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Publications

Publications by BIO

2022

Moodbuster (E-MODEL): The feasibility of digital cognitive behavioural therapy (CBT) for depressed older adults: Study protocol of two pilot feasibility studies (Preprint)

Authors
Amarti, K; Schulte, MHJ; Kleiboer, AM; van Genugten, CR; Oudega, M; Sonnenberg, C; Gonçalves, GC; Rocha, A; Riper, H;

Publication

Abstract
BACKGROUND

Internet-based interventions can be effective in the treatment of depression. However, internet-based interventions for older adults with depression are scarce and little is known about their feasibility and effectiveness.

OBJECTIVE

To present the design of two studies aiming to assess the feasibility of internet-based cognitive behavioural treatment (CBT) for older adults with depression (E-MODEL). We will assess the feasibility of an online, guided version of E-MODEL among depressed older adults from the general population as well as the feasibility of a blended format (combining integrated face-to-face sessions and internet-based modules) in specialised mental health care outpatient clinic.

METHODS

A single-group pretest-posttest design will be applied for both settings. The primary outcome of the studies will be feasibility in terms of (a) acceptance and satisfaction (measured with the Client Satisfaction Questionnaire-8, (b) usability (measured with the System Usability Scale) and (c) engagement (measured with the Twente Engagement with Ehealth Technologies Scale). Secondary outcomes include: (a) severity of depressive symptoms (PHQ-8), (b) participant and therapist experience with the digital technology (with the use of qualitative interviews), (c) working alliance between patient and practitioner (from both perspectives; WAI-SF), (d) technical alliance between patient and the platform (WAI-TECH-SF) and (e) uptake in terms of attemped and completed modules. N=30 older adults with mild to moderate depressive symptoms (score between 5 and 11 as measured with the Geriatric Depression Scale 15) will be recruited from the general population. N=15 older adults with moderate to severe depressive symptoms (GDS-15 score between 8 and 15) will be recruited from a specialised mental health care outpatient clinic.

RESULTS

A mixed-method approach of quantitative and qualitative analyses will be adopted. Both the primary and secondary outcomes will be additionally explored with an individual semistructured interview and synthesized descriptively. Descriptive statistics (Mean and SDs) will be used to examine the primary and secondary outcome measures. Within-group depression severity will be analyzed using a two-tailed paired sample t-test to investigate differences between time points. The interviews will be recorded and analyzed using thematic analysis.

CONCLUSIONS

The results of this pilot study will show whether this platform is feasible among the older adult population in a blended and guided format in the two settings as well as a first exploration of the size of the effect of E-MODEL in terms of decrease of depressive symptoms.

2022

Invasive and minimally invasive optical detection of pigment accumulation in brain cortex

Authors
Oliveira, LR; Gonçalves, TM; Pinheiro, MR; Fernandes, LE; Martins, IS; Silva, HF; Oliveira, HP; Tuchin, VV;

Publication
Journal of Biomedical Photonics and Engineering

Abstract
The estimation of the spectral absorption coefficient of biological tissues provides valuable information that can be used in diagnostic procedures. Such estimation can be made using direct calculations from invasive spectral measurements or though machine learning algorithms based on noninvasive or minimally invasive spectral measurements. Since in a noninvasive approach, the number of measurements is limited, an exploratory study to investigate the use of artificial generated data in machine learning techniques was performed to evaluate the spectral absorption coefficient of the brain cortex. Considering the spectral absorption coefficient that was calculated directly from invasive measurements as reference, the similar spectra that were estimated through different machine learning approaches were able to provide comparable information in terms of pigment, DNA and blood contents in the cortex. The best estimated results were obtained based only on the experimental measurements, but it was also observed that artificially generated spectra can be used in the estimations to increase accuracy, provided that a significant number of experimental spectra are available both to generate the complementary artificial spectra and to estimate the resulting absorption spectrum of the tissue. © 2022 Journal of Biomedical Photonics & Engineering. © J-BPE.

2022

Towards Machine Learning-Aided Lung Cancer Clinical Routines: Approaches and Open Challenges

Authors
Silva, F; Pereira, T; Neves, I; Morgado, J; Freitas, C; Malafaia, M; Sousa, J; Fonseca, J; Negrao, E; de Lima, BF; da Silva, MC; Madureira, AJ; Ramos, I; Costa, JL; Hespanhol, V; Cunha, A; Oliveira, HP;

Publication
JOURNAL OF PERSONALIZED MEDICINE

Abstract
Advancements in the development of computer-aided decision (CAD) systems for clinical routines provide unquestionable benefits in connecting human medical expertise with machine intelligence, to achieve better quality healthcare. Considering the large number of incidences and mortality numbers associated with lung cancer, there is a need for the most accurate clinical procedures; thus, the possibility of using artificial intelligence (AI) tools for decision support is becoming a closer reality. At any stage of the lung cancer clinical pathway, specific obstacles are identified and motivate the application of innovative AI solutions. This work provides a comprehensive review of the most recent research dedicated toward the development of CAD tools using computed tomography images for lung cancer-related tasks. We discuss the major challenges and provide critical perspectives on future directions. Although we focus on lung cancer in this review, we also provide a more clear definition of the path used to integrate AI in healthcare, emphasizing fundamental research points that are crucial for overcoming current barriers.

