Long-term musical training induces white matter plasticity in emotion and language networks.

Numerous studies have reported that long-term musical training can affect brain functionality and induce structural alterations in the brain. Singing is a form of vocal musical expression with an unparalleled capacity for communicating emotion; however, there has been relatively little research on neuroplasticity at the network level in vocalists (i.e., noninstrumental musicians). Our objective in this study was to elucidate changes in the neural network architecture following long-term training in the musical arts. We employed a framework based on graph theory to depict the connectivity and efficiency of structural networks in the brain, based on diffusion-weighted images obtained from 35 vocalists, 27 pianists, and 33 nonmusicians. Our results revealed that musical training (both voice and piano) could enhance connectivity among emotion-related regions of the brain, such as the amygdala. We also discovered that voice training reshaped the architecture of experience-dependent networks, such as those involved in vocal motor control, sensory feedback, and language processing. It appears that vocal-related changes in areas such as the insula, paracentral lobule, supramarginal gyrus, and putamen are associated with functional segregation, multisensory integration, and enhanced network interconnectivity. These results suggest that long-term musical training can strengthen or prune white matter connectivity networks in an experience-dependent manner.

PADAr: physician-oriented artificial intelligence-facilitating diagnosis aid for retinal diseases.

Purpose: Retinopathy screening via digital imaging is promising for early detection and timely treatment, and tracking retinopathic abnormality over time can help to reveal the risk of disease progression. We developed an innovative physician-oriented artificial intelligence-facilitating diagnosis aid system for retinal diseases for screening multiple retinopathies and monitoring the regions of potential abnormality over time. Approach: Our dataset contains 4908 fundus images from 304 eyes with image-level annotations, including diabetic retinopathy, age-related macular degeneration, cellophane maculopathy, pathological myopia, and healthy control (HC). The screening model utilized a VGG-based feature extractor and multiple-binary convolutional neural network-based classifiers. Images in time series were aligned via affine transforms estimated through speeded-up robust features. Heatmaps of retinopathy were generated from the feature extractor using gradient-weighted class activation mapping++, and individual candidate retinopathy sites were identified from the heatmaps using clustering algorithm. Nested cross-validation with a train-to-test split of 80% to 20% was used to evaluate the performance of the screening model. Results: Our screening model achieved 99% accuracy, 93% sensitivity, and 97% specificity in discriminating between patients with retinopathy and HCs. For discriminating between types of retinopathy, our model achieved an averaged performance of 80% accuracy, 78% sensitivity, 94% specificity, 79% F1-score, and Cohen's kappa coefficient of 0.70. Moreover, visualization results were also shown to provide reasonable candidate sites of retinopathy. Conclusions: Our results demonstrated the capability of the proposed model for extracting diagnostic information of the abnormality and lesion locations, which allows clinicians to focus on patient-centered treatment and untangles the pathological plausibility hidden in deep learning models.

Fuzzy logic-based mobile computing system for hand rehabilitation after neurological injury.

BACKGROUND: Effective neurological rehabilitation requires long term assessment and treatment. The rapid progress of virtual reality-based assistive technologies and tele-rehabilitation has increased the potential for self-rehabilitation of various neurological injuries under clinical supervision. OBJECTIVE: The objective of this study was to develop a fuzzy inference mechanism for a smart mobile computing system designed to support in-home rehabilitation of patients with neurological injury in the hand by providing an objective means of self-assessment. METHODS: A commercially available tablet computer equipped with a Bluetooth motion sensor was integrated in a splint to obtain a smart assistive device for collecting hand motion data, including writing performance and the corresponding grasp force. A virtual reality game was also embedded in the smart splint to support hand rehabilitation. Quantitative data obtained during the rehabilitation process were modeled by fuzzy logic. Finally, the improvement in hand function was quantified with a fuzzy rule database of expert opinion and experience. RESULTS: Experiments in chronic stroke patients showed that the proposed system is applicable for supporting in-home hand rehabilitation. CONCLUSIONS: The proposed virtual reality system can be customized for specific therapeutic purposes. Commercial development of the system could immediately provide stroke patients with an effective in-home rehabilitation therapy for improving hand problems.

