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Technology and Health Care is intended to serve as a forum for the presentation of original articles and technical notes, observing rigorous scientific standards. Furthermore, upon invitation, reviews, tutorials, discussion papers and minisymposia are featured.
The following types of contributions and areas are considered:
1. Original articles:
Technology development in medicine: New concepts, procedures and devices associated with the use of technology in medical research and clinical practice are presented to a readership with a widespread background in engineering and/or medicine.
Significance of medical technology and informatics for healthcare: The appropriateness, efficacy and usefulness deriving from the application of engineering methods, devices and informatics in medicine and with respect to public health are discussed.
2. Technical notes:
Short communications on novel technical developments with relevance for clinical medicine.
3. Reviews and tutorials (upon invitation only):
Tutorial and educational articles for persons with a primarily medical background on principles of engineering with particular significance for biomedical applications and vice versa are presented.
4. Minisymposia (upon invitation only):
Under the leadership of a Special Editor, controversial issues relating to healthcare are highlighted and discussed by various authors.
Abstract: BACKGROUND: Critically ill patients commonly suffer from infections that require antimicrobial therapy. In previous studies, liver dysfunction was shown to have an essential impact on the dose selection in these patients. This pilot study aims to assess the influence of liver dysfunction, measured by the novel LiMAx test, on clinical outcomes in critically ill patients treated with linezolid. METHODS: Twenty-nine critically ill patients were included and treated with linezolid. Indications for linezolid therapy were secondary or tertiary peritonitis (46.7%), bloodstream infection (6.7%) and 46.7% were other infections with gram-positive bacteria. Linezolid C min…, maximal liver function capacity (LiMAx test) and plasma samples were collected while linezolid therapy was in a steady-state condition. Furthermore, potential factors for the clinical outcome were investigated using logistic regression analysis. Clinical cure was defined as the resolution or significant improvement of clinical symptoms without using additional antibiotic therapy or intervention. RESULTS: Cured patients presented lower median linezolid C min yet a significantly higher mean LiMAx-value compared to the clinical failure group (1.9 mg/L vs. 5.1 mg/L) (349 μ g/kg/h vs. 131 μ g/kg/h). In the logistic regression model, LiMAx < 178 μ g/kg/h was the only independent predictor of clinical failure with a sensitivity of 77% and specificity of 93%. CONCLUSIONS: The LiMAx test predicts clinical failure more precisely than linezolid trough levels in critically ill surgical patients. Therefore liver failure may have a stronger impact on the outcome of critically ill surgical patients than low linezolid C min . While linezolid C min failed to predict patient’s outcome, LiMAx results were the only independent predictor of clinical failure.
Keywords: Linezolid, dosage, liver function test, clinical outcome
Abstract: BACKGROUND: Sleep staging is an important part of sleep research. Traditional automatic sleep staging based on machine learning requires extensive feature extraction and selection. OBJECTIVE: This paper proposed a deep learning algorithm without feature extraction based on one-dimensional convolutional neural network and long short-term memory. METHODS: The algorithm can automatically divide sleep into 5 phases including awake period, non-rapid eye movement sleep period (N1 ∼ N3) and rapid eye movement using the electroencephalogram signals. The raw signal was processed by the wavelet transform. Then, the processed signal was directly input…into the deep learning algorithm to obtain the staging result. RESULTS: The accuracy of staging is 93.47% using the Fpz-Cz electroencephalogram signal. When using the Fpz-Cz and electroencephalogram signal, the algorithm can obtain the highest accuracy of 94.15%. CONCLUSION: These results show that this algorithm is suitable for different physiological signals and can realize end-to-end automatic sleep staging without any manual feature extraction.
