Evaluation of a Clinical Decision Support System for the Prescription of Genetic Tests in the Gynecological Cancer Risk.

Clinical Decision Support System (CDSS) has been implemented to support physicians about the medical prescription of genetic testing. CDSS is based on open source software. A CDSS for prescribing these genetic tests in BRCA1 and BRCA2 and preventing gynecological cancer risks has been designed and performed in the 'Virgen del Rocío' University Hospital. Clinical evidence demonstrates that BRCA1 and BRCA2 mutations can develop gynecological cancer, but genetic testing has a high cost to the healthcare system. The developed technological architecture integrates open source tools like Mirth Connect and OpenClinica. The system allows general practitioners and gynecologists to classify patients as low risk (they do not require a specific treatment) or high risk (they should be attended by the Genetic Council), According to their genetic risk, recommending the prescription of genetic tests. The aim main of this paper is the evaluation of the developed CDSS, getting positive outcomes.

Joint segmentation of bones and muscles using an intensity and histogram-based energy minimization approach.

BACKGROUND AND OBJECTIVES: The segmentation of muscle and bone structures in CT is of interest to physicians and surgeons for surgical planning, disease diagnosis and/or the analysis of fractures or bone/muscle densities. Recently, the issue has been addressed in many research works. However, most studies have focused on only one of the two tissues and on the segmentation of one particular bone or muscle. This work addresses the segmentation of muscle and bone structures in 3D CT volumes. METHODS: The proposed bone and muscle segmentation algorithm is based on a three-label convex relaxation approach. The main novelty is that the proposed energy function to be minimized includes distance to histogram models of bone and muscle structures combined with gray-level information. RESULTS: 27 CT volumes corresponding to different sections from 20 different patients were manually segmented and used as ground-truth for training and evaluation purposes. Different metrics (Dice index, Jaccard index, Sensitivity, Specificity, Positive Predictive Value, accuracy and computational cost) were computed and compared with those used in some state-of-the art algorithms. The proposed algorithm outperformed the other methods, obtaining a Dice coefficient of 0.88 ±â€¯0.14, a Jaccard index of 0.80 ±â€¯0.19, a Sensitivity of 0.94 ±â€¯0.15 and a Specificity of 0.95 ±â€¯0.04 for bone segmentation, and 0.78 ±â€¯0.12, 0.65 ±â€¯0.16, 0.94 ±â€¯0.04 and 0.95 ±â€¯0.04 for muscle tissue. CONCLUSIONS: A fast, generalized method has been presented for segmenting muscle and bone structures in 3D CT volumes using a multilabel continuous convex relaxation approach. The results obtained show that the proposed algorithm outperforms some state-of-the art methods. The algorithm will help physicians and surgeons in surgical planning, disease diagnosis and/or the analysis of fractures or bone/muscle densities.

Three-dimensional printed models for surgical planning of complex congenital heart defects: an international multicentre study.

OBJECTIVES: To evaluate the impact of 3D printed models (3D models) on surgical planning in complex congenital heart disease (CHD). METHODS: A prospective case-crossover study involving 10 international centres and 40 patients with complex CHD (median age 3 years, range 1 month-34 years) was conducted. Magnetic resonance imaging and computed tomography were used to acquire and segment the 3D cardiovascular anatomy. Models were fabricated by fused deposition modelling of polyurethane filament, and dimensions were compared with medical images. Decisions after the evaluation of routine clinical images were compared with those after inspection of the 3D model and intraoperative findings. Subjective satisfaction questionnaire was provided. RESULTS: 3D models accurately replicate anatomy with a mean bias of -0.27 ± 0.73 mm. Ninety-six percent of the surgeons agree or strongly agree that 3D models provided better understanding of CHD morphology and improved surgical planning. 3D models changed the surgical decision in 19 of the 40 cases. Consideration of a 3D model refined the planned biventricular repair, achieving an improved surgical correction in 8 cases. In 4 cases initially considered for conservative management or univentricular palliation, inspection of the 3D model enabled successful biventricular repair. CONCLUSIONS: 3D models are accurate replicas of the cardiovascular anatomy and improve the understanding of complex CHD. 3D models did not change the surgical decision in most of the cases (21 of 40 cases, 52.5% cases). However, in 19 of the 40 selected complex cases, 3D model helped redefining the surgical approach.

