SIMUNEO: Control and Monitoring System for Lung Ultrasound Examination and Treatment of Neonatal Pneumothorax and Thoracic Effusion.

Training with real patients is a critical aspect of the learning and growth of doctors in training. However, this essential step in the educational process for clinicians can potentially compromise patient safety, as they may not be adequately prepared to handle real-life situations independently. Clinical simulators help to solve this problem by providing real-world scenarios in which the physicians can train and gain confidence by safely and repeatedly practicing different techniques. In addition, obtaining objective feedback allows subsequent debriefing by analysing the situation experienced and learning from other people's mistakes. This article presents SIMUNEO, a neonatal simulator in which professionals are able to learn by practicing the management of lung ultrasound and the resolution of pneumothorax and thoracic effusions. The article also discusses in detail the hardware and software, the main components that compose the system, and the communication and implementation of these. The system was validated through both usability questionnaires filled out by neonatology residents as well as through follow-up sessions, improvement, and control of the system with specialists of the department. Results suggest that the environment is easy to use and could be used in clinical practice to improve the learning and training of students as well as the safety of patients.

The value of metabolic parameters and textural analysis in predicting prognosis in locally advanced cervical cancer treated with chemoradiotherapy.

OBJECTIVE: The aim of the study was to assess the impact of clinical and metabolic parameters derived from 18F-FDG PET/CT (positron emission tomography-computed tomography) in patients with locally advanced cervical cancer (LACC) on prognosis. METHODS: Patients with LACC of stage IB2-IVA treated by primary radiochemotherapy followed by brachytherapy were enrolled in this retrospective study. Indexes derived from standardized uptake value (SUV), metabolic tumor volume (MTV), total lesion glycolysis (TLG), and textural features of the primary tumor were measured for each patient. Overall survival (OS) and recurrence-free survival (RFS) rates were calculated according to Kaplan-Meier and survival curves were compared using the log-rank test. Uni- and multivariate analyses were performed using the Cox regression model. RESULTS: A total of 116 patients were included. Median follow-up was 58 months (range: 1-129). A total of 36 (31%) patients died. Five-year OS and RFS rates were 69 and 60%, respectively. Univariate analyses indicated that FIGO stage, the presence of hydronephrosis, high CYFRA 21.1 levels, and textural features had a significant impact on OS and RFS. MTV as well as SCC-Ag concentration were also significantly associated with OS. On multivariate analysis, the presence of hydronephrosis, CYFRA 21.1, and sphericity were independent prognostics factors for OS and RFS. Also, SCC-Ag level, MTV, and GLZLM (gray-level zone length matrix) ZLNU (zone length non-uniformity) were significantly associated with OS. CONCLUSION: Classical prognostic factors and tumor heterogeneity on pretreatment PET/CT were significantly associated with prognosis in patients with LACC.

Correlating Personal Resourcefulness and Psychomotor Skills: An Analysis of Stress, Visual Attention and Technical Metrics.

Modern surgical education is focused on making use of the available technologies in order to train and assess surgical skill acquisition. Innovative technologies for the automatic, objective assessment of nontechnical skills are currently under research. The main aim of this study is to determine whether personal resourcefulness can be assessed by monitoring parameters that are related to stress and visual attention and whether there is a relation between these and psychomotor skills in surgical education. For this purpose, we implemented an application in order to monitor the electrocardiogram (ECG), galvanic skin response (GSR), gaze and performance of surgeons-in-training while performing a laparoscopic box-trainer task so as to obtain technical and personal resourcefulness' metrics. Eight surgeons (6 nonexperts and 2 experts) completed the experiment. A total of 22 metrics were calculated (7 technical and 15 related to personal resourcefulness) per subject. The average values of these metrics in the presence of stressors were compared with those in their absence and depending on the participants' expertise. The results show that both the mean normalized GSR signal and average surgical instrument's acceleration change significantly when stressors are present. Additionally, the GSR and acceleration were found to be correlated, which indicates that there is a relation between psychomotor skills and personal resourcefulness.

Can effective pedagogy be ensured in minimally invasive surgery e-learning?

