Novel Three-Dimensional Bladder Reconstruction Model from B-Mode Ultrasound Image to Improve the Accuracy of Bladder Volume Measurement.

Traditional bladder volume measurement from B-mode (two-dimensional) ultrasound has been found to produce inaccurate results, and thus in this work we aim to improve the accuracy of measurement from B-mode ultrasound. A total of 75 electronic medical records including ultrasonic images were reviewed retrospectively from 64 patients. We put forward a novel bladder volume measurement method, in which a three-dimensional (3D) reconstruction model was established from conventional two-dimensional (2D) ultrasonic images to estimate the bladder volume. The differences and relationships were analyzed among the actual volume, the traditional estimated volume, and the new reconstruction model estimated volume. We also compared the data in different volume groups from small volume to high volume. The mean actual volume is 531.8 mL and the standard deviation is 268.7 mL; the mean percentage error of traditional estimation is -28%. In our new bladder measurement method, the mean percentage error is -10.18% (N = 2), -4.72% (N = 3), -0.33% (N = 4), and 2.58% (N = 5). There is no significant difference between the actual volume and our new bladder measurement method (N = 4) in all data or the divided four groups. The estimated volumes from the traditional method or our new method are highly correlated with the actual volume. Our data show that the three-dimensional bladder reconstruction model provides an accurate measurement from conventional B-mode ultrasonic images compared with the traditional method. The accuracy is seen across different groups of volume, and thus we can conclude that this is a reliable and economical volume measurement model that can be applied in general software or in apps on mobile devices.

Lightweight and Resource-Constrained Learning Network for Face Recognition with Performance Optimization.

Despite considerable progress in face recognition technology in recent years, deep learning (DL) and convolutional neural networks (CNN) have revealed commendable recognition effects with the advent of artificial intelligence and big data. FaceNet was presented in 2015 and is able to significantly improve the accuracy of face recognition, while also being powerfully built to counteract several common issues, such as occlusion, blur, illumination change, and different angles of head pose. However, not all hardware can sustain the heavy computing load in the execution of the FaceNet model. In applications in the security industry, lightweight and efficient face recognition are two key points for facilitating the deployment of DL and CNN models directly in field devices, due to their limited edge computing capability and low equipment cost. To this end, this paper provides a lightweight learning network improved from FaceNet, which is called FN13, to break through the hardware limitation of constrained computational resources. The proposed FN13 takes the advantage of center loss to reduce the variations of the between-class features and enlarge the difference of the within-class features, instead of the triplet loss by using FaceNet. The resulting model reduces the number of parameters and maintains a high degree of accuracy, only requiring few grayscale reference images per subject. The validity of FN13 is demonstrated by conducting experiments on the Labeled Faces in the Wild (LFW) dataset, as well as an analytical discussion regarding specific disguise problems.

Anomaly Detection Outperforms Logistic Regression in Predicting Outcomes in Trauma Patients.

OBJECTIVE: Recent advancements in trauma resuscitation have shown a great benefit of early identification and control of hemorrhage, which is the most common cause of death in injured patients. We introduce a new analytical approach, anomaly detection (AD), as an alternative method to the traditional logistic regression (LR) method in predicting which injured patients receive transfusions, intensive care, and other interventions. METHODS: We abstracted routinely collected prehospital vital sign data from patient records (adult patients who survived more than 15 minutes after being directly admitted to a level 1 trauma center). The vital signs of the study cohort were analyzed using both LR and AD methods. Predictions on blood transfusions generated by these approaches were compared with hospital records using the respective areas under the receiver operating characteristic curves (AUROC). RESULTS: Of the patients seen at our trauma center between January 1, 2009, and December 31, 2010, 5,464 were included. AD significantly outperformed LR, identifying which patients would receive transfusions of uncrossmatched blood, transfusion of blood between the time of admission and 6 hours later, the need for intensive care, and in-hospital mortality (mean AUROC = 0.764 and 0.720, respectively). AD and LR provided similar predictions for the patients who would receive massive transfusion. Under the stratified 10 fold times 10 cross-validation test, AD also had significantly lower AUROC variance across subgroups than LR, suggesting AD is a more stable predictions model. CONCLUSIONS: AD provides enhanced predictions for clinically relevant outcomes in the trauma patient cohort studied and may assist providers in caring for acutely injured patients in the prehospital arena.

Reliable Collection of Real-Time Patient Physiologic Data from less Reliable Networks: a "Monitor of Monitors" System (MoMs).

Research and practice based on automated electronic patient monitoring and data collection systems is significantly limited by system down time. We asked whether a triple-redundant Monitor of Monitors System (MoMs) to collect and summarize key information from system-wide data sources could achieve high fault tolerance, early diagnosis of system failure, and improve data collection rates. In our Level I trauma center, patient vital signs(VS) monitors were networked to collect real time patient physiologic data streams from 94 bed units in our various resuscitation, operating, and critical care units. To minimize the impact of server collection failure, three BedMaster® VS servers were used in parallel to collect data from all bed units. To locate and diagnose system failures, we summarized critical information from high throughput datastreams in real-time in a dashboard viewer and compared the before and post MoMs phases to evaluate data collection performance as availability time, active collection rates, and gap duration, occurrence, and categories. Single-server collection rates in the 3-month period before MoMs deployment ranged from 27.8 % to 40.5 % with combined 79.1 % collection rate. Reasons for gaps included collection server failure, software instability, individual bed setting inconsistency, and monitor servicing. In the 6-month post MoMs deployment period, average collection rates were 99.9 %. A triple redundant patient data collection system with real-time diagnostic information summarization and representation improved the reliability of massive clinical data collection to nearly 100 % in a Level I trauma center. Such data collection framework may also increase the automation level of hospital-wise information aggregation for optimal allocation of health care resources.

