzhen, Shenzhen 518057, China
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Internet of Medical Things (IoMT)-Based Smart Healthcare System: Trends and Progress. COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE 2022; 2022:7218113. [PMID: 35880061 PMCID: PMC9308524 DOI: 10.1155/2022/7218113] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/30/2022] [Revised: 06/11/2022] [Accepted: 06/27/2022] [Indexed: 12/26/2022]
Abstract
Internet of Medical Thing (IoMT) is the most emerging era of the Internet of Thing (IoT), which is exponentially gaining researchers' attention with every passing day because of its wide applicability in Smart Healthcare systems (SHS). Because of the current pandemic situation, it is highly risky for an individual to visit the doctor for every small problem. Hence, using IoMT devices, we can easily monitor our day-to-day health records, and thereby initial precautions can be taken on our own. IoMT is playing a crucial role within the healthcare industry to increase the accuracy, reliability, and productivity of electronic devices. This research work provides an overview of IoMT with emphasis on various enabling techniques used in smart healthcare systems (SHS), such as radio frequency identification (RFID), artificial intelligence (AI), and blockchain. We are providing a comparative analysis of various IoMT architectures proposed by several researchers. Also, we have defined various health domains of IoMT, including the analysis of different sensors with their application environment, merits, and demerits. In addition, we have figured out key protocol design challenges, which are to be considered during the implementation of an IoMT network-based smart healthcare system. Considering these challenges, we prepared a comparative study for different data collection techniques that can be used to maintain the accuracy of collected data. In addition, this research work also provides a comprehensive study for maintaining the energy efficiency of an AI-based IoMT framework based on various parameters, such as the amount of energy consumed, packet delivery ratio, battery lifetime, quality of service, power drain, network throughput, delay, and transmission rate. Finally, we have provided different correlation equations for finding the accuracy and efficiency within the IoMT-based healthcare system using artificial intelligence. We have compared different data collection algorithms graphically based on their accuracy and error rate. Similarly, different energy efficiency algorithms are also graphically compared based on their energy consumption and packet loss percentage. We have analyzed our references used in this study, which are graphically represented based on their distribution of publication year and publication avenue.
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Denecke K, Baudoin CR. A Review of Artificial Intelligence and Robotics in Transformed Health Ecosystems. Front Med (Lausanne) 2022; 9:795957. [PMID: 35872767 PMCID: PMC9299071 DOI: 10.3389/fmed.2022.795957] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/15/2021] [Accepted: 06/15/2022] [Indexed: 11/13/2022] Open
Abstract
Health care is shifting toward become proactive according to the concept of P5 medicine-a predictive, personalized, preventive, participatory and precision discipline. This patient-centered care heavily leverages the latest technologies of artificial intelligence (AI) and robotics that support diagnosis, decision making and treatment. In this paper, we present the role of AI and robotic systems in this evolution, including example use cases. We categorize systems along multiple dimensions such as the type of system, the degree of autonomy, the care setting where the systems are applied, and the application area. These technologies have already achieved notable results in the prediction of sepsis or cardiovascular risk, the monitoring of vital parameters in intensive care units, or in the form of home care robots. Still, while much research is conducted around AI and robotics in health care, adoption in real world care settings is still limited. To remove adoption barriers, we need to address issues such as safety, security, privacy and ethical principles; detect and eliminate bias that could result in harmful or unfair clinical decisions; and build trust in and societal acceptance of AI.
