A validation study to analyze the reliability of center of pressure data in static posturography in dogs.

Introduction: Center of pressure (COP) parameters are frequently assessed to analyze movement disorders in humans and animals. Methodological discrepancies are a major concern when evaluating conflicting study results. This study aimed to assess the inter-observer reliability and test-retest reliability of body COP parameters including mediolateral and craniocaudal sway, total length, average speed and support surface in healthy dogs during quiet standing on a pressure plate. Additionally, it sought to determine the minimum number of trials and the shortest duration necessary for accurate COP assessment. Materials and methods: Twelve clinically healthy dogs underwent three repeated trials, which were analyzed by three independent observers to evaluate inter-observer reliability. Test-retest reliability was assessed across the three trials per dog, each lasting 20 seconds (s). Selected 20 s measurements were analyzed in six different ways: 1 × 20 s, 1 × 15 s, 2 × 10 s, 4 × 5 s, 10 × 2 s, and 20 × 1 s. Results: Results demonstrated excellent inter-observer reliability (ICC ≥ 0.93) for all COP parameters. However, only 5 s, 10 s, and 15 s measurements achieved the reliability threshold (ICC ≥ 0.60) for all evaluated parameters. Discussion: The shortest repeatable durations were obtained from either two 5 s measurements or a single 10 s measurement. Most importantly, statistically significant differences were observed between the different measurement durations, which underlines the need to standardize measurement times in COP analysis. The results of this study aid scientists in implementing standardized methods, thereby easing comparisons across studies and enhancing the reliability and validity of research findings in veterinary medicine.

Verification of initial-and-final-state opacity for unambiguous weighted automata.

Initial-and-final-state opacity (IFO) is a type of opacity that characterizes a system's ability to prevent the disclosure of information about whether its evolution starts at an initial state and ends at a final state. In this paper, we extend the notion of IFO from the logical automata to the framework of unambiguous weighted automata (UWAs) that do not contain any cycle composed solely of unobservable events. For the verification of IFO, we first construct a labeled observer and a trellis-based initial state estimator for a given UWA. Even though the labeled observer has much smaller state space compared to the trellis-based initial state estimator, it cannot be used when the set of secret state pairs or the set of non-secret state pairs is not in the Cartesian product form. Based on the labeled observer, we present a more efficient method to verify IFO in the case when the set of non-secret state pairs is expressed as a Cartesian product, regardless of whether the set of secret state pairs is in the Cartesian product form. Furthermore, we use the labeled observer to verify initial-state opacity for a UWA.

3D printed phantom with 12 000 submillimeter lesions to improve efficiency in CT detectability assessment.

BACKGROUND: The detectability performance of a CT scanner is difficult to precisely quantify when nonlinearities are present in reconstruction. An efficient detectability assessment method that is sensitive to small effects of dose and scanner settings is desirable. We previously proposed a method using a search challenge instrument: a phantom is embedded with hundreds of lesions at random locations, and a model observer is used to detect lesions. Preliminary tests in simulation and a prototype showed promising results. PURPOSE: In this work, we fabricated a full-size search challenge phantom with design updates, including changes to lesion size, contrast, and number, and studied our implementation by comparing the lesion detectability from a nonprewhitening (NPW) model observer between different reconstructions at different exposure levels, and by estimating the instrument sensitivity to detect changes in dose. METHODS: Designed to fit into QRM anthropomorphic phantoms, our search challenge phantom is a cylindrical insert 10 cm wide and 4 cm thick, embedded with 12 000 lesions (nominal width of 0.6 mm, height of 0.8 mm, and contrast of -350 HU), and was fabricated using PixelPrint, a 3D printing technique. The insert was scanned alone at a high dose to assess printing accuracy. To evaluate lesion detectability, the insert was placed in a QRM thorax phantom and scanned from 50 to 625 mAs with increments of 25 mAs, once per exposure level, and the average of all exposure levels was used as high-dose reference. Scans were reconstructed with three different settings: filtered-backprojection (FBP) with Br40 and Br59, and Sinogram Affirmed Iterative Reconstruction (SAFIRE) with strength level 5 and Br59 kernel. An NPW model observer was used to search for lesions, and detection performance of different settings were compared using area under the exponential transform of free response ROC curve (AUC). Using propagation of uncertainty, the sensitivity to changes in dose was estimated by the percent change in exposure due to one standard deviation of AUC, measured from 5 repeat scans at 100, 200, 300, and 400 mAs. RESULTS: The printed insert lesions had an average position error of 0.20 mm compared to printing reference. As the exposure level increases from 50 mAs to 625 mAs, the lesion detectability AUCs increase from 0.38 to 0.92, 0.42 to 0.98, and 0.41 to 0.97 for FBP Br40, FBP Br59, and SAFIRE Br59, respectively, with a lower rate of increase at higher exposure level. FBP Br59 performed best with AUC 0.01 higher than SAFIRE Br59 on average and 0.07 higher than FBP Br40 (all P < 0.001). The standard deviation of AUC was less than 0.006, and the sensitivity to detect changes in mAs was within 2% for FBP Br59. CONCLUSIONS: Our 3D-printed search challenge phantom with 12 000 submillimeter lesions, together with an NPW model observer, provide an efficient CT detectability assessment method that is sensitive to subtle effects in reconstruction and is sensitive to small changes in dose.

