IEEE Journal of Translational Engineering in Health and Medicine

Articles
Detecting Cataract Using Smartphones
Behnam Askarian, Peter Ho, Jo Woon Chong
Objective: Cataract, which is the clouding of the crystalline lens, is the most prevalent eye disease accounting for 51% of all eye diseases in the U.S. Cataract is a progressive disease, and its early detection is critical for preventing blindness.... Read more
Posted on 22 APR 2021
Articles
Privacy-Preserving Deep Speaker Separation for Smartphone-Based Passive Speech Assessment
Apiwat Ditthapron, Emmanuel O Agu, Adam C Lammert
Goal: Smartphones can be used to passively assess and monitor patients’ speech impairments caused by ailments such as Parkinson’s disease, Traumatic Brain Injury (TBI), Post-Traumatic Stress Disorder (PTSD) and neurodegenerative diseases such as Alzheimer’s disease and dementia. However, passive audio... Read more
Posted on 14 APR 2021
Featured Articles
Robot-Based Assessment of HIV-Related Motor and Cognitive Impairment for Neurorehabilitation
Kevin D. Bui, Carol A. Wamsley, Frances S. Shofer, Dennis L. Kolson, Michelle J. Johnson
There is a pressing need for strategies to slow or treat the progression of functional decline in people living with HIV. This paper explores a novel rehabilitation robotics approach to measuring cognitive and motor impairment in adults living with HIV,... Read more
Posted on 26 MAR 2021
Featured Articles
Vibration Stimulation as a Non-Invasive Approach to Monitor the Severity of Meniscus Tears
Mohsen Safaei, Nicholas B. Bolus, Daniel C. Whittingslow, Hyeon Ki Jeong, Alper Erturk, Omer T. Inan
Musculoskeletal disorders and injuries are one of the most prevalent medical conditions across age groups. Due to a high load-bearing function, the knee is particularly susceptible to injuries such as meniscus tears. Imaging techniques are commonly used to assess meniscus... Read more
Posted on 26 MAR 2021
Articles
Deep Learning Classification of Systemic Sclerosis Skin using the MobileNetV2 Model
Metin Akay, Yong Du, Cheryl L Sershen, Minghua Wu, Ting Y Chen, Shervin Assassi, Chandra Mohan, Yasemin M. Akay
Systemic sclerosis (SSc) is a rare autoimmune, systemic disease with prominent fibrosis of skin and internal organs. Early diagnosis of the disease is crucial for designing effective therapy and management plans. Machine learning algorithms, especially deep learning, have been found... Read more
Posted on 18 MAR 2021
Articles
Detecting effect of levodopa in Parkinson’s disease patients using sustained phonemes
Nemuel D. Pah, Mohammod A. Motin, Peter Kempster, Dinesh Kumar
Background: Parkinson’s disease (PD) is a multi-symptom neurodegenerative disease generally managed with medications, of which levodopa is the most effective. Determining the dosage of levodopa requires regular meetings where motor function can be observed. Speech impairment is an early symptom... Read more
Posted on 18 MAR 2021
Articles, Uncategorized
mHealth Technology Translation in a Limited Resources Community – Process, Challenges, and Lessons Learned from a Limited Resources Community of Chiang Mai Province, Thailand
Waraporn Boonchieng, Jintana Chaiwan, Bijaya Shrestha, Manash Shrestha, Adam J.O. Dede, Ekkarat Boonchieng
Objective: This report aims to provide practical advice about the implementation of a public health monitoring system using both geographic information system technology and mobile health, a term used for healthcare delivery via mobile devices. application amongst household residents and... Read more
Posted on 28 JAN 2021
Articles
Early detection of Acute Chest Syndrome through electronic recording and analysis of auscultatory percussion
Bekah Allen, Robert Molokie, Thomas J. Royston
Acute chest syndrome (ACS) is the leading cause of death among people with sickle cell disease. ACS is clinically defined and diagnosed by the presence of a new pulmonary infiltrate on chest imaging with accompanying fever and respiratory symptoms like... Read more
Posted on 6 OCT 2020
Articles
SARS-CoV-2 Detection from Voice
Gadi Pinkas, Yarden Karny, Aviad Malachi, Galia Barkai, Gideon Bachar, Vered Aharonson
Abstract: Automated voice-based detection of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) could facilitate the screening for COVID19. A dataset of cellular phone recordings from 88 subjects was recently collected. The dataset included vocal utterances, speech and coughs that were self-recorded... Read more
Posted on 25 SEP 2020
Articles
Can mHealth Technology Help Mitigate the Effects of the COVID-19 Pandemic?
Catherine P. Adans-Dester, Stacy Bamberg, Francesco Bertacchi, Brian Caulfield, Kara Chappie, Danilo Demarchi, M. Kelley Erb, Juan Estrada, Eric Fabara, Michael Freni, E. Karl Friedl, Roozbeh Ghaffari, Geoffrey Gill, Mark S. Greenberg, Reed W. Hoyt, Emil Jovanov, Christoph Kanzler, Dina Katabi, Meredith Kernan, Colleen Kigin, Sunghoon Ivan Lee, Steffen Leonhardt, Nigel Hamilton Lovell, Jose Mantilla, Thomas H. McCoy, Nell Meosky Luo, Glenn A. Miller, John Moore, Derek O'Keeffe, Jeffrey Palmer, Federico Parisi, Shyamal Patel, Ming Jack Po, Benito L. Pugliese, Thomas Quatieri, Tauhidur Rahman, Nathan Ramasarma, John A. Rogers, Guillermo U. Ruiz-Esparza, Stefano Sapienza, Gregory Schiurring, Lee Schwamm, Hadi Shafiee, Sara Kelly Silacci, Nathaniel M. Sims, Tanya Talkar, William J. Tharion, James A. Toombs, Christopher Uschnig, Gloria Vergara, Paul Wacnik, May D. Wang, James Welch, Lina Williamson, Ross Zafonte, Adrian Zai, Yuan-Ting Zhang, Guillermo J. Tearney, Rushdy Ahmad, David R. Walt, Paolo Bonato
Goal: The aim of the study herein reported was to review mobile health (mHealth) technologies and explore their use to monitor and mitigate the effects of the COVID-19 pandemic. Methods: A Task Force was assembled by recruiting individuals with expertise... Read more
Posted on 17 AUG 2020