INTRODUCTION: Effectiveness of e-learning diminishes without the support of a pedagogical model to guide its use. In minimally invasive surgery (MIS), this has been reported as a limitation when technology is used to deliver contents without a sound pedagogical background. MATERIAL AND METHODS: We describe how a generic pedagogical model, the 3D pedagogy framework, can be used for setting learning outcomes and activities in e-learning platforms focused on MIS cognitive skills. A demonstrator course on Nissen fundoplication was developed following the model step-by-step in the MISTELA learning platform. Course design was informed by Kolb's Experiential learning model. Content validation was performed by 13 MIS experts. RESULTS: Ten experts agreed on the suitability of content structuring done according to the pedagogical model. All experts agreed that the course provides means to assess the intended learning outcomes. CONCLUSIONS: This work showcases how a general-purpose e-learning framework can be accommodated to the needs of MIS training without limiting the course designers' pedagogical approach. Key advances for its success include: (1) proving the validity of the model in the wider scope of MIS skills and (2) raising awareness amongst stakeholders on the need of developing training plans with explicit, rather than assumed, pedagogical foundations. Abbreviations: MIS: minimally invasive surgery; TEL: technology enhanced learning.

Speech-Based Surgical Phase Recognition for Non-Intrusive Surgical Skills' Assessment in Educational Contexts.

Surgeons' procedural skills and intraoperative decision making are key elements of clinical practice. However, the objective assessment of these skills remains a challenge to this day. Surgical workflow analysis (SWA) is emerging as a powerful tool to solve this issue in surgical educational environments in real time. Typically, SWA makes use of video signals to automatically identify the surgical phase. We hypothesize that the analysis of surgeons' speech using natural language processing (NLP) can provide deeper insight into the surgical decision-making processes. As a preliminary step, this study proposes to use audio signals registered in the educational operating room (OR) to classify the phases of a laparoscopic cholecystectomy (LC). To do this, we firstly created a database with the transcriptions of audio recorded in surgical educational environments and their corresponding phase. Secondly, we compared the performance of four feature extraction techniques and four machine learning models to find the most appropriate model for phase recognition. The best resulting model was a support vector machine (SVM) coupled to a hidden-Markov model (HMM), trained with features obtained with Word2Vec (82.95% average accuracy). The analysis of this model's confusion matrix shows that some phrases are misplaced due to the similarity in the words used. The study of the model's temporal component suggests that further attention should be paid to accurately detect surgeons' normal conversation. This study proves that speech-based classification of LC phases can be effectively achieved. This lays the foundation for the use of audio signals for SWA, to create a framework of LC to be used in surgical training, especially for the training and assessment of procedural and decision-making skills (e.g., to assess residents' procedural knowledge and their ability to react to adverse situations).

High Correlation of Static First-Minute-Frame (FMF) PET Imaging after 18F-Labeled Amyloid Tracer Injection with [18F]FDG PET Imaging.

Dynamic early-phase PET images acquired with radiotracers binding to fibrillar amyloid-beta (Aß) have shown to correlate with [18F]fluorodeoxyglucose (FDG) PET images and provide perfusion-like information. Perfusion information of static PET scans acquired during the first minute after radiotracer injection (FMF, first-minute-frame) is compared to [18F]FDG PET images. FMFs of 60 patients acquired with [18F]florbetapir (FBP), [18F]flutemetamol (FMM), and [18F]florbetaben (FBB) are compared to [18F]FDG PET images. Regional standardized uptake value ratios (SUVR) are directly compared and intrapatient Pearson's correlation coefficients are calculated to evaluate the correlation of FMFs to their corresponding [18F]FDG PET images. Additionally, regional interpatient correlations are calculated. The intensity profiles of mean SUVRs among the study cohort (r = 0.98, p < 0.001) and intrapatient analyses show strong correlations between FMFs and [18F]FDG PET images (r = 0.93 ± 0.05). Regional VOI-based analyses also result in high correlation coefficients. The FMF shows similar information to the cerebral metabolic patterns obtained by [18F]FDG PET imaging. Therefore, it could be an alternative to the dynamic imaging of early phase amyloid PET and be used as an additional neurodegeneration biomarker in amyloid PET studies in routine clinical practice while being acquired at the same time as amyloid PET images.

Deep Learning Automated Segmentation for Muscle and Adipose Tissue from Abdominal Computed Tomography in Polytrauma Patients.