Continued use of an interactive computer game-based visual perception learning system in children with developmental delay.

This study developed an interactive computer game-based visual perception learning system for special education children with developmental delay. To investigate whether perceived interactivity affects continued use of the system, this study developed a theoretical model of the process in which learners decide whether to continue using an interactive computer game-based visual perception learning system. The technology acceptance model, which considers perceived ease of use, perceived usefulness, and perceived playfulness, was extended by integrating perceived interaction (i.e., learner-instructor interaction and learner-system interaction) and then analyzing the effects of these perceptions on satisfaction and continued use. Data were collected from 150 participants (rehabilitation therapists, medical paraprofessionals, and parents of children with developmental delay) recruited from a single medical center in Taiwan. Structural equation modeling and partial-least-squares techniques were used to evaluate relationships within the model. The modeling results indicated that both perceived ease of use and perceived usefulness were positively associated with both learner-instructor interaction and learner-system interaction. However, perceived playfulness only had a positive association with learner-system interaction and not with learner-instructor interaction. Moreover, satisfaction was positively affected by perceived ease of use, perceived usefulness, and perceived playfulness. Thus, satisfaction positively affects continued use of the system. The data obtained by this study can be applied by researchers, designers of computer game-based learning systems, special education workers, and medical professionals.

Effects of B Value on Quantification of Rapid Diffusion Kurtosis Imaging in Normal and Acute Ischemic Brain Tissues.

PURPOSE: Diffusion kurtosis imaging (DKI) has been widely used to characterize brain tissue alterations. Diffusion-weighting factor or b value plays an important role in the measurement of rapid DKI and may have influential effects on them. The purpose of this study was to investigate the effects of b value on rapid DKI indices in normal and acute ischemic brain tissues. MATERIALS AND METHODS: This study enrolled 10 healthy subjects and 4 acute ischemic stroke patients. Three repeated DKI data with 6 high b values (500, 750, 1000, 1500, 2000, 3000 s/mm) were acquired from healthy subjects, whereas nonrepeated DKI data with 3 high b values (1000, 2000, 3000 s/mm) were acquired from ischemic stroke patients. The DKI datasets were decomposed into several rapid DKI datasets consisting of 1 b0 and 2 high b values for comparisons. RESULTS: The results showed that b value significantly impacted the reproducibility and accuracy of DKI indices. The comparisons demonstrated that DKI with b = (0, 1000, 3000) s/mm exhibited more reproducible and accurate DKI indices than other DKI datasets in normal brain tissues, and similar results were noticed in acute ischemic brain tissue. CONCLUSIONS: We concluded that b value significantly impacted the quantification of DKI indices in both normal and acute ischemic brain tissues.

Epidemiology of cancer in end-stage renal disease dialysis patients: a national cohort study in Taiwan.

The incidence and mortality of site-specific cancers in patients with end-stage renal disease (ESRD) on maintenance dialysis have been rarely studied for Asian populations. We tapped Taiwan`s National Health Insurance Research Database to identify and recruit patients starting maintenance dialysis between 1999 and 2004. They were followed from initiation of dialysis until death, discontinuation of dialysis, or the end of 2008. We calculated the survival rate and mortality risk of dialysis patients with cancer. Of 40,833 dialysis patients, 2352 (5.8%) had been newly diagnosed with cancer. Being older, being male, and having chronic liver disease were factors associated with a higher risk for new cancer in ESRD dialysis patients. In men, liver cancer (20.63%) was the most frequent, followed by cancers of the bladder (16.88%) and kidney (11.61%). In women, bladder cancer (25.57%) was the most frequent, followed by cancers of the kidney (16.31%) and breast (11.20%). The 5-year survival rates for kidney and bladder cancer were higher than for other cancers; the survival rates for lung, stomach, and liver cancer were lower. In conclusion, the distribution of site-specific cancer was different between men and women in patients with ESRD on dialysis. More attention should be paid to teaching dialysis patients how to avoid the well-known cancer risks and carcinogens and individualized regular cancer screenings.