Reliability, costs, and radiation dose of dual-energy X-ray absorptiometry in diagnosis of radiologic sarcopenia in surgically menopausal women.

OBJECTIVE: The aim of this study was to evaluate and compare reliability, costs, and radiation dose of dual-energy X-ray absorptiometry (DXA) to MRI and CT in measuring muscle mass for the diagnosis of sarcopenia. METHODS: Thirty-four consecutive DXA scans performed in surgically menopausal women from November 2019 until March 2020 were analyzed by two observers. Observers analyzed muscle mass of the lower limbs in every scan twice. Reliability was assessed by calculating inter- and intra-observer variability. Reliability from CT and MRI as well as radiation dose from CT and DXA were collected from literature. Costs for each type of scan were calculated according to the guidelines for economic evaluation of the Dutch National Health Care Institute. RESULTS: The 34 participants had a median age of 58 years (IQR 53-65) and a median body mass index of 24.6 (IQR 21.7-29.7). Inter-observer variability had an intraclass correlation coefficient (ICC) of 0.997 (95% CI 0.994-0.998) with a relative variability of 0.037 ± 0.022%. Regarding intra-observer variability, observer 1 had an ICC of 0.998 (95% CI 0.996-0.999) with a relative variability of 0.019 ± 0.016% and observer 2 had an ICC of 0.997 (95% CI 0.993-0.998) with a relative variability of 0.016 ± 0.011%. DXA costs were €62, CT €77, and MRI €195. The estimated radiation dose of CT was 2.5-3.0 mSv, for DXA this was 2-4 µSv. CONCLUSIONS: DXA has lower costs and a lower radiation dose, with low inter- and intra-observer variability, compared to CT and MRI for assessing lower limb muscle mass. TRIAL REGISTRATION: Netherlands Trial Register; NL8068. CRITICAL RELEVANCE STATEMENT: DXA is a good alternative for CT and MRI in assessing lower limb muscle mass, with lower costs and lower radiation dose, while inter-observer and intra-observer variability are low. KEY POINTS: • Screening for sarcopenia should be optimized as the population ages. • DXA outperformed CT and MRI in the measured metrics. • DXA validity should be further evaluated as an alternative to CT and MRI for sarcopenia evaluation.

Observer-based differential evolution constrained control for safe reference tracking in robots.

Big torque inputs in controls could increase energy consumption, and big estimated perturbations in observers could produce device damages. Therefore, it would be interesting to propose a constrained control for safe reference tracking and a constrained observer for safe perturbation estimation in robots. Furthermore, the best gains in controls produce a balance between safe reference tracking and save energy consumption. Therefore, it would be interesting to propose a method to find the best gains. In this paper, an observer-based differential evolution constrained control is proposed for safe reference tracking in robots. The contributions are described as follows: (1) a constrained observer is proposed for safe perturbation estimation in robots, (2) a constrained control is proposed for safe reference tracking in robots, (3) a differential evolution optimizer is used to find the best gains in an observer-based constrained control, (4) the robust stability in an observer-based constrained control is assured, (5) the pseudo-code of an observer-based differential evolution constrained control is detailed. The proposed observer-based differential evolution constrained control is applied for safe reference tracking in two robots.

