The implication of oversampling on the effectiveness of force signals in the fault detection of endodontic instruments during RCT.

This work provides an innovative endodontic instrument fault detection methodology during root canal treatment (RCT). Sometimes, an endodontic instrument is prone to fracture from the tip, for causes uncertain the dentist's control. A comprehensive assessment and decision support system for an endodontist may avoid several breakages. This research proposes a machine learning and artificial intelligence-based approach that can help to diagnose instrument health. During the RCT, force signals are recorded using a dynamometer. From the acquired signals, statistical features are extracted. Because there are fewer instances of the minority class (i.e. faulty/moderate class), oversampling of datasets is required to avoid bias and overfitting. Therefore, the synthetic minority oversampling technique (SMOTE) is employed to increase the minority class. Further, evaluating the performance using the machine learning techniques, namely Gaussian Naïve Bayes (GNB), quadratic support vector machine (QSVM), fine k-nearest neighbor (FKNN), and ensemble bagged tree (EBT). The EBT model provides excellent performance relative to the GNB, QSVM, and FKNN. Machine learning (ML) algorithms can accurately detect endodontic instruments' faults by monitoring the force signals. The EBT and FKNN classifier is trained exceptionally well with an area under curve values of 1.0 and 0.99 and prediction accuracy of 98.95 and 97.56%, respectively. ML can potentially enhance clinical outcomes, boost learning, decrease process malfunctions, increase treatment efficacy, and enhance instrument performance, contributing to superior RCT processes. This work uses ML methodologies for fault detection of endodontic instruments, providing practitioners with an adequate decision support system.

A RUL Estimation System from Clustered Run-to-Failure Degradation Signals.

The prognostics and health management disciplines provide an efficient solution to improve a system's durability, taking advantage of its lifespan in functionality before a failure appears. Prognostics are performed to estimate the system or subsystem's remaining useful life (RUL). This estimation can be used as a supply in decision-making within maintenance plans and procedures. This work focuses on prognostics by developing a recurrent neural network and a forecasting method called Prophet to measure the performance quality in RUL estimation. We apply this approach to degradation signals, which do not need to be monotonical. Finally, we test our system using data from new generation telescopes in real-world applications.

A Robust Deep Neural Network for Rolling Element Fault Diagnosis under Various Operating and Noisy Conditions.

This study proposes a new intelligent diagnostic method for bearing faults in rotating machinery. The method uses a combination of nonlinear mode decomposition based on the improved fast kurtogram, gramian angular field, and convolutional neural network to detect the bearing state of rotating machinery. The nonlinear mode decomposition based on the improved fast kurtogram inherits the advantages of the original algorithm while improving the computational efficiency and signal-to-noise ratio. The gramian angular field can construct a two-dimensional image without destroying the time relationship of the signal. Therefore, the proposed method can perform fault diagnosis on rotating machinery under complex operating conditions. The proposed method is verified on the Paderborn dataset under heavy noise and multiple operating conditions to evaluate its effectiveness. Experimental results show that the proposed model outperforms wavelet denoising and the traditional adaptive decomposition method. The proposed model achieves over 99.6% accuracy in all four operating conditions provided by this dataset, and 93.8% accuracy in a strong noise environment with a signal-to-noise ratio of -4 dB.

Development of an In Vitro Hemorrhagic Hydrocephalus Model for Functional Evaluation of Magnetic Microactuators Against Shunt Obstructions.

