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Brain Health is defined as the development and preservation of optimal brain integrity and neural network functioning for a given age. Recent studies have related healthy habits with better maintenance of brain health across the lifespan. As a part of the Barcelona Brain Health Initiative (BBHI), a mHealth platform has been developed with the purpose of helping people to improve and monitor their healthy habits, facilitating the delivery of health coaching strategies. A decision support system (DSS), named Intelligent Coaching Assistant (ICA), has been developed to ease the work of professional brain health coaches, helping them design and monitor adherence to multidomain interventions in a more efficient manner. Personalized recommendations are based on users’ current healthy habits, individual preferences, and motivational aspects. Taking these inputs, an initial user profile is defined, and the ICA applies an algorithm for determining the most suitable personalized intervention plan. An initial validation has been done focusing on assessing the feasibility and usability of the solution, involving 20 participants for three weeks. We conclude that this kind of technology-based intervention is feasible and implementable in real-world settings. Importantly, the personalized intervention proposal generated by the DSS is feasible and its acceptability and usability are high.

Dynamic early-phase PET images acquired with radiotracers binding to fibrillar amyloid-beta (Aβ) 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 &lt, 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.

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.

Amyloid positron emission tomography (PET) brain imaging with radiotracers like [18F]florbetapir (FBP) or [18F]flutemetamol (FMM) is frequently used for the diagnosis of Alzheimer’s disease. Quantitative analysis is usually performed with standardized uptake value ratios (SUVR), which are calculated by normalizing to a reference region. However, the reference region could present high variability in longitudinal studies. Texture features based on the grey-level co-occurrence matrix, also called Haralick features (HF), are evaluated in this study to discriminate between amyloid-positive and negative cases. A retrospective study cohort of 66 patients with amyloid PET images (30 [18F]FBP and 36 [18F]FMM) was selected and SUVRs and 6 HFs were extracted from 13 cortical volumes of interest. Mann–Whitney U-tests were performed to analyze differences of the features between amyloid positive and negative cases. Receiver operating characteristic (ROC) curves were computed and their area under the curve (AUC) was calculated to study the discriminatory capability of the features. SUVR proved to be the most significant feature among all tests with AUCs between 0.692 and 0.989. All HFs except correlation also showed good performance. AUCs of up to 0.949 were obtained with the HFs. These results suggest the potential use of texture features for the classification of amyloid PET images.

Chronic thromboembolic pulmonary hypertension (CTEPH) is confirmed by visual analysis of single-photon emission computer tomography (SPECT) ventilation and perfusion (V/Q) images. Defects in the perfusion image discordant with the ventilation image indicate obstructed segments and the positive diagnosis of CTEPH. A quantitative metric and classification algorithm are proposed based on volumetric data from SPECT V/Q images. The difference in ventilation and perfusion volumes (VV-P) is defined as a quantitative metric to identify discordant defects in the SPECT images. The algorithm was validated with 22 patients grouped according to their diagnosis: (1) CTEPH and (2) respiratory pathology. Volumetric data from SPECT perfusion images was also compared before and after treatment for CTEPH. CTEPH was detected with a sensitivity of 0.67 and specificity of 0.80. The performance of volumetric data from SPECT perfusion images for the evaluation of treatment response was studied for two cases and improvement of pulmonary perfusion was observed in one case. This study uses volumetric data from SPECT V/Q images for the diagnosis of CTEPH and its differentiation from respiratory pathologies. The results indicate that the defined metric is a viable option for a quantitative analysis of SPECT V/Q images.

BACKGROUND: The increasing number of patients with acquired brain injury and the current subjectivity of the conventional upper extremity (UE) assessment tests require new objective assessment techniques. OBJECTIVE: This research proposes a novel objective motor assessment (OMA) methodology based on the Fugl-Meyer assessment (FMA). The goals are to automatically calculate the objective scores (OSs) of FMA-UE movements (as well as a global OS) and to interpret the estimated OSs. METHODS: Fifteen patients participated in the study. The OMA algorithm was designed to detect small-scale variations in UE movements. The OSs for 14 FMA-UE movements and the global OSs were automatically calculated using the algorithm and evaluated by 2 therapists. The interpretation of the global OSs was performed at 3 levels: by item, movement and globally. RESULTS: The global OSs calculated by our algorithm had a significant correlation with the therapists' scores (0.783 and 0.938

Healthy daily activities have a positive influence on many aspects of our lives. Habits have a deep impact on our health, they help to prevent the appearance of chronic and neurodegenerative diseases and will provide a healthy and active aging. This research work is aiming to analyze the need of new approaches on monitoring daily life activities, investigating new technologies and user modelling methods for healthy habits monitoring. mHealth platforms allow to perform a multivariable monitoring for allowing effective and personalized interventions. Data analytics, data mining and gamification methodologies are being applied to investigate user experience models. This user adaptation is commonly focused on personality and mood monitoring. Furthermore, new user models are built based on monitoring data, personality of user and the daily activity patterns extracted from intelligent and personalized monitoring. The final goal is contributing to improve user´s adherence to interventions, quality of life and quality of care in mHealth applications.