Learning Healthcare System for the Prescription of Genetic Testing in the Gynecological Cancer Risk.

Clinical evidence demonstrates that BRCA 1 and BRCA2 mutations can develop a gynecological cancer but genetic testing has a high cost to the healthcare system. Besides, several studies in the literature indicate that performing these genetic tests to the population is not cost-efficient. Currently, our physicians do not have a system to provide them the support for prescribing genetic tests. A Decision Support System for prescribing these genetic tests in BRCA1 and BRCA2 and preventing gynecological cancer risks has been designed, developed and deployed in the Virgen del Rocío University Hospital (VRUH). The technological architecture integrates a set of open source tools like Mirth Connect, OpenClinica, OpenCDS, and tranSMART in addition to several interoperability standards. The system allows general practitioners and gynecologists to classify patients as low risk (they do not require a specific treatment) or high risk (they should be attended by the Genetic Council). On the other hand, by means of this system we are also able to standardize criteria among professionals to prescribe these genetic tests. Finally, this system will also contribute to improve the assistance for this kind of patients.

Validation of a method for retroperitoneal tumor segmentation.

PURPOSE: In 2005, an application for surgical planning called AYRA[Formula: see text] was designed and validated by different surgeons and engineers at the Virgen del Rocío University Hospital, Seville (Spain). However, the segmentation methods included in AYRA and in other surgical planning applications are not able to segment accurately tumors that appear in soft tissue. The aims of this paper are to offer an exhaustive validation of an accurate semiautomatic segmentation tool to delimitate retroperitoneal tumors from CT images and to aid physicians in planning both radiotherapy doses and surgery. METHODS: A panel of 6 experts manually segmented 11 cases of tumors, and the segmentation results were compared exhaustively with: the results provided by a surgical planning tool (AYRA), the segmentations obtained using a radiotherapy treatment planning system (Pinnacle[Formula: see text]), the segmentation results obtained by a group of experts in the delimitation of retroperitoneal tumors and the segmentation results using the algorithm under validation. RESULTS: 11 cases of retroperitoneal tumors were tested. The proposed algorithm provided accurate results regarding the segmentation of the tumor. Moreover, the algorithm requires minimal computational time-an average of 90.5% less than that required when manually contouring the same tumor. CONCLUSION: A method developed for the semiautomatic selection of retroperitoneal tumor has been validated in depth. AYRA, as well as other surgical and radiotherapy planning tools, could be greatly improved by including this algorithm.

Three-dimensional segmentation of retroperitoneal masses using continuous convex relaxation and accumulated gradient distance for radiotherapy planning.

An innovative algorithm has been developed for the segmentation of retroperitoneal tumors in 3D radiological images. This algorithm makes it possible for radiation oncologists and surgeons semiautomatically to select tumors for possible future radiation treatment and surgery. It is based on continuous convex relaxation methodology, the main novelty being the introduction of accumulated gradient distance, with intensity and gradient information being incorporated into the segmentation process. The algorithm was used to segment 26 CT image volumes. The results were compared with manual contouring of the same tumors. The proposed algorithm achieved 90 % sensitivity, 100 % specificity and 84 % positive predictive value, obtaining a mean distance to the closest point of 3.20 pixels. The algorithm's dependence on the initial manual contour was also analyzed, with results showing that the algorithm substantially reduced the variability of the manual segmentation carried out by different specialists. The algorithm was also compared with four benchmark algorithms (thresholding, edge-based level-set, region-based level-set and continuous max-flow with two labels). To the best of our knowledge, this is the first time the segmentation of retroperitoneal tumors for radiotherapy planning has been addressed.

Planning, execution and monitoring of physical rehabilitation therapies with a robotic architecture.

