Evaluating the Diagnostic Potential of Connected Speech for Benign Laryngeal Disease Using Deep Learning Analysis.

OBJECTIVES: This study aimed to evaluate the performance of artificial intelligence (AI) models using connected speech and vowel sounds in detecting benign laryngeal diseases. STUDY DESIGN: Retrospective. METHODS: Voice samples from 772 patients, including 502 with normal voices and 270 with vocal cord polyps, cysts, or nodules, were analyzed. We employed deep learning architectures, including convolutional neural networks (CNNs) and time series models, to process the speech data. The primary endpoint was the area under the receiver's operating characteristic curve for binary classification. RESULTS: CNN models analyzing speech segments significantly outperformed those using vowel sounds in distinguishing patients with and without benign laryngeal diseases. The best-performing CNN model achieved areas under the receiver operating characteristic curve of 0.895 and 0.845 for speech and vowel sounds, respectively. Correlations between AI-generated disease probabilities and perceptual assessments were more pronounced in the connected-speech analyses. However, the time series models performed worse than the CNNs. CONCLUSION: Connected speech analysis is more effective than traditional vowel sound analysis for the diagnosis of laryngeal voice disorders. This study highlights the potential of AI technologies in enhancing the diagnostic capabilities of speech, advocating further exploration, and validation in this field.

Validation of Electrocardiogram Based Photoplethysmogram Generated Using U-Net Based Generative Adversarial Networks.

Photoplethysmogram (PPG) performs an important role in alarming atrial fibrillation (AF). While the importance of PPG is emphasized, there is insufficient amount of openly available atrial fibrillation PPG data. We propose a U-net-based generative adversarial network (GAN) which synthesize PPG from paired electrocardiogram (ECG). To measure the performance of the proposed GAN, we compared the generated PPG to reference PPG in terms of morphology similarity and also examined its influence on AF detection classifier performance. First, morphology was compared using two different metrics against the reference signal: percent root mean square difference (PRD) and Pearson correlation coefficient. The mean PRD and Pearson correlation coefficient were 27% and 0.94, respectively. Heart rate variability (HRV) of the reference AF ECG and the generated PPG were compared as well. The p-value of the paired t-test was 0.248, indicating that no significant difference was observed between the two HRV values. Second, to validate the generated AF PPG dataset, four different datasets were prepared combining the generated PPG and real AF PPG. Each dataset was used to optimize a classification model while maintaining the same architecture. A test dataset was prepared to test the performance of each optimized model. Subsequently, these datasets were used to test the hypothesis whether the generated data benefits the training of an AF classifier. Comparing the performance metrics of each optimized model, the training dataset consisting of generated and real AF PPG showed a test accuracy result of 0.962, which was close to that of the dataset consisting only of real AF PPG data at 0.961. Furthermore, both models yielded the same F1 score of 0.969. Lastly, using only the generated AF PPG dataset resulted in test accuracy of 0.945, indicating that the trained model was capable of generating valuable AF PPG. Therefore, it can be concluded that the generated AF PPG can be used to augment insufficient data. To summarize, this study proposes a GAN-based method to generate atrial fibrillation PPG that can be used for training atrial fibrillation PPG classification models.

Comparison of Patterns of Structural Progression in Primary Open Angle Glaucoma and Pseudoexfoliation Glaucoma.

