Yueh-Sheng Chen's research while affiliated with Chang Gung Memorial Hospital and other places

Patients with Parkinson’s disease (PD) may develop cognitive symptoms of impulse control disorders (ICDs) when chronically treated with dopamine agonist (DA) therapy for motor deficits. Motor and cognitive comorbidities critically increase the disability and mortality of the affected patients. This study proposes an electroencephalogram (EEG)-driven machine-learning scenario to automatically assess ICD comorbidity in PD. We employed a classic Go/NoGo task to appraise the capacity of cognitive and motoric inhibition with a low-cost, custom LEGO-like headset to record task-relevant EEG activity. Further, we optimized a support vector machine (SVM) and support vector regression (SVR) pipeline to learn discriminative EEG spectral signatures for the detection of ICD comorbidity and the estimation of ICD severity, respectively. With a dataset of 21 subjects with typical PD, 9 subjects with PD and ICD comorbidity (ICD), and 25 healthy controls (HC), the study results showed that the SVM pipeline differentiated subjects with ICD from subjects with PD with an accuracy of 66.3% and returned an around-chance accuracy of 53.3% for the classification of PD versus HC subjects without the comorbidity concern. Furthermore, the SVR pipeline yielded significantly higher severity scores for the ICD group than for the PD group and resembled the ICD vs. PD distinction according to the clinical questionnaire scores, which was barely replicated by random guessing. Without a commercial, high-precision EEG product, our demonstration may facilitate deploying a wearable computer-aided diagnosis system to assess the risk of DA-triggered cognitive comorbidity in patients with PD in their daily environment.

Background: Evidence showed that patients with Parkinson's disease (PD) who have a history of freezing of gait (FOG) have hypometric anticipatory postural adjustment (APA) during gait initiation (GI) compared to PD without FOG. Objectives: This study aimed to test the feasibility of center of pressure (COP) displacement during GI as the measure of APA in PD with and without a history of FOG. Methods: Patients with PD underwent COP trajectory measurements, including duration, length, velocity, and acceleration in different phases of APA (APA1, APA2a, APA2, and LOC), as well as evaluation of New Freezing of Gait Questionnaire (NFOG-Q), Tinetti balance and gait score, and Postural Instability and Gait Difficulty (PIGD) score in the on and off medication states. Results: The duration (seconds) of APA2a, APA2b, and LOC were highest while velocity in mediolateral direction (X) (m/s), including APA1, APA2a, APA2b, and LOC showed lowest in PD with FOG. Velocity in the mediolateral direction in different phases of APA increased in patients with FOG after dopaminergic therapy. APA2a (seconds) and APA2b (X) (m/s) were significantly associated with NFOG-Q part II, APA2b (X) (m/s) was significantly associated with NFOG-Q part III, and APA2a (seconds) was significantly associated with Tinetti balance and gait and PIGD score. Conclusions: PD with FOG history showed a favorable response of APAs to dopaminergic replacement. The APA parameters by COP trajectory, especially lateral COP shift toward the stance foot (APA2b (X) (m/s) and APA2a (seconds)) are surrogate markers to assess PD with FOG history.

Objective: We aimed to develop a deep learning artificial intelligence (AI) algorithm to detect impacted animal bones on lateral neck radiographs and to assess its effectiveness for improving the interpretation of lateral neck radiographs. Methods: Lateral neck radiographs were retrospectively collected for patients with animal bone impaction between January 2010 and March 2020. Radiographs were then separated into training, validation, and testing sets. A total of 1733 lateral neck radiographs were used to develop the deep learning algorithm. The testing set was assessed for the stand-alone deep learning AI algorithm and for human readers (radiologists, radiology residents, emergency physicians, ENT physicians) with and without the aid of the AI algorithm. Another radiograph cohort, collected from April 1, 2020, to June 30, 2020, was analyzed to simulate clinical application by comparing the deep learning AI algorithm with radiologists' reports. Results: In the testing set, the sensitivity, specificity, and accuracy of the AI model were 96%, 90%, and 93% respectively. Among the human readers, all physicians of different subspecialties achieved a higher accuracy with AI-assisted reading than without. In the simulation set, among the 20 cases positive for animal bones, the AI model accurately identified 3 more cases than the radiologists' reports. Conclusion: Our deep learning AI model demonstrated a higher sensitivity for detection of animal bone impaction on lateral neck radiographs without an increased false positive rate. The application of this model in a clinical setting may effectively reduce time to diagnosis, accelerate workflow, and decrease the use of CT.