Traditional methods of rehabilitation require continuous attention of therapists during the therapy sessions. This is a hard and expensive task in terms of time and effort. In many cases, the therapeutic objectives cannot be achieved due to the overwork or the difficulty for therapists to plan accurate sessions according to the medical criteria. For this purpose, a wide range of studies is opened in order to research new ways of rehabilitation, as in the field of social robotics. This work presents the current state of the THERAPIST project. Our main goal is to develop a cognitive architecture which provides a robot with enough autonomy to carry out an upper-limb rehabilitation therapy for patients with physical impairments, such as Cerebral Palsy and Obstetric Brachial Plexus Palsy.

New technologies applied to surgical processes: Virtual Reality and rapid prototyping.

AYRA is software of virtual reality for training, planning and optimizing surgical procedures. AYRA was developed under a research, development and innovation project financed by the Andalusian Ministry of Health, called VirSSPA. Nowadays AYRA has been successfully used in more than 1160 real cases and after proving its efficiency it has been introduced in the clinical practice at the Virgen del Rocío University Hospital . Furthermore, AYRA allows generating physical 3D biomodels using rapid prototyping technology. They are used for surgical planning support, intraoperative reference or defect reconstruction. In this paper, some of these tools and some real cases are presented.

3D printed models for planning endovascular stenting in transverse aortic arch hypoplasia.

OBJECTIVES: To evaluate whether three-dimensional (3D) printed models can be used to improve interventional simulation and planning in patients with aortic arch hypoplasia. BACKGROUND: Stenting of a hypoplastic transverse arch is technically challenging, and complications such as stent migration and partial obstruction of the origin of the head and neck vessels are highly dependent on operator skills and expertise. METHODS: Using magnetic resonance imaging (MRI) data, a 3D model of a repaired aortic coarctation of a 15-year-old boy with hypoplastic aortic arch was printed. Simulation of the endovascular stenting of the hypoplastic arch was carried out under fluoroscopic guidance in the 3D printed model, and subsequently in the patient. A Bland-Altman analysis was used to evaluate the agreement between measurements of aortic diameter in the 3D printed model and the patient's MRI and X-ray angiography. RESULTS: The 3D printed model proved to be radio-opaque and allowed simulation of the stenting intervention. The assessment of optimal stent position, size, and length was found to be useful for the actual intervention in the patient. There was excellent agreement between the 3D printed model and both MRI and X-ray angiographic images (mean bias and standard deviation of 0.36 ± 0.45 mm). CONCLUSIONS: 3D printed models accurately replicate patients' anatomy and are helpful in planning endovascular stenting in transverse arch hypoplasia. This opens a door for potential simulation applications of 3D models in the field of catheterization and cardiovascular interventions.

Social media for empowering people with diabetes: Current status and future trends.

The use of social media has become commonplace in society. Consequently, many people living with chronic conditions are turning to social media applications to support self-management. This paper presents a formative non-exhaustive review of research literature regarding the role of social media for diabetes type II empowerment. In our review, we identified several major areas for diabetes health social media research, namely: a) social network data analytics, b) mHealth and diabetes, c) gamification for diabetes, c) wearable, and d) MOOCs (Massive Open Online Courses). In all these areas, we analyzed how social media is being used and the challenges emerging from its application in the diabetes domain.

Three-dimensional patient-specific cardiac model for surgical planning in Nikaidoh procedure.

PURPOSE: To explore the use of three-dimensional patient-specific cardiovascular models using rapid prototyping techniques (fused deposition modelling) to improve surgical planning in patients with complex congenital heart disease. DESCRIPTION: Rapid prototyping techniques are used to print accurate three-dimensional replicas of patients' cardiovascular anatomy based on magnetic resonance images using computer-aided design systems. Models are printed using a translucent polylactic acid polymer. EVALUATION: As a proof of concept, a model of the heart of a 1.5-year-old boy with transposition of the great arteries, ventricular septal defect and pulmonary stenosis was constructed to help planning the surgical correction. The cardiac model allowed the surgeon to evaluate the location and dimensions of the ventricular septal defect as well as its relationship with the aorta and pulmonary artery. CONCLUSIONS: Cardiovascular models constructed by rapid prototyping techniques are extremely helpful for planning corrective surgery in patients with complex congenital malformations. Therefore they may potentially reduce operative time and morbi-mortality.

