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Khan M, Khurshid M, Vatsa M, Singh R, Duggal M, Singh K. On AI Approaches for Promoting Maternal and Neonatal Health in Low Resource Settings: A Review. Front Public Health 2022; 10:880034. [PMID: 36249249 PMCID: PMC9562034 DOI: 10.3389/fpubh.2022.880034] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/20/2022] [Accepted: 05/30/2022] [Indexed: 01/21/2023] Open
Abstract
A significant challenge for hospitals and medical practitioners in low- and middle-income nations is the lack of sufficient health care facilities for timely medical diagnosis of chronic and deadly diseases. Particularly, maternal and neonatal morbidity due to various non-communicable and nutrition related diseases is a serious public health issue that leads to several deaths every year. These diseases affecting either mother or child can be hospital-acquired, contracted during pregnancy or delivery, postpartum and even during child growth and development. Many of these conditions are challenging to detect at their early stages, which puts the patient at risk of developing severe conditions over time. Therefore, there is a need for early screening, detection and diagnosis, which could reduce maternal and neonatal mortality. With the advent of Artificial Intelligence (AI), digital technologies have emerged as practical assistive tools in different healthcare sectors but are still in their nascent stages when applied to maternal and neonatal health. This review article presents an in-depth examination of digital solutions proposed for maternal and neonatal healthcare in low resource settings and discusses the open problems as well as future research directions.
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Affiliation(s)
- Misaal Khan
- Department of Smart Healthcare, Indian Institute of Technology Jodhpur, Karwar, India,All India Institute of Medical Sciences Jodhpur, Jodhpur, India
| | - Mahapara Khurshid
- Department of Computer Science and Engineering, Indian Institute of Technology Jodhpur, Karwar, India
| | - Mayank Vatsa
- Department of Computer Science and Engineering, Indian Institute of Technology Jodhpur, Karwar, India,*Correspondence: Mayank Vatsa
| | - Richa Singh
- Department of Computer Science and Engineering, Indian Institute of Technology Jodhpur, Karwar, India
| | - Mona Duggal
- Post Graduate Institute of Medical Education and Research, Chandigarh, India
| | - Kuldeep Singh
- Department of Pediatrics, All India Institute of Medical Sciences Jodhpur, Jodhpur, India
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Cheng X, Zhu H, Mei L, Luo F, Chen X, Zhao Y, Chen S, Pan Y. Artificial Intelligence Based Pain Assessment Technology in Clinical Application of Real-World Neonatal Blood Sampling. Diagnostics (Basel) 2022; 12:diagnostics12081831. [PMID: 36010186 PMCID: PMC9406884 DOI: 10.3390/diagnostics12081831] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2022] [Revised: 07/12/2022] [Accepted: 07/26/2022] [Indexed: 11/16/2022] Open
Abstract
Background: Accurate neonatal pain assessment (NPA) is the key to neonatal pain management, yet it is a challenging task for medical staff. This study aimed to analyze the clinical practicability of the artificial intelligence based NPA (AI-NPA) tool for real-world blood sampling. Method: We performed a prospective study to analyze the consistency of the NPA results given by a self-developed automated NPA system and nurses’ on-site NPAs (OS-NPAs) for 232 newborns during blood sampling in neonatal wards, where the neonatal infant pain scale (NIPS) was used for evaluation. Spearman correlation analysis and the degree of agreement of the pain score and pain grade derived by the NIPS were applied for statistical analysis. Results: Taking the OS-NPA results as the gold standard, the accuracies of the NIPS pain score and pain grade given by the automated NPA system were 88.79% and 95.25%, with kappa values of 0.92 and 0.90 (p < 0.001), respectively. Conclusion: The results of the automated NPA system for real-world neonatal blood sampling are highly consistent with the results of the OS-NPA. Considering the great advantages of automated NPA systems in repeatability, efficiency, and cost, it is worth popularizing the AI technique in NPA for precise and efficient neonatal pain management.
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Affiliation(s)
- Xiaoying Cheng
- Quality Improvement Office, The Children’s Hospital, Zhejiang University School of Medicine, National Clinical Research Center for Child Health, Hangzhou 310052, China;
| | - Huaiyu Zhu
- College of Information Science and Electronic Engineering, Zhejiang University, Hangzhou 310027, China; (H.Z.); (Y.Z.)
| | - Linli Mei
- Administration Department of Nosocomial Infection, The Children’s Hospital, Zhejiang University School of Medicine, National Clinical Research Center for Child Health, Hangzhou 310052, China;
| | - Feixiang Luo
- Neonatal Intensive Care Unit, The Children’s Hospital, Zhejiang University School of Medicine, National Clinical Research Center for Child Health, Hangzhou 310052, China;
| | - Xiaofei Chen
- Gastroenterology Department, The Children’s Hospital, Zhejiang University School of Medicine, National Clinical Research Center for Child Health, Hangzhou 310052, China;
| | - Yisheng Zhao
- College of Information Science and Electronic Engineering, Zhejiang University, Hangzhou 310027, China; (H.Z.); (Y.Z.)
| | - Shuohui Chen
- Administration Department of Nosocomial Infection, The Children’s Hospital, Zhejiang University School of Medicine, National Clinical Research Center for Child Health, Hangzhou 310052, China;
- Correspondence: (S.C.); (Y.P.)
| | - Yun Pan
- College of Information Science and Electronic Engineering, Zhejiang University, Hangzhou 310027, China; (H.Z.); (Y.Z.)
