FCT: DF - Dissertações de Mestrado
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- Development of a Conductive Bioink for 3D Bioprintig of Cardiac TissuesPublication . Kerfah, Lydia; Almeida, Henrique; Borges, JoãoOne of the leading causes of death we currently face is cardiovascular disease. Myocardial infarc- tion is an acute event known for damaging cardiac tissue due to the poor regeneration capacity of cardi- omyocytes. The most common therapeutic method is heart transplantation; however, it presents several limitations, such as donor shortage and the need for immunosuppressive therapy to avoid transplant rejection. To address these challenges, the field of tissue engineering has highlighted the need for bio- engineered scaffolds capable of mimicking the electrical and mechanical properties of the heart and promoting its regeneration. This project focuses on developing a conductive bioink composed of meth- acrylated gelatine (GelMA), hyaluronic acid (HA), and MXenes (MX) for the three-dimensional (3D) bioprinting of cardiac tissues. HA is known for its strong influence on cell growth, water retention, and biocompatibility. At the same time, GelMA provides structural support, and MXenes enhance the me- chanical and conductive properties of the construct. Combining these materials aims to create a bioink capable of replicating cardiac tissue's electrical properties and cellular functions while maintaining the necessary mechanical stability for cardiac tissue engineering. Most of the results demonstrate that the bioinks containing HA in their composition exhibited better printability and supported minimal cell viability, achieving a balance between elasticity and viscosity, and high conductivity (0.39469 S/m e 0.38378 S/m). The inclusion of MXenes had a significant impact on the mechanical strength and stiff- ness of the hydrogels, confirmed by the higher value of Young's Modulus (4914.90 ± 52.24 kPa). The development of this innovative bioink could lead to the creation of functional conductive scaffolds, which would significantly advance cardiac tissue engineering, particularly in the treatment of myocar- dial infarction and other heart-related conditions.
- Exploring excitation-inhibition ratios in patients with epilepsy using scalp EEG recordings and mean-field modellingPublication . Lopes, Maria Leonor Pestana Gonçalves da Costa; Putten, Michel; Pereira, CarlaEpilepsy diagnosis primarily relies on identifying Interictal Epileptiform Discharges (IED) in scalp Electroencephalogram (EEG) recordings. However, the absence of visible IED does not preclude the disorder. In fact, only 30–55% of patients show IED on their first EEG. Most automated epilepsy research has focused on IED detection, leaving a gap in methods for distinguishing visually normal (IED-free) EEGs of epilepsy patients from those of Healthy Controls (HC). To address this gap, this study aims to explore whether normal-looking EEGs from patients with Left Temporal Lobe Epilepsy (LTE) contain subtle, imperceptible features of altered brain dynamics related to imbalance in Excitatory activity (E) and Inhibitory activity (I) and whether these features can be detected automatically, potentially offering new biomarkers beyond conventional visual analysis. To achieve this, spectral analysis techniques, including band-specific power, power ratios, and aperiodic activity were applied to EEG recordings to identify potential frequency- domain changes across brain regions. Additionally, a physiologically grounded corticothalamic mean-field model was used to estimate parameters reflecting cortical–thalamic interactions. Furthermore, a Deep Learning (DL) pipeline based on Convolutional Neural Networks (CNN) was trained solely on raw, IED-free EEG data to classify recordings from LTE patients and HC, evaluating whether the two groups could be distinguished without reliance on overt epileptiform activity. The findings suggested minor alterations in both the spectral analysis outcomes and the estimated parameters. Nonetheless, all parameters remained within physiological ranges, and none of the observed effects reached statistical significance. On another hand, despite being preliminary, the CNN achieved above- chance performance (accuracy ≈ 0.68), suggesting the potential presence of discriminative information in the data. Therefore, warranting further investigation. Overall, this study contributes to the search for reliable, non-invasive biomarkers for epilepsy, with the ultimate goal of enhancing diagnosis methods.
