Objective Metrics Research Articles

O-292 A three-dimensional ultrasound-based online system for automated ovarian follicle measurement

Abstract Study question Is OSIS Ovary (a three-dimensional ultrasound-based online system for automated ovarian follicle measurement) a reliable tool to track follicle growth during ovarian stimulation cycles? Summary answer We can conclude that OSIS Ovary constitutes a reliable follicle-measurement tool What is known already Ultrasound follicle tracking is a crucial part of cycle monitoring. When the number of follicles increases, the manual measurement process can become tedious, imprecise and can potentially compromise the clinical outcomes The use of automated volumetric software for follicle monitoring makes it possible to overcome these manual limitations, regardless of the moment in the cycle when the metrics are obtained and assessed, providing flexibility for both clinicians and patients At the same time, more objective metrics are obtained with automated volume segmentation than with manual follicle measurement Study design, size, duration A prospective cohort study was performed among 89 patients undergoing ovarian stimulation during in vitro fertilization or oocyte vitrification cycles They were invited to participate in the study between May 2020 and February 2021. All participants signed an informed consent form prior to the start of the study Participants/materials, setting, methods 534 ovarian ultrasound volumes and manual datasets were obtained from ovarian stimulation cycles from the 89 participant patients Three transvaginal (2D and 3D) ultrasounds scans were performed on volunteer patients on the three clinically relevant monitoring days: 4th- 5th day (first follicular assessment), 6th -7th day (introduction of the GnRH antagonist) and 8th-9th day (oocyte pickup scheduling) The mean follicle diameter and the follicle count were estimated manually (2D) and automated (3D with Osis Ovary) Main results and the role of chance Based on the mean follicle diameter and follicle count values obtained, the Pearson/intraclass correlation coefficients were 0.976/0.987 and 0.804/0.889 in ≥ 10mm follicles, 0.989/0.994 and 0.809/0.867 in ≥ 13mm follicles and 0.995/0.997 and 0.791/0.840 in ≥ 16mm follicles. The mean difference (MnD) for the mean diameter and follicle count was,respectively, 0.759/0.161 in ≥ 10mm follicles, 0.486/1.033 in ≥ 13mm follicles and 0.784/0.486 in ≥ 16mm follicles. The upper and lower limits of agreement (ULA and LLA) were 3.641/2.123 and5.392/3.070 in ≥ 10mm follicles, 3.496/2.522 and 4.285/2.218 in ≥ 13mm follicles, and3.723/2.153 and 2.432/1.459 in ≥ 16mm follicles. The limits of agreement range (LoAR) were5.764/8.462 in ≥ 10mm follicles, 6.048/6.503 in ≥ 13mm follicles and 5.876/3.891 in ≥ 16mm follicles Limitations, reasons for caution We can conclude that OSIS Ovary constitutes a reliable follicle-measurement tool. The performance observed can be considered very good and it must be progressively clinical studied to be assessed and optimized in the next future Wider implications of the findings Cloud-based features, such as multi-user functioning in any 3D ultrasound machine, could make OSIS Ovary an attractive resource for reproductive medicine clinics, making it easier to track follicle growth during ovarian stimulation cycles Trial registration number not applicable

Pathological insights derived from neuroimaging in amyotrophic lateral sclerosis: emerging clinical applications.

Neuroimaging has been instrumental in shaping current understanding of the pathoanatomical signature of amyotrophic lateral sclerosis (ALS) across clinically well defined patient cohorts. The potential utility of imaging as an objective disease marker, however, remains poorly defined. Increasingly advanced quantitative and computational imaging studies have highlighted emerging clinical applications for neuroimaging as a complementary clinical modality for diagnosis, monitoring, and modelling disease propagation. Multimodal neuroimaging has demonstrated novel approaches for capturing primary motor disease. Extra-motor subcortical dysfunction is increasingly recognized as key modulators of disease propagation. The neural signature of cortical and subcortical dysfunction in ALS has been well defined at the population level. Objective metrics of focal primary motor dysfunction are increasingly sensitive and translatable to the individual patient level. Integrity of extra-motor subcortical abnormalities are recognized to represent critical pathways of the ALS disease 'connectome', predicting pathological spread. Neuroimaging plays a pivotal role in capturing upper motor neuron pathology in ALS. Their potential clinical role as objective disease markers for disease classification, longitudinal monitoring, and prognosis in ALS have become increasingly well defined.