- Correspondence: (S.C.); (Y.P.)
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Hosseini E, Fang R, Zhang R, Chuah CN, Orooji M, Rafatirad S, Rafatirad S, Homayoun H. Convolution Neural Network for Pain Intensity Assessment from Facial Expression. Annu Int Conf IEEE Eng Med Biol Soc 2022; 2022:2697-2702. [PMID: 36085712 DOI: 10.1109/embc48229.2022.9871770] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
Pain is an unpleasant feeling that can reflect a patient's health situation. Since measuring pain is subjective, time-consuming, and needs continuous monitoring, automated pain intensity detection from facial expression holds great potential for smart healthcare applications. Convolutional Neural Networks (CNNs) are recently being used to identify features, map and model pain intensity from facial images, delivering great promise in helping practitioners detect disease. Limited research has been conducted to determine pain intensity levels across multiple classes. CNNs with simple learning schemes are limited in their ability to extract feature information from images. In order to develop a highly accurate pain intensity estimation system, this study proposes a Deep CNN (DCNN) model using the transfer learning technique, where a pre-trained DCNN model is adopted by replacing its dense upper layers, and the model is tuned using painful facial. We conducted experiments on the UNBC-McMaster shoulder pain archive database to estimate pain intensity in terms of seven-level thresholds using a given facial expression image. The experiments show our method achieves a promising improvement in terms of accuracy and performance to estimate pain intensity and outperform the-state-of-the-arts models.
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Chiang C, Chen Y, Tzeng H, Chang M, Chiou L, Pei Y. Deep Learning-Based Grimace Scoring Is Comparable to Human Scoring in a Mouse Migraine Model. J Pers Med 2022; 12:851. [PMID: 35743636 PMCID: PMC9225619 DOI: 10.3390/jpm12060851] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/22/2022] [Revised: 05/16/2022] [Accepted: 05/21/2022] [Indexed: 01/03/2023] Open
Abstract
Pain assessment is essential for preclinical and clinical studies on pain. The mouse grimace scale (MGS), consisting of five grimace action units, is a reliable measurement of spontaneous pain in mice. However, MGS scoring is labor-intensive and time-consuming. Deep learning can be applied for the automatic assessment of spontaneous pain. We developed a deep learning model, the DeepMGS, that automatically crops mouse face images, predicts action unit scores and total scores on the MGS, and finally infers whether pain exists. We then compared the performance of DeepMGS with that of experienced and apprentice human scorers. The DeepMGS achieved an accuracy of 70–90% in identifying the five action units of the MGS, and its performance (correlation coefficient = 0.83) highly correlated with that of an experienced human scorer in total MGS scores. In classifying pain and no pain conditions, the DeepMGS is comparable to the experienced human scorer and superior to the apprentice human scorers. Heatmaps generated by gradient-weighted class activation mapping indicate that the DeepMGS accurately focuses on MGS-relevant areas in mouse face images. These findings support that the DeepMGS can be applied for quantifying spontaneous pain in mice, implying its potential application for predicting other painful conditions from facial images.
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Campbell-Yeo M, Eriksson M, Benoit B. Assessment and Management of Pain in Preterm Infants: A Practice Update. Children (Basel) 2022; 9:244. [PMID: 35204964 PMCID: PMC8869922 DOI: 10.3390/children9020244] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Received: 12/14/2021] [Revised: 01/20/2022] [Accepted: 02/02/2022] [Indexed: 12/11/2022]
Abstract
Infants born preterm are at a high risk for repeated pain exposure in early life. Despite valid tools to assess pain in non-verbal infants and effective interventions to reduce pain associated with medical procedures required as part of their care, many infants receive little to no pain-relieving interventions. Moreover, parents remain significantly underutilized in provision of pain-relieving interventions, despite the known benefit of their involvement. This narrative review provides an overview of the consequences of early exposure to untreated pain in preterm infants, recommendations for a standardized approach to pain assessment in preterm infants, effectiveness of non-pharmacologic and pharmacologic pain-relieving interventions, and suggestions for greater active engagement of parents in the pain care for their preterm infant.