PRCIS: Primary open angle glaucoma and pseudoexfoliation glaucoma showed different progression patterns of the retinal nerve fiber layer and ganglion cell-inner plexiform layer thinning in OCT-guided progression analysis. PURPOSE: To compare the patterns of progression of retinal nerve fiber layer (RNFL) and macular ganglion cell-inner plexiform layer (GCIPL) thinning by guided progression analysis (GPA) of optical coherence tomography (OCT) in primary open angle glaucoma (POAG) and pseudoexfoliation glaucoma (PXG). MATERIALS AND METHODS: The progression of RNFL and GCIPL thinning was assessed by the GPA of Cirrus HD-OCT (Carl Zeiss Meditec, Dublin, CA). By overlaying the acquired images of the RNFL and GCIPL thickness-change maps, the topographic patterns of progressive RNFL and GCIPL thinning were evaluated. The rates of progression of RNFL and GCIPL thinning were analyzed and compared between patients with POAG and those with PXG. RESULTS: Of the 248 eyes of 248 patients with POAG (175 eyes of 175 patients) or PXG (73 eyes of 73 patients) enrolled, 156 POAG eyes and 48 PXG eyes were included. Progressive RNFL thinning was significantly more common in PXG than in POAG ( P =0.005). According to the RNFL progression-frequency maps, progression appeared mainly in the superotemporal and inferotemporal areas in POAG, whereas it had invaded more into the temporal area in PXG. According to the GCIPL maps, progression was most common in the inferotemporal area in both POAG and PXG. The average progression rate of GCIPL thinning was faster in PXG than in POAG ( P =0.013), and when analyzed in 2 halves (superior/inferior), the progression rate of the inferior half was faster in PXG than in POAG ( P =0.011). CONCLUSIONS: OCT GPA showed progression patterns of RNFL and GCIPL thinning in POAG and PXG. Understanding the specific patterns of progressive RNFL and GCIPL thinning according to glaucoma type may prove helpful to glaucoma-patient treatment and monitoring.

Deep-learning-based prediction of glaucoma conversion in normotensive glaucoma suspects.

BACKGROUND/AIMS: To assess the performance of deep-learning (DL) models for prediction of conversion to normal-tension glaucoma (NTG) in normotensive glaucoma suspect (GS) patients. METHODS: Datasets of 12 458 GS eyes were reviewed. Two hundred and ten eyes (105 eyes showing NTG conversion and 105 without conversion), followed up for a minimum of 7 years during which intraocular pressure (IOP) was lower than 21 mm Hg, were included. The features of two fundus images (optic disc photography and red-free retinal nerve fibre layer (RNFL) photography) were extracted by convolutional auto encoder. The extracted features as well as 15 clinical features including age, sex, IOP, spherical equivalent, central corneal thickness, axial length, average circumpapillary RNFL thickness, systolic/diastolic blood pressure and body mass index were used to predict NTG conversion. Prediction was performed using three machine-learning classifiers (ie, XGBoost, Random Forest, Gradient Boosting) with different feature combinations. RESULTS: All three algorithms achieved high diagnostic accuracy for NTG conversion prediction. The AUCs ranged from 0.987 (95% CI 0.978 to 1.000; Random Forest trained with both fundus images and clinical features) and 0.994 (95% CI 0.984 to 1.000; XGBoost trained with both fundus images and clinical features). XGBoost showed the best prediction performance for time to NTG conversion (mean squared error, 2.24). The top three important clinical features for time-to-conversion prediction were baseline IOP, diastolic blood pressure and average circumpapillary RNFL thickness. CONCLUSION: DL models, trained with both fundus images and clinical data, showed the potential to predict whether and when normotensive GS patients will show conversion to NTG.

Development of generalizable automatic sleep staging using heart rate and movement based on large databases.

Purpose: With the advancement of deep neural networks in biosignals processing, the performance of automatic sleep staging algorithms has improved significantly. However, sleep staging using only non-electroencephalogram features has not been as successful, especially following the current American Association of Sleep Medicine (AASM) standards. This study presents a fine-tuning based approach to widely generalizable automatic sleep staging using heart rate and movement features trained and validated on large databases of polysomnography. Methods: A deep neural network is used to predict sleep stages using heart rate and movement features. The model is optimized on a dataset of 8731 nights of polysomnography recordings labeled using the Rechtschaffen & Kales scoring system, and fine-tuned to a smaller dataset of 1641 AASM-labeled recordings. The model prior to and after fine-tuning is validated on two AASM-labeled external datasets totaling 1183 recordings. In order to measure the performance of the model, the output of the optimized model is compared to reference expert-labeled sleep stages using accuracy and Cohen's κ as key metrics. Results: The fine-tuned model showed accuracy of 76.6% with Cohen's κ of 0.606 in one of the external validation datasets, outperforming a previously reported result, and showed accuracy of 81.0% with Cohen's κ of 0.673 in another external validation dataset. Conclusion: These results indicate that the proposed model is generalizable and effective in predicting sleep stages using features which can be extracted from non-contact sleep monitors. This holds valuable implications for future development of home sleep evaluation systems.

