Objective Assessment Of Performance Research Articles

Use of optical see-through head-mounted devices in dentistry - a scoping review.

The aim of this scoping review was to identify the scientific evidence related to the utilization of Optical See- Through Head-Mounted Display (OST-HMD) in dentistry, and to determine future research needs. The research question was formulated using the "Population" (P), "Concept" (Cpt), and "Context" (Cxt) framework for scoping reviews. Existing literature was designated as P, OST-HMD as Cpt, and Dentistry as Cxt. An electronic search was conducted in PubMed, Embase, Web of Science, and CENTRAL. Two authors independently screened titles and abstracts and performed the full-text analysis. The search identified 286 titles after removing duplicates. Nine studies, involving 138 participants and 1760 performed tests were included in this scoping review. Seven of the articles were preclinical studies, one was a survey, and one was a clinical trial. The included manuscripts covered various dental fields: three studies in orthodontics, two in oral surgery, two in conservative dentistry, one in general dentistry, and the remaining one in prosthodontics. Five articles focused on educational purposes. Two brands of OST-HMD were used: in eight studies HoloLens Microsoft was used, while Google Glass was utilized in one article. The overall number of included studies was low; therefore, the available data from this review cannot yet support an evidence-based recommendation for the clinical use of OST-HMDs. However, the existing preclinical data indicate a significant capacity for clinical and educational implementation. Further adoption of these devices will facilitate more reliable and objective quality and performance assessments, as well as more direct comparisons with conventional workflows. More clinical studies must be conducted to substantiate the potential benefits and reliability for patients and clinicians.

“Seeing inside out”: revealing the effectiveness of otoscopy training in virtual reality enhanced practical exams - a randomized controlled trial

BackgroundThe study aimed to assess the impact of different training modalities on otoscopy performance during a practical exam using a high-fidelity simulator and to determine if objective evaluation of otoscopy is feasible using a simulator that records insertion depth and tympanic membrane coverage.MethodsParticipants were assigned to one of four groups: control and three intervention groups with varying training approaches. Participants received otoscopy training and then were assessed through a practical exam on a high-fidelity simulator that uses virtual reality to visualize the ear canal and middle ear. Performance was evaluated using a modified Objective Structured Assessment of Technical Skills checklist and Integrated Procedural Performance Instrument checklist. Insertion depth, tympanic membrane coverage, and correct diagnosis were recorded. Data were tested for normal distribution using the Shapiro-Wilk test. One-way ANOVA and, for non-normally distributed data, Kruskal-Wallis test combined with Dunn’s test for multiple comparisons were used. Interrater reliability was assessed using Cohen’s κ and Intraclass correlation coefficient.ResultsAll groups rated their training sessions positively. Performance on the OSATS checklist was similar among groups. IPPI scores indicated comparable patient handling skills. The feedback group examined larger tympanic membrane areas and had higher rates of correct diagnosis. The correct insertion depth was rarely achieved by all participants. Interrater reliability for OSATS was strong. IPPI reliability showed good correlation.ConclusionRegardless of training modality, participants perceived learning improvement and skill acquisition. Feedback improved examination performance, indicating simulator-guided training enhances skills. High-fidelity simulator usage in exams provides an objective assessment of performance.