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Affiliation(s)
- Marsha Campbell-Yeo
- School of Nursing, Faculty of Health, Dalhousie University, Halifax, NS B3H 4R2, Canada
- Department of Pediatrics, Psychology and Neuroscience, Dalhousie University, Halifax, NS B3H 4R2, Canada
- IWK Health, Halifax, NS B3K 6R8, Canada
| | - Mats Eriksson
- School of Health Sciences, Faculty of Medicine and Health, Örebro University, SE-701 82 Örebro, Sweden;
| | - Britney Benoit
- Rankin School of Nursing, St. Francis Xavier University, Antigonish, NS B2G 2N5, Canada;
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Susam B, Riek N, Akcakaya M, Xu X, de Sa V, Nezamfar H, Diaz D, Craig K, Goodwin M, Huang J. Automated Pain Assessment in Children using Electrodermal Activity and Video Data Fusion via Machine Learning. IEEE Trans Biomed Eng 2021; 69:422-431. [PMID: 34242161 DOI: 10.1109/tbme.2021.3096137] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
Abstract
Pain assessment in children continues to challenge clinicians and researchers, as subjective experiences of pain require inference through observable behaviors, both involuntary and deliberate. The presented approach supplements the subjective self-report-based method by fusing electrodermal activity (EDA) recordings with video facial expressions to develop an objective pain assessment metric. Such an approach is specifically important for assessing pain in children who are not capable of providing accurate self-pain reports, requiring nonverbal pain assessment. We demonstrate the performance of our approach using data recorded from children in post-operative recovery following laparoscopic appendectomy. We examined separately and combined the usefulness of EDA and video facial expression data as predictors of childrens self-reports of pain following surgery through recovery. Findings indicate that EDA and facial expression data independently provide above chance sensitivities and specificities, but their fusion for classifying clinically significant pain vs. clinically nonsignificant pain achieved substantial improvement, yielding 90.91% accuracy, with 100% sensitivity and 81.82% specificity. The multimodal measures capitalize upon different features of the complex pain response. Thus, this paper presents both evidence for the utility of a weighted maximum likelihood algorithm as a novel feature selection method for EDA and video facial expression data and an accurate and objective automated classification algorithm capable of discriminating clinically significant pain from clinically nonsignificant pain in children.
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Pouromran F, Radhakrishnan S, Kamarthi S. Exploration of physiological sensors, features, and machine learning models for pain intensity estimation. PLoS One 2021; 16:e0254108. [PMID: 34242325 DOI: 10.1371/journal.pone.0254108] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2020] [Accepted: 06/20/2021] [Indexed: 11/19/2022] Open
Abstract
In current clinical settings, typically pain is measured by a patient’s self-reported information. This subjective pain assessment results in suboptimal treatment plans, over-prescription of opioids, and drug-seeking behavior among patients. In the present study, we explored automatic objective pain intensity estimation machine learning models using inputs from physiological sensors. This study uses BioVid Heat Pain Dataset. We extracted features from Electrodermal Activity (EDA), Electrocardiogram (ECG), Electromyogram (EMG) signals collected from study participants subjected to heat pain. We built different machine learning models, including Linear Regression, Support Vector Regression (SVR), Neural Networks and Extreme Gradient Boosting for continuous value pain intensity estimation. Then we identified the physiological sensor, feature set and machine learning model that give the best predictive performance. We found that EDA is the most information-rich sensor for continuous pain intensity prediction. A set of only 3 features from EDA signals using SVR model gave an average performance of 0.93 mean absolute error (MAE) and 1.16 root means square error (RMSE) for the subject-independent model and of 0.92 MAE and 1.13 RMSE for subject-dependent. The MAE achieved with signal-feature-model combination is less than 1 unit on 0 to 4 continues pain scale, which is smaller than the MAE achieved by the methods reported in the literature. These results demonstrate that it is possible to estimate pain intensity of a patient using a computationally inexpensive machine learning model with 3 statistical features from EDA signal which can be collected from a wrist biosensor. This method paves a way to developing a wearable pain measurement device.
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Hassan T, Seus D, Wollenberg J, Weitz K, Kunz M, Lautenbacher S, Garbas JU, Schmid U. Automatic Detection of Pain from Facial Expressions: A Survey. IEEE Trans Pattern Anal Mach Intell 2021; 43:1815-1831. [PMID: 31825861 DOI: 10.1109/tpami.2019.2958341] [Citation(s) in RCA: 21] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/10/2023]
Abstract
Pain sensation is essential for survival, since it draws attention to physical threat to the body. Pain assessment is usually done through self-reports. However, self-assessment of pain is not available in the case of noncommunicative patients, and therefore, observer reports should be relied upon. Observer reports of pain could be prone to errors due to subjective biases of observers. Moreover, continuous monitoring by humans is impractical. Therefore, automatic pain detection technology could be deployed to assist human caregivers and complement their service, thereby improving the quality of pain management, especially for noncommunicative patients. Facial expressions are a reliable indicator of pain, and are used in all observer-based pain assessment tools. Following the advancements in automatic facial expression analysis, computer vision researchers have tried to use this technology for developing approaches for automatically detecting pain from facial expressions. This paper surveys the literature published in this field over the past decade, categorizes it, and identifies future research directions. The survey covers the pain datasets used in the reviewed literature, the learning tasks targeted by the approaches, the features extracted from images and image sequences to represent pain-related information, and finally, the machine learning methods used.
