Find research datasets worth reusing

Development of Applications of the ® PillCam Endoscopy System and Evaluation of Their Performance

ClinicalTrials.gov study NCT02775708. IPD Sharing: Not stated. Countries: 2. Publications: 0.

Accuracy and User Performance Evaluation of PixoTest HbA1c Measurement System

ClinicalTrials.gov study NCT06575231. IPD Sharing: NO. Countries: 0. Publications: 0.

REFLEx Study (ENDOTAK RELIANCE G Evaluation of Handling and Electrical Performance

ClinicalTrials.gov study NCT00146822. IPD Sharing: Not stated. Countries: 0. Publications: 0.

Evaluation of Performance of New IFIS Sleeve

ClinicalTrials.gov study NCT06067360. IPD Sharing: NO. Countries: 0. Publications: 0.

Performance and Safety Evaluation of the SenSura® Mio Baby Device in Subjects With a Stoma

ClinicalTrials.gov study NCT03929978. IPD Sharing: Not stated. Countries: 0. Publications: 0.

Evaluation of Safety and Performance of the Orbix Breast Lift System

ClinicalTrials.gov study NCT00774059. IPD Sharing: Not stated. Countries: 1. Publications: 0.

Clinical Performance Evaluation of the C2i Test

ClinicalTrials.gov study NCT05860543. IPD Sharing: NO. Countries: 0. Publications: 0.

A Performance Evaluation of the Harmony 1 Sensors in Adults and Pediatrics

ClinicalTrials.gov study NCT02456922. IPD Sharing: NO. Countries: 0. Publications: 0.

"Evaluation of Clinical Performance and Success Rate of CAD/CAM Versus Conventional Band and Loop Space Maintainer"

ClinicalTrials.gov study NCT05134714. IPD Sharing: NO. Countries: 0. Publications: 0.

Prospective Clinical Investigation to Evaluate the Safety and Performance of Juläine™ in Improving Gluteal Skin Laxity in Adults

ClinicalTrials.gov study NCT07332650. IPD Sharing: Not stated. Countries: 0. Publications: 0.

Real-life Performance Evaluation of the LiFlow X-ray Platform

ClinicalTrials.gov study NCT06999538. IPD Sharing: UNDECIDED. Countries: 0. Publications: 0.

Evaluation of Balance and Physical Performance Under Dual Task Conditions in Individuals With and Without Generalized Joint Hypermobility

ClinicalTrials.gov study NCT07044999. IPD Sharing: Not stated. Countries: 0. Publications: 0.

Evaluation of the Biop Colposcopy System's Safety and Performance (Accuracy of Its Registration Procedure)

ClinicalTrials.gov study NCT03750214. IPD Sharing: NO. Countries: 0. Publications: 0.

Performance Evaluation by Magnetic Resonance Imaging (MRI) of Intramuscular Thigh Injections With 3 Configurations of Needle-free Injector (ZENEO®)

ClinicalTrials.gov study NCT03225638. IPD Sharing: UNDECIDED. Countries: 0. Publications: 0.

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Thinking out of the box to evaluate the performance of engineered SOX17 in pluripotency reprogramming

GEO Series GSE227830. Mus musculus; Mus. 25 samples. Type: Expression profiling by high throughput sequencing; Genome binding/occupancy profiling by high throughput sequencing.

Empirical Evaluation of Diagnostic Algorithm Performance Using a Generic Framework

A variety of rule-based, model-based and datadriven techniques have been proposed for detection and isolation of faults in physical systems. However, there have been few efforts to comparatively analyze the performance of these approaches on the same system under identical conditions. One reason for this was the lack of a standard framework to perform this comparison. In this paper we introduce a framework, called DXF, that provides a common language to represent the system description, sensor data and the fault diagnosis results; a run-time architecture to execute the diagnosis algorithms under identical conditions and collect the diagnosis results; and an evaluation component that can compute performance metrics from the diagnosis results to compare the algorithms. We have used DXF to perform an empirical evaluation of 13 diagnostic algorithms on a hardware testbed (ADAPT) at NASA Ames Research Center and on a set of synthetic circuits typically used as benchmarks in the model-based diagnosis community. Based on these empirical data we analyze the performance of each algorithm and suggest directions for future development.