2022

Insomnia and nightmare profiles during the COVID-19 pandemic in Portugal: characterization and associated factors

Authors
Goncalves M.; Henriques A.; Costa A.R.; Correia D.; Severo M.; Severo M.; Lucas R.; Lucas R.; Barros H.; Santos A.C.; Ribeiro A.I.; Rocha A.; Lopes C.; Correia D.; Ramos E.; Gonçalves G.; Barros H.; Araújo J.; Talih M.; Tavares M.; Lunet N.; Meireles P.; Duarte R.; Camacho R.; Fraga S.; Correia S.; Silva S.; Leão T.;

Publication
SLEEP MEDICINE

Abstract
Objective/background: To describe and characterize insomnia symptoms and nightmare profiles in Portugal during the first six weeks of a national lockdown due to COVID-19. Patients/methods: An open cohort study was conducted to collect information of the general population during the first wave of SARS-CoV-2/COVID-19 pandemic in Portugal. We analyzed data from 5011 participants (>= 16 years) who answered a weekly questionnaire about their well-being. Two questions about the frequency of insomnia and nightmares about COVID-19 were consecutively applied during six weeks (March-May 2020). Latent class analysis was conducted and different insomnia and nightmare profiles were identified. Associations between individual characteristics and both profiles were estimated using odds ratios (ORs) and 95% confidence intervals (CI). Results: Five insomnia (No insomnia, Stable-mild, Decreasing-moderate, Stable-severe, Increasing-severe) and three nightmares profiles (Stable-mild, Stable-moderate, Stable-severe) were identified. Being female, younger, perceiving their income as insufficient and feelings of fear towards COVID-19 were associated with higher odds of insomnia (Women: OR = 6.98 95%CI: 4.18-11.64; >= 60 years: OR = 0.30 95%CI: 0.18-0.53; Insufficient income: adjusted OR (aOR) = 8.413 95% CI: 3.93-16.84; Often presenting fear of being infected with SARS-CoV-2 infection: aOR = 9.13 95%CI: 6.36-13.11), and nightmares (Women: OR = 2.60 95%CI: 1.74-3.86; >= 60 years: OR = 0.45 95%CI: 0.28-0.74; Insufficient income: aOR = 2.60 95%CI: 1.20e5.20; Often/almost always presenting fear of being infected with SARS-CoV-2 infection: aOR = 6.62 95%CI: 5.01-8.74). Having a diagnosis of SARS-CoV-2 virus infection was associated with worse patterns of nightmares about the pandemic. Conclusions: Social and psychological individual factors are important characteristics to consider in the developmentof therapeutic strategies to supportpeoplewithsleep problems during the COVID-19 pandemic.

2022

A data mining approach to classify serum creatinine values in patients undergoing continuous ambulatory peritoneal dialysis

Authors
Brito, C; Esteves, M; Peixoto, H; Abelha, A; Machado, J;

Publication
WIRELESS NETWORKS

Abstract
Continuous ambulatory peritoneal dialysis (CAPD) is a treatment used by patients in the end-stage of chronic kidney diseases. Those patients need to be monitored using blood tests and those tests can present some patterns or correlations. It could be meaningful to apply data mining (DM) to the data collected from those tests. To discover patterns from meaningless data, it becomes crucial to use DM techniques. DM is an emerging field that is currently being used in machine learning to train machines to later aid health professionals in their decision-making process. The classification process can found patterns useful to understand the patients' health development and to medically act according to such results. Thus, this study focuses on testing a set of DM algorithms that may help in classifying the values of serum creatinine in patients undergoing CAPD procedures. Therefore, it is intended to classify the values of serum creatinine according to assigned quartiles. The better results obtained were highly satisfactory, reaching accuracy rate values of approximately 95%, and low relative absolute error values.

2022

Attention-driven Spatial Transformer Network for Abnormality Detection in Chest X-Ray Images

Authors
Rocha, J; Pereira, SC; Pedrosa, J; Campilho, A; Mendonca, AM;

Publication
2022 IEEE 35TH INTERNATIONAL SYMPOSIUM ON COMPUTER-BASED MEDICAL SYSTEMS (CBMS)

Abstract
Backed by more powerful computational resources and optimized training routines, deep learning models have attained unprecedented performance in extracting information from chest X-ray data. Preceding other tasks, an automated abnormality detection stage can be useful to prioritize certain exams and enable a more efficient clinical workflow. However, the presence of image artifacts such as lettering often generates a harmful bias in the classifier, leading to an increase of false positive results. Consequently, healthcare would benefit from a system that selects the thoracic region of interest prior to deciding whether an image is possibly pathologic. The current work tackles this binary classification exercise using an attention-driven and spatially unsupervised Spatial Transformer Network (STN). The results indicate that the STN achieves similar results to using YOLO-cropped images, with fewer computational expenses and without the need for localization labels. More specifically, the system is able to distinguish between normal and abnormal CheXpert images with a mean AUC of 84.22%.

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