Exploration of the importance of geriatric frailty on health-related quality of life.

AIM: The ageing of the population is a demographic trend globally. Promoting the health of elderly persons has become a crucial priority. However, knowledge about the relationship between frailty and quality of life (QoL) remains limited. This research aimed to investigate the association between elderly frailty and QoL, and to identify whether frailty or current health status more significantly affects the QoL of elderly persons. METHODS: This research began in 2012, and by the end of the study in April 2013, 375 samples had been collected. Structured questionnaires and the face-to-face questionnaire interviews were used. Multiple linear regression, t-test, and one-way anova were administered. RESULTS: Elderly individuals who were hospitalized performed worse in the physical health QoL domain, whereas those who regularly attended outpatient follow-ups performed worse in the mental health QoL domain. QoL was only partially affected in both groups. All QoL domains were affected in elderly individuals with symptoms of frailty. Age, marital status, and financial status significantly affect health-related QoL. CONCLUSION: Geriatric frailty significantly affects general QoL. Early screening of frailty-related conditions among the elderly can prompt early and proper intervention for this population.

Death Does Matter--Cancer Risk in Patients With End-Stage Renal Disease: A Nationwide Population-Based Study With Competing Risk Analyses.

Patients with end-stage renal disease (ESRD) have a high mortality rate. We hypothesized that not accounting for death as a competing risk overestimates the event rate caused by ESRD. Thus, we examined the cancer risk for patients with ESRD (ESRD) after death as a competing risk event had been adjusted for. Patients with newly diagnosed ESRD (n = 64,299) between 1999 and 2007, together with age- and sex-matched controls without ESRD (ESRD) (n = 128,592) were enrolled (1:2). In a Cox proportional hazards model that included death as a competing risk, ESRD patients in Taiwan had a lower overall incidence (subdistribution hazard ratio [sdHR] = 1.29) of cancer than did ESRD patients in a Cox model that did not include death as a competing risk (HR = 1.70). After competing mortality had been adjusted for, ESRD patients ≥70 (sdHR = 0.82) and ESRD patients on long-term dialysis (> 5 follow-up years, sdHR = 0.62), had a lower risk for developing cancer than did ESRD patients. This finding supported our hypothesis that standard survival analyses overestimate the event rate, especially when the mortality rate is high. It also showed that ESRD patients, when they grow older, were far less likely to develop cancer and far more likely to die because of underlying illnesses that might also affect the risk of death because of ESRD.

Comparisons of Reproducibility and Mean Values of Diffusion Tensor Imaging-Derived Indices between Unipolar and Bipolar Diffusion Pulse Sequences.

Eddy current distortion is an important issue that may influence the quantitative measurements of diffusion tensor imaging (DTI). The corrections of eddy current artifacts could be performed using bipolar diffusion gradients or unipolar gradients with affine registration. Whether the diffusion pulse sequence affects the quantification of DTI indices and the technique that produces more reliable DTI indices in terms of reproducibility both remain unclear. Therefore, the purpose of this study was to compare the reproducibility and mean values of DTI-derived indices between unipolar and bipolar diffusion pulse sequences based on actual human brain data. Five repeated datasets of unipolar and bipolar DTI were acquired from 10 healthy subjects at different echo times (TEs). The reproducibility and mean values of DTI indices were assessed by calculating the coefficient of variation and mean values of the 5 repeated measurements. The results revealed that the reproducibility and mean values of DTI indices were significantly affected by the pulse sequence. Unipolar DTI exhibited significantly higher reproducibility than bipolar DTI even at the same TE, and the mean values of DTI indices were significantly different between them. Therefore, we concluded that the reproducibility and mean values of DTI indices were significantly influenced by diffusion pulse sequences.

Volume-outcome associations after major hepatectomy for hepatocellular carcinoma: a nationwide Taiwan study.