- ANÁLISE Multimodal do Controlo PosturalPublication . Valente, Laura Rebelo; Quaresma, Cláudia; Quintão, CarlaO estudo do movimento é essencial para a compreensão do comportamento e das funções motoras humanas, permitindo distinguir padrões saudáveis de patológicos. Neste âmbito, abordagens multimodais com integração de múltiplos sensores, têm-se revelado promissoras, possibilitando um estudo holístico do movimento. Contudo, além dos de- safios de integração, a falta de padronização de protocolos experimentais e o reduzido tamanho de bases normativas ainda limitam a generalização de muitos estudos. O presente estudo procurou adaptar metodologias de trabalhos anteriores para caracte- rizar o padrão de movimento de 40 participantes saudáveis, integrando dados fisiológicos, cinéticos e cinemáticos, recolhidos através de uma t-shirt inteligente Hexoskin, de uma plataforma de força PLUX e de sensores inerciais e de eletromiografia. Além da análise contínua dos sinais, foram extraídos parâmetros específicos, analisados tanto na coerência do seu comportamento ao longo do protocolo como nas relações estabelecidas entre si. Incluiu-se ainda uma aplicação ilustrativa da utilização deste protocolo para comparar padrões de movimento entre dois grupos etários distintos. O protocolo e a metodologia deste trabalho revelaram-se factíveis, tendo a integração de todos os sistemas de medida sido feita com êxito. Comprovou-se a correlação dos parâmetros, que, mesmo que moderadas, associa a instabilidade do centro de pressão à variabilidade angular do centro de massa e de parâmetros fisiológicos, em situações estáticas e dinâmicas. Adicionalmente, a ativação muscular observada refletiu um padrão de co-contração de músculos homólogos (predominante nos músculos Multífidos) e maior ativação dos músculos contralaterais ao movimento nas tarefas de alcance.
- Analysing Gastric and Intestinal Myoelectric Activity Across Digestive States with Non-Invasive ElectrogastrographyPublication . Sousa, Sara Raquel Gomes; Gamboa, HugoElectrogastrography (EGG) is a non-invasive technique used for recording the myoelectri- cal activity of the stomach and intestines. Despite its clinical potential, its application as a diagnostic tool for gastrointestinal pathologies remains limited, in part due to the lack of standardized methodologies and limited understanding of its signal’s characteristics across physiological states. This thesis aims to contribute to expanding scientific knowl- edge of EGG signals by studying their behaviour before and after food ingestion (pre- and post-prandial states), identifying distinctive patterns and relevant features. Four-channel abdominal recordings were acquired from thirteen healthy subjects using a wearable SmartBelt device during fasting and fed states. Signals were decomposed using Empirical Mode Decomposition (EMD) to isolate gastric and intestinal components, followed by extraction of time- and frequency-domain features. Feature ranking and selection highlighted the most informative features, enhancing classification models by improving performance, generalization, and reducing the risk of overfitting. Machine Learning (ML) classifiers, including Support Vector Machine (SVM), Random Forest (RF), K-Nearest Neighbors (KNN) and Logistic Regression (LR), were trained using a file-based split and 5-fold cross-validation. The RF model achieved the highest accuracy of 93.0% (precision = 0.81/1.00, recall = 1.00/0.90, F1-score = 0.90/0.95 for pre-/post- prandial). KNN followed with an accuracy of 88.0%, and SVM and LR both reached an accuracy of 83.0%. By focusing on the physiological differences reflected in EGG signals and evaluating the models’ performance in classifying them, this study provides new insights to the current available information regarding gastrointestinal myoelectrical activity. These findings highlight the potential of EGG signal in capturing physiological differences across digestive states and support the development of non-invasive methods for diagnosing gastrointestinal conditions.
- Characterization of radiation beams for metrology in Radiological Protection by X-ray spectrometryPublication . Karim, Maria; Kling, Andreas; Cruz, JoãoEnsuring accurate metrological verification of ionizing radiation monitoring equipment is a crucial task in the area of radiological protection of exposed workers and patients subjected to ionizing radiation. This work aims to enhance the metrological verification of X-ray detection equipment at the Portuguese Metrology Laboratory of Ionizing Radiation (Laboratório de Metrologia de Radiações Ionizantes, LMRI), located at the Technological and Nuclear Campus of Instituto Superior Técnico, University of Lisbon. The study explores the application of X-ray spectrometry employing cadmium telluride detectors to perform systematic studies on the properties of the X-ray beams available at LMRI allowing for the extension of calibration conditions beyond those currently available. For this purpose, the energy spectra of the beams were measured as a function of the distance from the source, tube current and acceleration voltage. Monte Carlo methods were employed to assess the detector’s response to monoenergetic radiation and to build a response function matrix for reconstructing continuous X-ray spectra from the measured data using deconvolution algorithms. As an additional asset, methods for determining the spectral endpoint were implemented based on a machine learning algorithm offering an alternative approach to precisely determine the acceleration potential of the X-ray tube.