Identification of tumour regression in neoadjuvantly treated pancreatic cancer is based on divergent and nonspecific criteria.

AIMS: Following the increased use of neoadjuvant therapy for pancreatic cancer, grading of tumour regression (TR) has become part of routine diagnostics. However, it suffers from marked interobserver variation, which is mainly ascribed to the subjectivity of the defining criteria of the categories in TR grading systems. We hypothesized that a further cause for the interobserver variation is the use of divergent and nonspecific morphological criteria to identify tumour regression. METHODS AND RESULTS: Twenty treatment-naïve pancreatic cancers and 20 pancreatic cancers treated with neoadjuvant chemotherapy were reviewed by three experienced pancreatic pathologists who, blinded for treatment status, categorized each tumour as treatment-naïve or neoadjuvantly treated, and annotated all tissue areas they considered showing tumour regression. Only 50%-65% of the cases were categorized correctly, and the annotated tissue areas were highly discrepant (only 3%-41% overlap). When the prevalence of various morphological features deemed to indicate TR was compared between treatment-naïve and neoadjuvantly treated tumours, only one pattern, characterized by reduced cancer cell density and prominent stroma affecting a large area of the tumour bed, occurred significantly more frequently, but not exclusively, in the neoadjuvantly treated group. Finally, stromal features, both morphological and biological, were investigated as possible markers for tumour regression, but failed to distinguish TR from native tumour stroma. CONCLUSION: There is considerable divergence in opinion between pathologists when it comes to the identification of tumour regression. Reliable identification of TR is only possible if it is extensive, while lesser degrees of treatment effect cannot be recognized with certainty.

The Value of Computed Tomography-Based Planning in Shoulder Arthroplasty Compared to Intra-/Interobserver Reliability of X-ray Planning.

Background: Reversed total shoulder arthroplasty (RTSA) is an established surgery for many pathologies of the shoulder and the demand continues to rise with an aging population. Preoperative planning is mandatory to support the surgeon's understanding of the patient's individual anatomy and, therefore, is crucial for the patient's outcome. Methods: In this observational study, we identified 30 patients who underwent RTSA with two- and three-dimensional preoperative planning. Each patient underwent new two-dimensional planning from a medical student and an orthopedic resident as well as through a mid-volume and high-volume shoulder surgeon, which was repeated after a minimum of 4 weeks. The intra- and interobserver reliability was then analyzed and compared to the 3D planning and the implanted prosthesis. The evaluated parameters were the size of the pegged glenoid baseplate, glenosphere, and humeral short stem. Results: The inter-rater reliability showed higher deviations in all four raters compared to the 3D planning of the base plate, glenosphere, and shaft. The intra-rater reliability showed a better correlation in more experienced raters, especially in the planning of the shaft. Conclusions: Our study shows that 3D planning is more accurate than traditional planning on plain X-rays, despite experienced shoulder surgeons showing better results in 2D planning than inexperienced ones.

Automated Web-based Software for CT Quality Control Testing of Low-contrast Detectability using Model Observers.

The Channelized Hotelling observer (CHO) is well correlated with human observer performance in many CT detection/classification tasks but has not been widely adopted in routine CT quality control and performance evaluation, mainly because of the lack of an easily available, efficient, and validated software tool. We developed a highly automated solution - CT image quality evaluation and Protocol Optimization (CTPro), a web-based software platform that includes CHO and other traditional image quality assessment tools such as modulation transfer function and noise power spectrum. This tool can allow easy access to the CHO for both the research and clinical community and enable efficient, accurate image quality evaluation without the need of installing additional software. Its application was demonstrated by comparing the low-contrast detectability on a clinical photon-counting-detector (PCD)-CT with a traditional energy-integrating-detector (EID)-CT, which showed UHR-T3D had 6.2% higher d' than EID-CT with IR (p = 0.047) and 4.1% lower d' without IR (p = 0.122).