Machine learning in employee performance evaluation: A HRM perspective

This research explores how machine learning, the tech whiz kid, is shaking up the traditional HR world of employee reviews. Companies are hungry for better ways to assess their workforce, and machine learning comes to the table with a buffet of data analysis tools. Productivity metrics, project wins, even that qualitative feedback, all get crunched by these algorithms to dish up comprehensive and objective performance pictures. This research dives into both the pros and cons of bringing this tech titan into HR, aiming to make evaluations not just accurate, but also fair and impactful. It's about finding the sweet spot where technology and human understanding join forces to power up performance reviews for the modern workplace. In the ever-evolving dance of the modern workplace, where performance reigns supreme, the old waltz of subjective evaluations stumbles toward obsolescence. This study steps into the spotlight, exploring the transformative potential of machine learning as the new lead partner in HRM, specifically for crafting nuanced and objective performance assessments. Imagine algorithms like virtuoso musicians, weaving together diverse data melodies – productivity's staccato riffs, project outcomes' triumphant crescendos, even the subtle whispers of qualitative feedback – to paint a vibrant portrait of individual contributions. But this data-driven tango isn't without its tricky steps. This research scrutinizes both the grace and potential missteps of machine learning in HRM, aiming to illuminate a path toward optimized performance evaluations that are not only accurate but also fair and effective. By bridging the gap between technology and human understanding, this study offers a roadmap for organizations waltzing towards a future where performance appraisals unlock the full potential of their workforce.

NextGen Training for Medical First Responders: Advancing Mass-Casualty Incident Preparedness through Mixed Reality Technology

Mixed reality (MR) technology has the potential to enhance the disaster preparedness of medical first responders in mass-casualty incidents through new training methods. In this manuscript, we present an MR training solution based on requirements collected from experienced medical first responders and technical experts, regular end-user feedback received through the iterative design process used to develop a prototype and feedback from two initial field trials. We discuss key features essential for an effective MR training system, including flexible scenario design, added realism through patient simulator manikins and objective performance assessment. Current technological challenges such as the responsiveness of avatars and the complexity of smart scenario control are also addressed, along with the future potential for integrating artificial intelligence. Furthermore, an advanced analytics and statistics tool that incorporates complex data integration, machine learning for data analysis and visualization techniques for performance evaluation is presented.

Analisa Penerapan Metode WASPAS Dalam Sistem Pendukung Keputusan Penilaian Kinerja Checker Terbaik Pada Kereta Api

Performance assessment in a work environment involving monitoring and checking activities plays an important role in maintaining quality and efficiency. This study analyzes the application of the Weighted Aggregated Sum Product Assessment (WASPAS) method as part of the Decision Support System in the process of evaluating the performance of the best checkers. The WASPAS method is used to address the complexity of measuring checker performance by considering various relevant criteria. This article describes the steps for implementing the WASPAS method and its integration into a decision support system. The results of the analysis show that this approach can help improve accuracy and objectivity in performance appraisal, providing a strong basis for management to make informed decisions regarding the selection of the best checkers. In this study, checker performance data were collected and analyzed carefully. The results of the analysis provide insight into a more accurate and objective checker performance assessment. The integration of the WASPAS method into DSS helps managers or decision makers overcome subjectivity in the assessment process, as well as avoid potential bias that could affect the final result. From the results of the study, it can be concluded that the application of the WASPAS method in SPK can increase the validity and reliability of checker performance assessments. This approach allows managers to carry out a more comprehensive and balanced evaluation of every aspect of checker performance. However, careful selection of criterion weights and sensitivity to changes in weights is required to ensure optimal results. Thus, this study provides a more in-depth look at how the WASPAS method can be applied in the DSS context for assessing the performance of the best checkers. The implications can be felt in making decisions that are more informed and accurate in managing individual performance in the work environment. From the results of calculations that have been carried out from 7 alternative Checker data using the WASPAS Method, the best alternative, namely the fifth alternative with the name Riski Sudrajat, obtains the highest score, namely 0.939. Therefore the fifth alternative was chosen as the Checker with the best performance on the Railway.

The Quartet Data Portal: integration of community-wide resources for multiomics quality control.

The Quartet Data Portal facilitates community access to well-characterized reference materials, reference datasets, and related resources established based on a family of four individuals with identical twins from the Quartet Project. Users can request DNA, RNA, protein, and metabolite reference materials, as well as datasets generated across omics, platforms, labs, protocols, and batches. Reproducible analysis tools allow for objective performance assessment of user-submitted data, while interactive visualization tools support rapid exploration of reference datasets. A closed-loop "distribution-collection-evaluation-integration" workflow enables updates and integration of community-contributed multiomics data. Ultimately, this portal helps promote the advancement of reference datasets and multiomics quality control.

