Editorial

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3 pages, 183 KiB

Open AccessEditorial

Mobile Diagnostic Devices for Digital Transformation in Personalized Healthcare

by Hatice Ceylan Koydemir and Aniruddha Ray

Diagnostics 2020, 10(12), 1008; https://doi.org/10.3390/diagnostics10121008 - 25 Nov 2020

Cited by 5 | Viewed by 2043

Abstract

Mobile devices have increasingly become an essential part of the healthcare system worldwide [...] Full article

(This article belongs to the Special Issue Mobile Diagnosis 2.0)

Research

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12 pages, 822 KiB

Open AccessArticle

New Mobile Device to Measure Verticality Perception: Results in Young Subjects with Headaches

by Daniel Rodríguez-Almagro, Esteban Obrero-Gaitán, Rafael Lomas-Vega, Noelia Zagalaz-Anula, María Catalina Osuna-Pérez and Alexander Achalandabaso-Ochoa

Diagnostics 2020, 10(10), 796; https://doi.org/10.3390/diagnostics10100796 - 07 Oct 2020

Cited by 4 | Viewed by 2050

Abstract

The subjective visual vertical (SVV) test has been frequently used to measure vestibular contribution to the perception of verticality. Recently, mobile devices have been used to efficiently perform this measurement. The aim of this study was to analyze the perception of verticality in [...] Read more.

The subjective visual vertical (SVV) test has been frequently used to measure vestibular contribution to the perception of verticality. Recently, mobile devices have been used to efficiently perform this measurement. The aim of this study was to analyze the perception of verticality in subjects with migraines and headaches. A cross-sectional study was conducted that included 28 patients with migraine, 74 with tension-type headache (TTH), and 93 healthy subjects. The SVV test was used through a new virtual reality system. The mean absolute error (MAE) of degrees deviation was also measured to qualify subjects as positive when it was greater than 2.5°. No differences in the prevalence of misperception in verticality was found among healthy subjects (31.18%), migraineurs (21.43%), or those with TTH (33.78%) (p = 0.480). The MAE was not significantly different between the three groups (migraine = 1.36°, TTH = 1.61°, and healthy = 1.68°) (F = 1.097, p = 0.336, and η2 = 0.011). The perception of verticality could not be explained by any variable usually related to headaches. No significant differences exist in the vestibular contribution to the perception of verticality between patients with headaches and healthy subjects. New tests measuring visual and somatosensory contribution should be used to analyze the link between the perception of verticality and headaches. Full article

(This article belongs to the Special Issue Mobile Diagnosis 2.0)

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12 pages, 2297 KiB

Open AccessArticle

Assessment of the Status of Patients with Parkinson’s Disease Using Neural Networks and Mobile Phone Sensors

by Yulia Shichkina, Elizaveta Stanevich and Yulia Irishina

Diagnostics 2020, 10(4), 214; https://doi.org/10.3390/diagnostics10040214 - 12 Apr 2020

Cited by 4 | Viewed by 3258

Abstract

Parkinson’s disease (PD) is one of the most common chronic neurological diseases and one of the significant causes of disability for middle-aged and elderly people. Monitoring the patient’s condition and its compliance is the key to the success of the correction of the [...] Read more.

Parkinson’s disease (PD) is one of the most common chronic neurological diseases and one of the significant causes of disability for middle-aged and elderly people. Monitoring the patient’s condition and its compliance is the key to the success of the correction of the main clinical manifestations of PD, including the almost inevitable modification of the clinical picture of the disease against the background of prolonged dopaminergic therapy. In this article, we proposed an approach to assessing the condition of patients with PD using deep recurrent neural networks, trained on data measured using mobile phones. The data was received in two modes: background (data from the phone’s sensors) and interactive (data directly entered by the user). For the classification of the patient’s condition, we built various models of the neural network. Testing of these models showed that the most efficient was a recurrent network with two layers. The results of the experiment show that with a sufficient amount of the training sample, it is possible to build a neural network that determines the condition of the patient according to the data from the mobile phone sensors with a high probability. Full article

(This article belongs to the Special Issue Mobile Diagnosis 2.0)

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13 pages, 2660 KiB

Open AccessArticle

Comparison of Night, Day and 24 h Motor Activity Data for the Classification of Depressive Episodes

by Julieta G. Rodríguez-Ruiz, Carlos E. Galván-Tejada, Laura A. Zanella-Calzada, José M. Celaya-Padilla, Jorge I. Galván-Tejada, Hamurabi Gamboa-Rosales, Huizilopoztli Luna-García, Rafael Magallanes-Quintanar and Manuel A. Soto-Murillo

Diagnostics 2020, 10(3), 162; https://doi.org/10.3390/diagnostics10030162 - 17 Mar 2020

Cited by 23 | Viewed by 4450

Abstract

Major Depression Disease has been increasing in the last few years, affecting around 7 percent of the world population, but nowadays techniques to diagnose it are outdated and inefficient. Motor activity data in the last decade is presented as a better way to [...] Read more.