The effectiveness of vitamin D3 supplementation in improving functional outcome of non-surgically treated symptomatic lumbar spinal stenosis: Randomized controlled clinical trial - Pilot study.

BACKGROUND: Although vitamin D is one of the essential nutrients associated with musculoskeletal system function, there is no standard treatment method for vitamin D deficiency. This study aimed to investigate the effects of vitamin D supplementation on the improvement in symptoms, functional recovery of the spine, and changes in the quality of life in patients with spinal stenosis. METHODS: In this prospective study, patients with spinal stenosis and serum 25-hydroxy vitamin D levels < 10 ng/mL were randomly assigned to a supplementation group (Group S) and a non-supplementation group (Group NS): 26 participants in Group S (16 females and 10 males) and 25 in Group NS (15 females and 10 males). The degree of lower back pain in both groups was assessed using the visual analog scale; spine function was assessed using the Oswestry disability index and Roland-Morris disability questionnaire; and patient quality of life was assessed using the 36-item short form health survey. We compared and analyzed the values that were measured at baseline, between 4 and 6 weeks (V1), 10 and 12 weeks (V2), and 22 and 26 weeks (V3). RESULTS: No statistically significant difference was observed in lower back pain, spine function, or quality of life between both groups at baseline. In terms of lower back pain in V1, Group S scored 4.15 ±â€…3.12, while Group NS scored 5.64 ±â€…1.85 (P = .045). In V2, Group S scored 3.15 ±â€…2.38, while Group NS scored 4.52 ±â€…1.87 (P = .027). Moreover, in V3, Group S scored 3.58 ±â€…1.65, while Group NS scored 4.60 ±â€…1.68 (P = .033), indicating a statistically significant improvement in each period. CONCLUSION: If a vitamin D deficiency that does not require surgical treatment exists in patients with lumbar spinal stenosis, high-dose vitamin D injections can improve lower back pain, which is the main symptom of lumbar spinal stenosis, as well as the functional outcomes of the spine and quality of life.

Prediction of Increased Intracranial Pressure in Traumatic Brain Injury Using Quantitative Electroencephalogram in a Porcine Experimental Model.

Continuous and non-invasive measurement of intracranial pressure (ICP) in traumatic brain injury (TBI) is important to recognize increased ICP (IICP), which can reduce treatment delays. The purpose of this study was to develop an electroencephalogram (EEG)-based prediction model for IICP in a porcine TBI model. Thirty swine were anaesthetized and underwent IICP by inflating a Foley catheter in the intracranial space. Single-channel EEG data were collected every 6 min in 10 mmHg increments in the ICP from baseline to 50 mmHg. We developed EEG-based models to predict the IICP (equal or over 25 mmHg) using four algorithms: logistic regression (LR), naive Bayes (NB), support vector machine (SVM), and random forest (RF). We assessed the performance of each model based on the accuracy, sensitivity, specificity, and AUC values. The accuracy of each prediction model for IICP was 0.773 for SVM, 0.749 for NB, 0.746 for RF, and 0.706 for LR. The AUC of each model was 0.860 for SVM, 0.824 for NB, 0.802 for RF, and 0.748 for LR. We developed a machine learning prediction model for IICP using single-channel EEG signals in a swine TBI experimental model. The SVM model showed good predictive power with the highest AUC value.