Enhanced Learning Using Digital Recordings with Integrated Standardized Patient Examinations (ISPEs) in Physical Therapy Education: A Qualitative Pilot Study

(1) Background: Integrated standardized patient examinations (ISPEs) allow students to demonstrate competence with curricular learning and communication. Digital recordings of these experiences provide an objective permanent record, allowing students to review and improve their performance. Although recordings have been utilized as a tool in physical therapy education, no studies have described the impact of reviewing recordings of ISPE. This qualitative pilot study aimed to investigate student perceptions and learning after reviewing their recordings of ISPE. (2) Methods: Second-year Doctor of Physical Therapy students (n= 23) participated in the study by completing an anonymous online survey after reviewing their recordings from three ISPEs. Thematic analysis was used to identify codes and central themes from the survey data. (3) Results: The results showed that 95.6% of students found the video review process beneficial. Five themes emerged: (i) digital recordings provide an objective performance assessment, (ii) approaches to self-review vary, (iii) it provides an opportunity for growth, (iv) a holistic review is possible, and (v) students need structure and guidance in the process. (4) Conclusions: Study findings indicate that a review of the recordings of ISPEs facilitates the development of clinical skills for physical therapy students. Implementing an explicit framework for reviewing the recordings may enhance the process and facilitate further promotion of reflection-on-action.

828 Real-Time Artificial Intelligence Instruction in Comparison to Human Expert Instruction in Surgical Technical Skills Teaching – a Randomized Controlled Trial

Abstract Aim Surgical simulators equipped with artificial intelligence (AI) systems provide risk-free training on realistically simulated complex patient cases, objective performance assessment, and tailored error avoidance feedback. This study compared the efficacy of real-time AI instruction with human instructor-mediated training in surgical technical skills teaching. Method Medical students (n = 98) were randomly allocated into three feedback groups. Everyone repeated a virtual brain tumour resection five times. The first repetition was done without receiving feedback to assess their baseline performance. Students in Group 1 did not receive real-time feedback, and they were shown visual feedback only after each procedure based on expert benchmarks. Students in Group 2 were instructed in real-time by the AI system. After each task, the students were shown their error-video clips generated by this system alongside the expert-level demonstrations relating to each error. Students in Group 3 were instructed by human instructors during the tasks. After each task, instructors summarized the areas of improvement and demonstrated correction techniques. All participant data was scored using a composite-score. Statistical analysis was conducted to compare learning from the first to the last task repetition. Results Students in Group 2 and Group 3 significantly improved in the performance score by the third and second repetition, respectively (p<0.01, p = 0.01) compared to their baseline performance. Students in Group 2 achieved significantly higher scores than those in Group 3 in the fifth repetition (p<0.01). Conclusions Artificial intelligence systems may provide competency-based curricula in surgical training with efficient surgical technical skills teaching methodologies.

Periodontal disease and sports performance: a systematic review and meta-analysis

ABSTRACT This systematic review and meta-analysis aimed to assess the association between athletic performance and periodontal disease (PD). Observational studies published up to April 2022 were searched in online databases (PubMed, Scopus, Web of Science, The Cochrane Library, LILACS, OpenGrey, and Google Scholar). PECO statement focused on studies with professional athletes (Participants) with PD (Exposition) compared to those with healthy periodontium (Comparison) and its effects on sports performance (outcome). Searches were conducted according to the PRISMA guidelines (registration protocol: 10.17605/OSF.IO/7ADH6) and risk of bias evaluation according to the Joanna Briggs Institute checklist. A total of 793 references were retrieved, of which eight were considered eligible. Two studies did not show an association between clinical PD parameters and performance. A meta-analysis with 396 participants suggested a relationship between PD and self-reported reduction in sports performance (odds ratio [OR] = 1.55; 95% confidence interval [CI] 1.04–2.31; p = 0.03; I2 = 0%) with moderate quality of evidence accordingly GRADE analysis. Despite the differences in performance evaluations between studies, PD was associated with reduced self-reported sports performance. Additional studies assessing confounding factors and objective assessment of PD and performance are needed to clarify the real effect of both conditions.