Evaluation of Treatment Outcomes and Quality of Life in Patients with Obstructive Sleep Apnea Receiving Oral Appliance Therapy

ABSTRACT Background: A frequent sleep condition linked to considerable morbidity and death is obstructive sleep apnea (OSA). Continuous positive airway pressure (CPAP) therapy is not the only noninvasive treatment option for OSA. Another option is oral appliance therapy (OAT). The purpose of this study was to assess the quality of life and treatment results for OSA patients receiving OAT in a tertiary care environment. Methods: Over the course of 1 year, prospective observational research was carried out at a tertiary care facility. Patients with consecutive Obsructive Sleep apnea as per polysomnography report were enrolled in the sudy. Medical history, baseline demographics, and sleep characteristics were noted. Both before and after the start of OAT, objective sleep metrics and subjective results were evaluated. Adverse events and therapeutic compliance were tracked. To assess the effectiveness and tolerability of the medication, statistical analysis was done. Findings: The research had 150 patients in all, with a mean age of 52.4 years and a 66.7% male preponderance. Objective sleep metrics showed considerable improvements after starting OAT, with the apnea-hypopnea index decreasing from 28.7 to 9.3 episodes per hour (P < 0.001). Improvement was also evident in subjective outcomes as ESS ratings dropped from 12.6 to 6.2 (P < 0.05). Evaluations of quality of life indicated improvements in the categories of physical and mental health (P < 0.001). There were few reported adverse effects and a good level of adherence to OAT. In tertiary care settings, OAT is an effective and well-tolerated therapeutic option for individuals with OSA. The noteworthy enhancements in treatment results and overall well-being highlight the practicality of OAT as a substitute for CPAP therapy. To fully understand the long-term effectiveness and cardiovascular advantages of OAT in the treatment of OSA, more investigation is necessary.

Video Enhancement to Reduce Reflection and Darkness Caused by the Sunglasses using Fusion Algorithm

The enhancement of video of a person wearing sunglasses to reduce the reflection and darkness is a very challenging task in computer vision applications such as video surveillance. The existence of reflections and darkness caused by sunglasses results in intrusive images. The absence of a clear eye diminishes the visible quality of the complete face image. The eyes under sunglasses are not identified clearly if the reflection and darkness exist in the sunglasses. This paper demonstrates the reduction of adverse artifacts such as reflection and darkness in the eye region caused by sunglasses. The system is carried out with the fusion algorithm which consists of three modules: eyeglass tracking on face images, reduction of reflection, and reduction of darkness through the image enhancement method. The image enhancement method includes a color balance algorithm and a histogram stretching algorithm. Firstly, an automatic glasses presence detection model, based on a Robust Local Binary Pattern identifies the imaging process of the ocular region covered by the sunglasses. Secondly, a non-convex optimization scheme, guided by landmarks on the glasses, effectively reduces reflections through several iterations. The image enhancement method incorporating Color Balance, and Histogram Stretching is used to identify eye regions within sunglasses. The resulting regenerated eye regions within sunglasses exhibit increased brightness, subtle darkness, and minimized reflection. The objective evaluation metrics such as peak signal-to-noise ratio, structural similarity index measure, and logarithmic mean square error are used to measure the strength of the proposed system. Qualitative evaluations are conducted to demonstrate the good quality of eyeglass face images with reduced reflection and darkness.

What CT Findings are Predictive of Post-Traumatic Enophthalmos in Orbital Fractures?