Major Depression Disease has been increasing in the last few years, affecting around 7 percent of the world population, but nowadays techniques to diagnose it are outdated and inefficient. Motor activity data in the last decade is presented as a better way to diagnose, treat and monitor patients suffering from this illness, this is achieved through the use of machine learning algorithms. Disturbances in the circadian rhythm of mental illness patients increase the effectiveness of the data mining process. In this paper, a comparison of motor activity data from the night, day and full day is carried out through a data mining process using the Random Forest classifier to identified depressive and non-depressive episodes. Data from Depressjon dataset is split into three different subsets and 24 features in time and frequency domain are extracted to select the best model to be used in the classification of depression episodes. The results showed that the best dataset and model to realize the classification of depressive episodes is the night motor activity data with 99.37% of sensitivity and 99.91% of specificity. Full article

(This article belongs to the Special Issue Mobile Diagnosis 2.0)

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17 pages, 1135 KiB

Open AccessArticle

Concurrent Validity and Reliability of an Inertial Measurement Unit for the Assessment of Craniocervical Range of Motion in Subjects with Cerebral Palsy

by Cristina Carmona-Pérez, Juan Luis Garrido-Castro, Francisco Torres Vidal, Sandra Alcaraz-Clariana, Lourdes García-Luque, Francisco Alburquerque-Sendín and Daiana Priscila Rodrigues-de-Souza

Diagnostics 2020, 10(2), 80; https://doi.org/10.3390/diagnostics10020080 - 01 Feb 2020

Cited by 15 | Viewed by 4093

Abstract

Objective: This study aimed to determine the validity and reliability of Inertial Measurement Units (IMUs) for the assessment of craniocervical range of motion (ROM) in patients with cerebral palsy (CP). Methods: twenty-three subjects with CP and 23 controls, aged between 4 and 14 [...] Read more.

Objective: This study aimed to determine the validity and reliability of Inertial Measurement Units (IMUs) for the assessment of craniocervical range of motion (ROM) in patients with cerebral palsy (CP). Methods: twenty-three subjects with CP and 23 controls, aged between 4 and 14 years, were evaluated on two occasions, separated by 3 to 5 days. An IMU and a Cervical Range of Motion device (CROM) were used to assess craniocervical ROM in the three spatial planes. Validity was assessed by comparing IMU and CROM data using the Pearson correlation coefficient, the paired t-test and Bland–Altman plots. Intra-day and inter-day relative reliability were determined using the Intraclass Correlation Coefficient (ICC). The Standard Error of Measurement (SEM) and the Minimum Detectable Change at a 90% confidence level (MDC₉₀) were obtained for absolute reliability. Results: High correlations were detected between methods in both groups on the sagittal and frontal planes (r > 0.9), although this was reduced in the case of the transverse plane. Bland–Altman plots indicated bias below 5º, although for the range of cervical rotation in the CP group, this was 8.2º. The distance between the limits of agreement was over 23.5º in both groups, except for the range of flexion-extension in the control group. ICCs were higher than 0.8 for both comparisons and groups, except for inter-day comparisons of rotational range in the CP group. Absolute reliability showed high variability, with most SEM below 8.5º, although with worse inter-day results, mainly in CP subjects, with the MDC₉₀ of rotational range achieving more than 20º. Conclusions: IMU application is highly correlated with CROM for the assessment of craniocervical movement in CP and healthy subjects; however, both methods are not interchangeable. The IMU error of measurement can be considered clinically acceptable; however, caution should be taken when this is used as a reference measure for interventions. Full article

(This article belongs to the Special Issue Mobile Diagnosis 2.0)

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11 pages, 1088 KiB

Open AccessArticle

Development of a Flow-Free Automated Colorimetric Detection Assay Integrated with Smartphone for Zika NS1

by Md Alamgir Kabir, Hussein Zilouchian, Mazhar Sher and Waseem Asghar

Diagnostics 2020, 10(1), 42; https://doi.org/10.3390/diagnostics10010042 - 14 Jan 2020

Cited by 25 | Viewed by 7823

Abstract

The Zika virus (ZIKV) is an emerging flavivirus transmitted to humans by Aedes mosquitoes that can potentially cause microcephaly, Guillain–Barré Syndrome, and other birth defects. Effective vaccines for Zika have not yet been developed. There is a necessity to establish an easily deployable, [...] Read more.