Fast soft-tissue deformations coupled with mixed reality toward the next-generation childbirth training simulator.

High-quality gynecologist and midwife training is particularly relevant to limit medical complications and reduce maternal and fetal morbimortalities. Physical and virtual training simulators have been developed. However, physical simulators offer a simplified model and limited visualization of the childbirth process, while virtual simulators still lack a realistic interactive system and are generally limited to imposed predefined gestures. Objective performance assessment based on the simulation numerical outcomes is still not at hand. In the present work, we developeda virtual childbirth simulator based on the Mixed-Reality (MR) technology coupled with HyperMSM (Hyperelastic Mass-Spring Model) formulation for real-time soft-tissue deformations, providing intuitive user interaction with the virtual physicalmodel and a quantitative assessment to enhance the trainee's gestures. Microsoft HoloLens 2 was used and the MR simulator was developed including a complete holographic obstetric model. A maternalpelvis system modelof a pregnant woman (including the pelvis bone, the pelvic floor muscles, the birth canal, the uterus, and the fetus) was generated, and HyperMSM formulation was applied to simulate the soft tissue deformations. To induce realistic reactions to free gestures, the virtual replicas of the user's detected hands were introduced into the physical simulation and were associated with a contact model between the hands and the HyperMSM models. The gesture of pulling any part of the virtual models with two hands was also implemented. Two labor scenarios were implemented within the MR childbirth simulator: physiological labor and forceps-assisted labor. A scoring system for the performance assessment was included based on real-time biofeedback. As results,our developedMR simulation application was developed in real-time with a refresh rate of 30-50 FPS on the HoloLens device. HyperMSM model was validated using FE outcomes: high correlation coefficients of [0.97-0.99] and weighted root mean square relative errors of 9.8% and 8.3% were obtained for the soft tissue displacement and energy density respectively. Experimental tests showed that the implemented free-user interaction system allows to apply the correct maneuvers (in particular the "Viennese" maneuvers) during the labor process, and is capable to induce a truthful reaction of the model. Obtainedresults confirm alsothe possibility of using our simulation's outcomes to objectively evaluate the trainee's performance with a reduction of 39% for the perineal strain energy density and 5.6mm for the vertical vaginal diameter when the "Viennese" technique is applied. This present study provides, for the first time, an interactive childbirth simulator with an MR immersive experience with direct free-hand interaction, real-time soft-tissue deformation feedback, and an objective performance assessment based on numerical outcomes. This offers a new perspective for enhancing next-generation training-based obstetric teaching. The used models of the maternal pelvic system and the fetus will be enhanced, and more delivery scenarios (e.g. instrumental delivery, breech delivery, shoulder dystocia) will be designed and integrated. The third stage of labor will be also investigated to include the delivery of the placenta, and the clamping and cutting of the umbilical cord.

Financial Analysis and Reporting of Construction Enterprise: An analysis of CRH plc

A financial analysis statement is a report that examines a company's financial health and performance in detail. The report will often contain a review of the business's financial reports, such as the balance sheet, income statement, and cash flow statement, in addition to other financial measures and ratios used to assess the company's financial status. It is a vital tool since it may assist managers in making decisions, give an objective assessment of the company's performance, permit comparison with industry benchmarks, and aid in regulatory compliance. Real solutions to human problems are provided through construction enterprises. Many projects are executed by multiple contractors, and a single project may include a variety of techniques and methods. It is classified as an asset-heavy business, which may offer distinct advantages over other types of enterprises. CRH plc is a multinational conglomerate of diverse construction materials companies headquartered in Dublin, Ireland. It makes and distributes a wide range of building goods. It is a traditional representative of a construction enterprise. We may use it as a model by studying its financial performance.