Improved Resolution and Image Quality of Musculoskeletal Magnetic Resonance Imaging using Deep Learning-based Denoising Reconstruction: A Prospective Clinical Study.

OBJECTIVE: To prospectively evaluate a deep learning-based denoising reconstruction (DLR) for improved resolution and image quality in musculoskeletal (MSK) magnetic resonance imaging (MRI). METHODS: Images from 137 contrast-weighted sequences in 40 MSK patients were evaluated. Each sequence was performed twice, first with the routine parameters and reconstructed with a routine reconstruction filter (REF), then with higher resolution and reconstructed with DLR, and with three conventional reconstruction filters (NL2, GA43, GA53). The five reconstructions (REF, DLR, NL2, GA43, and GA53) were de-identified, randomized, and blindly reviewed by three MSK radiologists using eight scoring criteria and a forced ranking. Quantitative SNR, CNR, and structure's full width at half maximum (FWHM) for resolution assessment were measured and compared. To account for repeated measures, Generalized Estimating Equations (GEE) with Bonferroni adjustment was used to compare the reader's scores, SNR, CNR, and FWHM between DLR vs. NL2, GA43, GA53, and REF. RESULTS: Compared to the routine REF images, the resolution was improved by 47.61% with DLR from 0.39 ± 0.15 mm2 to 0.20 ± 0.06 mm2 (p < 0.001). Per-sequence average scan time was shortened by 7.93% with DLR from 165.58 ± 21.86 s to 152.45 ± 25.65 s (p < 0.001). Based on the average scores, DLR images were rated significantly higher in all image quality criteria and the forced ranking (p < 0.001). CONCLUSION: This prospective clinical evaluation demonstrated that DLR allows approximately two times finer resolution and improved image quality compared to the standard-of-care images.

Inter- and intra-observer variability of qualitative visual breast-composition assessment in mammography among Japanese physicians: a first multi-institutional observer performance study in Japan.

BACKGROUND: Visual assessment of mammographic breast composition remains the most common worldwide, although subjective variability limits its reproducibility. This study aimed to investigate the inter- and intra-observer variability in qualitative visual assessment of mammographic breast composition through a multi-institutional observer performance study for the first time in Japan. METHODS: This study enrolled 10 Japanese physicians from five different institutions. They used the new Japanese breast-composition classification system 4th edition to subjectively evaluate the breast composition in 200 pairs of right and left normal mediolateral oblique mammograms (number determined using precise sample size calculations) twice, with a 1-month interval (median patient age: 59 years [range 40-69 years]). The primary endpoint of this study was the inter-observer variability using kappa (κ) value. RESULTS: Inter-observer variability for the four and two classes of breast-composition assessment revealed moderate agreement (Fleiss' κ: first and second reading = 0.553 and 0.587, respectively) and substantial agreement (Fleiss' κ: first and second reading = 0.689 and 0.70, respectively). Intra-observer variability for the four and two classes of breast-composition assessment demonstrated substantial agreement (Cohen's κ, median = 0.758) and almost perfect agreement (Cohen's κ, median = 0.813). Assessments of consensus between the 10 physicians and the automated software Volpara® revealed slight agreement (Cohen's κ; first and second reading: 0.104 and 0.075, respectively). CONCLUSIONS: Qualitative visual assessment of mammographic breast composition using the new Japanese classification revealed excellent intra-observer reproducibility. However, persistent inter-observer variability, presenting a challenge in establishing it as the gold standard in Japan.

Observer-based resilient dissipativity control for discrete-time memristor-based neural networks with unbounded or bounded time-varying delays.

This work focuses on the issue of observer-based resilient dissipativity control of discrete-time memristor-based neural networks (DTMBNNs) with unbounded or bounded time-varying delays. Firstly, the Luenberger observer is designed, and additionally based on the observed states, the observer-based resilient controller is proposed. An augmented system is presented by considering both the error system and the DTMBNNs with the controller. Secondly, a novel sufficient extended exponential dissipativity condition is obtained for the augmented system with unbounded time-varying delays by proposing a system solutions-based estimation approach. This method is based on system solutions and without constructing any Lyapunov-Krasovskii functionals (LKF), thereby reducing the complexity of theoretical derivation and computational workload. In addition, an algorithm is proposed to solve the nonlinear inequalities in the sufficient condition. Thirdly, the sufficient extended exponential dissipativity condition for the augmented system with bounded time-varying delays is also obtained. Finally, the effectiveness of the theoretical results is illustrated through two simulation examples.