Depression is a mental disorder which can become a serious health problem. This research line is focused on creating a depression detection service from text analysis. Sentiment Analysis and Natural Language Processing methods will be used to develop this service. The service will classify text in positive or negative depending on the emotions inferred from user’s input texts. In this study, five classifiers have been evaluated to determine which one fits better for this purpose. Naive Bayes, Decision Tree, Naive Bayes based on Bernoulli model and Maximum Entropy are the classifiers analyzed. A specific corpus which includes sentences that fit research needs was designed for testing purposes, whereas Sentiment140’s corpus was used with training purposes. An evaluation methodology formed by three tests was designed. Results show a promising starting point, but further analysis will be needed. Future related works will focus on expanding classification to get user’s mood and not only a binary classification. It will allow to include this service as input for developing personalized intervention and education mHealth systems.

Positron emission tomography (PET) with amyloid binding tracers and 18F-fluordesoxyglucose (FDG) is used in the diagnosis of Alzheimer’s disease and other dementias. The dual-phase amyloid PET protocol acquires an early perfusion image immediately after the radiotracer injection that resembles FDG images. We studied the correlation of early phase 18F-florbetapir (eFBP) and FDG PET images by the quantitation of regional radiotracer uptake. Subjects with a neurodegenerative disease and eFBP and FDG PET scans were retrospectively selected. The PET images were normalized to the Montreal Neurological Institute template using Statistical Parametric Mapping version 12. Regional standardized uptake value ratios (SUVR) were extracted with the cerebellum serving as the reference region. Pearson’s correlation tests were performed to evaluate the regional correlation between eFBP and FDG PET images based on the quantitative measurements. Overall high correlation coefficients of up to r = 0.95 were observed in cortical regions that are in line with other studies or exceed previous results.

In this article, we described a new mobile-Health (mHealth) supported clinical pathway of care for people living with medically stable HIV in terms of platform acceptability, usability and technical feasibility. The EmERGE mHealth platform was codesigned with clinicians and the community, developed using Scrum agile methodology, integrated with hospital information systems and validated in a large prospective cohort study of 2251 participants. The evaluation of this new paradigm of care was conducted using a tailored Health Technology Assessment: the Model for Assessment of Telemedicine Applications. Usability and acceptability were assessed through the System Usability Score and a Patient Reported Experience Measure. The EmERGE platform was successfully deployed across diverse care settings in five European countries and used by 2251 patients and more than 20 clinicians for up to 30 months. Results from the formal evaluation demonstrated that the EmERGE platform is feasible and acceptable, with a high level of usability (median System Usability Score (SUS) 85.0%) and very positive patient-reported experiences (94.2% would recommend to a friend). The EmERGE platform is a secure and General Data Protection Regulation (GDPR)-compliant system with a complete set of functionalities that could be easily adapted to other clinical conditions, clinical sites and health systems thanks to its modular technical architecture.

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).

VR P300-based BCI has proven to be a suitable method for training social attention skills in youngsters with autism spectrum disorder (ASD). In this study, we present a method that could be used in such an application to identify which object the user is paying attention to in a virtual environment by means of EEG recordings only. Temporal and time-frequency features were explored. Furthermore, the prediction accuracy of linear and nonlinear classification methods was assessed and compared, along with their computational times and complexity, and linear discriminant analysis (LDA) yielded the best overall performance (82%). The successful predictions and low computational times demonstrate the feasibility of the proposed solution for a VR-BCI neurorehabilitation tool.

Introducción: El análisis del movimiento es una valiosa herramienta para la evaluación de las habilidades psicomotrices en la laparoscopia. Sin embargo, requiere tecnologías para el seguimiento de la actividad de los instrumentos laparoscópicos durante el entrenamiento. En este artículo presentamos una técnica sin sensores para realizar el seguimiento de los movimientos de los instrumentos laparoscópicos basado en un sistema de cámara ortogonal y procesamiento de imágenes de video. Método: Los movimientos de los instrumentos laparoscópicos son capturados con dos cámaras web colocadas en configuración ortogonal. La posición y la orientación en el espacio de trabajo tridimensional se obtienen utilizando marcadores de color colocados en la punta de los instrumentos. Resultados: Las pruebas de precisión mostraron una resolución de 0.14 mm para el desplazamiento, con 1694 cm3 de espacio de trabajo total y 0.54° en los movimientos angulares. Los errores relativos medios del sistema de seguimiento fueron

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.