ANALYSIS OF THE LARGEST BANK OF KAZAKHSTAN – JSC «HALYK BANK OF KAZAKHSTAN»

Banks are an integral part of the modern monetary economy, their activities are related to the needs of reproduction. Banks form the basis of the market mechanism by which the country's economy operates. Commercial banks are designed to regulate the flow of all cash flows, primarily credit, to help ensure the most rational use of society's financial resources and the flow of capital to those sectors of the country's economy where the return on investment will be maximum. Determining the real state of the Bank is of great importance not only for the Bank itself, but also for numerous shareholders and depositors. They should be confident in the financial well-being of a particular Bank, the development of which brings them real benefits.Modern economic analysis is the basis for an objective assessment of the Bank's performance and management decision - making. In this regard, there is a need for in-depth research in the field of banking, as well as understanding of domestic and foreign experience.This article provides an analysis of the financial condition of Halyk Bank of Kazakhstan JSC: an assessment of the main financial indicators of the Bank, an analysis and assessment of changes in the capital of Halyk Bank of Kazakhstan JSC. In addition, the analysis of changes in customer funds and the analysis of changes in the Bank's obligations was carried out. The article also considers the analysis of the Bank's loan portfolio and its share in the total loan portfolio of STB, considers and pranalizes the income and expenses of Halyk Bank of Kazakhstan JSC.

A Decision Support System For The Selection Of The Best Employees At CV. Adiguna By Applying The Preferences Selection Index Method

Technological Developments at this time, competition in the business world is getting tougher, to support this, companies must also improve their quality. On CV. Adiguna to increase the resources of his employees in improving the quality of his company is of course supported and influenced by the performance of employees who are competent in their field. Where in CV. Adiguna of Bengkulu City in giving awards to the best employees every year so far it is still done manually, this can certainly increase the enthusiasm of employees at work and always run a business by fulfilling several criteria set by CV Adiguna of Bengkulu City. Therefore to provide an objective assessment of employee performance, a Decision Support System is needed to select the best employees and provide rewards to employees, then to support the above, it is necessary to implement several Decision Support Systems at CV Adiguna of Bengkulu City based on the criteria that have been determined by the management of CV Adiguna of Bengkulu City. Decision support system for selecting the best employees at CV Adiguna of Bengkulu City by applying the Preferences Selection Index method is a desktop-based application that has implemented the Preferences Selection Index (PSI) method. This application can be used to assist in the process of selecting the best employees on CV Adiguna of Bengkulu City uses 5 predetermined criteria, then the assessment of these 5 criteria is processed using the Preferences Selection Index (PSI) method to produce a ranking that will be used to determine the best employee. Based on the results of the tests that have been carried out, it can be concluded that the decision support system for selecting the best employees at CV Adiguna of Bengkulu City by applying the Preferences Selection Index method is able to run well and can overcome data input errors besides that calculations are done manually with those carried out by the application producing the same output.

Subjective and Objective Assessment of Chewing Performance in Older Adults with Different Dental Occlusion

Objectives: Satisfactory masticatory function is a significant indicator of improved oral health-related quality of life in older adults, either with or without artificial teeth. The purpose of this study was to examine masticatory performance of older adults with different prosthodontic reconstructions and occlusion, as well as to compare subjective and objective assessment of chewing performance. Material and Methods: This study included 100 participants aged 65 and more. The chewing function questionnaire (CFQ) with answers on the Likert scale 0–4 (0 = no difficulties; 4 = maximum difficulties) was used as an indicator of subjective masticatory assessment. The two-color chewing gum test was used as objective assessment of masticatory function (mixing ability), based on the Z-score values. Dental occlusion was assessed using functional teeth units (FTU). Results: The results obtained using the CFQ and the clinical mixing ability test are in compliance with each other (r = 0.742). A decrease in the number of natural teeth, especially posterior teeth, progressively reduced chewing ability and increased difficulties in mastication. Participants with less FTUs, or with FTUs that are a part of removable denture, had greater reduction in masticatory function compared to those with FTUs composed of natural teeth or fixed partial dentures. Conclusion: This study revealed a strong positive correlation between the subjective masticatory assessment in older adults using the validated questionnaire and the objective clinical test of the mixing ability of two chewing gums. Our data on influence of the number and the type of FTU on masticatory performance will be valuable when planning prosthodontic therapy.