Keywords: Sleep staging, deep learning, one-dimensional convolutional neural network, long short-term memory
Abstract: BACKGROUND: Many inpatients become anxious or frightened about scheduled treatment processes, and medical staff do not have sufficient time to provide emotional support. The recent advancement of information and communications technology (ICT) and the use of artificial intelligence (AI), including robots, in the health care field is being put to the test. OBJECTIVE: This study aimed to develop a bedside robot system to deliver information and provide emotional support to inpatients and to evaluate the usability and perceptions of the developed robot. METHODS: The first stage was accomplished by deriving essential functions from…the results of user demand surveys on robots and by implementing a prototype by mapping each essential function to the robot’s hardware and software. For the second stage, the robot was assessed for usability and perceptions in a simulation center, a hospital-like environment, by 10 nurses, 10 inpatients, and family caregivers. Usability and perception were evaluated using the think-aloud method, a survey, and individual interviews. RESULTS: Based on the usability evaluation, the perceived usefulness, ease of use, and satisfaction were 5.28 ± 1.27 points, 5.42 ± 1.55 points, and 5.27 ± 1.46 points out of 7, respectively. It was found that overall, the robot was positively perceived by participants. As a result of the qualitative data analysis, the participants perceived the robot as an object that had the positive effect of providing emotional support through communication. CONCLUSIONS: The bedside robot in this study, which incorporated human-robot interaction (HRI) technology, is an alternative suited to the new normal era that will contribute to ensuring that patients have more self-directed hospital stays as well as emotional support through information delivery and communication.
Keywords: Robotics, artificial intelligence, inpatients, patient-centered care, point-of-care systems, caregivers
Abstract: BACKGROUND: Inadequate scaffolding performance hinders the clinical application of the biodegradable zinc alloy stents. OBJECTIVE: In this study we propose a novel stent with the tenon-and-mortise structure to improve its scaffolding performance. METHODS: 3D models of stents were established in Pro/E. Based on the biodegradable zinc alloy material and two numerical simulation experiments were performed in ABAQUS. Firstly, the novel stent could be compressed to a small-closed ring by a crimp shell and can form a tenon-and-mortise structure after expanded by a balloon. Finally, 0.35 MPa was applied to the crimp shell to…test the scaffolding performance of the novel stent and meanwhile compare it with an ordinary stent. RESULTS: Results showed that the novel stent decreased the recoiling ratio by 70.7% compared with the ordinary stent, indicating the novel structure improved the scaffolding performance of the biodegradable zinc alloy stent. CONCLUSION: This study proposes a novel design that is expected to improve the scaffolding performance of biodegradable stents.
Keywords: Zinc alloy, biodegradable stent, scaffolding stiffness, finite element analysis
Abstract: BACKGROUND: According to the World Health Organization, one in ten adults will have Type 2 Diabetes Mellitus (T2DM) in the next few years. Autonomic dysfunction is one of the significant complications of T2DM. Autonomic dysfunction is usually assessed by standard Ewing’s test and resting Heart Rate Variability (HRV) indices. OBJECTIVE: Resting HRV has limited use in screening due to its large intra and inter-individual variations. Therefore, a combined approach of resting and orthostatic challenge HRV measurement with a machine learning technique was used in the present study. METHODS: A total of 213 subjects…of both genders between 20 to 70 years of age participated in this study from March 2018 to December 2019 at Smt. Kashibai Navale Medical College and General Hospital (SKNMCGH) in Pune, India. The volunteers were categorized according to their glycemic status as control (n = 51 Euglycemic) and T2DM (n = 162). The short-term ECG signal in the resting and after an orthostatic challenge was recorded. The HRV indices were extracted from the ECG signal as per HRV-Taskforce guidelines. RESULTS: We observed a significant difference in time, frequency, and non-linear resting HRV indices between the control and T2DM groups. A blunted autonomic response to an orthostatic challenge quantified by percentage difference was observed in T2DM compared to the control group. HRV patterns during rest and the orthostatic challenge were extracted by various machine learning algorithms. The Classification and Regression Tree (CART) model has shown better performance among all the machine learning algorithms. It has shown an accuracy of 84.04%, the sensitivity of 89.51%, a specificity of 66.67%, with an Area Under Receiver Operating Characteristic Curve (AUC) of 0.78 compared to resting HRV alone with 75.12% accuracy, 86.42% sensitivity, 39.22% specificity, with an AUC of 0.63 for differentiating autonomic dysfunction in non-diabetic control and T2DM. CONCLUSION: It was possible to develop a Classification and Regression Tree (CART) model to detect autonomic dysfunction. The technique of percentage difference between resting and orthostatic challenge HRV indicates the blunted autonomic response. The developed CART model can differentiate the autonomic dysfunction using both resting and orthostatic challenge HRV data compared to only resting HRV data in T2DM. Thus, monitoring HRV parameters using the CART model during rest and after orthostatic challenge may be a better alternative to detect autonomic dysfunction in T2DM as against only resting HRV.