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Affiliation(s)
- Kerstin Denecke
- Institute for Medical Information, Bern University of Applied Sciences, Bern, Switzerland
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10
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Kim T, Kim YG, Park S, Lee JK, Lee CH, Hyun SJ, Kim CH, Kim KJ, Chung CK. Diagnostic triage in patients with central lumbar spinal stenosis using a deep learning system of radiographs. J Neurosurg Spine 2022; 37:104-111. [PMID: 35061993 DOI: 10.3171/2021.11.spine211136] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/27/2021] [Accepted: 11/11/2021] [Indexed: 11/06/2022]
Abstract
OBJECTIVE Magnetic resonance imaging (MRI) is the gold-standard tool for diagnosing lumbar spinal stenosis (LSS), but it is difficult to promptly examine all suspected cases with MRI considering the modality's high cost and limited accessibility. Although radiography is an efficient screening technique owing to its low cost, rapid operability, and wide availability, its diagnostic accuracy is relatively poor. In this study, the authors aimed to develop a deep learning model with a convolutional neural network (CNN) for diagnosing severe central LSS using radiography and to evaluate radiological diagnostic features using gradient-weighted class activation mapping (Grad-CAM). METHODS Patients who had undergone both spinal MRI and radiography in the period from May 1, 2005, to December 31, 2017, were screened. According to the formal MRI report, participants were consecutively included in the severe central LSS or healthy control group, and radiographs for both groups were collected. A CNN-based transfer learning algorithm was developed to classify radiographic findings as LSS or normal (binary classification). The proposed models were evaluated using six performance metrics: area under the receiver operating characteristic curve (AUROC), accuracy, sensitivity, specificity, and positive and negative predictive values. RESULTS The VGG19 model achieved the highest accuracy with an AUROC of 90.0% (95% CI 89.8%-90.3%) by training 12,442 images. Accuracy was 82.8% (95% CI 82.5%-83.1%) by averaging 5-fold models. Feature points on Grad-CAM were reasonable, and the features could be categorized into reduced disc height, narrow foramina, short pedicle, and hyperdense facet joint. The AUROC in the extra validation was 89.3% (95% CI 88.7%-90.0%). Accuracy was 81.8% (95% CI 80.6%-83.0%) by averaging 5-fold models. Multivariate logistic regression analysis showed that a combination of demographic factors (age and sex) did not improve the model performance. CONCLUSIONS The algorithm trained by a CNN to identify central LSS on radiographs showed high diagnostic accuracy and is expected to be useful as a triage tool. The algorithm could accurately localize the stenotic lesion to assist physicians in the identification of LSS.
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Affiliation(s)
- Tackeun Kim
- 1Department of Neurosurgery, Seoul National University Bundang Hospital, Seongnam
| | - Young-Gon Kim
- 2Transdisciplinary Department of Medicine & Advanced Technology, Seoul National University Hospital, Seoul
- 3AI Institute, Seoul National University, Seoul
| | - Seyeon Park
- 2Transdisciplinary Department of Medicine & Advanced Technology, Seoul National University Hospital, Seoul
| | - Jae-Koo Lee
- 1Department of Neurosurgery, Seoul National University Bundang Hospital, Seongnam
| | - Chang-Hyun Lee
- 1Department of Neurosurgery, Seoul National University Bundang Hospital, Seongnam
- 4Department of Neurosurgery, Seoul National University Hospital, Seoul
| | - Seung-Jae Hyun
- 1Department of Neurosurgery, Seoul National University Bundang Hospital, Seongnam
- 5Seoul National University College of Medicine, Seoul; and
| | - Chi Heon Kim
- 4Department of Neurosurgery, Seoul National University Hospital, Seoul
- 5Seoul National University College of Medicine, Seoul; and
| | - Ki-Jeong Kim
- 1Department of Neurosurgery, Seoul National University Bundang Hospital, Seongnam
- 5Seoul National University College of Medicine, Seoul; and
| | - Chun Kee Chung
- 4Department of Neurosurgery, Seoul National University Hospital, Seoul
- 5Seoul National University College of Medicine, Seoul; and
- 6Department of Brain and Cognitive Sciences, Seoul National University College of Natural Sciences, Seoul, Republic of Korea
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Pulmonary Nodule Clinical Trial Data Collection and Intelligent Differential Diagnosis for Medical Internet of Things. CONTRAST MEDIA & MOLECULAR IMAGING 2022; 2022:2058284. [PMID: 35685674 PMCID: PMC9162868 DOI: 10.1155/2022/2058284] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/10/2022] [Revised: 04/29/2022] [Accepted: 05/11/2022] [Indexed: 12/04/2022]
Abstract