Feasibility of an inertial measurement unit sensor-based guiding system for benign paroxysmal positional vertigo treatment: A pilot study.

Performing an accurate canalith repositioning procedure (CRP) is important for treating benign paroxysmal positional vertigo, because inadequate rotational head angles can result in ineffective otolith mobilization and consequent treatment failure. Specialists-guided Epley maneuver reportedly had mean errors of 13.7°-24.4° while they were significantly larger (40.0°-51.5°) when self-administered. Similar results were obtained for the Barbeque maneuver: mean errors were 9.2°-13.0° by the specialists while they were significantly larger (22.9°-28.6°) when self-administered. Our study aimed to validate the feasibility of an inertial measurement unit sensor-based CRP (IMU-CRP) by analyzing the differences in accuracy in the rotational angles, comparing them with education-based conventional CRP (EDU-CRP). A pilot validation was also performed by analyzing the treatment success rate of IMU-CRP in patients with BPPV. This single-institution prospective, comparative effectiveness study examined 19 participants without active vertigo or prior knowledge of benign paroxysmal positional vertigo and CRP. Participants conducted the Epley and Barbeque roll maneuvers without and with auditory guidance (EDU-CRP vs. IMU-CRP, respectively) twice, and head rotation accuracies were compared. Differences in target angles based on the American Academy of Otolaryngology-Head and Neck Surgery guidelines were considered errors. For BPPV participants, treatment success was assessed based on the presence or absence of nystagmus, vertigo, and dizziness. For all the Epley and Barbeque roll maneuvers steps, the absolute errors were smaller for IMU- than for EDU-CRPs, with significant differences in steps 2-4 and 3-6 of the Epley and Barbeque roll maneuvers, respectively. A learning effect was found in steps 4 and 5 of the Barbeque roll maneuver but not in the Epley maneuver. The treatment success rates after 1 h were 71.4% and 100% for the Epley and Barbeque roll maneuvers, respectively. Real-time feedback on head rotation angles induced more appropriate movements in the Epley and Barbeque roll maneuvers. A guiding device based on head monitoring providing real-time auditory feedback may increase the self-administered CRP success rates in treating benign paroxysmal positional vertigo.

Patterns of Optical Coherence Tomography Imaging in Preperimetric Open Angle Glaucoma: A Comparative Study With Young-Age-Onset and Old-Age-Onset Eyes.

PRCIS: Optic coherence tomography imaging in preperimetric open angle glaucoma (OAG) differed between young-age-onset and old-age-onset eyes. Inferior and superior quadrants were thinner in young and old-age-onset eyes, respectively. Understanding the specific patterns of early glaucomatous damage based on age-at-onset may improve glaucoma diagnosis and monitoring. PURPOSE: To investigate the patterns of retinal nerve fiber layer (RNFL) and macular ganglion cell-inner plexiform layer (GCIPL) thinning in preperimetric OAG by optical coherence tomography based on age at onset ("young-age onset (<40 y)" vs. "old-age onset (≥40 y)". MATERIALS AND METHODS: The RNFL and GCIPL deviation images were acquired by Cirrus HD-optical coherence tomography, and overlaid, thus converted to a "deviation frequency map", respectively. The topographic thinning patterns and parameters of RNFL and GCIPL thickness measurements were compared. RESULTS: A total of 194 eyes of 194 patients with preperimetric OAG and 97 eyes of 97 age-matched normal subjects were analyzed. Young-age-onset eyes of preperimetric OAG mainly had RNFL defects inferotemporally (264-296 degrees) with GCIPL defects in the inferior region (213-357 degrees). Old-age-onset preperimetric OAG eyes had RNFL defects inferotemporally (266-294°) and superotemporally (33-67 degrees), with GCIPL defects in the inferior and superior regions (206-360 degrees, 0-22 degrees). The inferior quadrant of RNFL and GCIPL thicknesses were significantly thinner in young-age-onset eyes compared with old-age-onset eyes ( P =0.012, 0.016), while the superior quadrant of those were significantly thinner in the old-age-onset eyes ( P =0.003, 0.005). CONCLUSION: Young-age-onset and old-age-onset eyes of preperimetric OAG present different specific patterns of RNFL and GCIPL thinning.