Manual segmentation of muscle and adipose compartments from computed tomography (CT) axial images is a potential bottleneck in early rapid detection and quantification of sarcopenia. A prototype deep learning neural network was trained on a multi-center collection of 3413 abdominal cancer surgery subjects to automatically segment truncal muscle, subcutaneous adipose tissue and visceral adipose tissue at the L3 lumbar vertebral level. Segmentations were externally tested on 233 polytrauma subjects. Although after severe trauma abdominal CT scans are quickly and robustly delivered, with often motion or scatter artefacts, incomplete vertebral bodies or arms that influence image quality, the concordance was generally very good for the body composition indices of Skeletal Muscle Radiation Attenuation (SMRA) (Concordance Correlation Coefficient (CCC) = 0.92), Visceral Adipose Tissue index (VATI) (CCC = 0.99) and Subcutaneous Adipose Tissue Index (SATI) (CCC = 0.99). In conclusion, this article showed an automated and accurate segmentation system to segment the cross-sectional muscle and adipose area L3 lumbar spine level on abdominal CT. Future perspectives will include fine-tuning the algorithm and minimizing the outliers.

Automated Insulin Delivery: The Artificial Pancreas Technical Challenges.

BACKGROUND: The automation of glucose control has been an important goal of diabetes treatment for many decades. The first artificial pancreas experiences were in-hospital, closely supervised, small-scale, and short-term studies that demonstrated their superiority over continuous subcutaneous insulin infusion therapy. At present, long-term outpatient studies are being conducted in free-living scenarios. AREAS OF UNCERTAINTY: The integration of multiple devices increases patients' burden and the probability of technical risks. Control algorithms must be robust to manage disturbance variables, such as physical exercise, meal composition, stress, illness, and circadian variations in insulin sensitivity. Extra layers of safety could be achieved through remote supervision. Dual-hormone systems reduce the incidence and duration of hypoglycemia, but the availability of stable pumpable glucagon needs to be solved. Faster insulin analogues are expected to improve all types of artificial pancreas. THERAPEUTIC ADVANCES: Artificial pancreas safety and feasibility are being demonstrated in outpatient studies. Artificial pancreas use increases the time of sensor-measured glucose in near-normoglycemia and reduces the risk of hyperglycemia and hypoglycemia. The benefits are observed both in single- and dual-hormone algorithms and in full- or semi-closed loop control. A recent meta-analysis including 41 randomized controlled trials showed that artificial pancreas use achieves a reduction of time in hyperglycemia (2 hours less than control treatment) and in hypoglycemia (20 minutes less); mean levels of continuous glucose sensor fell by 8.6 mg/dL over 24 hours and by 14.6 mg/dL overnight. The OpenAPS community uses Do It Yourself artificial pancreas in the real world since 2013, and a recent retrospective cross-over study (n = 20) compared continuous glucose sensor readings before and after initiation: mean levels of blood glucose fell by 7.4 mg/dL over 24 hours and time in range increased from 75.8% to 82.2% (92 minutes more). CONCLUSIONS: The outpatient use of artificial pancreas is safe and improves glucose control in outpatients with type 1 diabetes compared with the use of any type of insulin-based treatment. The availability of open-source solutions and data sharing is needed to foster the development of new artificial pancreas approaches and to promote the wide use of Big Data tools for knowledge discovery, decision support, and personalization.

Technologies for Monitoring Lifestyle Habits Related to Brain Health: A Systematic Review.

Brain health refers to the preservation of brain integrity and function optimized for an individual's biological age. Several studies have demonstrated that our lifestyles habits impact our brain health and our cognitive and mental wellbeing. Monitoring such lifestyles is thus critical and mobile technologies are essential to enable such a goal. Three databases were selected to carry out the search. Then, a PRISMA and PICOTS based criteria for a more detailed review on the basis of monitoring lifestyle aspects were used to filter the publications. We identified 133 publications after removing duplicates. Fifteen were finally selected from our criteria. Many studies still use questionnaires as the only tool for monitoring and do not apply advanced analytic or AI approaches to fine-tune results. We anticipate a transformative boom in the near future developing and implementing solutions that are able to integrate, in a flexible and adaptable way, data from technologies and devices that users might already use. This will enable continuous monitoring of objective data to guide the personalized definition of lifestyle goals and data-driven coaching to offer the necessary support to ensure adherence and satisfaction.

Interpretation of motion analysis of laparoscopic instruments based on principal component analysis in box trainer settings.