Objective: To test the feasibility of objective assessments using the TekScan MatScan pressure mat plantar pressure measurement as a time-effective screening service for Parkinson's disease (PD) with and without freezing of gait (FOG) history. Design: A prospective cross-sectional study. Setting: The largest medical center in southern Taiwan. Interventions: Not applicable. Main outcome measure(s): Plantar pressure measurements including average peak pressure (PP), contact distribution (CA), and pressure time integral (PTI) in static and dynamic conditions as well as clinical scores during off-medication states. Participants: One hundred and three patients with PD and the other 22 age-sex-match healthy volunteers. Results: Plantar pressure assessment including PP, CA, and PTI on the total foot (TF) areas between PD and healthy control in the static conditions are similar. Patients with PD represented higher PTI on TF areas as well as hallux, midfoot area, and medial and lateral hells during dynamic conditions as compared with healthy control. The PP, CA, and PTI during the static condition and CA during the dynamic condition on the hallux showed statistical significance between PD with and without FOG history. Stepwise logistic regression after controlling with age and BMI showed only PTI on hallux (static conditions) was significantly associated with the presence of FOG. The receiver operating characteristic curve analysis in diagnostic accuracy for FOG in PTI were statistically significant (p=0.002, Area under the curve: 0.71). Conclusion: FOG screening using the TekScan MatScan pressure mat plantar pressure measurement could serve as a time-effective screening service at the outpatient clinic. Based on our study, PTI may be valuable in auxiliary diagnosis.

In this study, nonwoven fabrics, rigid polyurethane foam (RPUF), Basalt woven fabrics, and an aluminum foil film mold are used to produce multi-functional composite sheets with flame-retardant, sound-absorbing, and electromagnetic-shielding functions. The nonwoven layer is composed of Nomex fibers, flame-retardant PET fibers, and low-melting-point (LMPET) fibers via the needle rolling process. The optimal Nomex fiber/flame-retardant PET fiber/LMPET fiber (N/F/L) nonwoven fabrics are then combined with rigid polyurethane (PU) foam, Basalt woven fabric, and an aluminum foil film mold, thereby producing nonwoven/rigid polyurethane foam/Basalt woven fabric composite sheets that are wrapped in the aluminized foil film. The test results indicate that formed with a foaming density of 60 kg/m3 and 10 wt% of a flame retardant, the composite sheets exhibit electromagnetic interference shielding efficacy (EMI SE) that exceeds 40 dB and limiting oxygen index (LOI) that is greater than 26. The efficient and highly reproducible experimental design proposed in this study can produce multifunctional composite sheets that feature excellent combustion resistance, sound absorption, and EMI SE and are suitable for use in the transportation, industrial factories, and building wall fields.

Evidence supports the view that postural sway in a quiet stance increases with clinical disease severity and dopaminergic therapy in idiopathic Parkinson’s disease (PD), which, in turn, increases the risk of falling. This study evaluated the feasibility of combining disease-specific and balance-related measures as risk predictors for future falls in patients with PD. The patients with PD underwent postural sway measurements (area, length, and velocity traveled by the excursion of the center of pressure) and clinical functional scores (Parkinson’s Disease Rating Scale [UPDRS] and Tinetti balance and gait score assessment) in both the on- and off-states of dopaminergic therapy. The outcome was defined as the development of a new fall. The sway area, velocity, and length increased after the medication administration. The Cox proportional hazards model showed that only previous fall history, Tinetti balance and gait score (on-state), and levodopa equivalent daily dose (LEDD) were associated with the development of future falls. The cumulative risk of fall development showed that the sway length and velocity were associated with future falls after more than six months. The combined LEDD, Tinetti balance and gait score (on-state), and velocity and length of postural sway (on-state) had the highest diagnostic accuracy (area under the curve = 0.9, p < 0.0001). Dopaminergic therapy can improve clinical functional scores but worsen balance-related measures. Increased sway length and velocity during the medication state are hallmarks of future falls, particularly in advanced PD. Combining disease-specific and balance-related measures can serve as an auxiliary diagnosis as risk predictors for future falls.

