Browsing by Keyword "Health Informatics"
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Item 3D Active Surfaces for Liver Segmentation in Multisequence MRI Images(2016-08-01) Bereciartua, Arantza; Picon, Artzai; Galdran, Adrian; Iriondo, Pedro M.; COMPUTER_VISION; Tecnalia Research & InnovationBiopsies for diagnosis can sometimes be replaced by non-invasive techniques such as CT and MRI. Surgeons require accurate and efficient methods that allow proper segmentation of the organs in order to ensure the most reliable intervention planning. Automated liver segmentation is a difficult and open problem where CT has been more widely explored than MRI. MRI liver segmentation represents a challenge due to the presence of characteristic artifacts, such as partial volumes, noise and low contrast. In this paper, we present a novel method for multichannel MRI automatic liver segmentation. The proposed method consists of the minimization of a 3D active surface by means of the dual approach to the variational formulation of the underlying problem. This active surface evolves over a probability map that is based on a new compact descriptor comprising spatial and multisequence information which is further modeled by means of a liver statistical model. This proposed 3D active surface approach naturally integrates volumetric regularization in the statistical model. The advantages of the compact visual descriptor together with the proposed approach result in a fast and accurate 3D segmentation method. The method was tested on 18 healthy liver studies and results were compared to a gold standard made by expert radiologists. Comparisons with other state-of-the-art approaches are provided by means of nine well established quality metrics. The obtained results improve these methodologies, achieving a Dice Similarity Coefficient of 98.59.Item Analysis on the characterization of multiphoton microscopy images for malignant neoplastic colon lesion detection under deep learning methods(2021-01-01) Terradillos, Elena; Saratxaga, CristinaL; Mattana, Sara; Cicchi, Riccardo; Pavone, FrancescoS; Andraka, Nagore; Glover, BenjaminJ; Arbide, Nagore; Velasco, Jacques; Etxezarraga, MªCarmen; Picon, Artzai; VISUALColorectal cancer has a high incidence rate worldwide, with over 1.8 million new cases and 880,792 deaths in 2018. Fortunately, its early detection significantly increases the survival rate, reaching a cure rate of 90% when diagnosed at a localized stage. Colonoscopy is the gold standard technique for detection and removal of colorectal lesions with potential to evolve into cancer. When polyps are found in a patient, the current procedure is their complete removal. However, in this process, gastroenterologists cannot assure complete resection and clean margins which are given by the histopathology analysis of the removed tissue, which is performed at laboratory. Aims: In this paper, we demonstrate the capabilities of multiphoton microscopy (MPM) technology to provide imaging biomarkers that can be extracted by deep learning techniques to identify malignant neoplastic colon lesions and distinguish them from healthy, hyperplastic, or benign neoplastic tissue, without the need for histopathological staining. Materials and Methods: To this end, we present a novel MPM public dataset containing 14,712 images obtained from 42 patients and grouped into 2 classes. A convolutional neural network is trained on this dataset and a spatially coherent predictions scheme is applied for performance improvement. Results: We obtained a sensitivity of 0.8228 ± 0.1575 and a specificity of 0.9114 ± 0.0814 on detecting malignant neoplastic lesions. We also validated this approach to estimate the self-confidence of the network on its own predictions, obtaining a mean sensitivity of 0.8697 and a mean specificity of 0.9524 with the 18.67% of the images classified as uncertain. Conclusions: This work lays the foundations for performing in vivo optical colon biopsies by combining this novel imaging technology together with deep learning algorithms, hence avoiding unnecessary polyp resection and allowing in situ diagnosis assessment.Item Automatic 3D model-based method for liver segmentation in MRI based on Active Contours and Total Variation minimization(2015-07-01) Bereciartua, Arantza; Picon, Artzai; Galdran, Adrian; Iriondo, Pedro M.; COMPUTER_VISION; Tecnalia Research & InnovationLiver cancer is one of the leading causes of cancer-related mortality worldwide. Non-invasive techniques of medical imaging such as Computerized