We propose a fully automated methodology for hexahedral meshing of patient-specific structures of the human knee obtained from magnetic resonance images, i.e. femoral/tibial cartilages and menisci. We select eight patients from the Osteoarthritis Initiative and validate our methodology using MATLAB on a laptop computer. We obtain the patient-specific meshes in an average of three minutes, while faithfully representing the geometries with well-shaped elements. We hope to provide a fundamentally different means to test hypotheses on the mechanisms of disease progression by integrating our patient-specific FE meshes with data from individual patients. Download both our meshes and software at http://im.engr.uconn.edu/downloads.php.

Background Diffuse Large B cell lymphoma (DLBCL) is usually diagnosed between the 5th-6th decade of life. Its incidence increases with age. Chemotherapy (CT) with R-CHOP is the standard treatment for any age. However, in the clinical practice, alternative treatments with less toxicities are offered to elderly patients. This group of patients is usually not well represented in clinical trials. Our aim is to asses the benefit of standard treatment in this population, as well as discontinuation of treatment and its impact on overall survival (OS). As secondary endpoint, we have analyzed the survival rate in the subgroup of patients older than 75 years of age treated with CT. Methods A retrospective review was performed in patients ≥65 years who were diagnosed with DLBCL during the years 2013-2015 in the Hospital of Leon. Patients with histological confirmation of the primary diagnosis and who received treatment were included. Results 65 patients were analyzed. The average age was 75 years. 64% were male and 30% had stage 4 disease at the time of diagnosis. The CT schemes were: R-CHOP (N = 28), R-CEOP (N = 11) and R-miniCHOP (N = 8). 10 patients had grade 4 AEs. One death ocurred after receiving the first cicle. 70% completed at least 4 cicles of CT. Median OS was 51 months versus (vs) 11 months in favor of R-CHOP group (HR = 0,59, p = 0,112 IC 95%=0,30-1,13). Results were not statistically significant likely because of the small sample size. 45% of patients had a complete response, 20% had a partial response and 10% had no response to treatment.The main cause of death was disease progression. Median OS was 15 months in both groups, in patients older than 75 treated with CT vs Conclusions In clinical practice, age is an important variable to account for when deciding on the management approach for heamatological malignancies such as DLBCL. Although our results show a non-statistically significant benefit in OS of R-CHOP vs other schemes, this could be due to the small sample size. Given the prevalence of elderly population in our country, and the proved benefit of standard chemotherapy in the approach of DLBCL, more studies are needed. Legal entity responsible for the study The authors. Funding Has not received any funding. Disclosure All authors have declared no conflicts of interest.

Introduction: E-learning web environments, including the new TELMA platform, are increasingly being used to provide cognitive training in minimally invasive surgery (MIS) to surgeons. A complete validation of this MIS elearning platform has been performed to determine whether it complies with the three web quality dimensions: usability, content and functionality. Methods: 21 Surgeons participated in the validation trials. They performed a set of tasks in the TELMA platform, where an e-MIS validity approach was followed. Subjective (questionnaires and checklists) and objective (web analytics) metrics were analysed to achieve the complete validation of usability, content and functionality. Results: The TELMA platform allowed access to didactic content with easy and intuitive navigation. Surgeons performed all tasks with a close-to-ideal number of clicks and amount of time. They considered the design of the website to be consistent (95.24%), organised (90.48%) and attractive (85.71%). Moreover, they gave the content a high score (4.06 out of 5) and considered it adequate for teaching purposes. The surgeons scored the professional language and content (4.35), logo (4.24) and recommendations (4.20) the highest. Regarding functionality, the TELMA platform received an acceptance of 95.24% for navigation and 90.48% for interactivity. Conclusions: According to the study, it seems that TELMA had an attractive design, innovative content and interactive navigation, which are three key features of an e-learning platform. TELMA successfully met the three criteria necessary for consideration as a website of quality by achieving more than 70% of agreements regarding all usability, content and functionality items validated; this constitutes a preliminary requirement for an effective e-learning platform. However, the content completeness, authoring tool and registration process required improvement. Finally, the e-MIS validity methodology used to measure the three dimensions of web quality in this work can be applied to other clinical areas or training fields.