Reliability-based anti-disturbance control for systems with parametric stochastic uncertainty: A probabilistic LMI approach.

We propose a reliability-based anti-disturbance control (RADC) method for systems with parametric stochastic uncertainty based on the linear matrix inequality (LMI) and the limit state function. Differing from the existing anti-disturbance control, the parametric stochastic uncertainty is considered in both the concerned system and the exogenous disturbance system. With this consideration, the condition for system stability and performance robustness is described by a stochastic LMI which holds with a certain probability (reliability). Through the limit state function method, the stochastic LMI is subtly transformed into two probabilistic LMIs for two different cases. The proposed probabilistic LMIs contain two probabilistic parameters of reliability indexes that quantify the effect of parametric stochastic uncertainty. At different prescribed reliability indexes, controllers with different reliability can be flexibly and reliably designed. Two illustrative examples with Monte-Carlo verification are presented to demonstrate the feasibility and effectiveness of the proposed RADC method.

Probabilistic U-Net model observer for the DDC method in CT scan protocol optimization.

Optimizing complex imaging procedures within Computed Tomography, considering both dose and image quality, presents significant challenges amidst rapid technological advancements and the adoption of Machine Learning (ML) methods. A crucial metric in this context is the Difference-Detailed Curve, which relies on human observer studies. However, these studies are labor-intensive and prone to both inter- and intra-observer variability. To tackle these issues, a ML-based model observer utilizing the U-Net architecture and a Bayesian methodology is proposed. In order to train a model observer unaffected by the spatial arrangement of low-contrast objects, the image preprocessing incorporates a Gaussian Process-based noise model. Additionally, Gradient-weighted Class Activation Mapping is utilized to gain insights into the model observer's decision-making process. By training on data from a diverse group of observers, well-calibrated probabilistic predictions that quantify observer variability are achieved. Leveraging the principles of Beta regression, the Bayesian methodology is used to derive a model observer performance metric, effectively gauging the model observer's strength in terms of an 'effective number of observers'. Ultimately, this framework enables to predict the DDC distribution by applying thresholds to the inferred probabilities.

MPPT efficiency enhancement of a grid connected solar PV system using Finite Control set model predictive controller.

Maximum power point tracking (MPPT) is required to get the highest possible power generated from a photovoltaic (PV) cell. Numerous researchers have proposed different MPPT strategies to be able to collect maximum generated electricity from the photovoltaic cells. In this research paper, a MPPT model predictive control strategy for a grid-connected PV system is presented. Model predictive control (MPC) was used to develop and model the AC load energy tracking efficiency for the PV systems with a power rate of 20 kW at standard test conditions. For the purpose of obtaining the power tracking performance, a DC-DC boost converter, DC-AC two level three phase inverter, and control mechanism for a grid connected AC load system was examined and presented in this paper. To approximate the actual PV array properties, the PV model is used, and the MPPT approach is suggested as a way to regulate the DC-DC boost converter and get the most power possible from the PV array when compared to P&O and model predictive control system. A three-phase, two-level VSI is employed in this study that is controlled by a model predictive control system with SVPWM. The inverter's control structure is developed using a model predictive control system (inner loop current controller) with reference frame transformation (abc to dq) coordinates by utilizing PLL. The PLL is used to obtain critical information about the grid voltage. A RL filter is then used to lower the total harmonic distortion of the output and connect the inverter's output to the grid. The MATLAB R2019a environment is used to create the system model. The overall performance of the system for conventional perturb and observer is around 97.72%, while for Finite Control Set Model Predictive Controller is 99.80%, which is better than previous similar research with faster time response and less oscillation around maximum power point.

Paris classification of colonic polyps using CT colonography: prospective cohort study of interobserver variation.