Introduction Radiofrequency ablation (RFA) is recognized as an effective technique for the treatment of benign thyroid nodules (BTNs), although the long-term results are limited. This study aimed to evaluate the residual vital volume increase, regrowth, and new growth over a 2-year period after RFA among different nodule volume groups. Subjects and Methods This retrospective study evaluated 135 patients with 153 BTNs who underwent ultrasound guided RFA. The BTNs were categorized into small (<10 mL), medium (10–30 mL), and large (>30 mL) according to the initial volume of BTNs prior to ablation. The volume changes of each nodule were analyzed at 1, 3, 6, 12 and 24 months after RFA. New growth was defined as the growth in volume not found in the early follow-up on ultrasonography. Results The initial ablation ratio of all BTNs was 99.67%. The mean volume reduction ratio (VRR) of BTNs was 85.53% after 2-year follow-up. The small nodule group showed a lower VRR compared to the other two groups at the 1-month follow-up, and there was no difference of VRR at the subsequent follow-ups. The incidence of residual vital volume increase was 4.58%. The overall incidence of regrowth was 3.92% and the mean timing of regrowth was 16.71 months. New growth occurred in 18.95% of patients. No further treatment was required in the majority of cases. Conclusion RFA achieved a clinically relevant volume reduction in different sizes of single BTNs which persisted for at least 2 years, thereby preventing the need for retreatment.

(1) Background: The Alberta Stroke Program Early CT Score (ASPECTS) is a standardized scoring tool used to evaluate the severity of acute ischemic stroke (AIS) on non-contrast CT (NCCT). Our aim in this study was to automate ASPECTS. (2) Methods: We utilized a total of 258 patient images with suspected AIS symptoms. Expert ASPECTS readings on NCCT were used as ground truths. A deep learning-based automatic detection (DLAD) algorithm was developed for automated ASPECTS scoring based on 168 training patient images using a convolutional neural network (CNN) architecture. An additional 90 testing patient images were used to evaluate the performance of the DLAD algorithm, which was then compared with ASPECTS readings on NCCT as performed by physicians. (3) Results: The sensitivity, specificity, and accuracy of DLAD for the prediction of ASPECTS were 65%, 82%, and 80%, respectively. These results demonstrate that the DLAD algorithm was not inferior to radiologist-read ASPECTS on NCCT. With the assistance of DLAD, the individual sensitivity of the ER physician, neurologist, and radiologist improved. (4) Conclusion: The proposed DLAD algorithm exhibits a reasonable ability for ASPECTS scoring on NCCT images in patients presenting with AIS symptoms. The DLAD algorithm could be a valuable tool to improve and accelerate the decision-making process of front-line physicians.

Introduction This study aimed to investigate and compare the therapeutic efficacy and safety of ultrasound-guided radiofrequency ablation (RFA), between primary hyperparathyroidism (PHPT) and secondary hyperparathyroidism (SHPT) patients, with or without previous parathyroidectomy (PTX). Subjects and Methods A total of 21 patients (7 PHPT, 14 SHPT) underwent RFA for hyperparathyroidism (HPT) at Kaohsiung Chang Gung Memorial Hospital, Taiwan. Five of the 14 SHPT patients had previously received PTX. The laboratory data, volume change of each parathyroid nodule, symptomatic scores, and complications were analyzed and compared between all groups at 1 and 7 days, and at 1, 3, 6, and 12 months after RFA. Results After RFA, the volume reduction ratio (VRR) for all patients at the last follow-up was 93.76%, and clinical symptoms significantly improved. At 12 months, all PHPT patients achieved successful treatment of intact PTH (iPTH). In SHPT patients, the mean iPTH value significantly decreased 1-day post-RFA, subsequently exhibiting a transient rebound which proceeded to decrease, with 57.1% reaching successful treatment standards. SHPT patients with PTX showed a lower complication score, shorter ablation time, higher iPTH baseline and outcomes, and lower VRR, compared to patients without PTX. The serum calcium level significantly decreased to normal range in 85.7% of all patients at 12 months. Severe hypocalcemia occurred in 23.8% at 1 week, and all were corrected with calcium supplements. Conclusions RFA demonstrates a therapeutic efficacy similar to PTX. It can thus be considered an effective alternative treatment for PHPT, SHPT, or post-PTX patients who are unsuitable for another PTX.