BACKGROUND: Motion analysis parameters (MAPs) have been extensively validated for assessment of minimally invasive surgical skills. However, there are discrepancies on how specific MAPs, tasks, and skills match with each other, reflecting that motion analysis cannot be generalized independently of the learning outcomes of a task. Additionally, there is a lack of knowledge on the meaning of motion analysis in terms of surgical skills, making difficult the provision of meaningful, didactic feedback. In this study, new higher significance MAPs (HSMAPs) are proposed, validated, and discussed for the assessment of technical skills in box trainers, based on principal component analysis (PCA). METHODS: Motion analysis data were collected from 25 volunteers performing three box trainer tasks (peg grasping/PG, pattern cutting/PC, knot suturing/KS) using the EVA tracking system. PCA was applied on 10 MAPs for each task and hand. Principal components were trimmed to those accounting for an explained variance > 80% to define the HSMAPs. Individual contributions of MAPs to HSMAPs were obtained by loading analysis and varimax rotation. Construct validity of the new HSMAPs was carried out at two levels of experience based on number of surgeries. RESULTS: Three new HSMAPs per hand were defined for PG and PC tasks, and two per hand for KS task. PG presented validity for HSMAPs related to insecurity and economy of space. PC showed validity for HSMAPs related to cutting efficacy, peripheral unawareness, and confidence. Finally, KS presented validity for HSMAPs related with economy of space and knotting security. CONCLUSIONS: PCA-defined HSMAPs can be used for technical skills' assessment. Construct validation and expert knowledge can be combined to infer how competences are acquired in box trainer tasks. These findings can be exploited to provide residents with meaningful feedback on performance. Future works will compare the new HSMAPs with valid scoring systems such as GOALS.

Design and Technical Validation of a Telemedicine Service for Rural Healthcare in Ecuador.

BACKGROUND: Telemedicine is becoming increasingly important in Ecuador, especially in areas such as rural primary healthcare and medical education. Rural telemedicine programs in the country need to be strengthened by means of a technological platform adapted to local surroundings and offering advantages such as access to specialized care, continuing education, and so on, combined with modest investment requirements. INTRODUCTION: This present article presents the design of a Telemedicine Platform (TMP) for rural healthcare services in Ecuador and a preliminary technical validation with medical students and teachers. MATERIALS AND METHODS: An initial field study was designed to capture the requirements of the TMP. In a second phase, the TMP was validated in an academic environment along three consecutive academic courses. Assessment was by means of user polls and analyzing user interactions as registered automatically by the platform. The TMP was developed using Web-based technology and open code software. RESULTS: One hundred twenty-four students and 6 specialized faculty members participated in the study, conducting a total of 262 teleconsultations of clinical cases and 226 responses, respectively. CONCLUSION: The validation results show that the TMP is a useful communication tool for the documentation and discussion of clinical cases. Moreover, its usage may be recommended as a teaching methodology, to strengthen the skills of medical undergraduates. The results indicate that implementing the system in rural healthcare services in Ecuador would be feasible.

Supervised classification of psychomotor competence in minimally invasive surgery based on instruments motion analysis.

BACKGROUND: Objective assessment of psychomotor skills has become an important challenge in the training of minimally invasive surgical (MIS) techniques. Currently, no gold standard defining surgical competence exists for classifying residents according to their surgical skills. Supervised classification has been proposed as a means for objectively establishing competence thresholds in psychomotor skills evaluation. This report presents a study comparing three classification methods for establishing their validity in a set of tasks for basic skills' assessment. METHODS: Linear discriminant analysis (LDA), support vector machines (SVM), and adaptive neuro-fuzzy inference systems (ANFIS) were used. A total of 42 participants, divided into an experienced group (4 expert surgeons and 14 residents with >10 laparoscopic surgeries performed) and a nonexperienced group (16 students and 8 residents with <10 laparoscopic surgeries performed), performed three box trainer tasks validated for assessment of MIS psychomotor skills. Instrument movements were captured using the TrEndo tracking system, and nine motion analysis parameters (MAPs) were analyzed. The performance of the classifiers was measured by leave-one-out cross-validation using the scores obtained by the participants. RESULTS: The mean accuracy performances of the classifiers were 71 % (LDA), 78.2 % (SVM), and 71.7 % (ANFIS). No statistically significant differences in the performance were identified between the classifiers. CONCLUSIONS: The three proposed classifiers showed good performance in the discrimination of skills, especially when information from all MAPs and tasks combined were considered. A correlation between the surgeons' previous experience and their execution of the tasks could be ascertained from results. However, misclassifications across all the classifiers could imply the existence of other factors influencing psychomotor competence.