OBJECTIVE: The objective of this study was to explore volume-outcome associations after major hepatectomy for hepatocellular carcinoma (HCC). METHODS: This population-based cohort study retrospectively analyzed 23,107 major hepatectomies for HCC patients from 1998 to 2009. Relationships between hospital/surgeon volume and patient outcome were analyzed by propensity score matching (PSM). Five-year overall survival (OS) was estimated by Kaplan-Meier method, and differences were compared by log-rank test. RESULTS: The mean length of stay (LOS) after major hepatectomy was 18.1 days, and the mean hospital cost was US$5,088.2. After PSM, the mean OS in high- and low-volume hospitals was 71.1 months (standard deviation (SD) 0.7 months) and 68.6 months (SD 0.6 months), respectively; the mean OS in high- and low-volume surgeons was 78.5 months (SD 0.7 months) and 66.9 months (SD 0.7 months), respectively. The PSM analysis showed that treatment by high-volume hospitals and treatment by high-volume surgeons were both associated with significantly shorter LOS, lower hospital cost, and longer survival compared to their low-volume counterparts (P < 0.001). CONCLUSIONS: The results of this nationwide study support the regionalization of HCC treatment by hospital volume and by surgeon volume. High surgeon volume revealed both short- and long-term benefits. The applicability of PSM in volume-outcome analysis may also be confirmed.

Leuconostoc mesenteroides growth in food products: prediction and sensitivity analysis by adaptive-network-based fuzzy inference systems.

BACKGROUND: An adaptive-network-based fuzzy inference system (ANFIS) was compared with an artificial neural network (ANN) in terms of accuracy in predicting the combined effects of temperature (10.5 to 24.5°C), pH level (5.5 to 7.5), sodium chloride level (0.25% to 6.25%) and sodium nitrite level (0 to 200 ppm) on the growth rate of Leuconostoc mesenteroides under aerobic and anaerobic conditions. METHODS: THE ANFIS AND ANN MODELS WERE COMPARED IN TERMS OF SIX STATISTICAL INDICES CALCULATED BY COMPARING THEIR PREDICTION RESULTS WITH ACTUAL DATA: mean absolute percentage error (MAPE), root mean square error (RMSE), standard error of prediction percentage (SEP), bias factor (Bf), accuracy factor (Af), and absolute fraction of variance (R (2)). Graphical plots were also used for model comparison. CONCLUSIONS: The learning-based systems obtained encouraging prediction results. Sensitivity analyses of the four environmental factors showed that temperature and, to a lesser extent, NaCl had the most influence on accuracy in predicting the growth rate of Leuconostoc mesenteroides under aerobic and anaerobic conditions. The observed effectiveness of ANFIS for modeling microbial kinetic parameters confirms its potential use as a supplemental tool in predictive mycology. Comparisons between growth rates predicted by ANFIS and actual experimental data also confirmed the high accuracy of the Gaussian membership function in ANFIS. Comparisons of the six statistical indices under both aerobic and anaerobic conditions also showed that the ANFIS model was better than all ANN models in predicting the four kinetic parameters. Therefore, the ANFIS model is a valuable tool for quickly predicting the growth rate of Leuconostoc mesenteroides under aerobic and anaerobic conditions.

Using Akaike information criterion and minimum mean square error mode in compensating for ultrasonographic errors for estimation of fetal weight by new operators.

OBJECTIVES: The accuracy of ultrasound (US) measurements is operator dependent. In order to decrease the operator-dependent errors in estimated fetal weight (EFW), a model selection analysis was undertaken to select significant compensation weighting factors on ultrasonographic parameters to support artificial neural network (ANN), and thus to enhance the accuracy of fetal weight estimation. MATERIALS AND METHODS: In total, 2127 singletons were examined by prenatal US within 3 days before delivery for ANN development, and another 100 cases were selected from new operators for evaluation. First, correlation analysis was used to analyze the differences between the prenatal and postnatal parameters. Second, Akaike information criterion (AIC) was used to determine the number of database partition and optimal weightings for compensating the input parameters of the ANN model. Finally, minimum mean squared error (MMSE) mode was utilized to determine the optimal EFW. RESULTS: EFW of the proposed compensation model using AIC and MMSE showed mean absolute percent error of 5.1 ± 3.1% and mean absolute error of 158.9 ± 96.2 g. When comparing the accuracy of EFW, our model using AIC and MMSE was superior to those conventional EFW formulas (all p < 0.05). CONCLUSION: We proved that performing the parameter compensation (by AIC) and model compensations (by MMSE) for the ANN model can improve EFW accuracy. Our AIC-MMSE model of EFW will contribute to the improvement of accuracy when adding new US datasets measured by new operators.