Enophthalmos is a surgical indication for orbital fracture repair. However, guidelines to predict enophthalmos in orbital fractures are ambiguous. We systematically reviewed the existing literature on utilizing CT findings to establish objective metrics to predict enophthalmos in asymptomatic patients during initial trauma work-up. PRISMA guidelines were followed. PubMed and Embase were used to identify studies of interest. The Quality in Prognosis Studies (QUIPS) tool was used for risk of bias assessment. Random effects model meta-analyses of orbital volume change and fracture area values were completed. Regression analyses were performed to determine thresholds that predicted 2 mm enophthalmos. Of the initial 2236 abstracts, 36 met inclusion criteria. Thirty retrospective studies evaluated a total of 2851 patients and 6 prospective studies evaluated 211 patients. All 36 studies had predominantly low risk of bias. Predictors of enophthalmos assessed were orbital volume change (21 papers), fracture surface area (13 papers), inferior rectus muscle (IRM) displacements (7 papers), and fracture location (4 papers). Studies reporting on orbital volume change offered values ranging from 0.69 to 4.26 cm3. Fracture area predictor values ranged from 1.50 to 3.38 cm2. Meta-analyses confirmed the validity of both predictors. Pooled regression analyses demonstrated that 3.33 cm3 of orbital volume increase or fracture area of 3.12 cm2 were predictors of 2 mm enophthalmos. Both orbital volume change and fracture area measured on CT scan are good predictors of late post-traumatic enophthalmos. Pooled data indicates 3.12 cm2 of fracture area or 3.33 cm3 of orbital volume increase are predictive of enophthalmos.

Enhancing Monitoring Performance: A Microservices Approach to Monitoring with Spyware Techniques and Prediction Models.

In today's digital landscape, organizations face significant challenges, including sensitive data leaks and the proliferation of hate speech, both of which can lead to severe consequences such as financial losses, reputational damage, and psychological impacts on employees. This work considers a comprehensive solution using a microservices architecture to monitor computer usage within organizations effectively. The approach incorporates spyware techniques to capture data from employee computers and a web application for alert management. The system detects data leaks, suspicious behaviors, and hate speech through efficient data capture and predictive modeling. Therefore, this paper presents a comparative performance analysis between Spring Boot and Quarkus, focusing on objective metrics and quantitative statistics. By utilizing recognized tools and benchmarks in the computer science community, the study provides an in-depth understanding of the performance differences between these two platforms. The implementation of Quarkus over Spring Boot demonstrated substantial improvements: memory usage was reduced by up to 80% and CPU usage by 95%, and system uptime decreased by 119%. This solution offers a robust framework for enhancing organizational security and mitigating potential threats through proactive monitoring and predictive analysis while also guiding developers and software architects in making informed technological choices.

Evaluating emotional and subjective responses in synthetic art-related dialogues: A multi-stage framework with large language models

The appearance of Large Language Models (LLM) has implied a qualitative step forward in the performance of conversational agents, and even in the generation of creative texts. However, previous applications of these models in generating dialogues neglected the impact of ‘hallucinations’ in the context of generating synthetic dialogues, thus omitting this central aspect in their evaluations. For this reason, we propose an open-source and flexible framework called GenEvalGPT framework: a comprehensive multi-stage evaluation strategy utilizing diverse metrics. The objective is two-fold: first, the goal is to assess the extent to which synthetic dialogues between a chatbot and a human align with the specified commands, determining the successful creation of these dialogues based on the provided specifications; and second, to evaluate various aspects of emotional and subjective responses. Assuming that dialogues to be evaluated were synthetically produced from specific profiles, the first evaluation stage utilizes LLMs to reconstruct the original templates employed in dialogue creation. The success of this reconstruction is then assessed in a second stage using lexical and semantic objective metrics. On the other hand, crafting a chatbot’s behaviors demands careful consideration to encompass a diverse range of interactions it is meant to engage in. Synthetic dialogues play a pivotal role in this context, as they can be deliberately synthesized to emulate various behaviors. This is precisely the objective of the third stage: evaluating whether the generated dialogues adhere to the required aspects concerning emotional and subjective responses. To validate the capabilities of the proposed framework, we applied it to recognize whether the chatbot exhibited one of two distinct behaviors in the synthetically generated dialogues: being emotional and providing subjective responses, or remaining neutral. This evaluation will encompass traditional metrics and automatic metrics generated by the LLM. In our use case of art-related dialogues, our findings reveal that the capacity to recover templates or profiles is more effective for information or profile items that are objective and factual, in contrast to those related to mental states or subjective facts. For the emotional and subjective behavior assessment, rule-based metrics achieved a 79% of accuracy in detecting emotions or subjectivity (anthropic), and an 82% on the LLM automatic metrics. The combination of these metrics and stages could help to decide which of the generated dialogues should be maintained depending on the applied policy, which could vary from preserving between 57% to 93% of the initial dialogues.