In this paper, the medical Internet of things (IoT) is used to pool data from clinical trials of pulmonary nodules, and on this basis, intelligent differential diagnosis techniques are investigated. A filtered orthogonal frequency division multiplexing model based on polarisation coding is proposed, where the input data are fed to a modulator after polarisation cascade coding, and the system performance is analysed under a medical Internet of things modulated additive Gaussian white noise channel. The above polarisation-coded filtered orthogonal frequency division multiplexing system components are applied to electroencephalogram (EEG) signal transmission, to which a threshold compression module and a vector reconstruction module are added to address the system power burden associated with the acquisition and transmission of large amounts of real-time EEG data in the medical IoT. In the threshold compression module, the inherent characteristics of EEG signals are analysed, and the generated EEG data are decomposed into multiple symbolic streams and compressed by applying different thresholds to improve the compression ratio while ensuring the quality of service of the application. A deep neural network-based approach is proposed for the detection and diagnosis of lung nodules. Automatic identification and measurement of simulated lung nodules and the corresponding volumes of nodules in images under different conditions are applied. The sensitivity of each AIADS in identifying lung nodules under different convolution kernel conditions, false positives (FP), false negatives (FN), relative volume errors (RVE), the miss detection rate (MDR) for different types of lung nodules, and the performance of each system in predicting the four types of nodules are calculated. In this paper, an interpretable multibranch feature convolutional neural network model is proposed for the diagnosis of benign and malignant lung nodules. It is demonstrated that the proposed model not only yields interpretable lung nodule classification results but also achieves better lung nodule classification performance with an accuracy rate of 97.8%.
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Kumar Y, Koul A, Singla R, Ijaz MF. Artificial intelligence in disease diagnosis: a systematic literature review, synthesizing framework and future research agenda. JOURNAL OF AMBIENT INTELLIGENCE AND HUMANIZED COMPUTING 2022; 14:8459-8486. [PMID: 35039756 PMCID: PMC8754556 DOI: 10.1007/s12652-021-03612-z] [Citation(s) in RCA: 115] [Impact Index Per Article: 57.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/04/2020] [Accepted: 11/18/2021] [Indexed: 05/03/2023]
Abstract
Artificial intelligence can assist providers in a variety of patient care and intelligent health systems. Artificial intelligence techniques ranging from machine learning to deep learning are prevalent in healthcare for disease diagnosis, drug discovery, and patient risk identification. Numerous medical data sources are required to perfectly diagnose diseases using artificial intelligence techniques, such as ultrasound, magnetic resonance imaging, mammography, genomics, computed tomography scan, etc. Furthermore, artificial intelligence primarily enhanced the infirmary experience and sped up preparing patients to continue their rehabilitation at home. This article covers the comprehensive survey based on artificial intelligence techniques to diagnose numerous diseases such as Alzheimer, cancer, diabetes, chronic heart disease, tuberculosis, stroke and cerebrovascular, hypertension, skin, and liver disease. We conducted an extensive survey including the used medical imaging dataset and their feature extraction and classification process for predictions. Preferred reporting items for systematic reviews and Meta-Analysis guidelines are used to select the articles published up to October 2020 on the Web of Science, Scopus, Google Scholar, PubMed, Excerpta Medical Database, and Psychology Information for early prediction of distinct kinds of diseases using artificial intelligence-based techniques. Based on the study of different articles on disease diagnosis, the results are also compared using various quality parameters such as prediction rate, accuracy, sensitivity, specificity, the area under curve precision, recall, and F1-score.
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Affiliation(s)
- Yogesh Kumar
- Department of Computer Engineering, Indus Institute of Technology and Engineering, Indus University, Ahmedabad, 382115 India
| | | | - Ruchi Singla
- Department of Research, Innovations, Sponsored Projects and Entrepreneurship, CGC Landran, Mohali, India
| | - Muhammad Fazal Ijaz
- Department of Intelligent Mechatronics Engineering, Sejong University, Seoul, 05006 South Korea
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Tarek N, Mandour MA, El-Madah N, Ali R, Yahia S, Mohamed B, Mostafa D, El-Metwally S. Morse glasses: an IoT communication system based on Morse code for users with speech impairments. COMPUTING 2022; 104:789-808. [PMCID: PMC8202051 DOI: 10.1007/s00607-021-00959-1] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/22/2020] [Accepted: 05/11/2021] [Indexed: 05/29/2023]
Abstract
The advent of internet of things has opened the opportunities for people with disabilities, increased their inclusion and productivity in their living society. Most of the invented smart sensing devices including the wearable ones for users with speech impairments are expensive and not affordable for patients in the low income countries such as Egypt. Morse Glasses is a cost efficient wearable device based on IoT technology and a modified Morse code that tracks the patient’s eyes blinks and translates it into a generated speech. A sequence of Morse encoded alphabets/sentences along with the frequently used ones is displayed and heard on any android supported device that is installed Morse Glasses mobile application. With cost less than 30$, patients with motor neuron diseases such as Amyotrophic Lateral Sclerosis (ALS) can communicate easily with the others, express their needs and simply live their life normally.