Feasibility study of a novel rectal cooling system for hypothermic radical prostatectomy in a swine model.

Thermal damage and inflammatory responses of the sphincter and neurovascular bundles (NVBs) are responsible for post-prostatectomy incontinence and erectile dysfunction. Intraoperative hypothermia in the pelvic cavity may reduce the occurrence of these complications. We evaluated the feasibility of a novel rectal cooling system using an animal model. A novel rectal cooling system consisting of a cooling console and a multi-lumen rectal balloon was developed. We conducted animal tests on male pigs to evaluate the efficacy and safety of the system. The primary endpoint was to maintain the temperature of the NVBs at 25℃ (±5℃) during and after the electrocauterization of the bladder neck for 10 seconds. The safety endpoint was device-related complications or significant changes in the core body temperature of the pigs. The NVB temperature was below 30℃ within 3 minutes of activation of the rectal balloon. The temperature of the proximal NVB was consistently maintained below 25℃ in all cases. The temperature 1 cm from the bladder neck did not rise above 38℃ and dropped to the initial level within 1 minute after electrocauterization. During cooling, the minimum temperature at the apex of the prostate was reduced to 10.1℃. There were no device-related complications or significant changes in core body temperature throughout the experiment. Animal tests suggest the feasibility and safety of this novel rectal cooling system. A first-in-human trial to assess the safety and efficacy of this system during radical prostatectomy is warranted.

Augmented Reality-Based Visual Cue for Guiding Central Catheter Insertion in Pediatric Oncologic Patients.

BACKGROUND: Pediatric hemato-oncologic patients require central catheters for chemotherapy, and the junction of the superior vena cava and right atrium is considered the ideal location for catheter tips. Skin landmarks or fluoroscopic supports have been applied to identify the cavoatrial junction; however, none has been recognized as the gold standard. Therefore, we aim to develop a safe and accurate technique using augmented reality technology for the location of the cavoatrial junction in pediatric hemato-oncologic patients. METHODS: Fifteen oncology patients who underwent chest computed tomography were enrolled for Hickman catheter or chemoport insertion. With the aid of augmented reality technology, three-dimensional models of the internal jugular veins, external jugular veins, subclavian veins, superior vena cava, and right atrium were constructed. On inserting the central vein catheters, the cavoatrial junction identified using the three-dimensional models were marked on the body surface, the tip was positioned at the corresponding location, and the actual insertion location was confirmed using a portable x-ray machine. The proposed method was evaluated by comparing the distance from the cavoatrial junction to the augmented reality location with that to the conventional location on x-ray. RESULTS: The mean distance between the cavoatrial junction and augmented reality location on x-ray was 1.2 cm, which was significantly shorter than that between the cavoatrial junction and conventional location (1.9 cm; P = 0.027). CONCLUSIONS: Central catheter insertion using augmented reality technology is more safe and accurate than that using conventional methods and can be performed at no additional cost in oncology patients.

Effect of an anti-adhesion agent on vision-based assessment of cervical adhesions after thyroid surgery: randomized, placebo-controlled trial.