Restoration of Semantic-Based Super-Resolution Aerial Images

Currently, technologies for remote sensing image processing are actively developing, including both satellite images and aerial images obtained from video cameras of unmanned aerial vehicles. Often such images have artifacts such as low resolution, blurred image fragments, noise, etc. One way to overcome such limitations is to use modern technologies to restore super-resolution images based on deep learning methods. The specificity of aerial images is the presentation of texture and structural elements in a higher resolution than in satellite images, which objectively contributes to better results of restoration. The article provides a classification of super-resolution methods based on the main architectures of deep neural networks, namely convolutional neural networks, visual transformers and generative adversarial networks. The article proposes a method for reconstructing super-resolution aerial images SemESRGAN taking into account semantic features by using an additional deep network for semantic segmentation during the training stage. The total loss function, including adversarial losses, pixel-level losses, and perception losses (feature similarity), is minimized. Six annotated aerial and satellite image datasets CLCD, DOTA, LEVIR-CD, UAVid, AAD, and AID were used for the experiments. The results of image restoration using the proposed SemESRGAN method were compared with the basic architectures of convolutional neural networks, visual transformers and generative adversarial networks. Comparative results of image restoration were obtained using objective metrics PSNR and SSIM, which made it possible to evaluate the quality of restoration using various deep network models.

Evaluating Gesture Generation in a Large-scale Open Challenge: The GENEA Challenge 2022

This article reports on the second GENEA Challenge to benchmark data-driven automatic co-speech gesture generation. Participating teams used the same speech and motion dataset to build gesture-generation systems. Motion generated by all these systems was rendered to video using a standardised visualisation pipeline and evaluated in several large, crowdsourced user studies. Unlike when comparing different research articles, differences in results are here only due to differences between methods, enabling direct comparison between systems. The dataset was based on 18 hours of full-body motion capture, including fingers, of different persons engaging in a dyadic conversation. Ten teams participated in the challenge across two tiers: full-body and upper-body gesticulation. For each tier, we evaluated both the human-likeness of the gesture motion and its appropriateness for the specific speech signal. Our evaluations decouple human-likeness from gesture appropriateness, which has been a difficult problem in the field. The evaluation results show some synthetic gesture conditions being rated as significantly more human-like than 3D human motion capture. To the best of our knowledge, this has not been demonstrated before. On the other hand, all synthetic motion is found to be vastly less appropriate for the speech than the original motion-capture recordings. We also find that conventional objective metrics do not correlate well with subjective human-likeness ratings in this large evaluation. The one exception is the Fréchet gesture distance (FGD), which achieves a Kendall’s tau rank correlation of around -0.5. Based on the challenge results we formulate numerous recommendations for system building and evaluation.

The global patient-reported outcomes for multiple sclerosis initiative: bridging the gap between clinical research and care - updates at the 2023 plenary event.