A Survey of Metrics for Performance Evaluation of Prognostics

Prognostics is an emerging concept in condition basedmaintenance(CBM)ofcriticalsystems.Alongwith developing the fundamentals of being able to confidently predict Remaining Useful Life (RUL), the technology calls for fielded applications as it inches towards maturation. This requires a stringent performance evaluation so that the significance of the concept can be fully exploited. Currently, prognostics concepts lack standard definitions and suffer from ambiguous and inconsistent interpretations. This lack of standards is in part due to the varied end-user requirements for different applications, time scales, available information, domain dynamics, etc. to name a few issues. Instead, the research community has used a variety of metrics based largely on convenience with respect to their respective requirements. Very little attention has been focused on establishing a common ground to compare different efforts. This paper surveys the metrics that are already used for prognostics in a variety of domains including medicine, nuclear, automotive, aerospace, and electronics. It also considers other domains that involve prediction-related tasks, such as weather and finance. Differences and similarities between these domains and health maintenancehave been analyzed to help understand what performance evaluation methods may or may not be borrowed. Further, these metrics have been categorized in several ways that may be useful in deciding upon a suitable subset for a specific application. Some important prognostic concepts have been defined using a notational framework that enables interpretation of different metrics coherently. Last, but not the least, a list of metrics has been suggested to assess critical aspects of RUL predictions before they are fielded in real applications.

Metrics for Offline Evaluation of Prognostic Performance

Prognostic performance evaluation has gained significant attention in the past few years.*Currently, prognostics concepts lack standard definitions and suffer from ambiguous and inconsistent interpretations. This lack of standards is in part due to the varied end- user requirements for different applications, time scales, available information, domain dynamics, etc. to name a few. The research community has used a variety of metrics largely based on convenience and their respective requirements. Very little attention has been focused on establishing a standardized approach to compare different efforts. This paper presents several new evaluation metrics tailored for prognostics that were recently introduced and were shown to effectively evaluate various algorithms as compared to other conventional metrics. Specifically, this paper presents a detailed discussion on how these metrics should be interpreted and used. These metrics have the capability of incorporating probabilistic uncertainty estimates from prognostic algorithms. In addition to quantitative assessment they also offer a comprehensive visual perspective that can be used in designing the prognostic system. Several methods are suggested to customize these metrics for different applications. Guidelines are provided to help choose one method over another based on distribution characteristics. Various issues faced by prognostics and its performance evaluation are discussed followed by a formal notational framework to help standardize subsequent developments.

Evaluating Prognostics Performance for Algorithms Incorporating Uncertainty Estimates

Uncertainty Representation and Management (URM) are an integral part of the prognostic system development.1As capabilities of prediction algorithms evolve, research in developing newer and more competent methods for URM is gaining momentum.2Beyond initial concepts, more sophisticated prediction distributions are obtained that are not limited to assumptions of Normality and unimodal characteristics. Most prediction algorithms yield non-parametric distributions that are then approximated as known ones for analytical simplicity, especially for performance assessment methods. Although applying the prognostic metrics introduced earlier with their simple definitions has proven useful, a lot of information about the distributions gets thrown away. In this paper, several techniques have been suggested for incorporating information available from Remaining Useful Life (RUL) distributions, while applying the prognostic performance metrics. These approaches offer a convenient and intuitive visualization of algorithm performance with respect to metrics like prediction horizon and α-λ performance, and also quantify the corresponding performance while incorporating the uncertainty information. A variety of options have been shortlisted that could be employed depending on whether the distributions can be approximated to some known form or cannot be parameterized. This paper presents a qualitative analysis on how and when these techniques should be used along with a quantitative comparison on a real application scenario. A particle filter based prognostic framework has been chosen as the candidate algorithm on which to evaluate the performance metrics due to its unique advantages in uncertainty management and flexibility in accommodating non-linear models and non-Gaussian noise. We investigate how performance estimates get affected by choosing different options of integrating the uncertainty estimates. This allows us to identify the advantages and limitations of these techniques and their applicability towards a standardized performance evaluation method.

Evaluating Algorithm Performance Metrics Tailored for Prognostics

Prognostics has taken center stage in Condition Based Maintenance (CBM) where it is desired to estimate Remaining Useful Life (RUL) of a system so that remedial measures may be taken in advance to avoid catastrophic events or unwanted downtimes. Validation of such predictions is an important but difficult proposition and a lack of appropriate evaluation methods renders prognostics meaningless. Evaluation methods currently used in the research community are not standardized and in many cases do not sufficiently assess key performance aspects expected out of a prognostics algorithm. In this paper we introduce several new evaluation metrics tailored for prognostics and show that they can effectively evaluate various algorithms as compared to other conventional metrics. Four prognostic algorithms, Relevance Vector Machine (RVM), Gaussian Process Regression (GPR), Artificial Neural Network (ANN), and Polynomial Regression (PR), are compared. These algorithms vary in complexity and their ability to manage uncertainty around predicted estimates. Results show that the new metrics rank these algorithms in a different manner; depending on the requirements and constraints suitable metrics may be chosen. Beyond these results, this paper offers ideas about how metrics suitable to prognostics may be designed so that the evaluation procedure can be standardized.