Tomography (CT) and Magnetic Resonance Imaging (MRI) are often used by radiologists for diagnosis and surgery planning. With the aim of assuring the most reliable intervention planning to surgeons, new accurate methods and tools must be provided to locate and segment the regions of interest. Automated liver segmentation is a challenging problem for which promising results have been achieved mostly for CT. However, MRI is required by radiologists, since it offers better information for diagnosis purposes. MRI liver segmentation represents a challenge due to the presence of characteristic artifacts, such as partial volumes, noise, low contrast and poorly defined edges of the liver in relation to adjacent organs. In this paper, we present a method for MRI automatic 3D liver segmentation by means of an Active Contour model extended to 3D and minimized by Total Variation dual approach that has also been extended to 3D. A new approach to enhance the contrast in the input MRI image is proposed and it allows more accurate segmentation. The proposed methodology allows replacing the input image by a probability map obtained by means of a previously generated statistical model of the liver. An Accuracy of 98.89 and Dice Similarity Coefficient of 90.19 are in line with other state-of-the-art methodologies.Item A compact system for simultaneous stimulation and recording for closed-loop myoelectric control(2021-12) Garenfeld, Martin A.; Jorgovanovic, Nikola; Ilic, Vojin; Strbac, Matija; Isakovic, Milica; Dideriksen, Jakob L.; Dosen, Strahinja; Tecnalia Research & Innovation; SGBackground.Despite important advancements in control and mechatronics of myoelectric prostheses, the communication between the user and his/her bionic limb is still unidirectional, as these systems do not provide somatosensory feedback. Electrotactile stimulation is an attractive technology to close the control loop since it allows flexible modulation of multiple parameters and compact interface design via multi-pad electrodes. However, the stimulation interferes with the recording of myoelectric signals and this can be detrimental to control.Item A decision support system for electrode shaping in multi-pad FES foot drop correction(2017-07-03) Malešević, Jovana; Dedijer Dujović, Suzana; Savić, Andrej M.; Konstantinović, Ljubica; Vidaković, Aleksandra; Bijelić, Goran; Malešević, Nebojša; Keller, Thierry; Tecnalia Research & Innovation; SG; MercadoBackground: Functional electrical stimulation (FES) can be applied as an assistive and therapeutic aid in the rehabilitation of foot drop. Transcutaneous multi-pad electrodes can increase the selectivity of stimulation; however, shaping the stimulation electrode becomes increasingly complex with an increasing number of possible stimulation sites. We described and tested a novel decision support system (DSS) to facilitate the process of multi-pad stimulation electrode shaping. The DSS is part of a system for drop foot treatment that comprises a customdesigned multi-pad electrode, an electrical stimulator, and an inertial measurement unit. Methods: The system was tested in ten stroke survivors (3-96 months post stroke) with foot drop over 20 daily sessions. The DSS output suggested stimulation pads and parameters based on muscle twitch responses to short stimulus trains. The DSS ranked combinations of pads and current amplitudes based on a novel measurement of the quality of the induced movement and classified them based on the movement direction (dorsiflexion, plantar flexion, eversion and inversion) of the paretic foot. The efficacy of the DSS in providing satisfactory pad-current amplitude choices for shaping the stimulation electrode was evaluated by trained clinicians. The range of paretic foot motion was used as a quality indicator for the chosen patterns. Results: The results suggest that the DSS output was highly effective in creating optimized FES patterns. The position and number of pads included showed pronounced inter-patient and inter-session variability; however, zones for inducing dorsiflexion and plantar flexion within the multi-pad electrode were clearly separated. The range of motion achieved with FES was significantly greater than the corresponding active range of motion (p < 0.05) during the first three weeks of therapy. Conclusions: The proposed DSS in combination with a custom multi-pad electrode design covering the branches of peroneal and tibial nerves proved to be an effective tool for producing both the dorsiflexion and plantar flexion of a paretic