- Deteção de traços moleculares de triclosan em meios líquidos complexosPublication . Cunha, Gonçalo Teófilo Alves da; Henriques, Célia; Pires, Ana; Raposo, Maria de FátimaO triclosan (TCS) é um agente antibacteriano utilizado em produtos farmacêuticos e de hi- giene pessoal (PPCP), cada vez mais recorrente em reservas de água, o que representa riscos para a saúde pública. Torna-se essencial o desenvolvimento de métodos de deteção de TCS para monitorização. Devido às propriedades lipofílicas do TCS, o objetivo desta tese consis- tiu na criação de um sensor baseado em filmes ultrafinos de lípidos, capaz de detetar quan- tidades vestigiais de TCS em meios aquosos complexos. Para concretizar este objetivo, este trabalho foi dividido em duas partes: Na primeira, estudou-se a interação da molécula de TCS com uma bicamada lipídica de 1-palmitoyl-2-oleoyl-sn-glycero-3-phosphocholine (POPC) utilizando simulação de Dinâmica Molecular (MD). Verificou-se a fácil inserção da molécula de TCS na bicamada, preferencialmente entre 9 e 11 Å. O estudo do ângulo de inserção indicou liberdade de rotação, evitando orientações em que os cloros mais periféri- cos se alinhavam em paralelo com a normal da bicamada. Na segunda parte, desenvolve- ram-se sensores baseados em filmes lipídicos e analisaram-se as suas características elétricas em função da concentração de TCS em água da torneira. Suportes sólidos com elétrodos interdigitados de ouro, revestidos por filmes finos preparados por camada-por-camada uti- lizando o polieletrólito hidrocloreto de polialilamina (PAH) e o fosfolípido 1,2-Dipalmitoyl- sn-glycero-3-phosphoglycerol (DPPG), detetaram TCS em amostras de água da torneira do- pada utilizando espectroscopia de impedância como método de deteção. O LOD do sensor foi de 4.23 × 10⁻11 M e detetou-se deriva devido à possível degradação dos filmes. Os dados foram analisados por método de análise de componentes principais (PCA), que demonstra- ram a capacidade de distinguir claramente as amostras com diferentes concentrações de TCS num intervalo linear de 10-12 a 10-5 M. O PCA feito a partir dos valores de ângulo de fase e módulo de impedância, apresentou uma sensibilidade de 3.51 ± 0.21, sendo possível cal- cular a concentração de TCS em função de F1 através da calibração 𝐶 = 10𝐹1/3.51. Estes resultados permitem concluir que filmes lipídicos apresentam elevado potencial para o de- senvolvimento de uma língua eletrónica que permita detetar TCS em água
- Development of a Kinematic Data Analysis Pipeline for Patients with Lumbar Spinal StenosisPublication . Amaral, Afonso Miguel Abrantes de Brito; Mendes, César; Silva, LuísLumbar spinal stenosis (LSS) is a degenerative condition characterized by narrowing of the lumbar spinal canal, leading to pain typically exacerbated by standing and walking. Depending on severity, management ranges from conservative care to surgery. In practice, however, treatment decisions often rely heavily on subjective information. Similar challenges exist across many other pathologies, where objective biomechanical data could improve medical decision-making. Inertial measurement units (IMUs) provide a non-invasive and accessible means to capture patient gait kinematics, enabling more objective assessments. Yet, current gait analysis software is limited, most closed-source solutions output only tabular parameters, restricting transparency, reproducibility, and, critically preventing access to full time-series data required for advanced analyses such as feature engineering for pathology stratification. To address these limitations, the aim of this thesis was to develop and validate an open-source pipeline that transforms raw IMU signals into clinically interpretable gait parameters while preserving access to the underlying time-series. To achieve this, an automated workflow was implemented to process accelerometer, gyroscope, and magnetometer signals from 15 IMUs through preprocessing, sensor fusion, and biomechanical modeling in OpenSim. Joint-angle Validation was performed against an optical motion capture reference. The system successfully generated reliable lower-limb kinematics, with strong agreement against motion capture, while upper-limb kinematics showed inconsistent validity. From validated joints, several features discriminated healthy from patient groups, with large effect sizes in more severe cases. Although discrimination between conservative and surgical patients was limited, the framework proved robust for detecting pathological deviations in gait. This work delivers a transparent and reproducible end-to-end system that enables objective and explainable gait assessments, while allowing advanced feature extraction for pathology stratification. It demonstrates the clinical potential of IMU-based gait analysis to support decision-making, enhance treatment precision, and improve patient outcomes.