Laparoscopic surgery is commonly used in many surgical procedures but requires a learning process to develop the necessary skills. Virtual reality simulators play an essential role within the training curricula. This paper aims to determine whether training in SINERGIA VR simulator allows novice surgeons to improve their basic psychomotor laparoscopic skills.Forty-two people participated in this study, including 28 unexperience medical students and 14 expert surgeons who developed previously more than 100 laparoscopic procedures. Medical students made a pre-training test in LapMentor II; then, they trained in SINERGIA and they finally accomplished a post-training test in LapMentor II. Experts just made one trial in LapMentor II. A statistical analysis was carried out and results of pre- and post-training tests of novices were compared with Wilcoxon signed-rank test. Pre- and post-training tests of novices were also compared with results of experts with Mann-Whitney U test.Most metrics provided by LapMentor II and included in this study show significant differences when comparing pre- and post-training tests of novices. Analysis of pre-training test of novices and experts results show significant differences in all analyzed metrics for all studied tasks. On the other hand, LapMentor was not able to distinguish between experts and novices after training in SINERGIA for any metric in the camera manipulation task and for some metrics of the other tasks.Training in SINERGIA VR simulator allows improvement of basic psychomotor laparoscpic skills and transferring them to another virtual simulator. Therefore, it could be used in laparoscopic surgery training programs.

In this paper, the development of an assisting system for laparoscopic surgical training is presented. With this system, we expect to facilitate the training process at the first stages of training in laparoscopic surgery and to contribute to an objective evaluation of surgical skills. To achieve this, we propose the insertion of multimedia contents and outlines of work adapted to the level of experience of trainees and the detection of the movements of the laparoscopic instrument into the monitored image. A module to track the instrument is implemented focusing on the tip of the laparoscopic tool. This tracking method does not need the presence of artificial marks or special colours to distinguish the instruments. Similarly, the system has another method based on visual tracking to localize support multimedia content in a stable position of the field of vision. Therefore, this position of the support content is adapted to the movements of the camera or the working area. Experimental results are presented to show the feasibility of the proposed system for assisting in laparoscopic surgical training.

Motion analysis is employed to assess minimally invasive surgical psychomotor skills in box trainers. Tracking of laparoscopic instruments requires sensor-based systems that can be expensive, limit movements and modify their ergonomic properties. We evaluate the feasibility of using Leap Motion as a cheap, unobtrusive alternative. Four experiments were performed to determine its precision while tracking a laparoscopic instrument inside and outside a box trainer. Static long and short term precision of the Leap Motion was

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. 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

The present work covers the first validation efforts of the EVA Tracking System for the assessment of minimally invasive surgery (MIS) psychomotor skills. Instrument movements were recorded for 42 surgeons (4 expert, 22 residents, 16 novice medical students) and analyzed for a box trainer peg transfer task. Construct validation was established for 7/9 motion analysis parameters (MAPs). Concurrent validation was determined for 8/9 MAPs against the TrEndo Tracking System. Finally, automatic determination of surgical proficiency based on the MAPs was sought by 3 different approaches to supervised classification (LDA, SVM, ANFIS), with accuracy results of 61.9%, 83.3% and 80.9% respectively. Results not only reflect on the validation of EVA for skills’ assessment, but also on the relevance of motion analysis of instruments in the determination of surgical competence.

Acquired Brain Injury (ABI) has become one of the most common causes of neurological disability in developed countries. Cognitive disorders result in a loss of independence and therefore patients? quality of life. Cognitive rehabilitation aims to promote patients? skills to achieve their highest degree of personal autonomy. New technologies such as interactive video, whereby real situations of daily living are reproduced within a controlled virtual environment, enable the design of personalized therapies with a high level of generalization and a great ecological validity. This paper presents a graphical tool that allows neuropsychologists to design, modify, and configure interactive video therapeutic activities, through the combination of graphic and natural language. The tool has been validated creating several Activities of Daily Living and a preliminary usability evaluation has been performed showing a good clinical acceptance in the definition of complex interactive video therapies for cognitive rehabilitation.