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9
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Abstract
INTRODUCTION Objective pain assessment in non-verbal populations is clinically challenging due to their inability to express their pain via self-report. Repetitive exposures to acute or prolonged pain lead to clinical instability, with long-term behavioural and cognitive sequelae in newborn infants. Strong analgesics are also associated with medical complications, potential neurotoxicity and altered brain development. Pain scores performed by bedside nurses provide subjective, observer-dependent assessments rather than objective data for infant pain management; the required observations are labour intensive, difficult to perform by a nurse who is concurrently performing the procedure and increase the nursing workload. Multimodal pain assessment, using sensor-fusion and machine-learning algorithms, can provide a patient-centred, context-dependent, observer-independent and objective pain measure. METHODS AND ANALYSIS In newborns undergoing painful procedures, we use facial electromyography to record facial muscle activity-related infant pain, ECG to examine heart rate (HR) changes and HR variability, electrodermal activity (skin conductance) to measure catecholamine-induced palmar sweating, changes in oxygen saturations and skin perfusion, and electroencephalography using active electrodes to assess brain activity in real time. This multimodal approach has the potential to improve the accuracy of pain assessment in non-verbal infants and may even allow continuous pain monitoring at the bedside. The feasibility of this approach will be evaluated in an observational prospective study of clinically required painful procedures in 60 preterm and term newborns, and infants aged 6 months or less. ETHICS AND DISSEMINATION The Institutional Review Board of the Stanford University approved the protocol. Study findings will be published in peer-reviewed journals, presented at scientific meetings, taught via webinars, podcasts and video tutorials, and listed on academic/scientific websites. Future studies will validate and refine this approach using the minimum number of sensors required to assess neonatal/infant pain. TRIAL REGISTRATION NUMBER ClinicalTrials.gov Registry (NCT03330496).
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Affiliation(s)
- Jean-Michel Roué
- Neonatal & Pediatric Intensive Care Unit, Brest University Hospital, University of Western Brittany, Brest, France
| | - Iris Morag
- Shamir Medical Center (Assaf Harofeh), Neonatal Intensive Care Unit, Tel Aviv University Sackler Faculty of Medicine, Tel Aviv, Israel
| | - Wassim M Haddad
- School of Aerospace Engineering, Georgia Institute of Technology, Atlanta, Georgia, USA
| | | | - Kanwaljeet J S Anand
- Department of Pediatrics, Pain/Stress Neurobiology Laboratory, Maternal and Child Health Research Institute, Stanford University School of Medicine, Stanford, California, USA
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10
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Lin Q, Huang G, Li L, Zhang L, Liang Z, Anter AM, Zhang Z. Designing individual-specific and trial-specific models to accurately predict the intensity of nociceptive pain from single-trial fMRI responses. Neuroimage 2020; 225:117506. [PMID: 33127478 DOI: 10.1016/j.neuroimage.2020.117506] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2019] [Revised: 10/06/2020] [Accepted: 10/21/2020] [Indexed: 11/19/2022] Open
Abstract
Using machine learning to predict the intensity of pain from fMRI has attracted rapidly increasing interests. However, due to remarkable inter- and intra-individual variabilities in pain responses, the performance of existing fMRI-based pain prediction models is far from satisfactory. The present study proposed a new approach which can design a prediction model specific to each individual or each experimental trial so that the specific model can achieve more accurate prediction of the intensity of nociceptive pain from single-trial fMRI responses. More precisely, the new approach uses a supervised k-means method on nociceptive-evoked fMRI responses to cluster individuals or trials into a set of subgroups, each of which has similar and consistent fMRI activation patterns. Then, for a new test individual/trial, the proposed approach chooses one subgroup of individuals/trials, which has the closest fMRI patterns to the test individual/trial, as training samples to train an individual-specific or a trial-specific pain prediction model. The new approach was tested on a nociceptive-evoked fMRI dataset and achieved significantly higher prediction accuracy than conventional non-specific models, which used all available training samples to train a model. The generalizability of the proposed approach is further validated by training specific models on one dataset and testing these models on an independent new dataset. This proposed individual-specific and trial-specific pain prediction approach has the potential to be used for the development of individualized and precise pain assessment tools in clinical practice.
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Affiliation(s)
- Qianqian Lin
- School of Biomedical Engineering, Health Science Center, Shenzhen University, Shenzhen, Guangdong 518060, China; Guangdong Provincial Key Laboratory of Biomedical Measurements and Ultrasound Imaging, Shenzhen, Guangdong 518060, China; Department of Brain Functioning Research, The Seventh Hospital of Hangzhou, 305 Tianmushan Road, Hangzhou, Zhejiang, China
| | - Gan Huang
- School of Biomedical Engineering, Health Science Center, Shenzhen University, Shenzhen, Guangdong 518060, China; Guangdong Provincial Key Laboratory of Biomedical Measurements and Ultrasound Imaging, Shenzhen, Guangdong 518060, China
| | - Linling Li
- School of Biomedical Engineering, Health Science Center, Shenzhen University, Shenzhen, Guangdong 518060, China; Guangdong Provincial Key Laboratory of Biomedical Measurements and Ultrasound Imaging, Shenzhen, Guangdong 518060, China
| | - Li Zhang
- School of Biomedical Engineering, Health Science Center, Shenzhen University, Shenzhen, Guangdong 518060, China; Guangdong Provincial Key Laboratory of Biomedical Measurements and Ultrasound Imaging, Shenzhen, Guangdong 518060, China
| | - Zhen Liang
- School of Biomedical Engineering, Health Science Center, Shenzhen University, Shenzhen, Guangdong 518060, China; Guangdong Provincial Key Laboratory of Biomedical Measurements and Ultrasound Imaging, Shenzhen, Guangdong 518060, China
| | - Ahmed M Anter
- School of Biomedical Engineering, Health Science Center, Shenzhen University, Shenzhen, Guangdong 518060, China; Guangdong Provincial Key Laboratory of Biomedical Measurements and Ultrasound Imaging, Shenzhen, Guangdong 518060, China
| | - Zhiguo Zhang
- School of Biomedical Engineering, Health Science Center, Shenzhen University, Shenzhen, Guangdong 518060, China; Guangdong Provincial Key Laboratory of Biomedical Measurements and Ultrasound Imaging, Shenzhen, Guangdong 518060, China; Peng Cheng Laboratory, Shenzhen, Guangdong 518055, China.