OBJECTIVE: Occlusion of ventriculoperitoneal shunts placed after intraventricular hemorrhage occurs frequently. The objective of this study was to develop a hemorrhagic hydrocephalus model to assess the ability of an oscillating microactuator within the ventricular catheter (VC) to prevent shunt obstruction. METHODS: An in vitro hydrocephalus model with extreme risk of shunt obstruction was created. Phosphate-buffered saline, blood, and thrombin were driven through ventriculoperitoneal shunts for 8 hours. Five VCs were fitted with a microactuator and compared with 5 control VCs. The microactuator was actuated by an external magnetic field for 30 minutes. Pressure within the imitation lateral ventricle was measured. RESULTS: In the 5 control shunts, 6 obstructions developed (3 VC, 3 valve-distal catheter) compared with 1 obstruction (VC) in the 5 microactuator shunts. In the control and microactuator groups, the median volume exiting the shunts in 8 hours was 30 mL versus 256 mL. Median time to reach an intraventricular pressure of 40 mm Hg (13.8 minutes vs. >8 hours), median total time >40 mm Hg (6.2 hours vs. 0.0 hours), and median maximum pressure (192 mm Hg vs. 36 mm Hg) were significantly improved in the microactuator group (P < 0.01). CONCLUSIONS: In addition to protecting the VC, the microactuator appeared to prevent hematoma obstructing the valve or distal catheter, resulting in a much longer duration of low intraventricular pressures. A microactuator activated by placing the patient's head in an external magnetic field could reduce shunt obstructions in hemorrhagic hydrocephalus.

Subcutaneous implantable cardioverter-defibrillator and defibrillation testing: A propensity-matched pilot study.

BACKGROUND: To date, only a few comparisons between subcutaneous implantable cardioverter-defibrillator (S-ICD) patients undergoing and those not undergoing defibrillation testing (DT) at implantation (DT+ vs DT-) have been reported. OBJECTIVE: The purpose of this study was to compare long-term clinical outcomes of 2 propensity-matched cohorts of DT+ and DT- patients. METHODS: Among consecutive S-ICD patients implanted across 17 centers from January 2015 to October 2020, DT- patients were 1:1 propensity-matched for baseline characteristics with DT+ patients. The primary outcome was a composite of ineffective shocks and cardiovascular mortality. Appropriate and inappropriate shock rates were deemed secondary outcomes. RESULTS: Among 1290 patients, a total of 566 propensity-matched patients (283 DT+; 283 DT-) served as study population. Over median follow-up of 25.3 months, no significant differences in primary outcome event rates were found (10 DT+ vs 14 DT-; P = .404) as well as for ineffective shocks (5 DT- vs 3 DT+; P = .725). At multivariable Cox regression analysis, DT performance was associated with a reduction of neither the primary combined outcome nor ineffective shocks at follow-up. A high PRAETORIAN score was positively associated with both the primary outcome (hazard ratio 3.976; confidence interval 1.339-11.802; P = .013) and ineffective shocks alone at follow-up (hazard ratio 19.030; confidence interval 4.752-76.203; P = .003). CONCLUSION: In 2 cohorts of strictly propensity-matched patients, DT performance was not associated with significant differences in cardiovascular mortality and ineffective shocks. The PRAETORIAN score is capable of correctly identifying a large percentage of patients at risk for ineffective shock conversion in both cohorts.

Dutch Outcome in Implantable Cardioverter-Defibrillator Therapy: Implantable Cardioverter-Defibrillator-Related Complications in a Contemporary Primary Prevention Cohort.

Background One third of primary prevention implantable cardioverter-defibrillator patients receive appropriate therapy, but all remain at risk of defibrillator complications. Information on these complications in contemporary cohorts is limited. This study assessed complications and their risk factors after defibrillator implantation in a Dutch nationwide prospective registry cohort and forecasts the potential reduction in complications under distinct scenarios of updated indication criteria. Methods and Results Complications in a prospective multicenter registry cohort of 1442 primary implantable cardioverter-defibrillator implant patients were classified as major or minor. The potential for reducing complications was derived from a newly developed prediction model of appropriate therapy to identify patients with a low probability of benefitting from the implantable cardioverter-defibrillator. During a follow-up of 2.2 years (interquartile range, 2.0-2.6 years), 228 complications occurred in 195 patients (13.6%), with 113 patients (7.8%) experiencing at least one major complication. Most common ones were lead related (n=93) and infection (n=18). Minor complications occurred in 6.8% of patients, with lead-related (n=47) and pocket-related (n=40) complications as the most prevailing ones. A surgical reintervention or additional hospitalization was required in 53% or 61% of complications, respectively. Complications were strongly associated with device type. Application of stricter implant indication results in a comparable proportional reduction of (major) complications. Conclusions One in 13 patients experiences at least one major implantable cardioverter-defibrillator-related complication, and many patients undergo a surgical reintervention. Complications are related to defibrillator implantations, and these should be discussed with the patient. Stricter implant indication criteria and careful selection of device type implanted may have significant clinical and financial benefits.