Intelligent Therapy Assistant (ITA) for cognitive rehabilitation in patients with acquired brain injury.

BACKGROUND: This paper presents the design, development and first evaluation of an algorithm, named Intelligent Therapy Assistant (ITA), which automatically selects, configures and schedules rehabilitation tasks for patients with cognitive impairments after an episode of Acquired Brain Injury. The ITA is integrated in "Guttmann, Neuro Personal Trainer" (GNPT), a cognitive tele-rehabilitation platform that provides neuropsychological services. METHODS: The ITA selects those tasks that are more suitable for the specific needs of each patient, considering previous experiences, and improving the personalization of the treatment. The system applies data mining techniques to cluster the patients according their cognitive impairment profile. Then, the algorithm rates every rehabilitation task, based on its cognitive structure and the clinical impact of executions done by similar patients. Finally, it configures the most suitable degree of difficulty, depending on the impairment of the patient and his/her evolution during the treatment. RESULTS: The ITA has been evaluated during 18 months by 582 patients. In order to evaluate the effectiveness of the ITA, a comparison between the traditional manual planning procedure and the one presented in this paper has been done, taking into account: a) the selected tasks assigned to rehabilitation sessions; b) the difficulty level configured for the sessions; c) and the improvement of their cognitive capacities after completing treatment. CONCLUSIONS: The obtained results reveal that the rehabilitation treatment proposed by the ITA is as effective as the one performed manually by therapists, arising as a new powerful support tool for therapists. The obtained results make us conclude that the proposal done by the ITA is very close to the one done by therapists, so it is suitable for real treatments.

Systems and technologies for objective evaluation of technical skills in laparoscopic surgery.

Minimally invasive surgery is a highly demanding surgical approach regarding technical requirements for the surgeon, who must be trained in order to perform a safe surgical intervention. Traditional surgical education in minimally invasive surgery is commonly based on subjective criteria to quantify and evaluate surgical abilities, which could be potentially unsafe for the patient. Authors, surgeons and associations are increasingly demanding the development of more objective assessment tools that can accredit surgeons as technically competent. This paper describes the state of the art in objective assessment methods of surgical skills. It gives an overview on assessment systems based on structured checklists and rating scales, surgical simulators, and instrument motion analysis. As a future work, an objective and automatic assessment method of surgical skills should be standardized as a means towards proficiency-based curricula for training in laparoscopic surgery and its certification.

Automatic Skeleton Segmentation in CT Images Based on U-Net.

Bone metastasis, emerging oncological therapies, and osteoporosis represent some of the distinct clinical contexts which can result in morphological alterations in bone structure. The visual assessment of these changes through anatomical images is considered suboptimal, emphasizing the importance of precise skeletal segmentation as a valuable aid for its evaluation. In the present study, a neural network model for automatic skeleton segmentation from bidimensional computerized tomography (CT) slices is proposed. A total of 77 CT images and their semimanual skeleton segmentation from two acquisition protocols (whole-body and femur-to-head) are used to form a training group and a testing group. Preprocessing of the images includes four main steps: stretcher removal, thresholding, image clipping, and normalization (with two different techniques: interpatient and intrapatient). Subsequently, five different sets are created and arranged in a randomized order for the training phase. A neural network model based on U-Net architecture is implemented with different values of the number of channels in each feature map and number of epochs. The model with the best performance obtains a Jaccard index (IoU) of 0.959 and a Dice index of 0.979. The resultant model demonstrates the potential of deep learning applied in medical images and proving its utility in bone segmentation.

Robustness of [18F]FDG PET/CT radiomic analysis in the setting of drug-induced cardiotoxicity.

