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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: Although flow diversion is a promising procedure for aneurysm treatment, the safety and efficacy of this strategy have not been sufficiently characterized. Both mechanical properties and flow reduction effects are important factors in the design of an optimal stent. OBJECTIVE: We aimed to clarify the contributions of strut size and pitch to the mechanical properties (radial stiffness and longitudinal flexibility) and geometric characteristics (porosity and pore density) related to flow reduction effects. METHODS: Crimping and bending behaviors of the stents were simulated with the finite element method. The relationships between the mechanical…properties and geometric characteristics were investigated by changing the strut size and pitch. RESULTS: Within the porosity range of 79–82%, the radial stiffness of the stent was similarly influenced by either the strut size or pitch. However, the longitudinal flexibility tended to be influenced more by strut size than by pitch. CONCLUSIONS: Adjusting the strut size rather than the pitch can change the mechanical properties while minimizing the change in porosity or pore density related to flow reduction effects.
Keywords: Intracranial aneurysm, radial stiffness, longitudinal flexibility, finite element analysis
Abstract: BACKGROUND: Neuromuscular electrical stimulation (NMES) is commonly used in rehabilitation. However, the optimal combination of phase-duration and amplitude for enhancing motor output is not yet resolved. OBJECTIVE: To test the effects of increasing phase-duration and amplitude on isometric knee extension force and discomfort, while controlling the effects of electrode-skin resistance and body mass index (BMI). METHODS: Twenty-one healthy volunteers participated in the study. Stimulation was set at 250 μ sec phase-duration and 45 Hz to evoke 10% of maximal voluntary isometric contraction of the quadriceps. Electrode-skin resistance was measured. Then, electrically induced…contraction (EIC) forces and discomfort level were measured under four conditions: Moderate (25%) or substantial increase (50%) from baseline amplitude with constant phase-duration and moderate (25%) or substantial increase (50%) in phase-duration with amplitude constant. RESULTS: Compared with baseline, EIC force was significantly higher in all intensification conditions, while discomfort was significantly greater in all conditions except for moderate increase in phase-duration (p = 0.44). Amplitude intensification produced significantly higher force and greater discomfort than phase-duration. Electrode-skin resistance and BMI were not significant covariates. CONCLUSIONS: Greater force is elicited by increasing amplitude than by similar increase in phase-duration; however, the associated discomfort is also higher. Clinicians may use phase-duration while conditioning for NMES.
Abstract: BACKGROUND AND OBJECTIVE: The aim of this study was to investigate if the cultivation of sonicate fluid (SFC) in blood culture bottles (BCB) leads to a higher rate of bacterial isolations than agar plate culture (APC) and to investigate whether the utilization of BCB leads to a reduction in culture time. METHODS: We performed a retrospective analysis of 206 revision total knee and total hip arthroplasty patients comparing the results of both synovial fluid culture and SFC in both BCB and conventional APC. RESULTS: The use of BCB improved both the rate of…positive bacterial isolations and reduced the culture time for synovial fluid as well as SFC. Fifty-one patients showed a bacterial isolation in SFC-APC and 101 in SFC-BCB. For synovial fluid 24 patients showed a bacterial isolation on APC and 37 showed a bacterial isolation in BCB. The synovial fluid cultures showed growth on APC after an average of 2.8 days vs. 1.8 days in BCB. The SFC-APC showed growth after an average of 4.2 days vs. 2.9 days for SFC-BCB. CONCLUSIONS: The culture of synovial and sonicate fluid in BCB leads to more positive bacterial isolations and quicker bacterial growth than conventional agar plate cultures.