Significant advancements have been achieved in delineating the progress of the Global PROMS (PROMS) Initiative. The PROMS Initiative, a collaborative endeavor by the European Charcot Foundation and the Multiple Sclerosis International Federation, strives to amplify the influence of patient input on MS care and establish a cohesive perspective on Patient-Reported Outcomes (PROs) for diverse stakeholders. This initiative has established an expansive, participatory governance framework launching four dedicated working groups that have made substantive contributions to research, clinical management, eHealth, and healthcare system reform. The initiative prioritizes the global integration of patient (For the purposes of the Global PROMS Initiative, the term "patient" refers to the people with the disease (aka People with Multiple Sclerosis - pwMS): any individual with lived experience of the disease. People affected by the disease/Multiple Sclerosis: any individual or group that is affected by the disease: E.g., family members, caregivers will be also engaged as the other stakeholders in the initiative). insights into the management of MS care. It merges subjective PROs with objective clinical metrics, thereby addressing the complex variability of disease presentation and progression. Following the completion of its second phase, the initiative aims to help increasing the uptake of eHealth tools and passive PROs within research and clinical settings, affirming its unwavering dedication to the progressive refinement of MS care. Looking forward, the initiative is poised to continue enhancing global surveys, rethinking to the relevant statistical approaches in clinical trials, and cultivating a unified stance among 'industry', regulatory bodies and health policy making regarding the application of PROs in MS healthcare strategies.

Nasal Airflow Dynamics following LeFort I Advancement in Cleft Nasal Deformities: A Retrospective Preliminary Study.

Unilateral cleft lip and palate (UCLP) nasal deformity impacts airflow patterns and pressure distribution, leading to nasal breathing difficulties. This study aims to create an integrated approach using computer-aided design (CAD) and computational fluid dynamics (CFD) to simulate airway function and assess outcomes in nasal deformities associated with unilateral cleft lip and palate (UCLP) after LeFort I osteotomy advancement. Significant alterations were observed in nasal geometry, airflow velocity, pressure dynamics, volumetric flow rate, and nasal resistance postoperatively, indicating improved nasal airflow. The cross-sectional area increased by 26.6%, airflow rate by 6.53%, and nasal resistance decreased by 6.23%. The study offers quantitative insights into the functional impacts of such surgical interventions, contributing to a deeper understanding of UCLP nasal deformity treatment and providing objective metrics for assessing surgical outcome.

Dual-branch deep cross-modal interaction network for semantic segmentation with thermal images

Semantic segmentation using RGB (Red-Green-Blue) images and thermal datas is an indispensable component of autonomous driving. The key to RGB-Thermal (RGB and Thermal) semantic segmentation is achieving the interaction and fusion of features between RGB and thermal images. Therefore, we propose a dual-branch deep cross-modal interaction network (DCIT) based on Encoder–Decoder structure. This framework consists of two parallel networks for feature extraction from RGB and Thermal data. Specifically, in each feature extraction stage of the Encoder, we design a Cross Feature Regulation Modules (CFRM) to align and correct modality specific features by reducing the inter-modality feature differences and eliminating intra-modality noise. Then, the modality features are aggregated through Cross Modal Feature Fusion Module (CMFFM) based on cross linear attention to capture global information from modality features. Finally, Adaptive Multi-Scale Cross-positional Fusion Module (AMCFM) utilizes the fused features to integrate consistent semantic information in the Decoder stage. Our framework can improve the interaction of cross modal features. Extensive experiments on urban scene datasets demonstrate that our proposed framework outperforms other RGB-Thermal semantic segmentation methods in terms of objective metrics and subjective visual assessments.

Remote expert viewing, laboratory tests or objective metrics: which one(s) to trust?