A fault diagnosis method based on Auxiliary Classifier Generative Adversarial Network for rolling bearing.

Rolling bearing fault diagnosis is one of the challenging tasks and hot research topics in the condition monitoring and fault diagnosis of rotating machinery. However, in practical engineering applications, the working conditions of rotating machinery are various, and it is difficult to extract the effective features of early fault due to the vibration signal accompanied by high background noise pollution, and there are only a small number of fault samples for fault diagnosis, which leads to the significant decline of diagnostic performance. In order to solve above problems, by combining Auxiliary Classifier Generative Adversarial Network (ACGAN) and Stacked Denoising Auto Encoder (SDAE), a novel method is proposed for fault diagnosis. Among them, during the process of training the ACGAN-SDAE, the generator and discriminator are alternately optimized through the adversarial learning mechanism, which makes the model have significant diagnostic accuracy and generalization ability. The experimental results show that our proposed ACGAN-SDAE can maintain a high diagnosis accuracy under small fault samples, and have the best adaptation performance across different load domains and better anti-noise performance.

Managing head injury risks in competitive skateboarding: what do we know?

OBJECTIVES: The broad objective of this paper is to inform policy, practice and research regarding the management of head injury risks in competitive skateboarding. The main motivation for the current study was the question of mandating helmet use in competitive skateboarding. The specific aims are to present current knowledge on (A) head injury risks in skateboarding, (B) preliminary biomechanical data on falls and head injury risks in a selection of competitive skateboarding events similar to those planned for the Summer Olympics, (C) standards for skateboard-styled helmets and (D) impact performance of helmets commonly used in skateboarding. METHODS: A narrative review of the published literature on head injuries in skateboarding was conducted. Videos of skateboarding competitions from Vans Park Professional League, Street League Skateboarding and Dew Tour were reviewed to describe crashes and falls. Standards databases including the International Organization for Standardization (ISO), British Standards Institution (BSI), Snell, United States Consumer Product Safety Commission (CPSC) and American Society for Testing and Materials (ASTM) were searched for skateboarding-styled helmet standards. A sample of helmets considered suitable for skateboarding was tested in standard impact tests. RESULTS: The majority of previous literature focused on the paediatric population in a recreational setting with little data from competitive skateboarding. Head injuries comprised up to 75% of all injuries and helmet use was less than 35%. Video analysis identified high rates of falls and crashes during competitive skateboarding, but also a capacity for the athletes to control falls and limit head impacts. Less than 5% of competitive skateboarders wore helmets. In addition to dedicated national skateboard helmet standards, there are several national standards for skateboard-styled helmets. All helmets, with the exception of one uncertified helmet, had similar impact attenuation performance; that is, at 0.8 m drop height, 114-148 g; at 1.5 m, 173-220 g; and at 2.0 m, 219-259 g. Impact performance in the second impact was degraded in all helmets tested. CONCLUSION: Helmets styled for skateboarding are available 'off the shelf' that will offer protection to the head against skull fractures and intracranial injuries in competitive skateboarding. There is an urgent need to commence a programme of research and development to understanding and control head injury risks.

GAN-SAE based fault diagnosis method for electrically driven feed pumps.

The running of high-speed electrically driven feed pump has a direct impact on the safety of personnel equipment and economic benefits of power plant, as the result, intelligent condition monitoring and fault diagnosis of electrically driven feed pump becomes an urgent need. In the practical process of electrically driven feed pump fault diagnosis, the running of the equipment is in normal state for a long time, occasionally, with faults, which makes the fault data very rare in a large number of monitoring data, and makes it difficult to extract the internal fault features behind the original time series data, When the deep learning theory is used in practice, the imbalance between the fault data and the normal data occurs in the operation data set. In order to solve the problem of data imbalance, this paper proposes a fault diagnosis method of GAN-SAE. This method first makes compensation for the imbalance of sample data based on the Generative Adversarial Network (GAN), and then uses the Stacked Auto Encoder (SAE) method to extract the signal features. By designing the fault diagnosis program, compared with only using SAE, back propagation neural networks (BP) and multi-hidden layer neural networks(MNN) method, the GAN-SAE method can offer better capability of extracting features, and the accuracy of fault diagnosis of electrically driven feed pump could be improved to 98.89%.