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11
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Abstract
Models designed to detect abnormalities that reflect disease from facial structures are an emerging area of research for automated facial analysis, which has important potential value in smart healthcare applications. However, most of the proposed models directly analyze the whole face image containing the background information, and rarely consider the effects of the background and different face regions on the analysis results. Therefore, in view of these effects, we propose an end-to-end attention network with spatial transformation to estimate different pain intensities. In the proposed method, the face image is first provided as input to a spatial transformation network for solving the problem of background interference; then, the attention mechanism is used to adaptively adjust the weights of different face regions of the transformed face image; finally, a convolutional neural network (CNN) containing a Softmax function is utilized to classify the pain levels. The extensive experiments and analysis are conducted on the benchmarking and publicly available database, namely the UNBC-McMaster shoulder pain. More specifically, in order to verify the superiority of our proposed method, the comparisons with the basic CNNs and the-state-of-the-arts are performed, respectively. The experiments show that the introduced spatial transformation and attention mechanism in our method can significantly improve the estimation performances and outperform the-state-of-the-arts.
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Affiliation(s)
- Xuwu Xin
- The Second Affiliated Hospital of Shantou University Medical College, Shantou, China
| | - Xiaoyan Lin
- The Second Affiliated Hospital of Shantou University Medical College, Shantou, China
| | - Shengfu Yang
- The First Affiliated Hospital of Jinan University, Guangzhou, China
| | - Xin Zheng
- Shantou Chaonan Minsheng Hospital, Shantou, China
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Davis KD, Aghaeepour N, Ahn AH, Angst MS, Borsook D, Brenton A, Burczynski ME, Crean C, Edwards R, Gaudilliere B, Hergenroeder GW, Iadarola MJ, Iyengar S, Jiang Y, Kong JT, Mackey S, Saab CY, Sang CN, Scholz J, Segerdahl M, Tracey I, Veasley C, Wang J, Wager TD, Wasan AD, Pelleymounter MA. Discovery and validation of biomarkers to aid the development of safe and effective pain therapeutics: challenges and opportunities. Nat Rev Neurol 2020; 16:381-400. [PMID: 32541893 PMCID: PMC7326705 DOI: 10.1038/s41582-020-0362-2] [Citation(s) in RCA: 180] [Impact Index Per Article: 45.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 04/21/2020] [Indexed: 02/06/2023]
Abstract
Pain medication plays an important role in the treatment of acute and chronic pain conditions, but some drugs, opioids in particular, have been overprescribed or prescribed without adequate safeguards, leading to an alarming rise in medication-related overdose deaths. The NIH Helping to End Addiction Long-term (HEAL) Initiative is a trans-agency effort to provide scientific solutions to stem the opioid crisis. One component of the initiative is to support biomarker discovery and rigorous validation in collaboration with industry leaders to accelerate high-quality clinical research into neurotherapeutics and pain. The use of objective biomarkers and clinical trial end points throughout the drug discovery and development process is crucial to help define pathophysiological subsets of pain, evaluate target engagement of new drugs and predict the analgesic efficacy of new drugs. In 2018, the NIH-led Discovery and Validation of Biomarkers to Develop Non-Addictive Therapeutics for Pain workshop convened scientific leaders from academia, industry, government and patient advocacy groups to discuss progress, challenges, gaps and ideas to facilitate the development of biomarkers and end points for pain. The outcomes of this workshop are outlined in this Consensus Statement.
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Affiliation(s)
- Karen D Davis
- Department of Surgery and Institute of Medical Science, University of Toronto, Toronto, ON, Canada.
- Division of Brain, Imaging and Behaviour, Krembil Brain Institute, Toronto Western Hospital, University Health Network, Toronto, ON, Canada.