OGATE: A framework for autonomous controllers assessment

Autonomous robots can face a variety of applications integrating Artificial Intelligence (AI) techniques, for instance Planning & Scheduling (P&S). While robotics and planning systems are commonly well assessed through test benchs and metrics, in autonomous robots literature it is usual to present isolated case studies for evaluating such works. For instance, experiments are presented in very specific circumstances and often the data provided is not enough to enable a characterization of the autonomous features performance, providing only a demonstration of effectiveness. The main issue is the absence of a framework to assess autonomous controllers from a general perspective. We propose a research focused on a set of general applicable metrics to enable assessment of autonomous controllers. In this way, our objective is to analyse the deliberation and reaction capabilities of an autonomous robot in real operative scenarios. Such metrics are implemented in OGATE, a domain independent tool that automatically carries on with controllers’ testing, generating objective and reproducible performance assessments. To test this framework we have used two autonomous controllers that rely on different technologies for P&S. Results show that we are able to obtain relevant data, enabling the characterization of different P&S integration in robotics.

The Effect of ESG News on the Chinese Stock Market

The relation between corporate environmental, social, and governance (ESG) performance and firm value has received increasing attention. However, the recent literature on corporate ESG performance evaluation suffers from potentially biased data sources, incomplete coverage, and uncertainty among agencies. The objectivity, timeliness, and breadth of news allow the media to provide a good perspective on ESG instead. This study investigated the effects of ESG-related news on the stock market to confirm that news is a helpful complement to the assessment of ESG performance. The results show that 1) ESG news has a greater and more significant impact on the stock market than news unrelated to ESG. 2) Of the three fields of ESG news, environmental news has a greater and more significant impact, which varies widely by topic. 3) Environmental news has different impacts on listed firms in different regions. This study provides new ideas for a complete and objective assessment of corporate ESG performance and the management of a corporate media image from the use of ESG news.

The effect of the capacity-building program in primary and secondary education (external evaluation) on the level of performance evaluation of physical education teachers

Significant changes are occurring in the education system in Iraq. These changes aim to ensure the improvement of the quality of education in schools in cooperation with the British Council, as this council is one of the leading international organizations in the United Kingdom specialized in the field of education and cultural relations, as every school in central and southern Iraq received An external evaluation by a committee of three educational supervisors in the academic year ( 2018-2019 ) , and year( 2019-2020 ) where the evaluation process provided, for the first time, an objective assessment of each school's performance and effectiveness in accordance with the twenty-five standards for schools. The research aims to identify the degree of annual evaluation of the physical education teacher according to the form of performance evaluation after the application of the program. The researcher used the experimental method for its suitability to the nature of the research. The research community included teachers of sports education in the representation of the Ministry of Education in Erbil, whose number is (102) and distributed over (116) schools. The research sample included (10) teachers distributed among 10 schools for the displaced, namely (Al-Wehdah, Al-Shams 3, Al-Shams 1, Lana Al-Ahlia, Hataw, the Emirates Martyrs, Sejala, Al-Huwaira, Al-Muzaffariyah, Salah Al-Din). The data were statistically treated and the researcher reached the validity of a program External evaluation in increasing the annual evaluation scores of physical education teacher.