Abstract: BACKGROUND: The spectral analysis of the heart rate variability (HRV) shows a decrease in the power of the high frequency (HF) component in preeclamptic pregnancy compared with normal pregnancy; such a decrease is associated with an increase in the low frequency (LF) and the very low frequency (VLF) power. The physiological interpretation is that preeclamptic pregnancy is associated with a facilitation of sympathetic regulation and an attenuation of parasympathetic influence of HR compared with non-pregnancy and normal pregnancy. OBJECTIVE: To use an efficient nased on spectral analysis non-invasive technique to identify preeclamptic pregnant subjects from normal…pregnant in Oman. METHODS: The soft-decision wavelet-based technique is implemented to find the power of the HRV bands in high resolution manner compared to the classical fast Fourier Transform method. Data was obtained from 20 preeclamptic pregnant subjects and 20 normal pregnant controls of the same pregnancy duration, obtained from Nizwa and Sultan Qaboos University hospitals in Oman. RESULTS: The soft-decision wavelet method succeeds to identify patients from normal pregnant with specificity, sensitivity and accuracy of 90%, 80% and 85%, respectively, compared to the FFT which results in 75% specificity, sensitivity and accuracy. CONCLUSION: The LF power obtained by Soft-decision wavelet decomposition is shown to be a successful feature for identification of preeclampsia.
Keywords: Preeclampsia, normal pregnant, identification, HRV, soft-decision wavelet spectral analysis
Abstract: One major challenge of bioprinting is to develop a viable bioink to act as an extracellular matrix. This study investigated a novel method for bioprinting using a pectin based bioink. Besides pectin, Pluronic ® F-127 was incorporated into the bioink to obtain the desired shape during the initial bioprinting process at 37 ∘ C. Once an object was printed it was treated with Ca 2 + (pectin cross-linker) to create the final tissue/organ structure. The results indicated that pectin/Pluronic ® F-127 is a potential…bioink. Moreover, this methodology provides a novel and fast approach for bioprinting.
Abstract: BACKGROUND: Mammography is considered the gold standard for early breast cancer detection but it is very difficult to interpret mammograms for many reason. Computer aided diagnosis (CAD) is an important development that may help to improve the performance in breast cancer detection. OBJECTIVE: We present a CAD system based on feature extraction techniques for detecting abnormal patterns in digital mammograms. METHODS: Computed features based on gray-level co-occurrence matrices (GLCM) are used to evaluate the effectiveness of textural information possessed by mass regions. A total of 20 texture features are extracted from each mammogram.…The ability of feature set in differentiating normal, benign and malign tissue is investigated using a Support Vector Machine (SVM) classifier, Naive Bayes classifier and K-Nearest Neighbor (k-NN) classifier. The efficiency of classification is provided using cross-validation technique. Support Vector Machine was originally designed for binary classification. We constructed a three-class SVM classifier by combining two binary classifiers and then compared his performance with classifiers intended for multi-class classification. To evaluate the classification performance, confusion matrix and Receiver Operating Characteristic (ROC) analysis were performed. RESULTS: Obtained results indicate that SVM classification results are better than the k-NN and Naive Bayes classification results, with accuracy ratio of 65% according to 51.6% and 38.1%, respectively.The unbalanced classification that occurs in all three classification tests is reason for unsatisfactory accuracy. CONCLUSIONS: Obtained experimental results indicate that the proposed three-class SVM classifier is more suitable for practical use than the other two methods.
Abstract: BACKGROUND: Robot-Assisted Gait Training (RAGT) is a widespread approach for locomotion rehabilitation but information about intervention frequency and duration is still lacking. OBJECTIVE: To evaluate the effect of frequency and duration of a RAGT on motor outcome of children affected by Cerebral Palsy (CP). METHODS: Forty-four CP children (age 4–17) underwent one among four different intensive trainings with equal dose of intervention, combining Task-Oriented Physiotherapy (TOP) and RAGT: 40 sessions (4 sessions/week) over 10 weeks of sole TOP (group1) or RAGT (group2) or RAGT and TOP (2 + 2 sessions/week;…group3); 40 sessions in shorter period (4 weeks) of RAGT and TOP (5 + 5 sessions/week; group4). Each child was assessed before, after the training and after 3 months with: Ashworth, gross motor function measure (GMFM)-88, GMFM-66, six minutes walking test and gait analysis. RESULTS: No differences among the 4 protocols were highlighted although both groups with exclusive physiotherapy and RAGT obtained significant improvements in GMFM-88, GMFM-E and GMFM-66 while the mixed approaches did not show significant changes. CONCLUSION : Single-treatment approaches seem to be more effective than mixed approaches, independently from the duration (4 or 10 weeks). RAGT seems to have similar effect with respect to the traditional TOP, at least over 10 weeks.