Many patients experience cervical adhesions after thyroid surgery. To date, however, no studies have objectively measured the effects of anti-adhesion agents on cervical adhesion symptoms. This study evaluated the effects of an anti-adhesion agent on cervical adhesions after thyroid surgery, as determined using a system that measures the extent of marker movement objectively. One hundred patients were randomized in a 1:1 ratio to undergo thyroid surgery with or without the anti-adhesion agent Collabarrier. Using specially manufactured recording equipment, the position of the marker on neck skin was measured before surgery, and 2 weeks, 3 months, and 9 months after surgery. Relative change in marker distance, calculated by subtracting the marker position before surgery from the marker positions 2 weeks, 3 months, and 9 months after surgery, differed significantly in the groups of patients who underwent thyroid surgery with and without the anti-adhesion agent (P < 0.05). A novel measuring system can objectively evaluate the effectiveness of a thyroid anti-adhesion agent. The anti-adhesion agent used significantly reduced adhesions compared with the control group. The trial is registered at www.cris.nih.go.kr (KCT0005745; date of registration, 08/01/2021).

EEG-Based Prediction of the Recovery of Carotid Blood Flow during Cardiopulmonary Resuscitation in a Swine Model.

The recovery of cerebral circulation during cardiopulmonary resuscitation (CPR) is important to improve the neurologic outcomes of cardiac arrest patients. To evaluate the feasibility of an electroencephalogram (EEG)-based prediction model as a CPR feedback indicator of high- or low-CBF carotid blood flow (CBF), the frontal EEG and hemodynamic data including CBF were measured during animal experiments with a ventricular fibrillation (VF) swine model. The most significant 10 EEG parameters in the time, frequency and entropy domains were determined by neighborhood component analysis and Student's t-test for discriminating high- or low-CBF recovery with a division criterion of 30%. As a binary CBF classifier, the performances of logistic regression, support vector machine (SVM), k-nearest neighbor, random forest and multilayer perceptron algorithms were compared with eight-fold cross-validation. The three-order polynomial kernel-based SVM model showed the best accuracy of 0.853. The sensitivity, specificity, F1 score and area under the curve of the SVM model were 0.807, 0.906, 0.853 and 0.909, respectively. An automated CBF classifier derived from non-invasive EEG is feasible as a potential indicator of the CBF recovery during CPR in a VF swine model.

Ultrasonic blood flowmeter with a novel Xero algorithm for a mechanical circulatory support system.

Mechanical circulatory support systems (MCSSs) are crucial devices for transplants in patients with heart failure. The blood flowing through the MCSS can be recirculated or even stagnated in the event of critical blood flow issues. To avoid emergencies due to abnormal changes in the flow, continuous changes of the flowrate should be measured with high accuracy and robustness. For better flowrate measurements, a more advanced ultrasonic blood flowmeter (UFM), which is a noninvasive measurement tool, is needed. In this paper, we propose a novel UFM sensor module using a novel algorithm (Xero) that can exploit the advantages of both conventional cross-correlation (Xcorr) and zero-crossing (Zero) algorithms, using only the zero-crossing-based algorithm. To ensure the capability of our own developed and optimized ultrasonic sensor module for MCSSs, the accuracy, robustness, and continuous monitoring performance of the proposed algorithm were compared to those of conventional algorithms after application to the developed sensor module. The results show that Xero is superior to other algorithms for flowrate measurements under different environments and offers an error rate of at least 0.92%, higher robustness for changing fluid temperatures than conventional algorithms, and sensitive responses to sudden changes in flowrates. Thus, the proposed UFM system with Xero has a great potential for flowrate measurements in MCSSs.

Suture tie-down forces and cyclic contractile forces after an undersized tricuspid annuloplasty using a 3-dimensional rigid ring in an ovine model.