The Zika virus (ZIKV) is an emerging flavivirus transmitted to humans by Aedes mosquitoes that can potentially cause microcephaly, Guillain–Barré Syndrome, and other birth defects. Effective vaccines for Zika have not yet been developed. There is a necessity to establish an easily deployable, high-throughput, low-cost, and disposable point-of-care (POC) diagnostic platform for ZIKV infections. We report here an automated magnetic actuation platform suitable for a POC microfluidic sandwich enzyme-linked immunosorbent assay (ELISA) using antibody-coated superparamagnetic beads. The smartphone integrated immunoassay is developed for colorimetric detection of ZIKV nonstructural protein 1 (NS1) antigen using disposable chips to accommodate the reactions inside the chip in microliter volumes. An in-house-built magnetic actuator platform automatically moves the magnetic beads through different aqueous phases. The assay requires a total of 9 min to automatically control the post-capture washing, horseradish peroxidase (HRP) conjugated secondary antibody probing, washing again, and, finally, color development. By measuring the saturation intensity of the developed color from the smartphone captured video, the presented assay provides high sensitivity with a detection limit of 62.5 ng/mL in whole plasma. These results advocate a great promise that the platform would be useful for the POC diagnosis of Zika virus infection in patients and can be used in resource-limited settings. Full article

(This article belongs to the Special Issue Mobile Diagnosis 2.0)

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Review

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24 pages, 2459 KiB

Open AccessReview

Detection of Bacterial and Viral Pathogens Using Photonic Point-of-Care Devices

by Peuli Nath, Alamgir Kabir, Somaiyeh Khoubafarin Doust, Zachary Joseph Kreais and Aniruddha Ray

Diagnostics 2020, 10(10), 841; https://doi.org/10.3390/diagnostics10100841 - 19 Oct 2020

Cited by 30 | Viewed by 7600

Abstract

Infectious diseases caused by bacteria and viruses are highly contagious and can easily be transmitted via air, water, body fluids, etc. Throughout human civilization, there have been several pandemic outbreaks, such as the Plague, Spanish Flu, Swine-Flu, and, recently, COVID-19, amongst many others. [...] Read more.

Infectious diseases caused by bacteria and viruses are highly contagious and can easily be transmitted via air, water, body fluids, etc. Throughout human civilization, there have been several pandemic outbreaks, such as the Plague, Spanish Flu, Swine-Flu, and, recently, COVID-19, amongst many others. Early diagnosis not only increases the chance of quick recovery but also helps prevent the spread of infections. Conventional diagnostic techniques can provide reliable results but have several drawbacks, including costly devices, lengthy wait time, and requirement of trained professionals to operate the devices, making them inaccessible in low-resource settings. Thus, a significant effort has been directed towards point-of-care (POC) devices that enable rapid diagnosis of bacterial and viral infections. A majority of the POC devices are based on plasmonics and/or microfluidics-based platforms integrated with mobile readers and imaging systems. These techniques have been shown to provide rapid, sensitive detection of pathogens. The advantages of POC devices include low-cost, rapid results, and portability, which enables on-site testing anywhere across the globe. Here we aim to review the recent advances in novel POC technologies in detecting bacteria and viruses that led to a breakthrough in the modern healthcare industry. Full article

(This article belongs to the Special Issue Mobile Diagnosis 2.0)

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Other

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10 pages, 2967 KiB

Open AccessBrief Report

Mobile Laboratory Reveals the Circulation of Dengue Virus Serotype I of Asian Origin in Medina Gounass (Guediawaye), Senegal

by Idrissa Dieng, Boris Gildas Hedible, Moussa Moïse Diagne, Ahmed Abd El Wahed, Cheikh Tidiane Diagne, Cheikh Fall, Vicent Richard, Muriel Vray, Manfred Weidmann, Ousmane Faye, Amadou Alpha Sall and Oumar Faye

Diagnostics 2020, 10(6), 408; https://doi.org/10.3390/diagnostics10060408 - 16 Jun 2020

Cited by 21 | Viewed by 3396

Abstract

With the growing success of controlling malaria in Sub-Saharan Africa, the incidence of fever due to malaria is in decline, whereas the proportion of patients with non-malaria febrile illness (NMFI) is increasing. Clinical diagnosis of NMFI is hampered by unspecific symptoms, but early [...] Read more.

With the growing success of controlling malaria in Sub-Saharan Africa, the incidence of fever due to malaria is in decline, whereas the proportion of patients with non-malaria febrile illness (NMFI) is increasing. Clinical diagnosis of NMFI is hampered by unspecific symptoms, but early diagnosis is a key factor for both better patient care and disease control. The aim of this study was to determine the arboviral aetiologies of NMFI in low resource settings, using a mobile laboratory based on recombinase polymerase amplification (RPA) assays. The panel of tests for this study was expanded to five arboviruses: dengue virus (DENV), zika virus (ZIKV), yellow fever virus (YFV), chikungunya virus (CHIKV), and rift valley fever virus (RVFV). One hundred and four children aged between one month and 115 months were enrolled and screened. Three of the 104 blood samples of children <10 years presented at an outpatient clinic tested positive for DENV. The results were confirmed by RT-PCR, partial sequencing, and non-structural protein 1 (NS1) antigen capture by ELISA (Biorad, France). Phylogenetic analysis of the derived DENV-1 sequences clustered them with sequences of DENV-1 isolated from Guangzhou, China, in 2014. In conclusion, this mobile setup proved reliable for the rapid identification of the causative agent of NMFI, with results consistent with those obtained in the reference laboratory’s settings. Full article

(This article belongs to the Special Issue Mobile Diagnosis 2.0)

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