Virtual reality AYRA software for preoperative planning in facial allotransplantation.

PURPOSE: The purpose of this study was to validate a virtual reality software for the recording of anthropometric measurements as a first step towards matching donors with recipients in the preoperative planning process which precedes the harvest of a facial allograft. METHODS: Anthropometric measurements of both soft and bone tissue were recorded in 5 cryopreserved human heads to compare conventional analogue measurements with digital measurements obtained from 3-dimensional (3D) reconstructions produced using AYRA software. To test the degree of correlation between both measuring methods, intraclass correlation coefficient (ICC) was applied to each pair of measurements. RESULTS: ICCs calculated were greater than 0.6 (substantial or almost perfect correlation) for all of the pairs of variables, with the exception of 2 of the measurements studied in bone tissue. CONCLUSIONS: In facial transplantation, preoperative planning is crucial to select an allograft whose anatomical compatibility with the recipient defect is as close as possible. The dimensions of the potential face donor must be congruent to ensure the procedure's feasibility and the adequate insertion of the allograft into the defect. The recording of anthropometric measurements with the virtual reality software displayed an equivalent correlation to those produced using a conventional analogue method. The 3D reconstructions obtained by using a virtual reality software can play a useful role to facilitate the characterization of the donor face.

THERAPIST: Towards an Autonomous Socially Interactive Robot for Motor and Neurorehabilitation Therapies for Children.

BACKGROUND: Neurorehabilitation therapies exploiting the use-dependent plasticity of our neuromuscular system are devised to help patients who suffer from injuries or diseases of this system. These therapies take advantage of the fact that the motor activity alters the properties of our neurons and muscles, including the pattern of their connectivity, and thus their functionality. Hence, a sensor-motor treatment where patients makes certain movements will help them (re)learn how to move the affected body parts. But these traditional rehabilitation processes are usually repetitive and lengthy, reducing motivation and adherence to the treatment, and thus limiting the benefits for the patients. OBJECTIVE: Our goal was to create innovative neurorehabilitation therapies based on THERAPIST, a socially assistive robot. THERAPIST is an autonomous robot that is able to find and execute plans and adapt them to new situations in real-time. The software architecture of THERAPIST monitors and determines the course of action, learns from previous experiences, and interacts with people using verbal and non-verbal channels. THERAPIST can increase the adherence of the patient to the sessions using serious games. Data are recorded and can be used to tailor patient sessions. METHODS: We hypothesized that pediatric patients would engage better in a therapeutic non-physical interaction with a robot, facilitating the design of new therapies to improve patient motivation. We propose RoboCog, a novel cognitive architecture. This architecture will enhance the effectiveness and time-of-response of complex multi-degree-of-freedom robots designed to collaborate with humans, combining two core elements: a deep and hybrid representation of the current state, own, and observed; and a set of task-dependent planners, working at different levels of abstraction but connected to this central representation through a common interface. Using RoboCog, THERAPIST engages the human partner in an active interactive process. But RoboCog also endows the robot with abilities for high-level planning, monitoring, and learning. Thus, THERAPIST engages the patient through different games or activities, and adapts the session to each individual. RESULTS: RoboCog successfully integrates a deliberative planner with a set of modules working at situational or sensorimotor levels. This architecture also allows THERAPIST to deliver responses at a human rate. The synchronization of the multiple interaction modalities results from a unique scene representation or model. THERAPIST is now a socially interactive robot that, instead of reproducing the phrases or gestures that the developers decide, maintains a dialogue and autonomously generate gestures or expressions. THERAPIST is able to play simple games with human partners, which requires humans to perform certain movements, and also to capture the human motion, for later analysis by clinic specialists. CONCLUSIONS: The initial hypothesis was validated by our experimental studies showing that interaction with the robot results in highly attentive and collaborative attitudes in pediatric patients. We also verified that RoboCog allows the robot to interact with patients at human rates. However, there remain many issues to overcome. The development of novel hands-off rehabilitation therapies will require the intersection of multiple challenging directions of research that we are currently exploring.