The development of an eye movement-based deep learning system for laparoscopic surgical skills assessment

The development of valid, reliable, and objective methods of skills assessment is central to modern surgical training. Numerous rating scales have been developed and validated for quantifying surgical performance. However, many of these scoring systems are potentially flawed in their design in terms of reliability. Eye-tracking techniques, which provide a more objective investigation of the visual-cognitive aspects of the decision-making process, recently have been utilized in surgery domains for skill assessment and training, and their use has been focused on investigating differences between expert and novice surgeons to understand task performance, identify experienced surgeons, and establish training approaches. Ten graduate students at the National Taiwan University of Science and Technology with no prior laparoscopic surgical skills were recruited to perform the FLS peg transfer task. Then k-means clustering algorithm was used to split 500 trials into three dissimilar clusters, grouped as novice, intermediate, and expert levels, by an objective performance assessment parameter incorporating task duration with error score. Two types of data sets, namely, time series data extracted from coordinates of eye fixation and image data from videos, were used to implement and test our proposed skill level detection system with ensemble learning and a CNN algorithm. Results indicated that ensemble learning and the CNN were able to correctly classify skill levels with accuracies of 76.0% and 81.2%, respectively. Furthermore, the incorporation of coordinates of eye fixation and image data allowed the discrimination of skill levels with a classification accuracy of 82.5%. We examined more levels of training experience and further integrated an eye tracking technique and deep learning algorithms to develop a tool for objective assessment of laparoscopic surgical skill. With a relatively unbalanced sample, our results have demonstrated that the approach combining the features of visual fixation coordinates and images achieved a very promising level of performance for classifying skill levels of trainees.

Multi-Coil MRI Reconstruction Challenge-Assessing Brain MRI Reconstruction Models and Their Generalizability to Varying Coil Configurations.

Deep-learning-based brain magnetic resonance imaging (MRI) reconstruction methods have the potential to accelerate the MRI acquisition process. Nevertheless, the scientific community lacks appropriate benchmarks to assess the MRI reconstruction quality of high-resolution brain images, and evaluate how these proposed algorithms will behave in the presence of small, but expected data distribution shifts. The multi-coil MRI (MC-MRI) reconstruction challenge provides a benchmark that aims at addressing these issues, using a large dataset of high-resolution, three-dimensional, T1-weighted MRI scans. The challenge has two primary goals: (1) to compare different MRI reconstruction models on this dataset and (2) to assess the generalizability of these models to data acquired with a different number of receiver coils. In this paper, we describe the challenge experimental design and summarize the results of a set of baseline and state-of-the-art brain MRI reconstruction models. We provide relevant comparative information on the current MRI reconstruction state-of-the-art and highlight the challenges of obtaining generalizable models that are required prior to broader clinical adoption. The MC-MRI benchmark data, evaluation code, and current challenge leaderboard are publicly available. They provide an objective performance assessment for future developments in the field of brain MRI reconstruction.

Depth Estimation for Egocentric Rehabilitation Monitoring Using Deep Learning Algorithms

Upper limb impairment is one of the most common problems for people with neurological disabilities, affecting their activity, quality of life (QOL), and independence. Objective assessment of upper limb performance is a promising way to help patients with neurological upper limb disorders. By using wearable sensors, such as an egocentric camera, it is possible to monitor and objectively assess patients’ actual performance in activities of daily life (ADLs). We analyzed the possibility of using Deep Learning models for depth estimation based on a single RGB image to allow the monitoring of patients with 2D (RGB) cameras. We conducted experiments placing objects at different distances from the camera and varying the lighting conditions to evaluate the performance of the depth estimation provided by two deep learning models (MiDaS & Alhashim). Finally, we integrated the best performing model for depth-estimation (MiDaS) with other Deep Learning models for hand (MediaPipe) and object detection (YOLO) and evaluated the system in a task of hand-object interaction. Our tests showed that our final system has a 78% performance in detecting interactions, while the reference performance using a 3D (depth) camera is 84%.