Comparison of classification algorithms with wrapper-based feature selection for predicting osteoporosis outcome based on genetic factors in a taiwanese women population.

An essential task in a genomic analysis of a human disease is limiting the number of strongly associated genes when studying susceptibility to the disease. The goal of this study was to compare computational tools with and without feature selection for predicting osteoporosis outcome in Taiwanese women based on genetic factors such as single nucleotide polymorphisms (SNPs). To elucidate relationships between osteoporosis and SNPs in this population, three classification algorithms were applied: multilayer feedforward neural network (MFNN), naive Bayes, and logistic regression. A wrapper-based feature selection method was also used to identify a subset of major SNPs. Experimental results showed that the MFNN model with the wrapper-based approach was the best predictive model for inferring disease susceptibility based on the complex relationship between osteoporosis and SNPs in Taiwanese women. The findings suggest that patients and doctors can use the proposed tool to enhance decision making based on clinical factors such as SNP genotyping data.

Efficient fetal size classification combined with artificial neural network for estimation of fetal weight.

OBJECTIVES: A novel analysis was undertaken to select a significant ultrasonographic parameter (USP) for classifying fetuses to support artificial neural network (ANN), and thus to enhance the accuracy of fetal weight estimation. METHODS: In total, 2127 singletons were examined by prenatal ultrasound within 3 days before delivery. First, correlation analysis was used to determine a significant USP for fetal grouping. Second, K-means algorithm was utilized for fetal size classification based on the selected USP. Finally, stepwise regression analysis was used to examine input parameters of the ANN model. RESULTS: The estimated fetal weight (EFW) of the new model showed mean absolute percent error (MAPE) of 5.26 ± 4.14% and mean absolute error (MAE) of 157.91 ± 119.90 g. Comparison of EFW accuracy showed that the new model significantly outperformed the commonly-used EFW formulas (all p < 0.05). CONCLUSION: We proved the importance of choosing a specific grouping parameter for ANN to improve EFW accuracy.

A two-stage approach to positioning and identification of tracheal intubation using LED-based lightwand and acoustic models.

This paper proposes a two-stage approach to positioning and identifying tracheal intubations by computer-assisted diagnosis system. First, an innovated LED-based lightwand is developed for positioning the opening of the trachea and inserting the endotracheal tube rapidly. The proposed LED-based lightwand instrument significantly reduces the effects of temperature reaction without changing transillumination through the tissue. After intubation, breath sound analysis instrumentation is developed and assists for identifying the accurate tube position. To overcome the fast and overstepped variations of amplitude, a high sensitivity omni dimensional microphone and an automatic gain control device with linear phase property are calibrated and conducted into experiments of operating intubations. User-friendly interface software is also developed to analyze and transcribe the physiological characteristics of the collected breath sound corpus. Several subjective and objective experiments are performed to examine the practicability of the proposed approach and systems. The preliminary results show that the proposed LED-based lightwand instrument outperformed in the tip temperature without the influence on transillumination. The proposed parameters of average vibration parameter variance (AVPV) and frequency-domain energy variance (FEV) revealed the potential for distinguishing between the tracheal and esophageal intubations.

A hospital registration system using syndromes' descriptions analysis and information retrieval technology.

Correct registration in the hospital can avoid wasting medical resources and shorten the time of diagnoses. The purpose of this research is to build a hospital registration system to help patients register in the hospital. This system can assist patients, who are not familiar with medical topics, to determine which department they should register by the spontaneous descriptions. Patients' spontaneous descriptions of syndromes were transcribed into text formation. These descriptive texts were analyzed by the keywords punctuation process and syndromes's descriptions analysis process to get the important features, including the syndrome words, degree words, affected region of body, frequency, time and place. Then, an information retrieval based department making process was employed to calculate the relation score between each department and these important features. Finally, the system suggested the most possible departments they should register to the patients. In the experiment, 50 descriptions collected from 50 patients are as the input to the system. The preliminary result shows the top one correct rate is 88%. The proposed system also shows the higher performance than the baseline system.