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Affiliation(s)
- Nayera Tarek
- Medical Informatics Program, Faculty of Computers and Information, Mansoura University, 35516 Mansoura, Egypt
| | - Mariam Abo Mandour
- Medical Informatics Program, Faculty of Computers and Information, Mansoura University, 35516 Mansoura, Egypt
| | - Nada El-Madah
- Computer Science Department, Faculty of Computers and Information, Mansoura University, 35516 Mansoura, Egypt
| | - Reem Ali
- Medical Informatics Program, Faculty of Computers and Information, Mansoura University, 35516 Mansoura, Egypt
| | - Sara Yahia
- Medical Informatics Program, Faculty of Computers and Information, Mansoura University, 35516 Mansoura, Egypt
| | - Bassant Mohamed
- Medical Informatics Program, Faculty of Computers and Information, Mansoura University, 35516 Mansoura, Egypt
| | - Dina Mostafa
- Medical Informatics Program, Faculty of Computers and Information, Mansoura University, 35516 Mansoura, Egypt
| | - Sara El-Metwally
- Computer Science Department, Faculty of Computers and Information, Mansoura University, 35516 Mansoura, Egypt
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14
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Xie Y, Lu L, Gao F, He SJ, Zhao HJ, Fang Y, Yang JM, An Y, Ye ZW, Dong Z. Integration of Artificial Intelligence, Blockchain, and Wearable Technology for Chronic Disease Management: A New Paradigm in Smart Healthcare. Curr Med Sci 2021; 41:1123-1133. [PMID: 34950987 PMCID: PMC8702375 DOI: 10.1007/s11596-021-2485-0] [Citation(s) in RCA: 24] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2021] [Accepted: 12/03/2021] [Indexed: 12/19/2022]
Abstract
Chronic diseases are a growing concern worldwide, with nearly 25% of adults suffering from one or more chronic health conditions, thus placing a heavy burden on individuals, families, and healthcare systems. With the advent of the "Smart Healthcare" era, a series of cutting-edge technologies has brought new experiences to the management of chronic diseases. Among them, smart wearable technology not only helps people pursue a healthier lifestyle but also provides a continuous flow of healthcare data for disease diagnosis and treatment by actively recording physiological parameters and tracking the metabolic state. However, how to organize and analyze the data to achieve the ultimate goal of improving chronic disease management, in terms of quality of life, patient outcomes, and privacy protection, is an urgent issue that needs to be addressed. Artificial intelligence (AI) can provide intelligent suggestions by analyzing a patient's physiological data from wearable devices for the diagnosis and treatment of diseases. In addition, blockchain can improve healthcare services by authorizing decentralized data sharing, protecting the privacy of users, providing data empowerment, and ensuring the reliability of data management. Integrating AI, blockchain, and wearable technology could optimize the existing chronic disease management models, with a shift from a hospital-centered model to a patient-centered one. In this paper, we conceptually demonstrate a patient-centric technical framework based on AI, blockchain, and wearable technology and further explore the application of these integrated technologies in chronic disease management. Finally, the shortcomings of this new paradigm and future research directions are also discussed.