We present a study on the validity of quality assessment in the context of the development of visual media coding schemes. The work is motivated by the need for reliable means for decision-taking in standardization efforts of MPEG and JVET, i.e., the adoption or rejection of coding tools during the development process of the coding standard. The study includes results considering three means: objective quality metrics, remote expert viewing, which is a method designed in the context of MPEG standardization, and formal laboratory visual evaluation. The focus of this work is on the comparison of pairs of coded video sequences, e.g., a proposed change and an anchor scheme at a given rate point. An aggregation of performance measurements across multiple rate points, such as the Bjøntegaard Delta rate, is out of the scope of this paper. The paper details the test setup for the subjective assessment methods and the objective quality metrics under consideration. The results of the three approaches are reviewed, analyzed, and compared with respect to their suitability for the decision-taking task. The study indicates that, subject to the chosen test content and test protocols, the results of remote expert viewing using a forced-choice scale can be considered more discriminatory than the results of naïve viewers in the laboratory tests. The results further that, in general, the well-established quality metrics, such as PSNR, SSIM, or MS-SSIM, exhibit a high rate of correct decision-making when their results are compared with both types of viewing tests. Among the learning-based metrics, VMAF and AVQT appear to be most robust. For the development process of a coding standard, the selection of the most suitable means must be guided by the context, where a small number of carefully selected objective metrics, in combination with viewing tests for unclear cases, appears recommendable.

Pharmacologic Sleep Aids in the Intensive Care Unit: A Systematic Review.

Background: Patients in the intensive care unit (ICU) often experience poor sleep quality. Pharmacologic sleep aids are frequently used as primary or adjunctive therapy to improve sleep, although their benefits in the ICU remain uncertain. This review aims to provide a comprehensive assessment of the objective and subjective effects of medications used for sleep in the ICU, as well as their adverse effects. Methods: PubMed, Web of Science, Scopus, Embase, and Cochrane Central Register of Controlled Trials were systematically searched from their inception until June 2023 for comparative studies assessing the effects of pharmacologic sleep aids on objective and subjective metrics of sleep. Results: Thirty-four studies with 3498 participants were included. Medications evaluated were melatonin, ramelteon, suvorexant, propofol, and dexmedetomidine. The majority of studies were randomized controlled trials. Melatonin and dexmedetomidine were the best studied agents. Objective sleep metrics included polysomnography (PSG), electroencephalography (EEG), bispectral index, and actigraphy. Subjective outcome measures included patient questionnaires and nursing observations. Evidence for melatonin as a sleep aid in the ICU was mixed but largely not supportive for improving sleep. Evidence for ramelteon, suvorexant, and propofol was too limited to offer definitive recommendations. Both objective and subjective data supported dexmedetomidine as an effective sleep aid in the ICU, with PSG/EEG in 303 ICU patients demonstrating increased sleep duration and efficiency, decreased arousal index, decreased percentage of stage N1 sleep, and increased absolute and percentage of stage N2 sleep. Mild bradycardia and hypotension were reported as side effects of dexmedetomidine, whereas the other medications were reported to be safe. Several ongoing studies have not yet been published, mostly on melatonin and dexmedetomidine. Conclusions: While definitive conclusions cannot be made for most medications, dexmedetomidine improved sleep quantity and quality in the ICU. These benefits need to be balanced with possible hemodynamic side effects.

MCRNet: Underwater image enhancement using multi-color space residual network

The selective attenuation and scattering of light in underwater environments cause color distortion and contrast reduction in underwater images, which can impede the ever-growing demand for underwater robot operations. To address these issues, we propose a Multi-Color space Residual Network (MCRNet) for underwater image enhancement. Our method takes advantage of the unique features of color representation in the RGB, HSV, and Lab color spaces. By utilizing the distinct feature representations of images in different color spaces, we can highlight and fuse the most informative features of the three color spaces. Our approach employs a self-attention mechanism in the multi-color space feature fusion module. Extensive experiments demonstrate that our method achieves satisfactory results in color correction and contrast improvement of underwater images, particularly in severely degraded scenes. Consequently, our method outperforms state-of-the-art methods in both subjective visual comparison and objective evaluation metrics.