INTRODUCTION: Motion metrics have become an important source of information when addressing the assessment of surgical expertise. However, their direct relationship with the different surgical skills has not been fully explored. The purpose of this study is to investigate the relevance of motion-related metrics in the evaluation processes of basic psychomotor laparoscopic skills, as well as their correlation with the different abilities sought to measure. METHODS: A framework for task definition and metric analysis is proposed. An explorative survey was first conducted with a board of experts to identify metrics to assess basic psychomotor skills. Based on the output of that survey, three novel tasks for surgical assessment were designed. Face and construct validation study was performed, with focus on motion-related metrics. Tasks were performed by 42 participants (16 novices, 22 residents and 4 experts). Movements of the laparoscopic instruments were registered with the TrEndo tracking system and analyzed. RESULTS: Time, path length and depth showed construct validity for all three tasks. Motion smoothness and idle time also showed validity for tasks involving bi-manual coordination and tasks requiring a more tactical approach respectively. Additionally, motion smoothness and average speed showed a high internal consistency, proving them to be the most task-independent of all the metrics analyzed. CONCLUSION: Motion metrics are complementary and valid for assessing basic psychomotor skills, and their relevance depends on the skill being evaluated. A larger clinical implementation, combined with quality performance information, will give more insight on the relevance of the results shown in this study.

INTRODUCTION: The EVA (Endoscopic Video Analysis) tracking system a new tracking system for extracting motions of laparoscopic instruments based on non-obtrusive video tracking was developed. 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 centre to track the 3D 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 (p=0,97), average speed (p=0,94) or economy of volume (p=0,85), proving the viability of EVA. CONCLUSIONS: EVA has been successfully used in the training setup showing potential of endoscopic video analysis to assess laparoscopic psychomotor skills. The results encourage further implementation of video tracking in training setups and in image guided 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 Jesus Uson 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.

Automatic analysis of Minimally Invasive Surgical video has the potential to drive new solutions for alleviating needs of safe and reproducible training programs, objective and transparent evaluation systems and navigation tools to assist surgeons and improve patient safety. Surgical video is an always available source of information, which can be used without any additional intrusive hardware in the operating room. This paper is focused on surgical video analysis methods and techniques. It describes authors' contributions in two key aspects, the 3D reconstruction of the surgical field and the segmentation and tracking of tools and organs based on laparoscopic video images. Results are given to illustrate the potential of this field of research, like the calculi of the 3D position and orientation of a tool from its 2D image, or the translation of a preoperative resection plan into a hepatectomy surgical procedure using the shading information of the image. Research efforts are required to further develop these technologies in order to harness all the valuable information available in any video-based surgery.

1.1 Laparoscopic surgery Minimally Invasive Surgery (MIS) has changed the way surgery is performed in Operating Rooms (OR). MIS techniques are increasing their relevance in almost all surgical specialities, and have become the recommended standard in many procedures, displacing open surgery. Laparoscopy, one of the most common MIS approaches, has been adopted by several surgical sub-specialties including gastrointestinal, gynaecological and urological surgery (Fig. 1). It has become the standard technique for certain pathologies, like those associated with anti-reflux diseases, and procedures such as cholecystectomy (Cuschieri, 2005).

Video-endoscopic image analysis is an important source of information for extracting relevant 3D information of the surgical scene in minimally invasive surgery. This work proposes a new approach for getting depth information and relative position between instruments and organs based on video image analysis. The proposed image analysis extracts information from: (1) the projective geometry of the surgical setting in the image to determine laparoscopic tool’s pose, and (2) the scene illumination captured in the image for getting a depth-map of the surgical scene. Results have verified the potential of video-analysis based methods for obtaining 3D information of the whole surgical scene. This information offers new possibilities for developing new augmented reality surgical applications and for improving surgical training with objective metrics. The main benefit of this approach is the lack of extra elements which disturb surgeons’ performance in the clinical routine.

A trend in abdominal surgery is the transition from minimally invasive surgery to surgeries where augmented reality is used. Endoscopic video images are proposed to be employed for extracting useful information to help surgeons performing the operating techniques. This work introduces an illumination model into the design of automatic segmentation algorithms and 3D reconstruction methods. Results obtained from the implementation of our methods to real images are supposed to be an initial step useful for designing new methodologies that will help surgeons operating MIS techniques.

Vision-based tracking of laparoscopic tools offers new possibilities for improving surgical training and for developing new augmented reality surgical applications. We present an original method to determine not only the tip position, but also the orientation of a laparoscopic tool respect to the camera coordinate frame. A simple mathematical formulation shows how segmented tool edges and camera field of view define the tool 3D orientation. Then, 3D position of the tool tip is determined by image 2D coordinates of any known point of the tool and by tool's diameter. Accuracy is evaluated in real image sequences with known ground truth. Results show a positioning error of 9,28 mmRMS, what is explained by inaccuracies in the estimation of tool edges. The main advantage of proposed method is its robustness to occlusions of the tool tip.