Keywords: Type 2 diabetes mellitus (T2DM), Heart Rate Variability (HRV), autonomic dysfunction, orthostatic challenge, machine learning, classification and regression tree (CART)
Abstract: BACKGROUND: African Americans living with dementia are considered less likely to seek formal institutionalized elder care and more likely to be managed in the home by family-member caregivers. Assistive technologies (the use of smart visual devices like tablets and phones) can be used effectively to guide memory-impaired individuals with a sequence of pictures showing steps to complete activities of daily living, e.g., bathing, toileting, dressing. Assistive technology so far has not been generally embraced in African American communities. OBJECTIVES: Determine, if African American family caregivers, given the opportunity, would embrace the use of assistive technology and…if they would perceive its use beneficial. METHODS: We assessed a group of eight family caregivers’ overall care-burden scores, and their user-satisfaction scores after using assistive technology for three months. RESULTS: We found significant reduction in caregiver burden, positive changes in behavior and emotion scores, and high ratings on user satisfaction. CONCLUSIONS: The findings reported here comprise the first systematic study of the use of assistive technology by caregivers in an underserved population. They set the stage for exploring meaningful strategies and variables that will better engage underserved populations to take advantage of assistive technologies available in healthcare.
Keywords: Caregiving, African Americans, dementia, assistive technology, quality of life, activities of daily living
Abstract: BACKGROUND AND OBJECTIVE: The aim of this study was to compare the efficacy of photobiomodulation therapy (PBMT) and photodynamic therapy (PDT) as adjuncts to mechanical debridement (MD) for the treatment of peri-implantitis. The present study is based on the null hypothesis that there is no difference in the peri-implant inflammatory parameters (modified plaque index [mPI], modified gingival index [mGI], probing depth [PD]) and crestal bone loss (CBL) following MD either with PBMT or PDT in patients with peri-implantitis. METHODS: Forty-nine patients with peri-implantitis were randomly categorized into three groups. In Groups 1 and 2, patients underwent…MD with adjunct PBMT and PDT, respectively. In Group 3, patients underwent MD alone (controls). Peri-implant inflammatory parameters were measured at baseline and 3-months follow-up. P -values < 0.01 were considered statistically significant. RESULTS: At baseline, peri-implant clinicoradiographic parameters were comparable in all groups. Compared with baseline, there was a significant reduction in mPI (P < 0.001), mGI (P < 0.001) and PD (P < 0.001) in Groups 1 and 2 at 3-months follow-up. In Group 3, there was no difference in the scores of mPI, mGI and PD at follow-up. At 3-months follow-up, there was no difference in mPI, mGI and PD among patients in Groups 1 and 2. The mPI (P < 0.001), mGI (P < 0.001) and PD (P < 0.001) were significantly higher in Group 3 than Groups 1 and 2. The CBL was comparable in all groups at follow-up. CONCLUSION: PBMT and PDT seem to be useful adjuncts to MD for the treatment of peri-implant soft-tissue inflammation among patients with peri-implantitis.