| | - Nima Aghaeepour
- Department of Anesthesiology, Perioperative and Pain Medicine, Stanford University School of Medicine, Stanford, CA, USA
| | | | - Martin S Angst
- Department of Anesthesiology, Perioperative and Pain Medicine, Stanford University School of Medicine, Stanford, CA, USA
| | - David Borsook
- Center for Pain and the Brain, Harvard Medical School, Boston, MA, USA
| | | | | | | | - Robert Edwards
- Pain Management Center, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
| | - Brice Gaudilliere
- Department of Anesthesiology, Perioperative and Pain Medicine, Stanford University School of Medicine, Stanford, CA, USA
| | - Georgene W Hergenroeder
- The Vivian L. Smith Department of Neurosurgery, The University of Texas Health Science Center at Houston, McGovern Medical School, Houston, TX, USA
| | - Michael J Iadarola
- Department of Perioperative Medicine, Clinical Center, NIH, Rockville, MD, USA
| | - Smriti Iyengar
- Division of Translational Research, National Institute of Neurological Disorders and Stroke, NIH, Rockville, MD, USA
| | - Yunyun Jiang
- The Biostatistics Center, Milken Institute School of Public Health, The George Washington University, Washington, DC, USA
| | - Jiang-Ti Kong
- Department of Anesthesiology, Perioperative and Pain Medicine, Stanford University School of Medicine, Stanford, CA, USA
| | - Sean Mackey
- Department of Anesthesiology, Perioperative and Pain Medicine, Stanford University School of Medicine, Stanford, CA, USA
| | - Carl Y Saab
- Department of Neuroscience and Department of Neurosurgery, Carney Institute for Brain Science, Brown University, Providence, RI, USA
| | - Christine N Sang
- Department of Anesthesiology, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
| | - Joachim Scholz
- Neurocognitive Disorders, Pain and New Indications, Biogen, Cambridge, MA, USA
| | | | - Irene Tracey
- Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK
| | | | - Jing Wang
- Department of Anesthesiology, Perioperative Care and Pain Medicine, NYU School of Medicine, New York, NY, USA
| | - Tor D Wager
- Department of Psychological and Brain Sciences, Dartmouth College, Hanover, NH, USA
| | - Ajay D Wasan
- Anesthesiology and Perioperative Medicine and Psychiatry, University of Pittsburgh, Pittsburgh, PA, USA
| | - Mary Ann Pelleymounter
- Division of Translational Research, National Institute of Neurological Disorders and Stroke, NIH, Rockville, MD, USA
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Rashidi P, Edwards DA, Tighe PJ. Primer on machine learning: utilization of large data set analyses to individualize pain management. Curr Opin Anaesthesiol 2019; 32:653-60. [PMID: 31408024 DOI: 10.1097/ACO.0000000000000779] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/06/2023]
Abstract
PURPOSE OF REVIEW Pain researchers and clinicians increasingly encounter machine learning algorithms in both research methods and clinical practice. This review provides a summary of key machine learning principles, as well as applications to both structured and unstructured datasets. RECENT FINDINGS Aside from increasing use in the analysis of electronic health record data, machine and deep learning algorithms are now key tools in the analyses of neuroimaging and facial expression recognition data used in pain research. SUMMARY In the coming years, machine learning is likely to become a key component of evidence-based medicine, yet will require additional skills and perspectives for its successful and ethical use in research and clinical settings.
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Naranjo-Hernández D, Reina-Tosina J, Roa LM. Sensor Technologies to Manage the Physiological Traits of Chronic Pain: A Review. Sensors (Basel) 2020; 20:E365. [PMID: 31936420 PMCID: PMC7014460 DOI: 10.3390/s20020365] [Citation(s) in RCA: 24] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/31/2019] [Revised: 01/03/2020] [Accepted: 01/05/2020] [Indexed: 12/15/2022]
Abstract
Non-oncologic chronic pain is a common high-morbidity impairment worldwide and acknowledged as a condition with significant incidence on quality of life. Pain intensity is largely perceived as a subjective experience, what makes challenging its objective measurement. However, the physiological traces of pain make possible its correlation with vital signs, such as heart rate variability, skin conductance, electromyogram, etc., or health performance metrics derived from daily activity monitoring or facial expressions, which can be acquired with diverse sensor technologies and multisensory approaches. As the assessment and management of pain are essential issues for a wide range of clinical disorders and treatments, this paper reviews different sensor-based approaches applied to the objective evaluation of non-oncological chronic pain. The space of available technologies and resources aimed at pain assessment represent a diversified set of alternatives that can be exploited to address the multidimensional nature of pain.
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Affiliation(s)
- David Naranjo-Hernández
- Biomedical Engineering Group, University of Seville, 41092 Seville, Spain; (J.R.-T.); (L.M.R.)
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Affiliation(s)
- Laleh Jalilian
- Department of Anesthesiology and Perioperative Medicine, UCLA David Geffen School of Medicine, Los Angeles, California
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Cheng D, Liu D, Philpotts LL, Turner DP, Houle TT, Chen L, Zhang M, Yang J, Zhang W, Deng H. Current state of science in machine learning methods for automatic infant pain evaluation using facial expression information: study protocol of a systematic review and meta-analysis. BMJ Open 2019; 9:e030482. [PMID: 31831532 PMCID: PMC6924806 DOI: 10.1136/bmjopen-2019-030482] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/16/2019] [Revised: 11/18/2019] [Accepted: 11/18/2019] [Indexed: 11/04/2022] Open
Abstract
INTRODUCTION Infants can experience pain similar to adults, and improperly controlled pain stimuli could have a long-term adverse impact on their cognitive and neurological function development. The biggest challenge of achieving good infant pain control is obtaining objective pain assessment when direct communication is lacking. For years, computer scientists have developed many different facial expression-centred machine learning (ML) methods for automatic infant pain assessment. Many of these ML algorithms showed rather satisfactory performance and have demonstrated good potential to be further enhanced for implementation in real-world clinical settings. To date, there is no prior research that has systematically summarised and compared the performance of these ML algorithms. Our proposed meta-analysis will provide the first comprehensive evidence on this topic to guide further ML algorithm development and clinical implementation. METHODS AND ANALYSIS We will search four major public electronic medical and computer science databases including Web of Science, PubMed, Embase and IEEE Xplore Digital Library from January 2008 to present. All the articles will be imported into the Covidence platform for study eligibility screening and inclusion. Study-level extracted data will be stored in the Systematic Review Data Repository online platform. The primary outcome will be the prediction accuracy of the ML model. The secondary outcomes will be model utility measures including generalisability, interpretability and computational efficiency. All extracted outcome data will be imported into RevMan V.5.2.1 software and R V3.3.2 for analysis. Risk of bias will be summarised using the latest Prediction Model Study Risk of Bias Assessment Tool. ETHICS AND DISSEMINATION This systematic review and meta-analysis will only use study-level data from public databases, thus formal ethical approval is not required. The results will be disseminated in the form of an official publication in a peer-reviewed journal and/or presentation at relevant conferences. PROSPERO REGISTRATION NUMBER CRD42019118784.