OBJECTIVES: This study was conducted to measure suture tie-down forces and evaluate cyclic contractile forces (CCFs) in beating hearts after undersized 3-dimensional (3D) rigid-ring tricuspid valve annuloplasty (TAP). METHODS: Eight force transducers were attached to the 3D rigid TAP ring. Segments 1 to 8 were attached from the mid-septal to anterior-septal commissural area in a counterclockwise order. Two-sizes-down ring TAPs were performed in 6 sheep. Tie-down forces and CCF were recorded and analysed at the 8 annular segments and at 3 levels of peak right ventricular pressure (RVP: 30, 50 and 70 mmHg). RESULTS: The overall average tie-down forces and CCF were 4.34 ± 2.26 newtons (N) and 0.23 ± 0.09 N, respectively. The CCF at an RVP of 30 mmHg were higher at 3 commissural areas (segments 3, 5 and 8) than at the other segments. The increases in the CCF following changes in the RVP were statistically significant only at the 3 commissural areas (P = 0.012). However, mean CCFs remained low at all annular positions (ranges of average CCF = 0.06-0.46 N). CONCLUSIONS: The risk of suture dehiscence after down-sized 3D rigid-ring TAP might be minimal because the absolute forces remained low in all annular positions even in the condition of high RVP. However, careful suturing in the septal annular area and commissures is necessary to prevent an annular tear during a down-sized 3D rigid-ring TAP.

Prediction of cerebral perfusion pressure during CPR using electroencephalogram in a swine model of ventricular fibrillation.

BACKGROUND: Measuring the quality of cardiopulmonary resuscitation (CPR) is important for improving outcomes in cardiac arrest. Cerebral perfusion pressure (CePP) could represent cerebral circulation during CPR, but it is difficult to measure non-invasively. In this study, we developed the electroencephalogram (EEG) based brain index (EBRI) derived from EEG signals by machine learning techniques, which could estimate CePP accurately in a porcine cardiac arrest model. METHODS: We conducted a randomised crossover study using nine female pigs. After 1 min of untreated ventricular fibrillation, we performed CPR with 12 different 2-min tilting angle sessions, including two different head-up tilt (HUT) angles (30°, 15°) twice, horizontal angle (0°) four times and two different head-down tilt (HDT) angles (-15°, -30°) twice with the random order. We collected EEG signals using a single channel EEG electrode in real-time during CPR. We derived the EBRI models to predict the CePP classified by the 5 or 10 groups using three different machine learning algorithms, including the support vector machine (SVM), k-nearest neighbour (KNN) and random forest classification (RFC) method. We assessed the accuracy, sensitivity and specificity of each model. RESULTS: The accuracy of the EBRI model using an SVM algorithm in the 5-group CePP classification was 0.935 with a standard deviation (SD) from 0.923 to 0.946. The accuracy in the 10-group classification was 0.904 (SD: 0.896, 0.913). The accuracy of the EBRI using the KNN method in the 5-group classification was 0.927 (SD: 0.920, 0933) and in the 10-group was 0.894 (SD: 0.880, 0.907). The accuracy of the RFC algorithm was 0.947 (SD: 0.931, 0.963) in the 5-group classification and 0.920 (SD: 0.911, 0.929) in the 10-group classification. CONCLUSION: We developed the EBRI model using non-invasive acquisition of EEG signals to predict CePP during CPR. The accuracy the EBRI model was 0.935, 0.927 and 0.947 for each machine learning algorithm, and the EBRI could be used as a surrogate indicator for measuring cerebral perfusion during CPR.

Quantitative assessment of self-treated canalith repositioning procedures using inertial measurement unit sensors.

BACKGROUND: Low success and high recurrence of benign paroxysmal positional vertigo (BPPV) after home-based self-treated Epley and Barbeque (BBQ) roll maneuvers is an important issue. OBJECTIVE: To quantify the cause of low success rate of self-treated Epley and BBQ roll maneuvers and provide a clinically acceptable criterion to guide self-treatment head rotations. METHODS: Twenty-five participants without active BPPV wore a custom head-mount rotation monitoring device for objective measurements. Self-treatment and specialist-assisted maneuvers were compared for head rotation accuracy. Absolute differences between the head rotation evaluation criteria (American Academy of Otolaryngology guidelines) and measured rotation angles were considered as errors. Self-treatment and specialist-treated errors in maneuvers were compared. Between-trial variations and age effects were evaluated. RESULTS: A significantly large error and between-trial variation occurred in step 4 of the self-treated Epley maneuver, with a considerable error in the second trial. The cumulative error of all steps of self-treated BBQ roll maneuver was significantly large. Age effect occurred only in the self-treated BBQ roll maneuver. Errors in specialist-treated maneuvers ranged from 10 to 20 degrees. CONCLUSIONS: Real-time feedback of head movements during simultaneous head-body rotations could increase success rates of self-treatments. Specialist-treated maneuvers can be used as permissible rotation margin criteria.