foot. The results support the use of multi-pad electrode technology in combination with automatic electrode shaping algorithms for the rehabilitation of foot drop.Item Designing a generalised reward for Building Energy Management Reinforcement Learning agents(IEEE, 2021-09-08) Martinez, Ruben Mulero; Goikolea, Benat Arregi; Beitia, Inigo Mendialdua; Martinez, Roberto Garay; Mulero, Rubén; Arregi, Beñat; Mendialdua, Iñigo; Garay, Roberto; Solic, Petar; Nizetic, Sandro; Rodrigues, Joel J. P. C.; Rodrigues, Joel J.P.C.; Gonzalez-de-Artaza, Diego Lopez-de-Ipina; Perkovic, Toni; Catarinucci, Luca; Patrono, Luigi; DIGITALIZACIÓN Y AUTOMATIZACIÓN DE LA CONSTRUCCIÓN; EDIFICACIÓN DE ENERGÍA POSITIVA; Tecnalia Research & InnovationThe reduction of the carbon footprint of buildings is a challenging task, partly due to the conflicting goals of maximising occupant comfort and minimising energy consumption. An intelligent management of Heating, Ventilation and Air Conditioning (HVAC) systems is creating a promising research line in which the creation of suitable algorithms could reduce energy consumption maintaining occupants' comfort. In this regard, Reinforcement Learning (RL) approaches are giving a good balance between data requirements and intelligent operations to control building systems. However, there is a gap concerning how to create a generalised reward signal that can train RL agents without delimiting the problem to a specific or controlled scenario. To tackle it, an analysis and discussion is presented about the necessary requirements for the creation of generalist rewards, with the objective of laying the foundations that allow the creation of generalist intelligent agents for building energy management.Item Development of computer games for assessment and training in post-stroke arm telerehabilitation(2012) Rodriguez-De-Pablo, Cristina; Perry, Joel C.; Cavallaro, Francesca I.; Zabaleta, Haritz; Keller, Thierry; Tecnalia Research & InnovationStroke is the leading cause of long term disability among adults in industrialized nations. The majority of these disabilities include deficiencies in arm function, which can make independent living very difficult. Research shows that better results in rehabilitation are obtained when patients receive more intensive therapy. However this intensive therapy is currently too expensive to be provided by the public health system, and at home few patients perform the repetitive exercises recommended by their therapists. Computer games can provide an affordable, enjoyable, and effective way to intensify treatment, while keeping the patient as well as their therapists informed about their progress. This paper presents the study, design, implementation and user-testing of a set of computer games for at-home assessment and training of upper-limb motor impairment after stroke.Item European evidence-based recommendations for clinical assessment of upper limb in neurorehabilitation (CAULIN): data synthesis from systematic reviews, clinical practice guidelines and expert consensus: data synthesis from systematic reviews, clinical practice guidelines and expert consensus(2021-12) Prange-Lasonder, Gerdienke B.; Alt Murphy, Margit; Lamers, Ilse; Hughes, Ann-Marie; Buurke, Jaap H.; Feys, Peter; Keller, Thierry; Klamroth-Marganska, Verena; Tarkka, Ina M.; Timmermans, Annick; Burridge, Jane H.; Tecnalia Research & InnovationBackground: Technology-supported rehabilitation can help alleviate the increasing need for cost-effective rehabilitation of neurological conditions, but use in clinical practice remains limited. Agreement on a core set of reliable, valid and accessible outcome measures to assess rehabilitation outcomes is needed to generate strong evidence about effectiveness of rehabilitation approaches, including technologies. This paper collates and synthesizes a core set from multiple sources; combining existing evidence, clinical practice guidelines and expert consensus into European recommendations for Clinical Assessment of Upper Limb In Neurorehabilitation (CAULIN). Methods: Data from systematic reviews, clinical practice guidelines and expert consensus (Delphi methodology) were systematically extracted and synthesized using strength of evidence rating criteria, in addition to recommendations on assessment procedures. Three sets were defined: a core set: strong evidence for validity, reliability, responsiveness and clinical utility AND recommended by at least two sources; an extended set: strong evidence OR recommended by at least two sources and a