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Affiliation(s)
- Yi Xie
- Department of Orthopedic Surgery, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430022, China.,Laboratory of Intelligent Medicine, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430022, China
| | - Lin Lu
- Department of Orthopedic Surgery, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430022, China.,Laboratory of Intelligent Medicine, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430022, China
| | - Fei Gao
- Department of Orthopedic Surgery, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430022, China.,Laboratory of Intelligent Medicine, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430022, China
| | - Shuang-Jiang He
- Laboratory of Intelligent Medicine, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430022, China.,School of Electronic Information and Communications, Huazhong University of Science and Technology, Wuhan, 430074, China
| | - Hui-Juan Zhao
- Laboratory of Intelligent Medicine, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430022, China.,School of Electronic Information and Communications, Huazhong University of Science and Technology, Wuhan, 430074, China
| | - Ying Fang
- Laboratory of Intelligent Medicine, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430022, China
| | - Jia-Ming Yang
- Laboratory of Intelligent Medicine, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430022, China
| | - Ying An
- Laboratory of Intelligent Medicine, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430022, China.,Wuhan Fourth Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430032, China
| | - Zhe-Wei Ye
- Department of Orthopedic Surgery, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430022, China.,Laboratory of Intelligent Medicine, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430022, China
| | - Zhe Dong
- School of Cyber Science and Engineering, Huazhong University of Science and Technology, Wuhan, 430074, China.
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15
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Massaad E, Ha Y, Shankar GM, Shin JH. Clinical Prediction Modeling in Intramedullary Spinal Tumor Surgery. ACTA NEUROCHIRURGICA. SUPPLEMENT 2021; 134:333-339. [PMID: 34862557 DOI: 10.1007/978-3-030-85292-4_37] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Subscribe] [Scholar Register] [Indexed: 11/27/2022]
Abstract
Artificial intelligence is poised to influence various aspects of patient care, and neurosurgery is one of the most uprising fields where machine learning is being applied to provide surgeons with greater insight about the pathophysiology and prognosis of neurological conditions. This chapter provides a guide for clinicians on relevant aspects of machine learning and reviews selected application of these methods in intramedullary spinal cord tumors. The potential areas of application of machine learning extend far beyond the analyses of clinical data to include several areas of artificial intelligence, such as genomics and computer vision. Integration of various sources of data and application of advanced analytical approaches could improve risk assessment for intramedullary tumors.
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Affiliation(s)
- Elie Massaad
- Department of Neurosurgery, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Yoon Ha
- Department of Neurosurgery, Spine and Spinal Cord Institute, Yonsei University College of Medicine, Seoul, South Korea
| | - Ganesh M Shankar
- Department of Neurosurgery, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - John H Shin
- Department of Neurosurgery, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA.
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16
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Sahu KS, Majowicz SE, Dubin JA, Morita PP. NextGen Public Health Surveillance and the Internet of Things (IoT). Front Public Health 2021; 9:756675. [PMID: 34926381 PMCID: PMC8678116 DOI: 10.3389/fpubh.2021.756675] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2021] [Accepted: 11/12/2021] [Indexed: 11/23/2022] Open
Abstract
Recent advances in technology have led to the rise of new-age data sources (e.g., Internet of Things (IoT), wearables, social media, and mobile health). IoT is becoming ubiquitous, and data generation is accelerating globally. Other health research domains have used IoT as a data source, but its potential has not been thoroughly explored and utilized systematically in public health surveillance. This article summarizes the existing literature on the use of IoT as a data source for surveillance. It presents the shortcomings of current data sources and how NextGen data sources, including the large-scale applications of IoT, can meet the needs of surveillance. The opportunities and challenges of using these modern data sources in public health surveillance are also explored. These IoT data ecosystems are being generated with minimal effort by the device users and benefit from high granularity, objectivity, and validity. Advances in computing are now bringing IoT-based surveillance into the realm of possibility. The potential advantages of IoT data include high-frequency, high volume, zero effort data collection methods, with a potential to have syndromic surveillance. In contrast, the critical challenges to mainstream this data source within surveillance systems are the huge volume and variety of data, fusing data from multiple devices to produce a unified result, and the lack of multidisciplinary professionals to understand the domain and analyze the domain data accordingly.