BACKGROUND: The Paris classification categorises colorectal polyp morphology. Interobserver agreement for Paris classification has been assessed at optical colonoscopy (OC) but not CT colonography (CTC). We aimed to determine the following: (1) interobserver agreement for the Paris classification using CTC between radiologists; (2) if radiologist experience influenced classification, gross polyp morphology, or polyp size; and (3) the extent to which radiologist classifications agreed with (a) colonoscopy and (b) a combined reference standard. METHODS: Following ethical approval for this non-randomised prospective cohort study, seven radiologists from three hospitals classified 52 colonic polyps using the Paris system. We calculated interobserver agreement using Fleiss kappa and mean pairwise agreement (MPA). Absolute agreement was calculated between radiologists; between CTC and OC; and between CTC and a combined reference standard using all available imaging, colonoscopic, and histopathological data. RESULTS: Overall interobserver agreement between the seven readers was fair (Fleiss kappa 0.33; 95% CI 0.30-0.37; MPA 49.7%). Readers with < 1500 CTC experience had higher interobserver agreement (0.42 (95% CI 0.35-0.48) vs. 0.33 (95% CI 0.25-0.42)) and MPA (69.2% vs 50.6%) than readers with ≥ 1500 experience. There was substantial overall agreement for flat vs protuberant polyps (0.62 (95% CI 0.56-0.68)) with a MPA of 87.9%. Agreement between CTC and OC classifications was only 44%, and CTC agreement with the combined reference standard was 56%. CONCLUSION: Radiologist agreement when using the Paris classification at CT colonography is low, and radiologist classification agrees poorly with colonoscopy. Using the full Paris classification in routine CTC reporting is of questionable value. CLINICAL RELEVANCE STATEMENT: Interobserver agreement for radiologists using the Paris classification to categorise colorectal polyp morphology is only fair; routine use of the full Paris classification at CT colonography is questionable. KEY POINTS: • Overall interobserver agreement for the Paris classification at CT colonography (CTC) was only fair, and lower than for colonoscopy. • Agreement was higher for radiologists with < 1500 CTC experience and for larger polyps. There was substantial agreement when classifying polyps as protuberant vs flat. • Agreement between CTC and colonoscopic polyp classification was low (44%).

Interpretation of Complete Tumor Response on MRI Following Chemoradiotherapy of Rectal Cancer: Inter-Reader Agreement and Associated Factors in Multi-Center Clinical Practice.

OBJECTIVE: To measure inter-reader agreement and identify associated factors in interpreting complete response (CR) on magnetic resonance imaging (MRI) following chemoradiotherapy (CRT) for rectal cancer. MATERIALS AND METHODS: This retrospective study involved 10 readers from seven hospitals with experience of 80-10210 cases, and 149 patients who underwent surgery after CRT for rectal cancer. Using MRI-based tumor regression grading (mrTRG) and methods employed in daily practice, the readers independently assessed mrTRG, CR on T2-weighted images (T2WI) denoted as mrCRT2W, and CR on all images including diffusion-weighted images (DWI) denoted as mrCRoverall. The readers described their interpretation patterns and how they utilized DWI. Inter-reader agreement was measured using multi-rater kappa, and associated factors were analyzed using multivariable regression. Correlation between sensitivity and specificity of each reader was analyzed using Spearman coefficient. RESULTS: The mrCRT2W and mrCRoverall rates varied widely among the readers, ranging 18.8%-40.3% and 18.1%-34.9%, respectively. Nine readers used DWI as a supplement sequence, which modified interpretations on T2WI in 2.7% of cases (36/1341 [149 patients × 9 readers]) and mostly (33/36) changed mrCRT2W to non-mrCRoverall. The kappa values for mrTRG, mrCRT2W, and mrCRoverall were 0.56 (95% confidence interval: 0.49, 0.62), 0.55 (0.52, 0.57), and 0.54 (0.51, 0.57), respectively. No use of rectal gel, larger initial tumor size, and higher initial cT stage exhibited significant association with a higher inter-reader agreement for assessing mrCRoverall (P ≤ 0.042). Strong negative correlations were observed between the sensitivity and specificity of individual readers (coefficient, -0.718 to -0.963; P ≤ 0.019). CONCLUSION: Inter-reader agreement was moderate for assessing CR on post-CRT MRI. Readers' varying standards on MRI interpretation (i.e., threshold effect), along with the use of rectal gel, initial tumor size, and initial cT stage, were significant factors associated with inter-reader agreement.