supplementary set: some evidence OR recommended by at least one of the sources. Results: In total, 12 measures (with primary focus on stroke) were included, encompassing body function and activity level of the International Classification of Functioning and Health. The core set recommended for clinical practice and research: Fugl-Meyer Assessment of Upper Extremity (FMA-UE) and Action Research Arm Test (ARAT); the extended set recommended for clinical practice and/or clinical research: kinematic measures, Box and Block Test (BBT), Chedoke Arm Hand Activity Inventory (CAHAI), Wolf Motor Function Test (WMFT), Nine Hole Peg Test (NHPT) and ABILHAND; the supplementary set recommended for research or specific occasions: Motricity Index (MI); Chedoke-McMaster Stroke Assessment (CMSA), Stroke Rehabilitation Assessment Movement (STREAM), Frenchay Arm Test (FAT), Motor Assessment Scale (MAS) and body-worn movement sensors. Assessments should be conducted at pre-defined regular intervals by trained personnel. Global measures should be applied within 24 h of hospital admission and upper limb specific measures within 1 week. Conclusions: The CAULIN recommendations for outcome measures and assessment procedures provide a clear, simple, evidence-based three-level structure for upper limb assessment in neurological rehabilitation. Widespread adoption and sustained use will improve quality of clinical practice and facilitate meta-analysis, critical for the advancement of technology-supported neurorehabilitation.Item HoMEcare aRm rehabiLItatioN (MERLIN): telerehabilitation using an unactuated device based on serious games improves the upper limb function in chronic stroke: telerehabilitation using an unactuated device based on serious games improves the upper limb function in chronic stroke(2021-03) Rozevink, Samantha G.; van der Sluis, Corry K.; Garzo, Ainara; Keller, Thierry; Hijmans, Juha M.; Tecnalia Research & Innovation; Medical TechnologiesHoMEcare aRm rehabiLItatioN (MERLIN) is an unactuated version of the robotic device ArmAssist combined with a telecare platform. Stroke patients are able to train the upper limb function using serious games at home. The aim of this study is to investigate the effect of MERLIN training on the upper limb function of patients with unilateral upper limb paresis in the chronic phase of stroke (> 6 months post stroke). Patients trained task specific serious games for three hours per week during six weeks using an unactuated version of a robotic device. Progress was monitored and game settings were tailored through telerehabilitation. Measurements were performed six weeks pre-intervention (T0), at the start (T1), end (T2) and six weeks post-intervention (T3). Primary outcome was the Wolf Motor Function Test (WMFT). Secondary outcomes were other arm function tests, quality of life, user satisfaction and motivation.Item Human-Centered Design Components in Spiral Model to Improve Mobility of Older Adults(Springer, Cham, 2019) Khakurel, Jayden; Porras, Jari; Melkas, Helinä; Garzo, Ainara; Medical TechnologiesAs humans grow older, their cognitive needs change more frequently due to distal and proximal life events. Designers and developers need to come up with better designs that integrate older users’ needs in a short period of time with more interaction with the users. Therefore, the positioning of human end users in the center of the design itself is not the key to the success of design artifacts while designing applications for older adults to use a smartphone as a promising tool for journey planner while using public transportation. This study analyzed the use of human-centered design (HCD) components, the spiral model, and the design for failure (DfF) approach to improve the interactions between older users and designers/developers in gathering usability needs in the concept stage and during the development of the app with short iterative cycles. To illustrate the importance of the applied approach, a case study with particular focus on older adults is presented.Item On the design of EEG-based movement decoders for completely paralyzed stroke patients(2018-11-20) Spüler, Martin; López-Larraz, Eduardo; Ramos-Murguialday, Ander; Tecnalia Research & Innovation; Medical TechnologiesBackground: Brain machine interface (BMI) technology has demonstrated its efficacy for rehabilitation of paralyzed chronic stroke patients. The critical component in BMI-training consists of the associative connection (contingency) between the intention and the feedback provided. However, the