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Affiliation(s)
- Kirti Sundar Sahu
- School of Public Health Sciences, University of Waterloo, Waterloo, ON, Canada
| | - Shannon E. Majowicz
- School of Public Health Sciences, University of Waterloo, Waterloo, ON, Canada
| | - Joel A. Dubin
- School of Public Health Sciences, University of Waterloo, Waterloo, ON, Canada
- Department of Statistics and Actuarial Science, University of Waterloo, Waterloo, ON, Canada
| | - Plinio Pelegrini Morita
- School of Public Health Sciences, University of Waterloo, Waterloo, ON, Canada
- Institute of Health Policy, Management, and Evaluation, University of Toronto, Toronto, ON, Canada
- Department of Systems Design Engineering, University of Waterloo, Waterloo, ON, Canada
- Ehealth Innovation, Techna Institute, University Health Network, Toronto, ON, Canada
- Research Institute for Aging, University of Waterloo, Waterloo, ON, Canada
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17
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Lee JH, Lee JM, Hwang J, Park JY, Kim M, Kim DH, Lee JI, Nam KH, Han IH. User perception of medical service robots in hospital wards: a cross-sectional survey. Yeungnam Univ J Med 2021; 39:116-123. [PMID: 34607408 PMCID: PMC8913912 DOI: 10.12701/yujm.2021.01319] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/19/2021] [Accepted: 08/21/2021] [Indexed: 11/04/2022] Open
Abstract
Background Recently, there have been various developments in medical service robots (MSRs). However, few studies have examined the perceptions of those who use it. The purpose of this study is to identify user perceptions of MSRs. Methods We conducted a survey of 320 patients, doctors, and nurses. The contents of the survey were organized as follows: external appearances, perceptions, expected utilization, possible safety accidents, and awareness of their responsibilities. Statistical analyses were performed using t-test, chi-square test, and analysis of variance. Results The most preferred appearance was the animal type, with a screen. The overall average score of positive questions was 3.64±0.98 of 5 points and that of negative questions was 3.24±0.99. Thus, the results revealed that the participants had positive perceptions of MSR. The overall average of all expected utilization was 4.05±0.84. The most expected utilization was to guide hospital facilities. The most worrisome accident was exposure to personal information. Moreover, participants thought that the overall responsibility of the robot user (hospital) was greater than that of the robot manufacturer in the case of safety accidents. Conclusion The perceptions of MSRs used in hospital wards were positive, and the overall expected utilization was high. It is necessary to recognize safety accidents for such robots, and sufficient attention is required when developing and manufacturing robots.
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Affiliation(s)
- Jung Hwan Lee
- Department of Neurosurgery and Medical Research Institute, Pusan National University Hospital, Busan, Korea
| | - Jae Meen Lee
- Department of Neurosurgery and Medical Research Institute, Pusan National University Hospital, Busan, Korea
| | - Jaehyun Hwang
- Department of Neurosurgery and Medical Research Institute, Pusan National University Hospital, Busan, Korea
| | - Joo Young Park
- Department of Neurosurgery and Medical Research Institute, Pusan National University Hospital, Busan, Korea
| | - Mijeong Kim
- Department of Neurosurgery and Medical Research Institute, Pusan National University Hospital, Busan, Korea
| | - Dong Hwan Kim
- Department of Neurosurgery and Medical Research Institute, Pusan National University Hospital, Busan, Korea
| | - Jae Il Lee
- Department of Neurosurgery and Medical Research Institute, Pusan National University Hospital, Busan, Korea
| | - Kyoung Hyup Nam
- Department of Neurosurgery and Medical Research Institute, Pusan National University Hospital, Busan, Korea
| | - In Ho Han
- Department of Neurosurgery and Medical Research Institute, Pusan National University Hospital, Busan, Korea
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18
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Montag C, Elhai JD, Dagum P. On Blurry Boundaries When Defining Digital Biomarkers: How Much Biology Needs to Be in a Digital Biomarker? Front Psychiatry 2021; 12:740292. [PMID: 34658973 PMCID: PMC8514660 DOI: 10.3389/fpsyt.2021.740292] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/12/2021] [Accepted: 09/06/2021] [Indexed: 11/24/2022] Open
Abstract
Recent years have seen a rise in research where so called "digital biomarkers" represent the focal study interest. Many researchers understand that digital biomarkers describe digital footprints providing insights into healthy and pathological human (neuro-)biology. Beyond that the term digital biomarker is also used at times to describe more general concepts such as linking digital footprints to human behavior (which itself can be described as the result of a biological system). Given the lack of consensus on how to define a digital biomarker, the present short mini-review provides i) an overview on various definitions and ii) distinguishes between direct (narrow) or indirect (broad) concepts of digital biomarkers. From our perspective, digital biomarkers meant as a more direct (or narrow) concept describe digital footprints being directly linked to biological variables, such as stemming from molecular genetics, epigenetics, endocrinology, immunology or brain imaging, to name a few. More indirect concepts of digital biomarkers encompass digital footprints being linked to human behavior that may act as latent variables indirectly linked to biological variables.