Keywords: Robot-Assisted Gait Training, lower limb rehabilitation, cerebral palsy, frequency and duration of intervention
Abstract: BACKGROUND: Microbeam radiotherapy (MRT) is a treatment in which radiation field is divided into several separate fields of 10–100 μ m width and 100–400 μ m spacing. In this treatment, normal tissue can tolerate high doses that are delivered to its small volumes. OBJECTIVE: MCNPX 2.4 Monte Carlo code was used to calculate the dose distribution of MRT in a lung tumor in a simulated Rando phantom. METHODS: The effects of tissue inhomogeneities, using contrast media and changing the number of beams were investigated. Dose volume histograms and beam profiles of target and…organs at risk were assessed and the dose uniformity in the target region was evaluated using homogeneity. The conformity indices also used to quantify the conformation of the shape of prescribed isodose volume to the shape and size of the target. RESULTS: Tissue inhomogeneity of this region did not interfere significantly with target dose homogeneity. The use of contrast media or increasing the number of beams improved target dose homogeneity and decreased the dose to surrounding tissues. CONCLUSIONS: The results suggest that further investigation and evaluation of MRT for treatment of chest tumors is worthwhile.
Keywords: Microbeam radiotherapy, dose volume histogram, homogeneity index, conformity index
Abstract: BACKGROUND: Speech disorders such as dysphonia and dysarthria represent an early and common manifestation of Parkinson’s disease. Class prediction is an essential task in automatic speech treatment, particularly in the Parkinson’s disease case. Many classification experiments have been performed which focus on the automatic detection of Parkinson’s disease patients from healthy speakers but results are still not optimistic. A major problem in accomplishing this task is high dimensionality of speech data. OBJECTIVE: In this work, the potential of Principal Component Analysis (PCA) based modeling in dimensionality reduction is taken into consideration as the data smoothening tool…with multiclass target expression data. METHODS: On the basis of suggested PCA-based modeling, the power of class prediction using logistic regression (LR) and C5.0 in numeric data is investigated in publicly available Parkinson’s disease dataset Silverman voice treatment (LSVT) to develop an advanced classification model. RESULTS: The main advantage of our model is the effective reduction of the number of factors from p = 309 to k = 32 for LSVT Voice Rehabilitation dataset, with a fine classification accuracy of 100% and 99.92% for PCA-LR and PCA-C5.0 respectively. In addition, using only 9 dysphonia features, classification accuracy was (99.20%) and (99.11%) for PCA-LR, and PCA-C5.0 respectively. CONCLUSIONS: Our combined dimension reduction and data smoothening approaches have significant potential to minimize the number of features and increase the classification accuracy and then automatically classify subjects into Parkinson’s disease patients or healthy speakers.
Abstract: Mammogram classification is a crucial and challenging problem, because it helps in early diagnosis of breast cancer and supports radiologists in their decision to analyze similar mammograms out of a database by recognizing the classes of current mammograms. This paper proposes an effective method for classifying mammograms using random forests with wavelet based center-symmetric local binary pattern (WCS-LBP). To classify mammograms, multi-resolution CS-LBP texture characteristics from non-overlapping regions of the mammograms are captured. Further, we examine most relevant features using support vector machine-recursive feature elimination (SVM-RFE). Finally, we feed the selected features to decision trees and construct random forests which…are an ensemble of random decision trees. Using wavelet based local CS-LBP features with random forest, we classify the test images into different categories having the maximum posterior probability. The proposed method shows the improved performance as compared with other variant features and state-of-art methods. The obtained performance measures are 97.3% accuracy, 97.3% precision, 97.2% recall, 97.2% F-measure and 94.1% Matthews correlation coefficient (MCC).
Keywords: Computer-aided diagnosis, center symmetric-local binary pattern, discrete wavelet transform, random forest classifier, SVM-RFE, content-based image retrieval