Remaining useful life prediction is essential for cutting tool utilization evaluation and replacement decision-making. However, it is very difficult to build a mechanism model for the time-varying ...and non-linear cutting tool wear and life decreasing process. Based on big samples, artificial intelligence–based models have weak interpretability and un-quantized uncertainty. In fact, the cutting tool degradation is a stochastic process, and cutting tool remaining useful life is a random variable. Then, a non-linear Wiener-based cutting tool wear and remaining useful life prediction model is proposed for a specific cutting tool. The probability density function of remaining useful life is derived to quantize uncertainty. On the basis of Bayesian model, unknown parameters are estimated and updated by using history data and real-time data, respectively. Measurement variability is also considered to improve the accuracy and reliability. Experimental study verifies the approach’s effectiveness and accuracy. Detailed comparisons validate the approach’s advantages over existing models. By quantizing the uncertainty of cutting tool remaining useful life prediction with confidence intervals, the model is meaningful for cutting tool selection and replacement decision-making.
Prior studies of patients receiving maintenance hemodialysis have shown that, on average, blood pressure (BP) measured predialysis is higher than BP measured at home. We hypothesized that a subset of ...hemodialysis patients has BP that is higher when measured at home than when measured predialysis and this subgroup of patients has a higher prevalence of left ventricular hypertrophy.
Prospective cohort.
97 hypertensive hemodialysis patients enrolled in the Blood Pressure in Dialysis Study (BID), a randomized trial of comparing target predialysis BP ≤140/90 to 155-165/90 mm Hg.
Differences between predialysis and next-day home systolic BP measured ≥6 times over 1 year.
Left ventricular mass index (LVMI) by cardiac magnetic resonance imaging.
A hierarchical clustering analysis divided patients into 3 clusters based on the average and variability of differences in systolic predialysis and home BP. Clusters were compared with respect to clinical factors and LVMI.
Mean differences between predialysis and home systolic BP were 19.1 (95% CI, 17.0 to 21.1) mm Hg for cluster 1 ("home lower"), 3.7 (95% CI, 1.6 to 5.8) mm Hg for cluster 2 ("home and predialysis similar"), and −9.7 (95% CI, −12.0 to −7.4) mm Hg for cluster 3 ("home higher"). Systolic BP declined during dialysis in clusters 1 and 2 but increased in cluster 3. Interdialytic weight gains did not differ. After adjusting for sex and treatment arm, LVMI was higher in cluster 3 than in clusters 1 and 2: differences in means of 10.6 ± 4.96 (SE) g/m2 (P = 0.04) and 12.0 ± 5.08 g/m2 (P = 0.02), respectively.
Limited statistical power.
Nearly one-third of participants had home BPs higher than predialysis BPs. These patients had LVMI higher than those with similar or lower BPs at home, indicating that their BP may have been undertreated.
Objectives
In the Cancer Core Europe Consortium (CCE), standardized biomarkers are required for therapy monitoring oncologic multicenter clinical trials. Multiparametric functional MRI and ...particularly diffusion-weighted MRI offer evident advantages for noninvasive characterization of tumor viability compared to CT and RECIST. A quantification of the inter- and intraindividual variation occurring in this setting using different hardware is missing. In this study, the MRI protocol including DWI was standardized and the residual variability of measurement parameters quantified.
Methods
Phantom and volunteer measurements (single-shot T2w and DW-EPI) were performed at the seven CCE sites using the MR hardware produced by three different vendors. Repeated measurements were performed at the sites and across the sites including a traveling volunteer, comparing qualitative and quantitative ROI-based results including an explorative radiomics analysis.
Results
For DWI/ADC phantom measurements using a central post-processing algorithm, the maximum deviation could be decreased to 2%. However, there is no significant difference compared to a decentralized ADC value calculation at the respective MRI devices. In volunteers, the measurement variation in 2 repeated scans did not exceed 11% for ADC and is below 20% for single-shot T2w in systematic liver ROIs. The measurement variation between sites amounted to 20% for ADC and < 25% for single-shot T2w. Explorative radiomics classification experiments yield better results for ADC than for single-shot T2w.
Conclusion
Harmonization of MR acquisition and post-processing parameters results in acceptable standard deviations for MR/DW imaging. MRI could be the tool in oncologic multicenter trials to overcome the limitations of RECIST-based response evaluation.
Key Points
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Harmonizing acquisition parameters and post-processing homogenization, standardized protocols result in acceptable standard deviations for multicenter MR–DWI studies.