Superpixel conditional generation adversarial network for CMR artifact correction

Cardiac Magnetic Resonance (CMR) is widely used in diagnosing cardiac diseases for its excellent contrast of cardiovascular features. However, due to the long imaging time of CMR scanning, the patient's respiration, limb shaking, and heart beating will lead to a certain degree of motion artifacts in the image, seriously degrade the image quality and affect the doctor's clinical judgment. This paper proposes a superpixel conditional Generative Adversarial Network (spcGAN) based on a conditional Generative Adversarial Network (cGAN) by applying superpixel to both generator and discriminator parts. In the generator section, a generator network based on superpixel segmentation and pooling is proposed for feature extraction at the superpixel level to enhance the reconstruction of image edge texture and structural details. In the discriminator part, superpixel pooling is used to construct a superpixel discriminator. It is fused with the traditional convolutional discriminator to produce a superpixel-based dual discriminator, which makes the discriminator consider the image's local structure and details. Based on the generator and discriminator structure proposed in this paper, superpixel pooling and edge texturing loss functions are designed for optimization. Adequate ablation experiments and comparison experiments are conducted in terms of experimental results. Three types of objective metrics, Peak Signal-to-Noise Ratio (PSNR), Structural Similarity (SSIM), and Focus Measurement (Tenengrad), were selected as references. The experimental results show that the effect of removing motion artifacts from authentic CMR images on the three datasets is most significant in the dataset produced in this paper. The results obtained from the fusion between the designed generator, discriminator, and loss function are the most obvious. Compared with the existing methods, the spcGAN proposed in this paper performs better.

Cracking the code: interpreting content and phrases used in maternal-fetal medicine fellowship letters of recommendation

BackgroundLetters of recommendation for Maternal-Fetal Medicine(MFM) fellowship are a critical part of the applicant selection process. However, data regarding best practices for how to write LOR for MFM is limited. Similarly, within letters of recommendation, differences in the “code” or meaning of summative words/phrases used at the end of letters of recommendation are seen between surgery, pediatrics, and medicine. However, data regarding code MFM Letters of recommendation are quite limited. ObjectiveWe sought to describe what Maternal-Fetal Medicine program directors value in letters of recommendation for fellowship applicants and how PDs interpret commonly used summative words/phrases. Study DesignAfter IRB exemption, subject matter experts developed an e-survey querying the importance of various letters of recommendation “best practices” described by other specialties. Content and face validation were performed prior to dissemination. This cross-sectional survey was administered to MFM program directors in February 2023. The primary outcome was the relative importance of letters of recommendation content areas. Secondary outcomes included the strength of each summative “code” phrase. Descriptive analysis was performed and principal component analysis (PCA) was then used to reduce the list of phrases to their underlying dimensions. Statistical analysis was performed by SPSS 29.0. ResultsOf 104 MFM program directors sent the survey, 70 (67%) responded. MFM program directors reviewed an average of 78 applications (SD, 30) with 60% writing ≥3 letters/year. Ninety-one percent of respondents noted that letters of recommendation are important/very important in shaping impressions of an applicant. Respondents reported the depth of interaction with an applicant, the applicant's specific behavior traits, the applicant's abilities and a summative statement including strength of the recommendation as important content for MFM fellowship letters of recommendation. Letter length, use of bold/italics, and restating the applicant's curriculum vitae were considered not important. Following PCA with varimax rotation, 14 specific phrases used in letters of recommendation were reduced to 5 themes: high qualitative assessments, average qualitative assessments, objective metrics, exceeding expectations and grit. These themes accounted for 64.6% of the variance in the model (KMO 0.7, Bartlett's Test of Sphericity p<.01). Phrases that respondents considered positive included: “Top 5%,” “Want to keep,” and “highest recommendation,” (all mean score≥4.5/5), while “expected level,” “showed improvement,” and “2nd quartile” were negatively associated code words (all mean score <2.5/5). ConclusionMFM program directors reported that descriptions of an applicant's abilities, behavior traits, and depth of the writer's interactions with the applicant were all important components of an MFM fellowship letters of recommendation. Letter length, bold/italics, and highlights from the CV were not important. A clear “code” emerged regarding summative phrases included in letters of recommendation. Dissemination of these data might help less experienced letter writers send a clearer message and ensure all letter writers have a shared mental model.