- Minimum Viable Dataset: Towards Data-efficient Machine LearningPublication . Capelo, Bárbara Sofia Francisco; Gamboa, Hugo; Folgado, DuarteHealthcare generates petabytes of data each year, creating unique avenues for scientific progress, but also significant computational challenges for training Deep Learning models. Although performance has long followed the “scale-is-everything” paradigm, where larger datasets and models yield better results, emerging evidence shows that carefully curated smaller datasets can achieve comparable performance far more efficiently. This dissertation investigates how to compress large collections of healthcare data into smaller, efficient datasets that preserve task-relevant knowledge while easing com- putational demands. To this end, it introduces the Minimum Viable Dataset (MVD), a minimal yet highly informative subset of real data, optionally enriched with synthetic samples, designed to retain essential knowledge while substantially reducing training requirements. Previous work has focused on developing and benchmarking data summa- rization methods using performance metrics alone, which often overlook dataset diversity and representativeness. This dissertation takes a complementary approach, using data quality metrics to guide both the creation and evaluation of summarization methods, providing a more principled path toward efficient and reliable model training. The main contributions are threefold: (1) the formalization of the Minimum Viable Dataset (MVD) concept and its taxonomy; (2) a systematic study of data quality metrics for evaluating subsets; and (3) a hybrid strategy that leverages coreset-informed subsets to initialize dataset distillation methods. These approaches were primarily developed and validated on a dataset of hematological cell images, and further assessed across additional standardized biomedical image collections. The results show that MVDs provide a reproducible framework for efficient dataset construction and yield design directions for future dataset summarization methods. By retaining essential knowledge while reducing training requirements, MVDs are positioned as a step toward scalable and resource-conscious AI in healthcare, with particular relevance for low- and middle-income countries where computational resources are limited.
- Study of phase modulation techniques to concentrate laser radiation in turbid media for phototherapyPublication . Cruz, Ricardo Manuel Ramos; Coelho, João; Vieira, PedroDiagnostic and therapeutic techniques leveraging electromagnetic radiation as their pri- mary effector are abundant in the context of biomedical engineering. Its interaction mecha- nisms with matter though are often difficult to control, with this unpredictability stemming mainly from scattering phenomena for biological tissues. This study aims to address this through iterative feedback-based phase-only wavefront modulation. As such, an experimental testing procedure where two different feedback mech- anisms are evaluated is implemented. For both setups an 808 nm wavelength laser beam is used, with its already lower absorption and scattering characteristics for biological tissues mak- ing it relevant for potential applications. Also common to both experimental setups are the two different tissue simulating optical phantoms considered. Set to mimic the optical proper- ties of 1.1 mm thick human skin, they were composed of a mixture of agar, distilled water and milk, with the concentration of milk setting the scattering behavior of the turbid samples. The first feedback signal to be tested was gathered by a NIR camera placed behind the phantoms. The latter received the transmitted laser beam's profile after it had been modulated by a spatial light modulator (SLM). This feedback is then sent to the computer and processed in MATLAB by the iterative optimization algorithms implemented. These will finally send new phase masks to the SLM and continue this feedback loop until a desired number of iterations is reached. Given the placing of a sensor behind the tissue is not feasible for in-vivo applica- tions, in view of making the present study hold its relevance for such frameworks, the use of backscattered radiation as feedback to the modulation procedure was scrutinized. For both feedback mechanisms and scattering environments the population-based op- timization algorithm largely provided the best results in comparison with its single solution counterparts. No discernable differences were reported between different turbidity levels, proving the reliability of both techniques across the reported human skin scattering range. The backscattered feedback, although having shown to provide a noisier setting, with this remain- ing largely unaddressed given it was inherent to the available material, it nevertheless proved to be a valid framework for electromagnetic wave propagation management in the absorption and scattering regimes found in biological tissue.
- Concept Learning: Understanding and Extracting Domain-specific concepts in medical ObservationsPublication . Silva, Matilde Gonçalinho; Gamboa, Hugo; Carreiro, AndréIn clinical environments, high-risk decisions require trust and understanding, which can hinder the use of high-performance models such as deep neural networks due to their complexity and opacity. Despite the development of numerous techniques aimed at enhancing interpretability and transparency, the explanations provided are not always human-interpretable and remain underexplored as a means of improving downstream tasks. Furthermore, clinical data often faces the challenge of limited annotated data, despite a large volume of unlabeled data. This study aims to extract human-interpretable concepts from X-ray images and respective reports using multimodal learning with self-supervision, which allows learning directly from the input data without annotations. Moreover, the objective is to enhance the interpretability and performance in clinical tasks. Therefore, the work integrates a vision language model with self-supervision techniques to extract domain-specific textual concepts from the joint representation of X-ray images and reports, while also investigating changes in the model framework to improve the alignment between the concepts and image features. Finally, these extracted concepts are incorporated as features into an X-ray image classification model to assess their impact on performance. A key contribution is the development of a novel dataset generator with controllable ground truth concepts, providing a flexible and scalable tool to test concept-based ap- proaches and identifying appropriate methods for medical data. Additionally, the results demonstrate that the incorporation of large language models within self-supervised vision-language frameworks significantly enhances the extraction of concepts, leading to direct improvements in diagnostic classification performance and interpretability. These findings highlight the potential benefits of integrating multimodal frameworks with self-supervision for concept extraction, thereby establishing a foundation for more inter- pretable, transparent, and reliable artificial intelligence-driven healthcare solutions.