Development of Ferromagnetic Superspins in Bare Cu Nanoparticles by Electronic Charge Redistribution.

We report on the results of investigating the ferromagnetic properties of bare Cu nanoparticles. Three sets of bare Cu nanoparticle assemblies with mean particle diameters of 6.6, 8.1, and 11.1 nm were fabricated, employing the gas condensation method. Curie-Weiss paramagnetic responses to a weak driving magnetic field were detected, showing the appearance of particle superspins that overcomes the diamagnetic responses from the inner core. The isothermal magnetization displays a Langevin field profile together with magnetic hysteresis appearing even at 300 K, demonstrating the existence of ferromagnetic superspins in the Cu nanoparticles. Shifting of a noticeable amount of electronic charge from being distributed near the lattice sites in bulk form toward their neighboring ions in nanoparticles was found. The extended 3d and 4s band mixture are the main sources for the development of localized 3d holes for the development of ferromagnetic particle superspins in Cu nanoparticles.

Huge Inverse Magnetization Generated by Faraday Induction in Nano-Sized Au@Ni Core@Shell Nanoparticles.

We report on the design and observation of huge inverse magnetizations pointing in the direction opposite to the applied magnetic field, induced in nano-sized amorphous Ni shells deposited on crystalline Au nanoparticles by turning the applied magnetic field off. The magnitude of the induced inverse magnetization is very sensitive to the field reduction rate as well as to the thermal and field processes before turning the magnetic field off, and can be as high as 54% of the magnetization prior to cutting off the applied magnetic field. Memory effect of the induced inverse magnetization is clearly revealed in the relaxation measurements. The relaxation of the inverse magnetization can be described by an exponential decay profile, with a critical exponent that can be effectively tuned by the wait time right after reaching the designated temperature and before the applied magnetic field is turned off. The key to these effects is to have the induced eddy current running beneath the amorphous Ni shells through Faraday induction.

Blood Transfusion Indicators Following Trauma in the Non-Massively Bleeding Patient.

BACKGROUND: Establishing transfusion guidelines during trauma resuscitation is challenging. Our objective was to evaluate indications for transfusion in trauma patients who emergently received ≤2 units of red blood cells (RBC) during the first hour of resuscitation. METHODS: A single center retrospective study included non-massively bleeding trauma patients stratified into 2 groups: 1) with a clinical indication for transfusion and 2) with no indication for transfusion. Admission vital signs (VS), injury severity score (ISS), shock index, and laboratory values were compared between the two groups using the Wilcoxon rank-sum test. RESULTS: Among 111 non-massively bleeding trauma patients, 40 presented no indication for transfusion. All patients presented similar ISS and VS. The 71 patients presenting with an indication for transfusion had higher bicarbonate (22.6 vs 20.8) and lower lactate levels (4.7 v 6.6) (p<0.05). CONCLUSION: Lactate and bicarbonate blood levels may be potential indicators for RBC transfusion need during trauma resuscitation in non-massively bleeding patients.

Open chest cardiac massage offers no benefit over closed chest compressions in patients with traumatic cardiac arrest.

BACKGROUND: Open chest cardiac massage (OCCM) is a commonly performed procedure after traumatic cardiac arrest (TCA). OCCM has been reported to be superior to closed chest compressions (CCC) in animal models and in non-TCA. The purpose of this study is to prospectively compare OCCM versus CCC in TCA using end-tidal carbon dioxide (ETCO2), the criterion standard for determining the effectiveness of chest compressions and detection of return of spontaneous circulation (ROSC), as the surrogate for cardiac output and marker for adequacy of resuscitation. METHODS: This prospective observational study enrolled patients over a 9-month period directly presenting to a level 1 trauma center after TCA. Continuous high-resolution ETCO2 measurements were collected every 6 seconds for periods of CCC and OCCM, respectively. Patients receiving CCC only were compared with patients receiving CCC followed by OCCM. Student's t tests were used to compare ETCO2 within and between groups. RESULTS: Thirty-three patients were enrolled (16 OCCM, 17 CCC-only). Mean time of CCC before OCCM was 66 seconds. Within the OCCM group, final, peak, mean, and median ETCO2 levels significantly increased when comparing the initial CCC period to the OCCM interval. Using a time-matched comparison, significant increases were observed in the final and peak but not mean and median values when comparing the first minute of CCC to the remaining time in the CCC-only group. However, when periods of OCCM were compared with equivalent periods of CCC-only, there were no differences in the initial, final, peak, mean, or median ETCO2 values. Correspondingly, no difference in rates of ROSC was observed between groups (OCCM 23.5% vs. CCC 38.9%; p = 0.53). CONCLUSION: Although we could not control for confounders, we found no significant improvement in ETCO2 or ROSC with OCCM. With newer endovascular techniques for aortic occlusion, thoracotomy solely for performing OCCM provides no benefit over CCC. LEVEL OF EVIDENCE: Therapeutic study, level III.