Abstract: BACKGROUND: Simplified and easy-to-use monitoring approaches are crucial for the early diagnosis and prevention of obstructive sleep apnea (OSA) and its complications. OBJECTIVE: In this study, the OSA detection and arrhythmia classification algorithms based on single-channel photoplethysmography (PPG) are proposed for the early screening of OSA. METHODS: Thirty clinically diagnosed OSA patients participated in this study. Fourteen features were extracted from the PPG signals. The relationship between the number of features as inputs of the support vector machine (SVM) and performance of apnea events detection was evaluated. Also, a multi-classification algorithm based on…the modified Hausdorff distance was proposed to recognize sinus rhythm and four arrhythmias highly related with SA. RESULTS: The feature set composed of meanPP, SDPP, RMSSD, meanAm, and meank1 could provide a satisfactory balance between the performance and complexity of the algorithm for OSA detection. Also, the arrhythmia classification algorithm achieves the average sensitivity, specificity and accuracy of 83.79%, 95.91% and 93.47%, respectively in the classification of all four types of arrhythmia and regular rhythm. CONCLUSION: Single channel PPG-based OSA detection and arrhythmia classification in this study can provide a feasible and promising approach for the early screening and diagnosis of OSA and OSA-related arrhythmias.
Abstract: BACKGROUND: Trunk control ability is an important component of functional independence after the onset of stroke. Recently, it has been reported that robot-assisted functional training is effective for stroke patients. However, most studies on robot-assisted training have been conducted on upper and lower extremities. OBJECTIVE: The purpose of this study was to evaluate the effects of robot-assisted trunk control training on trunk postural control and balance ability in stroke patients. METHODS: Forty participants with hemiparetic stroke were recruited and randomly divided into two groups: the RT (robot-assisted trunk control training) group (n =…20) and the control group (n = 20). All participants underwent 40 sessions of conventional trunk stabilization training based on the Bobath concept (for 30 minutes, five-times per week for 8 weeks). After to each training session, 15 minutes of robotassisted trunk control training was given in the RT group, whereas the control group received stretching exercise for the same amount of time. Robot-assisted trunk control training was conducted in three programs: sitting balance, sit-to stand, and standing balance using a robot system specially designed to improve trunk control ability. To measure trunk postural control ability, trunk impairment scale (TIS) was used. Center of pressure (COP) distance, limits of stability (LOS), Berg Balance Scale (BBS) and functional reach test (FRT) were used to analyze balance abilities. RESULTS: In TIS, COP distance, LOS, BBS and FRT, there were significant improvements in both groups after intervention. More significant changes were shown in the RT group than the control group (p < 0.05). CONCLUSIONS: Our findings indicate that robot-assisted trunk control training is beneficial and effective to improve trunk postural control and balance ability in stroke patients. Therefore robot-assisted training may be suggested as an effective intervention to improve trunk control ability in patients with stroke.
Abstract: BACKGROUND: Wearable technologies have been developed for healthy aging. The technology for electromyography (EMG)-controlled functional electrical stimulation (FES) systems has been developed, but research on how helpful it is in daily life has been insufficient. OBJECTIVE: The purpose of this study was to investigate the effect of the EMG-controlled FES system on muscle morphology, balance, and gait in older adults. METHODS: Twenty-nine older adults were evaluated under two randomly assigned conditions (non-FES and FES assists). Muscle morphology, balance, gait function, and muscle effort during gait were measured using ultrasonography, a physical test, a…gait analysis system, and EMG. RESULTS: The EMG-controlled FES system improved gait speed by 11.1% and cadence by 15.6% (P < 0.01). The symmetry ratio of the bilateral gastrocnemius was improved by 9.9% in the stance phase and 11.8% in the swing phase (P < 0.05). The degrees of coactivation of the knee and ankle muscles were reduced by 45.1% and 50.5%, respectively (P < 0.05). Balance improved by 6–10.7% (P < 0.01). CONCLUSION: The EMG-controlled FES system is useful for balance and gait function by increasing muscle symmetry and decreasing muscle coactivation during walking in older adults.
Keywords: Aging, wearable technology, muscle symmetry, gait function