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Total measurement variation does not to exceed 11% for ADC in repeated measurements in repeated MR acquisitions, and below 20% for an identical volunteer travelling between sites.
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Radiomic classification experiments were able to identify stable features allowing for reliable discrimination of different physiological tissue samples, even when using heterogeneous imaging data.
Purpose
During biopharmaceutical drug manufacturing, storage, and distribution, proteins in both liquid and solid dosage forms go through various processes that could lead to protein aggregation. The ...extent of aggregation in the sub-micron range can be measured by analyzing a liquid or post-reconstituted powder sample using Micro-Flow Imaging (MFI) technique. MFI is widely used in biopharmaceutical industries due to its high sensitivity in detecting and analyzing particle size distribution. However, the MFI's sensitivity to various factors makes accurate measurement challenging. Therefore, in light of the inherent variability of the method, this work aims to explore the capabilities of an adopted coupled sensitivity analysis and machine learning algorithm to quantify the influencing factors on the formed sub-visible particles and method variability.
Methods
The proposed algorithm consists of two interconnected components, namely a surrogate model with a neural network and a sensitivity analyzer. A machine learning tool based on artificial neural networks (ANN) is constructed with MFI data. The best fit with an optimized configuration is found. Sensitivity and uncertainty analysis is performed using this network as the surrogate model to understand the impacts of input parameters on MFI data.
Results
Results reveal the most impactful reconstitution preparation factors and others that are masked by the instrument variabilities. It is shown that instrument inaccuracy is a function of size category, with higher variabilities associated with larger size ranges.
Conclusion
Utilizing this tool while assessing the sensitivity of outputs to various parameters, measurement variabilities for analytical characterization tests can be quantified.
The accurate, objective, and reproducible evaluation of tumor response to therapy is indispensable in clinical trials. This study aimed at investigating the reliability and reproducibility of a ...computer-aided contouring (CAC) tool in tumor measurements and its impact on evaluation of tumor response in terms of RECIST 1.1 criteria. A total of 200 cancer patients were retrospectively collected in this study, which were randomly divided into two sets of 100 patients for experiential learning and testing. A total of 744 target lesions were identified by a senior radiologist in distinctive body parts, of which 278 lesions were in data set 1 (learning set) and 466 lesions were in data set 2 (testing set). Five image analysts were respectively instructed to measure lesion diameter using manual and CAC tools in data set 1 and subsequently tested in data set 2. The interobserver variability of tumor measurements was validated by using the coefficient of variance (CV), the Pearson correlation coefficient (PCC), and the interobserver correlation coefficient (ICC). We verified that the mean CV of manual measurement remained constant between the learning and testing data sets (0.33 vs. 0.32,
= 0.490), whereas it decreased for the CAC measurements after learning (0.24 vs. 0.19,
< 0.001). The interobserver measurements with good agreement (CV < 0.20) were 29.9% (manual) vs. 49.0% (CAC) in the learning set (
< 0.001) and 30.9% (manual) vs. 64.4% (CAC) in the testing set (
< 0.001). The mean PCCs were 0.56 ± 0.11 mm (manual) vs. 0.69 ± 0.10 mm (CAC) in the learning set (
= 0.013) and 0.73 ± 0.07 mm (manual) vs. 0.84 ± 0.03 mm (CAC) in the testing set (
< 0.001). ICCs were 0.633 (manual) vs. 0.698 (CAC) in the learning set (
< 0.001) and 0.716 (manual) vs. 0.824 (CAC) in the testing set (
< 0.001). The Fleiss' kappa analysis revealed that the overall agreement was 58.7% (manual) vs. 58.9% (CAC) in the learning set and 62.9% (manual) vs. 74.5% (CAC) in the testing set. The 80% agreement of tumor response evaluation was 55.0% (manual) vs. 66.0% in the learning set and 60.6% (manual) vs. 79.7% (CAC) in the testing set. In conclusion, CAC can reduce the interobserver variability of radiological tumor measurements and thus improve the agreement of imaging evaluation of tumor response.