Level-based analysis of screw loosening with cortical bone trajectory screws in patients with lumbar degenerative disease.

This study aimed to verify the relationship between the number of fusion level and the risk of screw loosening by using cortical bone trajectory (CBT) screws in patients with lumbar degenerative disease.We retrospectively reviewed the serial plain radiograph images of lumbar degenerative disease patients who had undergone posterior fixation and fusion surgery with CBT from 2014. All included patients should have been followed-up with computed tomography scan or plain radiograph for at least 6 months after operation. We individually evaluated the prevalence of screw loosening according to each vertebral level. We also determined whether the number of screw fixation affected the prevalence of screw loosening and whether S1 fixation increased the risk of screw loosening.The screw-loosening rates were high at the S1 level. Moreover, although fixation involved to S1, the loosening rates evidently increased (Fisher exact test, P = .002). The screw-loosening rate was 6.56% in 2 level fusion. However, it increased with the number of fusion levels (3 level: 25.00%, 4 level: 51.16%, and 5 level: 62.50%). To investigate if the number of fusion level affected the S1 screw loosening, we classified the cohort of patients into either involving S1 (S1+ group) or not (S1- group) according to different fusion levels (). The screw loosening between 2 group in 2 (5.56% vs 6.98%) and 3 fusion level (26.32% vs 22.73%) did not exhibit any significant difference. Interestingly, significantly high screw loosening was found in 4 fusion level (60.00% vs 15.38%), indicating that the higher fusion level (4 level) can directly increase the risk of S1 screw loosening.Our data confirmed that the screw-loosening rate increases rate when long segment CBT fixation involves to S1. Therefore, in case of long-segment fixation by using CBT screw, surgeons should be aware of the fusion level of S1.

New respirator performance monitor (RePM) for powered air-purifying respirators.

Powered air-purifying respirators (PAPRs) that offer protection from particulates are deployed in different workplace environments. Usage of PAPRs by healthcare workers is rapidly increasing; these respirators are often considered the best option in healthcare settings, particularly during public health emergency situations, such as outbreaks of pandemic diseases. At the same time, lack of user training and certain vigorous work activities may lead to a decrease in a respirator's performance. There is a critical need for real-time performance monitoring of respiratory protective devices, including PAPRs. In this effort, a new robust and low-cost real-time performance monitor (RePM) capable of evaluating the protection offered by a PAPR against aerosol particles at a workplace was developed. The new device was evaluated on a manikin and on human subjects against a pair of condensation nuclei counters (P-Trak) used as the reference protection measurement system. The outcome was expressed as a manikin-based protection factor (mPF) and a Simulated Workplace Protection Factor (SWPF) determined while testing on subjects. For the manikin-based testing, the data points collected by the two methods were plotted against each other; a near-perfect correlation was observed with a correlation coefficient of 0.997. This high correlation is particularly remarkable since RePM and condensation particle counter (CPC) measure in different particle size ranges. The data variability increased with increasing mPF. The evaluation on human subjects demonstrated that RePM prototype provided an excellent Sensitivity (96.3% measured on human subjects at a response time of 60 sec) and a Specificity of 100%. The device is believed to be the first of its kind to quantitatively monitor PAPR performance while the wearer is working; it is small, lightweight, and does not interfere with job functions.

Filtration Efficiency of Hospital Face Mask Alternatives Available for Use During the COVID-19 Pandemic.