BACKGROUND AND OBJECTIVES: Standardization of radiomic data acquisition protocols is still at a very early stage, revealing a strong need to work towards the definition of uniform image processing methodologies The aim of this study is to identify sources of variability in radiomic data derived from image discretization and resampling methodologies prior to image feature extraction. Furthermore, to identify robust potential image-based biomarkers for the early detection of cardiotoxicity. METHODS: Image post-acquisition processing, interpolation, and volume of interest (VOI) segmentation were performed. Four experiments were conducted to assess the reliability in terms of the intraclass correlation coefficient (ICC) of the radiomic features and the effects of the variation of voxel size and gray level discretization. Statistical analysis was performed separating the patients according to cardiotoxicity diagnosis. Differences of texture features were studied with Mann-Whitney U test. P-values <0.05 after multiple testing correction were considered statistically significant. Additionally, a non-supervised k-Means clustering algorithm was evaluated. RESULTS: The effect of the variation in the voxel size demonstrated a non-dependency relationship with the values of the radiomic features, regardless of the chosen discretization method. The median ICC values were 0.306 and 0.872 for absolute agreement and consistency, respectively, when varying the discretization bin number. The median ICC values were 0.678 and 0.878 for absolute agreement and consistency, respectively, when varying the discretization bin size. A total of 16 first order, 6 Gray Level Co-occurrence Matrix (GLCM), 4 Gray Level Dependence Matrix (GLDM) and 4 Gray Level Run Length Matrix (GLRLM) features demonstrated statistically significant differences between the diagnosis groups for interim scans (P<0.05) for the fixed bin size (FBS) discretization methodology. However, no statistically significant differences between diagnostic groups were found for the fixed bin number (FBN) discretization methodology. Two clusters based on the radiomic features were identified. CONCLUSIONS: Gray level discretization has a major impact on the repeatability of the radiomic features. The selection of the optimal processing methodology has led to the identification of texture-based patterns for the differentiation of early cardiac damage profiles.

Precision nutrition impact on metabolic health and quality of life in aging population after a 3-month intervention: A randomized intervention.

OBJECTIVES: Innovative precision dietary procedures are required to promote healthy aging. This study aimed to evaluate the effects of a personalised strategy based on the inclusion of individualised foods and digital tools on overall health status and quality of life within a follow-up of 3 months in older adults with overweight or obesity. METHODS: 127 men and women aged between 50 and 80 years with overweight/obesity participated in the study-between January 2020 and September 2020 at the Center for Nutrition Research-University of Navarra and IMDEA-ALIMENTACIÓN-and were randomly assigned to a usual-care group (standard recommendations) or precision group (precision nutrition strategy based on the inclusion of individualised foods and a mobile application). Anthropometry, body fat percentage, biochemical parameters, diet, and quality of life (SF-36 Health Survey) were assessed at baseline and after 3 months. RESULTS: Both strategies were found to improve overall metabolic health; however, the precision approach demonstrated significantly better outcomes. The precision strategy reduced body weight at 3 months (-4.3 kg; p < 0.001) with significant improvements in body fat percentage, blood pressure and general metabolic health (glycated haemoglobin; alanine aminotransferase; aspartate aminotransferase; hepatic steatosis index) in comparison with the standard recommendations. The precision approach significantly enhanced the quality of life (SF-36) of individuals, with additional improvements in emotional well-being (p = 0.024) and vitality (p = 0.008). Adherence to the Mediterranean diet was significantly associated with a higher quality of life and vitality. CONCLUSION: These results support the benefit of precision nutrition approaches for promoting healthy aging and emotional well-being, enhancing the quality of life in aging populations, during the COVID-19 pandemic.

Toward Telemonitoring in Immune-Mediated Inflammatory Diseases: Protocol for a Mixed Attention Model Study.