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Affiliation(s)
- Christian Montag
- Department of Molecular Psychology, Institute of Psychology and Education, Ulm University, Ulm, Germany
| | - Jon D. Elhai
- Department of Psychology, University of Toledo, Toledo, OH, United States
- Department of Psychiatry, University of Toledo, Toledo, OH, United States
| | - Paul Dagum
- Applied Cognition, Los Altos, CA, United States
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19
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Wearables and Internet of Things (IoT) Technologies for Fitness Assessment: A Systematic Review. SENSORS 2021; 21:s21165418. [PMID: 34450860 PMCID: PMC8400146 DOI: 10.3390/s21165418] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/21/2021] [Revised: 08/03/2021] [Accepted: 08/06/2021] [Indexed: 12/28/2022]
Abstract
Wearable and Internet of Things (IoT) technologies in sports open a new era in athlete’s training, not only for performance monitoring and evaluation but also for fitness assessment. These technologies rely on sensor systems that collect, process and transmit relevant data, such as biomarkers and/or other performance indicators that are crucial to evaluate the evolution of the athlete’s condition, and therefore potentiate their performance. This work aims to identify and summarize recent studies that have used wearables and IoT technologies and discuss its applicability for fitness assessment. A systematic review of electronic databases (WOS, CCC, DIIDW, KJD, MEDLINE, RSCI, SCIELO, IEEEXplore, PubMed, SPORTDiscus, Cochrane and Web of Science) was undertaken according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. From the 280 studies initially identified, 20 were fully examined in terms of hardware and software and their applicability for fitness assessment. Results have shown that wearable and IoT technologies have been used in sports not only for fitness assessment but also for monitoring the athlete’s internal and external workloads, employing physiological status monitoring and activity recognition and tracking techniques. However, the maturity level of such technologies is still low, particularly with the need for the acquisition of more—and more effective—biomarkers regarding the athlete’s internal workload, which limits its wider adoption by the sports community.
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20
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Youn BY, Ko Y, Moon S, Lee J, Ko SG, Kim JY. Digital Biomarkers for Neuromuscular Disorders: A Systematic Scoping Review. Diagnostics (Basel) 2021; 11:diagnostics11071275. [PMID: 34359358 PMCID: PMC8307187 DOI: 10.3390/diagnostics11071275] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2021] [Revised: 06/30/2021] [Accepted: 07/13/2021] [Indexed: 11/16/2022] Open
Abstract
Biomarkers play a vital role in clinical care. They enable early diagnosis and treatment by identifying a patient's condition and disease course and act as an outcome measure that accurately evaluates the efficacy of a new treatment or drug. Due to the rapid development of digital technologies, digital biomarkers are expected to grow tremendously. In the era of change, this scoping review was conducted to see which digital biomarkers are progressing in neuromuscular disorders, a diverse and broad-range disease group among the neurological diseases, to discover available evidence for their feasibility and reliability. Thus, a total of 10 studies were examined: 9 observational studies and 1 animal study. Of the observational studies, studies were conducted with amyotrophic lateral sclerosis (ALS), Duchenne muscular dystrophy (DMD), and spinal muscular atrophy (SMA) patients. Non-peer reviewed poster presentations were not considered, as the articles may lead to erroneous results. The only animal study included in the present review investigated the mice model of ALS for detecting rest disturbances using a non-invasive digital biomarker.