Importance: Procuring respiratory protection for clinicians and other health care workers has become a major challenge of the coronavirus disease 2019 (COVID-19) pandemic and has resulted in nonstandard practices such as the use of expired respirators and various decontamination processes to prolong the useful life of respirators in health care settings. In addition, imported, non-National Institute for Occupational Safety and Health (NIOSH)-approved respirators have been donated or acquired by hospitals as a potential replacement for limited NIOSH-approved N95 respirators. Objective: To assess fitted filtration efficiencies (FFEs) for face mask alternatives used during the COVID-19 pandemic. Design, Setting, and Participants: For this quality-improvement study conducted between April and June 2020, we used the Occupational Safety and Health Administration's Quantitative Fit Testing Protocol for Filtering Facepiece Respirators in a laboratory atmosphere supplemented with sodium chloride particles to assess the FFEs of a variety of respirators worn by a male volunteer and female volunteer. Main Outcomes and Measures: The FFEs of respirators commonly worn by clinicians and other health care workers and available respirator alternatives during the COVID-19 pandemic. Results: Of the 29 different fitted face mask alternatives tested on 1 man and 1 woman, expired N95 respirators with intact elastic straps and respirators subjected to ethylene oxide and hydrogen peroxide sterilization had unchanged FFE (>95%). The performance of N95 respirators in the wrong size had slightly decreased performance (90%-95% FFE). All of the respirators not listed as approved in this evaluation (n = 6) failed to achieve 95% FFE. Neither of the 2 imported respirators authorized for use by the Centers for Disease Control and Prevention that were not NIOSH-approved tested in this study achieved 95% FFE, and the more effective of the 2 functioned at approximately 80% FFE. Surgical and procedural face masks had filtering performance that was lower relative to that of N95 respirators (98.5% overall FFE), with procedural face masks secured with elastic ear loops showing the lowest efficiency (38.1% overall FFE). Conclusions and Relevance: This quality-improvement study evaluating 29 face mask alternatives for use by clinicians interacting with patients during the COVID-19 pandemic found that expired N95 respirators and sterilized, used N95 respirators can be used when new N95 respirators are not available. Other alternatives may provide less effective filtration.

El efecto de la fatiga cíclica sobre los pilares de implantes dentales / The effect of the cyclic fatigue in dental implant abutments

INTRODUCCIÓN: La conexión implante-pilar ha sido sugerida como crucial para el éxito a largo plazo de las restauraciónes sobre implantes oseointegración y para prevenir futuras complicaciónes biológicas y mecánicas. El objetivo de este estudio fué evaluar la influencia del test de fatiga cíclica en el comportamiento de las conexiones internas implante-pilar. MÉTODOS: 36 pilares mutiposición de implantes de titanio de conexión interna fueron divididos en 2 grupos: 18 pilares slim o curvos y 10 pilares rectos con diferentes alturas. Los pilares fueron apretados con una llave de torque a 30 Ncm. Una carga cíclica entre 175 N y 100 N fué aplicada con 30º de inclinación axial al sistema de implantes durante 5 millones de ciclos. RESULTADOS: Los tests biomecánicos muestran una fractura de los implantes y de los tornillos de retención a una carga límite de 100 N de los pilares slim curvos y de 130 N en los pilares rectos. La fracturas aparecieron en la zona de unión entre el cuello y el cuerpo del implante y en los tornillos protéscios. No se han encontrado fracturas en los pilares multiposición. CONCLUSIONES: Este estudio indican que los tests de fatiga cíclica son importantes para analizar la respuesta biomecánica de los diferentes pilares en las conexiones implante-pilar de los sistemas de implantes

INTRODUCTION: The connection implant-abutment has been suggested to be crucial for the long-term success of implant restorations and to prevent future biological and mechanic complications. The aim of this study was to evaluate the influence of fatigue cyclic test in the behavior of internal connection implant-abutments. METHODS: Thirty six titanium abutments of internal connection implants were divided in two groups: 18 slim and 18 right multiunit abutments with different length. Abutments were tightened to 30 Ncm with a torque controller. A cyclic load between 175 N and 100 N according to different implant abutments at a 30-degree angle to the long axis was applied to the implants for a 5 million cycles. RESULTS: Biomechanical testing showed implant and screw retention fracture in a limit load of 100 N of slim implant abutments and 130 N of right implant abutments. Fracture cracks were located in the area between neck and body of implants and screw retention. No abutment fractures were found. CONCLUSIONS: This study indicate that fatigue cyclic test are very important to analize the biomechanical behavior of different abutments in connection implant-abutment of implants systems

Machine learning for pattern detection in cochlear implant FDA adverse event reports.