Objective. This article proposes a comprehensive literature review of past works addressing hearing protection device (HPD) comfort with the aim of identifying the main sources of variability in ...comfort evaluation. Methods. A literature review of study samples was performed: documents were hand searched and Internet searched using PubMed, Web of Science, Google Scholar, ProQuest Dissertations and Theses Professional, Scopus or Google search engines. While comfort constructs and measurement methods are reviewed for both earplugs and earmuff HPD types, results and analyses are provided for earplugs only. Results. The literature shows that the multiple sources of the perceived comfort measurement variability are related to the complexity of the concept of comfort and to the various physical and psychosocial characteristics of the triad 'environment/person/earplug', which differ from one study to the other. Conclusions. Considering the current state of knowledge and in order to decrease comfort measurements variability, it is advised to: (a) use a multidimensional construct of comfort and derive a comfort index for each comfort dimension;, (b) use exhaustive and valid questionnaires; (c) quantify as many triad characteristics as possible and use them as independent or control variables; (d) assess the quality of the earplug fitting and the attenuation efficiency.
Abstract Objective Understanding magnitudes of variability when measuring tumor size may be valuable in improving detection of tumor change and thus evaluating tumor response to therapy in clinical ...trials and care. Our study explored intra- and inter-reader variability of tumor uni-dimensional (1D), bi-dimensional (2D), and volumetric (VOL) measurements using manual and computer-aided methods (CAM) on CT scans reconstructed at different slice intervals. Materials and methods Raw CT data from 30 patients enrolled in oncology clinical trials was reconstructed at 5, 2.5, and 1.25 mm slice intervals. 118 lesions in the lungs, liver, and lymph nodes were analyzed. For each lesion, two independent radiologists manually and, separately, using computer software, measured the maximum diameter (1D), maximum perpendicular diameter, and volume (CAM only). One of them blindly repeated the measurements. Intra- and inter-reader variability for the manual method and CAM were analyzed using linear mixed-effects models and Bland–Altman method. Results For the three slice intervals, the maximum coefficients of variation for manual intra-/inter-reader variability were 6.9%/9.0% (1D) and 12.3%/18.0% (2D), and for CAM were 5.4%/9.3% (1D), 11.3%/18.8% (2D) and 9.3%/18.0% (VOL). Maximal 95% reference ranges for the percentage difference in intra-reader measurements for manual 1D and 2D, and CAM VOL were (−15.5%, 25.8%), (−27.1%, 51.6%), and (−22.3%, 33.6%), respectively. Conclusions Variability in measuring the diameter and volume of solid tumors, manually and by CAM, is affected by CT slice interval. The 2.5 mm slice interval provides the least measurement variability. Among the three techniques, 2D has the greatest measurement variability compared to 1D and 3D.
Measurement has been an important activity for centuries. The advent of Six Sigma initiatives has increased the assessment of measurement repeatability and reproducibility using Gage R&R studies. It ...is common practice that, when the operator-part interaction in Gage R&R studies is statistically significant, the associated variance component is used in estimating the test method reproducibility. In this article it is shown that the Operator-Part interaction may be due to systematic behavior of one or more operators thereby unnecessarily increasing the test method reproducibility variance. Several examples are included to demonstrate how the analysis is performed and how the results can be interpreted.
Purpose
The Cobb technique is the universally accepted method for measuring the severity of spinal deformities. Traditionally, Cobb angles have been measured using protractor and pencil on hardcopy ...radiographic films. The new generation of mobile ‘smartphones’ make accurate angle measurement possible using an integrated accelerometer, providing a potentially useful clinical tool for assessing Cobb angles. The purpose of this study was to compare Cobb angle measurements performed using a smartphone and traditional protractor in a series of 20 adolescent idiopathic scoliosis patients.
Methods
Seven observers measured major Cobb angles on 20 pre-operative postero-anterior radiographs of Adolescent Idiopathic Scoliosis patients with both a standard protractor and using an Apple iPhone. Five of the observers repeated the measurements at least a week after the original measurements.
Results
The mean absolute difference between pairs of smartphone/protractor measurements was 2.1°, with a small (1°) bias toward lower Cobb angles with the iPhone. 95% confidence intervals for intra-observer variability were ±3.3° for the protractor and ±3.9° for the iPhone. 95% confidence intervals for inter-observer variability were ±8.3° for the iPhone and ±7.1° for the protractor. Both of these confidence intervals were within the range of previously published Cobb measurement studies.
Conclusions
We conclude that the iPhone is an equivalent Cobb measurement tool to the manual protractor, and measurement times are about 15% less. The widespread availability of inclinometer-equipped mobile phones and the ability to store measurements in later versions of the angle measurement software may make these new technologies attractive for clinical measurement applications.
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