Point-of-care testing of plasma free hemoglobin and hematocrit for mechanical circulatory support.

Hematological analysis is essential for patients who are supported by a mechanical circulatory support (MCS). The laboratory methods used to analyze blood components are conventional and accurate, but they require a mandatory turn-around-time for laboratory results, and because of toxic substances, can also be hazardous to analysis workers. Here, a simple and rapid point-of-care device is developed for the measurement of plasma free hemoglobin (PFHb) and hematocrit (Hct), based on colorimetry. The device consists of camera module, minimized centrifuge system, and the custom software that includes the motor control algorithm for the centrifuge system, and the image processing algorithm for measuring the color components of blood from the images. We show that our device measured PFHb with a detection limit of 0.75 mg/dL in the range of (0-100) mg/dL, and Hct with a detection limit of 2.14% in the range of (20-50)%. Our device had a high correlation with the measurement method generally used in clinical laboratories (PFHb R = 0.999, Hct R = 0.739), and the quantitative analysis resulted in precision of 1.44 mg/dL for PFHb value of 14.5 mg/dL, 1.36 mg/dL for PFHb value of 53 mg/dL, and 1.24% for Hct 30%. Also, the device can be measured without any pre-processing when compared to the clinical laboratory method, so results can be obtained within 5 min (about an 1 h for the clinical laboratory method). Therefore, we conclude that the device can be used for point-of-care measurement of PFHb and Hct for MCS.

Clinical outcome prediction from analysis of microelectrode recordings using deep learning in subthalamic deep brain stimulation for Parkinson`s disease.

BACKGROUND: Deep brain stimulation (DBS) of the subthalamic nucleus (STN) is an effective treatment for improving the motor symptoms of advanced Parkinson's disease (PD). Accurate positioning of the stimulation electrodes is necessary for better clinical outcomes. OBJECTIVE: We applied deep learning techniques to microelectrode recording (MER) signals to better predict motor function improvement, represented by the UPDRS part III scores, after bilateral STN DBS in patients with advanced PD. If we find the optimal stimulation point with MER by deep learning, we can improve the clinical outcome of STN DBS even under restrictions such as general anesthesia or non-cooperation of the patients. METHODS: In total, 696 4-second left-side MER segments from 34 patients with advanced PD who underwent bilateral STN DBS surgery under general anesthesia were included. We transformed the original signal into three wavelets of 1-50 Hz, 50-500 Hz, and 500-5,000 Hz. The wavelet-transformed MER was used for input data of the deep learning. The patients were divided into two groups, good response and moderate response groups, according to DBS on to off ratio of UPDRS part III score for the off-medication state, 6 months postoperatively. The ratio were used for output data in deep learning. The Visual Geometry Group (VGG)-16 model with a multitask learning algorithm was used to estimate the bilateral effect of DBS. Different ratios of the loss function in the task-specific layer were applied considering that DBS affects both sides differently. RESULTS: When we divided the MER signals according to the frequency, the maximal accuracy was higher in the 50-500 Hz group than in the 1-50 Hz and 500-5,000 Hz groups. In addition, when the multitask learning method was applied, the stability of the model was improved in comparison with single task learning. The maximal accuracy (80.21%) occurred when the right-to-left loss ratio was 5:1 or 6:1. The area under the curve (AUC) was 0.88 in the receiver operating characteristic (ROC) curve. CONCLUSION: Clinical improvements in PD patients who underwent bilateral STN DBS could be predicted based on a multitask deep learning-based MER analysis.