Importance: Medical device performance and safety databases can be analyzed for patterns and novel opportunities for improving patient safety and/or device design. Objective: The objective of this analysis was to use supervised machine learning to explore patterns in reported adverse events involving cochlear implants. Design: Adverse event reports for the top three CI manufacturers were acquired for the analysis. Four supervised machine learning algorithms were used to predict which adverse event description pattern corresponded with a specific cochlear implant manufacturer and adverse event type. Setting: U.S. government public database. Participants: Adult and pediatric cochlear patients. Exposure: Surgical placement of a cochlear implant. Main Outcome Measure: Classification prediction accuracy (% correct predictions). Results: Most adverse events involved patient injury (n = 16,736), followed by device malfunction (n = 10,760), and death (n = 16). The random forest, linear SVC, naïve Bayes and logistic algorithms were able to predict the specific CI manufacturer based on the adverse event narrative with an average accuracy of 74.8%, 86.0%, 88.5% and 88.6%, respectively. Conclusions & relevance: Using supervised machine learning algorithms, our classification models were able to predict the CI manufacturer and event type with high accuracy based on patterns in adverse event text descriptions.

A novel method to eliminate the influence of absorbed lipids on the characterization of ultra-high molecular weight polyethylene using Fourier-transform infrared spectroscopy.

BACKGROUND: Fourier-transform infrared spectroscopy (FTIR) is one of the standard methods to analyze ultra-high molecular weight polyethylene (UHMWPE) in orthopedic implants. For retrieved components, lipid extraction using an organic solvent prior to the measurement is necessary to eliminate the influence of lipids absorbed in vivo. However, its influence on the measurement has not been substantially investigated. OBJECTIVE: To investigate the influence of lipid extraction on the FTIR analysis of UHMWPE and to develop a novel method to obtain reliable results without inconvenient lipid extraction. METHODS: FTIR analysis was repeatedly performed on UHMWPE specimens from retrieved components before and after lipid extraction under various conditions. A method to calculate the extent of influence of the absorbed lipids from the FTIR spectra was developed using a peak separation technique. RESULTS: An elevated temperature was necessary for lipid extraction; however, it had the potential to influence the results if the conditions were not properly controlled. The results obtained using the peak separation technique coincided with those obtained after lipid extraction. CONCLUSION: The use of the peak separation technique enables the efficient acquisition of reliable results without the need for lipid extraction.

Gunshot damage to monoplace hyperbaric chamber acrylic.

Introduction: Health care workers are vulnerable to workplace violence, including active shooter incidents. Little is known about how firearms could damage monoplace chamber acrylic and whether a breached pressurized chamber presents additional threat to the patient or bystanders. Methods: In a remote area where firearm discharge is permitted, we tested the durability of sections of monoplace hyperbaric chamber acrylic under various firearm discharges. Firearms were discharged at acrylic sections from a distance of 17 feet at 45 degrees and 10 degrees from perpendicular while wearing protective gear. Firearm calibers ranged from .22 caliber handgun to 5.56 mm AR-15 rifle. We also conducted similar testing on a monoplace hyperbaric chamber pressurized with >99% oxygen to a differential pressure of 14.7 psig (2.0 atmospheres absolute at sea level). Handguns were remotely fired at a distance of 12 feet from the chamber (30 degrees from perpendicular), while the rifles were fired at a distance of 60 feet from the chamber. Result: Higher-caliber handguns penetrated or fractured the acrylic sections only after multiple shots. The tested rifles caused full-thickness penetration and fracture with a single shot. However, the pressurized monoplace hyperbaric chamber required two shots from the AR-15 rifle, separated by approximately 60 mm, to penetrate the acrylic, resulting in rapid depressurization. The chamber otherwise remained intact, with no explosion or conflagration observed. Conclusion: An intact or pressurized chamber performs differently than stand-alone acrylic sections under firearms testing. In a worst-case active shooter scenario, the pressurized monoplace chamber tested posed no additional threat to bystanders beyond the significant risk of ricochet.