A novel output feedback consensus control approach for generic linear multi-agent systems under input saturation over a directed graph topology.

This paper considers an output feedback consensus control approach for the generic linear multi-agent systems (MASs) under input saturation over a directed graph. A region of stability-based approach has been established for dealing with the input saturation. A conventional Luenberger observer for estimating the states of followers by themselves and an advanced cooperative observer for estimating the state of leader by followers have been applied for an estimated state feedback control. The stability conditions have been derived by considering a three-term-based combined Lyapunov function. Moreover, computationally simple controller and estimator design conditions have been obtained by resorting to a decoupling approach A set of initial conditions has been investigated to achieve the leader-following consensus of MASs under the input saturation constraint. To the best of our knowledge, an output feedback consensus approach, providing a consensus region, for generic linear MASs under input saturation over directed graphs without requiring the exact state of the leader has been explored for the first time. In contrast to the existing methods, the proposed approach considers an output feedback approach (rather than the state feedback), accounts for both linear and nonlinear saturation regions, applies an estimate of the state of the leader through cooperative observer, and is based on a generalized sector condition for the saturation nonlinearity. In addition, it offers a computationally simple design solution owing to the proposed decoupling method. Simulation results are provided to validate the efficacy of the designed protocol for F-18 aircraft and unmanned ground vehicles.

Clinical outcome measures in vision and eye care.

Traditionally, clinical outcome assessments have focused on the patient's perspective through patient-reported outcome assessments; however, given the complexity, integration, and interactions of various participants within the clinical ophthalmology setting, we propose that additional diverse clinical perspectives should be explored in order to appreciate fully the value of care provided to patients. In this review we introduce a framework by which clinical outcome assessments (COAs) can be organized. Our COA framework is composed of five outcome measurements that encompass the perspectives of each player in a patient's care: clinical data-reported outcomes, patient-reported outcomes, clinician-reported outcomes, observer-reported outcomes, and reviewer-reported outcomes. By establishing a standard for evaluating patient care, we hope to address gaps in expectations of patient care and encourage more thoughtful patient-clinician relationships.

Assessment of bias in scoring of AI-based radiotherapy segmentation and planning studies using modified TRIPOD and PROBAST guidelines as an example.

BACKGROUND AND PURPOSE: Studies investigating the application of Artificial Intelligence (AI) in the field of radiotherapy exhibit substantial variations in terms of quality. The goal of this study was to assess the amount of transparency and bias in scoring articles with a specific focus on AI based segmentation and treatment planning, using modified PROBAST and TRIPOD checklists, in order to provide recommendations for future guideline developers and reviewers. MATERIALS AND METHODS: The TRIPOD and PROBAST checklist items were discussed and modified using a Delphi process. After consensus was reached, 2 groups of 3 co-authors scored 2 articles to evaluate usability and further optimize the adapted checklists. Finally, 10 articles were scored by all co-authors. Fleiss' kappa was calculated to assess the reliability of agreement between observers. RESULTS: Three of the 37 TRIPOD items and 5 of the 32 PROBAST items were deemed irrelevant. General terminology in the items (e.g., multivariable prediction model, predictors) was modified to align with AI-specific terms. After the first scoring round, further improvements of the items were formulated, e.g., by preventing the use of sub-questions or subjective words and adding clarifications on how to score an item. Using the final consensus list to score the 10 articles, only 2 out of the 61 items resulted in a statistically significant kappa of 0.4 or more demonstrating substantial agreement. For 41 items no statistically significant kappa was obtained indicating that the level of agreement among multiple observers is due to chance alone. CONCLUSION: Our study showed low reliability scores with the adapted TRIPOD and PROBAST checklists. Although such checklists have shown great value during development and reporting, this raises concerns about the applicability of such checklists to objectively score scientific articles for AI applications. When developing or revising guidelines, it is essential to consider their applicability to score articles without introducing bias.