Computer-aided auscultation learning system for nursing technique instruction.

Pulmonary auscultation is a physical assessment skill learned by nursing students for examining the respiratory system. Generally, a sound simulator equipped mannequin is used to group teach auscultation techniques via classroom demonstration. However, nursing students cannot readily duplicate this learning environment for self-study. The advancement of electronic and digital signal processing technologies facilitates simulating this learning environment. This study aims to develop a computer-aided auscultation learning system for assisting teachers and nursing students in auscultation teaching and learning. This system provides teachers with signal recording and processing of lung sounds and immediate playback of lung sounds for students. A graphical user interface allows teachers to control the measuring device, draw lung sound waveforms, highlight lung sound segments of interest, and include descriptive text. Effects on learning lung sound auscultation were evaluated for verifying the feasibility of the system. Fifteen nursing students voluntarily participated in the repeated experiment. The results of a paired t test showed that auscultative abilities of the students were significantly improved by using the computer-aided auscultation learning system.

Joint optimization of word alignment and epenthesis generation for Chinese to Taiwanese sign synthesis.

This work proposes a novel approach to translate Chinese to Taiwanese sign language and to synthesize sign videos. An aligned bilingual corpus of Chinese and Taiwanese Sign Language (TSL) with linguistic and signing information is also presented for sign language translation. A two-pass alignment in syntax level and phrase level is developed to obtain the optimal alignment between Chinese sentences and Taiwanese sign sequences. For sign video synthesis, a scoring function is presented to develop motion transition-balanced sign videos with rich combinations of intersign transitions. Finally, the maximum a posteriori (MAP) algorithm is employed for sign video synthesis based on joint optimization of two-pass word alignment and intersign epenthesis generation. Several experiments are conducted in an educational environment to evaluate the performance on the comprehension of sign expression. The proposed approach outperforms the IBM Model 2 in sign language translation. Moreover, deaf students perceived sign videos generated by the proposed method to be satisfactory.

Text generation from Taiwanese Sign Language using a PST-based language model for augmentative communication.

This paper proposes a novel approach to the generation of Chinese sentences from ill-formed Taiwanese Sign Language (TSL) for people with hearing impairments. First, a sign icon-based virtual keyboard is constructed to provide a visualized interface to retrieve sign icons from a sign database. A proposed language model (LM), based on a predictive sentence template (PST) tree, integrates a statistical variable n-gram LM and linguistic constraints to deal with the translation problem from ill-formed sign sequences to grammatical written sentences. The PST tree trained by a corpus collected from the deaf schools was used to model the correspondence between signed and written Chinese. In addition, a set of phrase formation rules, based on trigger pair category, was derived for sentence pattern expansion. These approaches improved the efficiency of text generation and the accuracy of word prediction and, therefore, improved the input rate. For the assessment of practical communication aids, a reading-comprehension training program with ten profoundly deaf students was undertaken in a deaf school in Tainan, Taiwan. Evaluation results show that the literacy aptitude test and subjective satisfactory level are significantly improved.

Error-tolerant sign retrieval using visual features and maximum a posteriori estimation.

This paper proposes an efficient error-tolerant approach to retrieving sign words from a Taiwanese Sign Language (TSL) database. This database is tagged with visual gesture features and organized as a multilist code tree. These features are defined in terms of the visual characteristics of sign gestures by which they are indexed for sign retrieval and displayed using an anthropomorphic interface. The maximum a posteriori estimation is exploited to retrieve the most likely sign word given the input feature sequence. An error-tolerant mechanism based on mutual information criterion is proposed to retrieve a sign word of interest efficiently and robustly. A user-friendly anthropomorphic interface is also developed to assist learning TSL. Several experiments were performed in an educational environment to investigate the system's retrieval accuracy. Our proposed approach outperformed a dynamic programming algorithm in its task and shows tolerance to user input errors.