BACKGROUND: Rheumatic and musculoskeletal diseases (RMDs) are chronic diseases that may alternate between asymptomatic periods and flares. These conditions require complex treatments and close monitoring by rheumatologists to mitigate their effects and improve the patient's quality of life. Often, delays in outpatient consultations or the patient's difficulties in keeping appointments make such close follow-up challenging. For this reason, it is very important to have open communication between patients and health professionals. In this context, implementing telemonitoring in the field of rheumatology has great potential, as it can facilitate the close monitoring of patients with RMDs. The use of these tools helps patients self-manage certain aspects of their disease. This could result in fewer visits to emergency departments and consultations, as well as enable better therapeutic compliance and identification of issues that would otherwise go unnoticed. OBJECTIVE: The main objective of this study is to evaluate the implementation of a hybrid care model called the mixed attention model (MAM) in clinical practice and determine whether its implementation improves clinical outcomes compared to conventional follow-up. METHODS: This is a multicenter prospective observational study involving 360 patients with rheumatoid arthritis (RA) and spondylarthritis (SpA) from 5 Spanish hospitals. The patients will be followed up by the MAM protocol, which is a care model that incorporates a digital tool consisting of a mobile app that patients can use at home and professionals can review asynchronously to detect incidents and follow patients' clinical evolution between face-to-face visits. Another group of patients, whose follow-up will be conducted in accordance with a traditional face-to-face care model, will be assessed as the control group. Sociodemographic characteristics, treatments, laboratory parameters, assessment of tender and swollen joints, visual analog scale for pain, and electronic patient-reported outcome (ePRO) reports will be collected for all participants. In the MAM group, these items will be self-assessed via both the mobile app and during face-to-face visits with the rheumatologist, who will do the same for patients included in the traditional care model. The patients will be able to report any incidence related to their disease or treatment through the mobile app. RESULTS: Participant recruitment began in March 2024 and will continue until December 2024. The follow-up period will be extended by 12 months for all patients. Data collection and analysis are scheduled for completion in December 2025. CONCLUSIONS: This paper aims to provide a detailed description of the development and implementation of a digital solution, specifically an MAM. The goal is to achieve significant economic and psychosocial impact within our health care system by enhancing control over RMDs. TRIAL REGISTRATION: ClinicalTrials.gov NCT06273306; https://clinicaltrials.gov/ct2/show/NCT06273306. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): PRR1-10.2196/55829.

TELMA: Technology-enhanced learning environment for minimally invasive surgery.

BACKGROUND: Cognitive skills training for minimally invasive surgery has traditionally relied upon diverse tools, such as seminars or lectures. Web technologies for e-learning have been adopted to provide ubiquitous training and serve as structured repositories for the vast amount of laparoscopic video sources available. However, these technologies fail to offer such features as formative and summative evaluation, guided learning, or collaborative interaction between users. METHODOLOGY: The "TELMA" environment is presented as a new technology-enhanced learning platform that increases the user's experience using a four-pillared architecture: (1) an authoring tool for the creation of didactic contents; (2) a learning content and knowledge management system that incorporates a modular and scalable system to capture, catalogue, search, and retrieve multimedia content; (3) an evaluation module that provides learning feedback to users; and (4) a professional network for collaborative learning between users. Face validation of the environment and the authoring tool are presented. RESULTS: Face validation of TELMA reveals the positive perception of surgeons regarding the implementation of TELMA and their willingness to use it as a cognitive skills training tool. Preliminary validation data also reflect the importance of providing an easy-to-use, functional authoring tool to create didactic content. CONCLUSION: The TELMA environment is currently installed and used at the Jesús Usón Minimally Invasive Surgery Centre and several other Spanish hospitals. Face validation results ascertain the acceptance and usefulness of this new minimally invasive surgery training environment.

EVA: laparoscopic instrument tracking based on Endoscopic Video Analysis for psychomotor skills assessment.

INTRODUCTION: The EVA (Endoscopic Video Analysis) tracking system is a new system for extracting motions of laparoscopic instruments based on nonobtrusive video tracking. The feasibility of using EVA in laparoscopic settings has been tested in a box trainer setup. METHODS: EVA makes use of an algorithm that employs information of the laparoscopic instrument's shaft edges in the image, the instrument's insertion point, and the camera's optical center to track the three-dimensional position of the instrument tip. A validation study of EVA comprised a comparison of the measurements achieved with EVA and the TrEndo tracking system. To this end, 42 participants (16 novices, 22 residents, and 4 experts) were asked to perform a peg transfer task in a box trainer. Ten motion-based metrics were used to assess their performance. RESULTS: Construct validation of the EVA has been obtained for seven motion-based metrics. Concurrent validation revealed that there is a strong correlation between the results obtained by EVA and the TrEndo for metrics, such as path length (ρ = 0.97), average speed (ρ = 0.94), or economy of volume (ρ = 0.85), proving the viability of EVA. CONCLUSIONS: EVA has been successfully validated in a box trainer setup, showing the potential of endoscopic video analysis to assess laparoscopic psychomotor skills. The results encourage further implementation of video tracking in training setups and image-guided surgery.