Technological interventions in functional capacity evaluations: An insight into current applications

BACKGROUND: Functional Capacity Evaluation (FCE) is a crucial component within return-to-work decision making. However, clinician-based physical FCE interpretation may introduce variability and biases. The rise of technological applications such as machine learning and artificial intelligence, could ensure consistent and precise results. OBJECTIVE: This review investigates the application of information and communication technologies (ICT) in physical FCEs specific for return-to-work assessments. METHODS: Adhering to the PRISMA guidelines, a search was conducted across five databases, extracting study specifics, populations, and technological tools employed, through dual independent reviews. RESULTS: Nine studies were identified that used ICT in FCEs. These technologies included electromyography, heart rate monitors, cameras, motion detectors, and specific software. Notably, although some devices are commercially available, these technologies were at a technology readiness level of 5–6 within the field of FCE. A prevailing trend was the combined use of diverse technologies rather than a single, unified solution. Moreover, the primary emphasis was on the application of technology within study protocols, rather than a direct evaluation of the technology usability and feasibility. CONCLUSION: The literature underscores limited ICT integration in FCEs. The current landscape of FCEs, marked by a high dependence on clinician observations, presents challenges regarding consistency and cost-effectiveness. There is an evident need for a standardized technological approach that introduces objective metrics to streamline the FCE process and potentially enhance its outcomes.

Reference-based super-resolution reconstruction of remote sensing images based on a coarse-to-fine feature matching transformer

Remote sensing image super-resolution reconstruction technology mines the deep details of remote sensing image, which has been widely used in the fields of intelligent and precise agriculture, intelligent transportation, earth surface object recognition, and so on. To obtain more detailed information,we design a reference-based super-resolution reconstruction network. Firstly, the coarse-to-fine feature matching strategy is adopted for the features of the input image. Global coarse matching is performed on the center patch of each block, and then pixel-level local fine matching is performed on the edge patches of the block. This both reduces the amount of computation and improves the matching accuracy. A threshold is set to determine whether the feature matching results meet the criteria for feature transfer. Finally, different scale features are fused through several convolutional layers and sampling operations, obtaining the reconstruction features after a fourfold increase in resolution. The ultimate super-resolution image is generated through a decoder. We have performed training and testing on remote sensing datasets. Compared to the current state-of-the-art methods, our proposed method is visually more details and outperforms other methods in terms of objective evaluation metrics.

Sleep After Concussion: A Scoping Review of Sensor Technologies.

Sleep disturbances following a concussion/mild traumatic brain injury are associated with longer recovery times and more comorbidities. Sensor technologies can directly monitor sleep-related physiology and provide objective sleep metrics. This scoping review determines how sensor technologies are currently used to monitor sleep following a concussion. We searched Ovid (Medline, Embase), Web of Science, CINAHL, Compendex Engineering Village, and PsycINFO from inception to June 20, 2022, following Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines for scoping reviews. Included studies objectively monitored sleep in participants with concussion. We screened 1081 articles and included 37 in the review. A total of 17 studies implemented polysomnography (PSG) months to years after injury for a median of two nights and provided a wide range of sleep metrics, including sleep-wake times, sleep stages, arousal indices, and periodic limb movements. Twenty-two studies used actigraphy days to weeks after injury for a median of 10 days and nights and provided information limited to sleep-wake times. Sleep stages were most reported in PSG studies, and sleep efficiency was most reported in actigraphy studies. For both technologies there was high variability in reported outcome measures. Sleep sensing technologies may be used to identify how sleep affects concussion recovery. However, high variability in sensor deployment methodologies makes cross-study comparisons difficult and highlights the need for standardization. Consensus on how sleep sensing technologies are used post-concussion may lead to clinical integration with subjective methods for improved sleep monitoring during the recovery period.