relationship between the BMI design and its performance in stroke patients is still an open question. Methods: In this study we compare different methodologies to design a BMI for rehabilitation and evaluate their effects on movement intention decoding performance. We analyze the data of 37 chronic stroke patients who underwent 4 weeks of BMI intervention with different types of association between their brain activity and the proprioceptive feedback. We simulate the pseudo-online performance that a BMI would have under different conditions, varying: (1) the cortical source of activity (i.e., ipsilesional, contralesional, bihemispheric), (2) the type of spatial filter applied, (3) the EEG frequency band, (4) the type of classifier; and also evaluated the use of residual EMG activity to decode the movement intentions. Results: We observed a significant influence of the different BMI designs on the obtained performances. Our results revealed that using bihemispheric beta activity with a common average reference and an adaptive support vector machine led to the best classification results. Furthermore, the decoding results based on brain activity were significantly higher than those based on muscle activity. Conclusions: This paper underscores the relevance of the different parameters used to decode movement, using EEG in severely paralyzed stroke patients. We demonstrated significant differences in performance for the different designs, which supports further research that should elucidate if those approaches leading to higher accuracies also induce higher motor recovery in paralyzed stroke patients.Item Prospect of smart home-based detection of subclinical depressive disorders(IEEE, 2011) Leon, Enrique; Montejo, Manuel; Dorronsoro, Iñigo; Tecnalia Research & Innovation; SGAging is associated with changing physical, social, emotional, and financial circumstances that are often new to the elder. The affective distress that stems from coping with them could play a negative effect on the health of seniors and lead to severe cases of depression, an emotional disorder that could lead to fatal consequences. The combination of novel methods of ambulatory detection of emotional states, body area networks providing information from numerous bodily parameters, and sophisticated pervasive technologies offers new possibilities in the detection of and intervention in cases of subclinical depression. In this paper we present the technical aspects and rationale behind systems that can use emotional valence monitoring to quantify prolonged emotional negativity and identify the activities associated with such negativity. We argue that this as a suitable mechanism to facilitate ambient-mediated self-regulation and remote peer-support.Item Robot-supported assessment of balance in standing and walking(2017-08-14) Shirota, Camila; van Asseldonk, Edwin; Matjačić, Zlatko; Vallery, Heike; Barralon, Pierre; Maggioni, Serena; Buurke, Jaap H.; Veneman, Jan F.; Tecnalia Research & Innovation; Medical TechnologiesClinically useful and efficient assessment of balance during standing and walking is especially challenging in patients with neurological disorders. However, rehabilitation robots could facilitate assessment procedures and improve their clinical value. We present a short overview of balance assessment in clinical practice and in posturography. Based on this overview, we evaluate the potential use of robotic tools for such assessment. The novelty and assumed main benefits of using robots for assessment are their ability to assess 'severely affected' patients by providing assistance-as-needed,as well as to provide consistent perturbations during standing and walking while measuring the patient's reactions. We provide a classification of robotic devices on three aspects relevant to their potential application for balance assessment: 1) how the device interacts with the body, 2) in what sense the device is mobile, and 3) on what surface the person stands or walks when using the device. As examples, nine types of robotic devices are described, classified and evaluated for their suitability for balance assessment. Two example cases of robotic assessments based on perturbations during walking are presented. We conclude that robotic devices are promising and can become useful and relevant tools for assessment of balance in patients with neurological disorders, both in research and in clinical use. Robotic assessment holds the promise to provide increasingly detailed assessment that allows to