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Affiliation(s)
- Dan Cheng
- Department of Anesthesiology, Pain and Perioperative Medicine, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan, China
- Department of Anesthesia, Critical Care and Pain Medicine, Massachusetts General Hospital, Boston, Massachusetts, USA
| | - Dianbo Liu
- Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
| | | | - Dana P Turner
- Department of Anesthesia, Critical Care and Pain Medicine, Massachusetts General Hospital, Boston, Massachusetts, USA
| | - Timothy T Houle
- Department of Anesthesia, Critical Care and Pain Medicine, Massachusetts General Hospital, Boston, Massachusetts, USA
| | - Lucy Chen
- Department of Anesthesia, Critical Care and Pain Medicine, Massachusetts General Hospital, Boston, Massachusetts, USA
| | - Miaomiao Zhang
- Department of Engineering, University of Virginia, Charlottesville, Virginia, USA
| | - Jianjun Yang
- Department of Anesthesiology, Pain and Perioperative Medicine, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan, China
| | - Wei Zhang
- Department of Anesthesiology, Pain and Perioperative Medicine, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan, China
| | - Hao Deng
- Department of Anesthesia, Critical Care and Pain Medicine, Massachusetts General Hospital, Boston, Massachusetts, USA
- DRPH Program, Johns Hopkins University Bloomberg School of Public Health, Baltimore, Maryland, USA
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Abstract
Hospitalized newborn infants experience pain that can have negative short- and long-term consequences and thus should be prevented and treated. National and international guidelines state that adequate pain management requires valid pain assessment. Nociceptive signals cause a cascade of physical and behavioral reactions that alone or in combination can be observed and used to assess the presence and intensity of pain. Units that are caring for newborn infants must adopt sufficient pain assessment tools to cover the gestational ages and pain types that occurs in their setting. Pain assessment should be performed on a regular basis and any detection of pain should be acted on. Future research should focus on developing and validating pain assessment tools for specific situations.
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Affiliation(s)
- Mats Eriksson
- Faculty of Medicine and Health, School of Health Sciences, Örebro University, S-701 85, Örebro, Sweden.
| | - Marsha Campbell-Yeo
- Faculty of Medicine and Health, School of Health Sciences, Örebro University, S-701 85, Örebro, Sweden; School of Nursing, Faculty of Health, Departments of Pediatrics, Psychology & Neuroscience, Dalhousie University, 5850/5890 University Ave, Halifax, NS, B3K 6R8, Canada; Centre for Pediatric Pain Research, IWK Health Centre, Halifax, Canada.
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Tavakolian M, Hadid A. A Spatiotemporal Convolutional Neural Network for Automatic Pain Intensity Estimation from Facial Dynamics. Int J Comput Vis 2019; 127:1413-25. [DOI: 10.1007/s11263-019-01191-3] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2022]
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Padmanabhan R, Meskin N, Ionescu CM, Haddad WM. A nonovershooting tracking controller for simultaneous infusion of anesthetics and analgesics. Biomed Signal Process Control 2019. [DOI: 10.1016/j.bspc.2018.09.015] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
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Dutta P, M N. Facial Pain Expression Recognition in Real-Time Videos. J Healthc Eng 2018; 2018:7961427. [PMID: 30510672 DOI: 10.1155/2018/7961427] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/11/2018] [Revised: 07/08/2018] [Accepted: 08/05/2018] [Indexed: 11/29/2022]
Abstract
Recognition of pain in patients who are incapable of expressing themselves allows for several possibilities of improved diagnosis and treatment. Despite the advancements that have already been made in this field, research is still lacking with respect to the detection of pain in live videos, especially under unfavourable conditions. To address this gap in existing research, the current study proposed a hybrid model that allowed for efficient pain recognition. The hybrid, which consisted of a combination of the Constrained Local Model (CLM), Active Appearance Model (AAM), and Patch-Based Model, was applied in conjunction with image algebra. This contributed to a system that enabled the successful detection of pain from a live stream, even with poor lighting and a low-resolution recording device. The final process and output allowed for memory for storage that was reduced up to 40%–55% and an improved processing time of 20%–25%. The experimental system met with success and was able to detect pain for the 22 analysed videos with an accuracy of 55.75%–100.00%. To increase the fidelity of the proposed technique, the hybrid model was tested on UNBC‐McMaster Shoulder Pain Database as well.