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Affiliation(s)
- Bo-Young Youn
- Department of Global Public Health and Korean Medicine Management, Graduate School, Kyung Hee University, Seoul 02447, Korea; (B.-Y.Y.); (S.M.)
| | - Youme Ko
- Department of Preventive Medicine, Kyung Hee University, Seoul 02447, Korea; (Y.K.); (S.-G.K.)
| | - Seunghwan Moon
- Department of Global Public Health and Korean Medicine Management, Graduate School, Kyung Hee University, Seoul 02447, Korea; (B.-Y.Y.); (S.M.)
| | - Jinhee Lee
- Department of Korean Medicine, Graduate School, Kyung Hee University, Seoul 02447, Korea;
| | - Seung-Gyu Ko
- Department of Preventive Medicine, Kyung Hee University, Seoul 02447, Korea; (Y.K.); (S.-G.K.)
| | - Jee-Young Kim
- Department of Neurology, Cheongna Best Rehabilitation Hospital, Incheon 22883, Korea
- Correspondence:
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21
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Lee JH, Lee JI, Kim DH, Nam KH, Jeon TJ, Han IH. Validation of a gyroscope-based wearable device for real-time position monitoring of patients in a hospital. Technol Health Care 2020; 29:843-848. [PMID: 33285656 DOI: 10.3233/thc-202575] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
BACKGROUND Monitoring patients' position is important, but there have been few studies related to validation. OBJECTIVE The objective of this study was to assess the validity of position monitoring measured using a wearable device by comparing the device's measurements to a patient's actual position. METHODS We constructed a wearable device with a three-axis gyroscope and applied it to 10 patients who were unable to change their position independently. We compared the actual angle of the position and the angle transmitted from the wearable device using a Bland-Altman plot and a receiver operating characteristic curve. RESULTS We compared the actual angle of the position and the angle transmitted from the wearable device using a Bland-Altman plot, but it was difficult to observe statistical similarity. The angles transmitted from the wearable device in the lateral and supine positions showed significant differences. The cutoff value separating the lateral and supine positions was found to be 27.1∘ (sensitivity = 100%, specificity = 99.9%). CONCLUSIONS Through our method, the measured values from the gyroscope-based wearable device did not accurately reflect the patient's actual position. However, the wearable device was able to distinguish the lateral position from the supine position.
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22
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Papa A, Papa V, Lopetuso LR, Gasbarrini A, Tursi A. Covid-19 and the management of patients with inflammatory bowel disease: a practical decalogue for the post-pandemic phase. Therap Adv Gastroenterol 2020; 13:1756284820968747. [PMID: 33149764 PMCID: PMC7586260 DOI: 10.1177/1756284820968747] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/22/2020] [Accepted: 10/01/2020] [Indexed: 02/04/2023] Open
Abstract
The coronavirus disease 2019 (COVID-19) pandemic caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has raised several concerns for patients with chronic immune-mediated diseases, including inflammatory bowel disease (IBD). As the outbreak appears to be in the descending phase, at least in some part of the world, as in most European countries, guidance is urgently needed to provide optimal care for our IBD patients in order to gradually and safely reduce the gap in care that has been accumulated in the months of lockdown and to face all the backlogs. Therefore, we have provided a decalogue of practical recommendations for gastroenterologists to manage patients with IBD in the post-peak phase of the COVID-19 pandemic. They include all the aspects of IBD care, not only pharmacological ones but also endoscopy, surgery, psychological treatment, telemedicine, diagnostics and educational tasks provided by doctors and patient associations.
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Affiliation(s)
| | - Valerio Papa
- Catholic University, Rome, Italy,Department of Digestive Surgery, Policlinico Universitario “A. Gemelli” IRCCS Foundation, Rome, Italy
| | - Loris Riccardo Lopetuso
- Department of Medical and Surgical Sciences, Division of Internal Medicine and Gastroenterology, Policlinico Universitario “A. Gemelli” IRCCS Foundation, Rome, Italy,Department of Medicine and Ageing Sciences, “G. d’Annunzio” University of Chieti-Pescara, Chieti, Italy,Center for Advanced Studies and Technology (CAST), “G. d’Annunzio” University of Chieti-Pescara, Chieti, Italy
| | - Antonio Gasbarrini
- Department of Medical and Surgical Sciences, Division of Internal Medicine and Gastroenterology, Policlinico Universitario “A. Gemelli” IRCCS Foundation, Rome, Italy Catholic University, Rome, Italy
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