individually tailor rehabilitation training, which may eventually improve training effectiveness.Item Smart Protocols for Physical Therapy of Foot Drop Based on Functional Electrical Stimulation: A Case Study: A case study(2021-04-26) Malešević, Jovana; Konstantinović, Ljubica; Bijelić, Goran; Malešević, Nebojša; SG; MercadoFunctional electrical stimulation (FES) is used for treating foot drop by delivering electrical pulses to the anterior tibialis muscle during the swing phase of gait. This treatment requires that a patient can walk, which is mostly possible in the later phases of rehabilitation. In the early phase of recovery, the therapy conventionally consists of stretching exercises, and less commonly of FES delivered cyclically. Nevertheless, both approaches minimize patient engagement, which is inconsistent with recent findings that the full rehabilitation potential could be achieved by an active psycho-physical engagement of the patient during physical therapy. Following this notion, we proposed smart protocols whereby the patient sits and ankle movements are FES-induced by self-control. In six smart protocols, movements of the paretic ankle were governed by the non-paretic ankle with different control strategies, while in the seventh voluntary movements of the paretic ankle were used for stimulation triggering. One stroke survivor in the acute phase of recovery participated in the study. During the therapy, the patient’s voluntary ankle range of motion increased and reached the value of normal gait after 15 sessions. Statistical analysis did not reveal the differences between the protocols in FES-induced movements.Item Unravelling the effect of data augmentation transformations in polyp segmentation(2020-12) Sánchez-Peralta, Luisa F.; Picón, Artzai; Sánchez-Margallo, Francisco M.; Pagador, J. Blas; COMPUTER_VISIONPurpose: Data augmentation is a common technique to overcome the lack of large annotated databases, a usual situation when applying deep learning to medical imaging problems. Nevertheless, there is no consensus on which transformations to apply for a particular field. This work aims at identifying the effect of different transformations on polyp segmentation using deep learning. Methods: A set of transformations and ranges have been selected, considering image-based (width and height shift, rotation, shear, zooming, horizontal and vertical flip and elastic deformation), pixel-based (changes in brightness and contrast) and application-based (specular lights and blurry frames) transformations. A model has been trained under the same conditions without data augmentation transformations (baseline) and for each of the transformation and ranges, using CVC-EndoSceneStill and Kvasir-SEG, independently. Statistical analysis is performed to compare the baseline performance against results of each range of each transformation on the same test set for each dataset. Results: This basic method identifies the most adequate transformations for each dataset. For CVC-EndoSceneStill, changes in brightness and contrast significantly improve the model performance. On the contrary, Kvasir-SEG benefits to a greater extent from the image-based transformations, especially rotation and shear. Augmentation with synthetic specular lights also improves the performance. Conclusion: Despite being infrequently used, pixel-based transformations show a great potential to improve polyp segmentation in CVC-EndoSceneStill. On the other hand, image-based transformations are more suitable for Kvasir-SEG. Problem-based transformations behave similarly in both datasets. Polyp area, brightness and contrast of the dataset have an influence on these differences.Item A usability study in patients with stroke using MERLIN, a robotic system based on serious games for upper limb rehabilitation in the home setting(2021-02) Guillén-Climent, Silvia; Garzo, Ainara; Muñoz-Alcaraz, María Nieves; Casado-Adam, Pablo; Arcas-Ruiz-Ruano, Javier; Mejías-Ruiz, Manuela; Mayordomo-Riera, Fernando Jesús; Tecnalia Research & Innovation; Medical TechnologiesNeuroscience and neurotechnology are transforming stroke rehabilitation. Robotic devices, in addition to telerehabilitation, are increasingly being used to train the upper limbs after stroke, and their use at home allows us to extend institutional rehabilitation by increasing and prolonging therapy. The aim of this study is to assess the usability of the MERLIN robotic system based on serious games for upper limb rehabilitation in people with stroke in the home environment.