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Abstract
OBJECTIVE This paper introduces a primer in the health care practice, namely a mathematical model and methodology for detecting and analysing nociceptor stimulation followed by related tissue memory effects. METHODS Noninvasive nociceptor stimulus protocol and prototype device for measuring bioimpedance is provided. Various time instants, sensor location, and stimulus train have been analysed. RESULTS The method and model indicate that nociceptor stimulation perceived as pain in awake healthy volunteers is noninvasively detected. The existence of a memory effect is proven from data. Sensor location had minimal effect on detection level, while day-to-day variability was observed without being significant. CONCLUSION Following the experimental study, the model enables a comprehensive management of chronic pain patients, and possibly other analgesia, or pain related regulatory loops. SIGNIFICANCE A device and methodology for noninvasive for detecting nociception stimulation have been developed. The proposed method and models have been validated on healthy volunteers.
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Zamzmi G, Kasturi R, Goldgof D, Zhi R, Ashmeade T, Sun Y. A Review of Automated Pain Assessment in Infants: Features, Classification Tasks, and Databases. IEEE Rev Biomed Eng 2017; 11:77-96. [PMID: 29989992 DOI: 10.1109/rbme.2017.2777907] [Citation(s) in RCA: 41] [Impact Index Per Article: 5.9] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
Bedside caregivers assess infants' pain at constant intervals by observing specific behavioral and physiological signs of pain. This standard has two main limitations. The first limitation is the intermittent assessment of pain, which might lead to missing pain when the infants are left unattended. Second, it is inconsistent since it depends on the observer's subjective judgment and differs between observers. Intermittent and inconsistent assessment can induce poor treatment and, therefore, cause serious long-term consequences. To mitigate these limitations, the current standard can be augmented by an automated system that monitors infants continuously and provides quantitative and consistent assessment of pain. Several automated methods have been introduced to assess infants' pain automatically based on analysis of behavioral or physiological pain indicators. This paper comprehensively reviews the automated approaches (i.e., approaches to feature extraction) for analyzing infants' pain and the current efforts in automatic pain recognition. In addition, it reviews the databases available to the research community and discusses the current limitations of the automated pain assessment.
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Kaltwang S, Todorovic S, Pantic M. Doubly Sparse Relevance Vector Machine for Continuous Facial Behavior Estimation. IEEE Trans Pattern Anal Mach Intell 2016; 38:1748-1761. [PMID: 26595911 DOI: 10.1109/tpami.2015.2501824] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/05/2023]
Abstract
Certain inner feelings and physiological states like pain are subjective states that cannot be directly measured, but can be estimated from spontaneous facial expressions. Since they are typically characterized by subtle movements of facial parts, analysis of the facial details is required. To this end, we formulate a new regression method for continuous estimation of the intensity of facial behavior interpretation, called Doubly Sparse Relevance Vector Machine (DSRVM). DSRVM enforces double sparsity by jointly selecting the most relevant training examples (a.k.a. relevance vectors) and the most important kernels associated with facial parts relevant for interpretation of observed facial expressions. This advances prior work on multi-kernel learning, where sparsity of relevant kernels is typically ignored. Empirical evaluation on challenging Shoulder Pain videos, and the benchmark DISFA and SEMAINE datasets demonstrate that DSRVM outperforms competing approaches with a multi-fold reduction of running times in training and testing.
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Gholami B, Bailey JM, Haddad WM, Tannenbaum AR. Clinical Decision Support and Closed-Loop Control for Cardiopulmonary Management and Intensive Care Unit Sedation Using Expert Systems. IEEE Trans Control Syst Technol 2012; 20:1343-1350. [PMID: 23620646 PMCID: PMC3633236 DOI: 10.1109/tcst.2011.2162412] [Citation(s) in RCA: 9] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/16/2023]
Abstract
Patients in the intensive care unit (ICU) who require mechanical ventilation due to acute respiratory failure also frequently require the administration of sedative agents. The need for sedation arises both from patient anxiety due to the loss of personal control and the unfamiliar and intrusive environment of the ICU, and also due to pain or other variants of noxious stimuli. While physicians select the agent(s) used for sedation and cardiovascular function, the actual administration of these agents is the responsibility of the nursing staff. If clinical decision support systems and closed-loop control systems could be developed for critical care monitoring and lifesaving interventions as well as the administration of sedation and cardiopulmonary management, the ICU nurse could be released from the intense monitoring of sedation, allowing her/him to focus on other critical tasks. One particularly attractive strategy is to utilize the knowledge and experience of skilled clinicians, capturing explicitly the rules expert clinicians use to decide on how to titrate drug doses depending on the level of sedation. In this paper, we extend the deterministic rule-based expert system for cardiopulmonary management and ICU sedation framework presented in [1] to a stochastic setting by using probability theory to quantify uncertainty and hence deal with more realistic clinical situations.
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Affiliation(s)
- Behnood Gholami
- Schools of Electrical and Computer and Biomedical Engineering, Georgia Institute of Technology, Atlanta, GA 30332-0150 USA ()
| | - James M. Bailey
- Department of Anesthesiology, Northeast Georgia Medical Center, Gainesville, GA 30503 USA ()
| | - Wassim M. Haddad
- School of Aerospace Engineering, Georgia Institute of Technology, Atlanta, GA 30332-0150 USA ()
| | - Allen R. Tannenbaum
- Schools of Electrical and Computer and Biomedical Engineering, Georgia Institute of Technology, Atlanta, GA 30332-0150 USA ()
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