Connecting the Dots: Graph Neural Network Powered Ensemble and Classification of Medical Images

Deep learning models have demonstrated remarkable results for various computer vision tasks, including the realm of medical imaging. However, their application in the medical domain is limited due to the requirement for large amounts of training data, which can be both challenging and expensive to obtain. To mitigate this, pre-trained models have been fine-tuned on domain-specific data, but such an approach can suffer from inductive biases. Furthermore, deep learning models struggle to learn the relationship between spatially distant features and their importance, as convolution operations treat all pixels equally. Pioneering a novel solution to this challenge, we employ the Image Foresting Transform to optimally segment images into superpixels. These superpixels are subsequently transformed into graph-structured data, enabling the proficient extraction of features and modeling of relationships using Graph Neural Networks (GNNs). Our method harnesses an ensemble of three distinct GNN architectures to boost its robustness. In our evaluations targeting pneumonia classification, our methodology surpassed prevailing Deep Neural Networks (DNNs) in performance, all while drastically cutting down on the parameter count. This not only trims down the expenses tied to data but also accelerates training and minimizes bias. Consequently, our proposition offers a sturdy, economically viable, and scalable strategy for medical image classification, significantly diminishing dependency on extensive training data sets.Comment: Our code is available at https://github.com/aryan-at-ul/AICS_2023_submissio

Compact & Capable: Harnessing Graph Neural Networks and Edge Convolution for Medical Image Classification

Graph-based neural network models are gaining traction in the field of representation learning due to their ability to uncover latent topological relationships between entities that are otherwise challenging to identify. These models have been employed across a diverse range of domains, encompassing drug discovery, protein interactions, semantic segmentation, and fluid dynamics research. In this study, we investigate the potential of Graph Neural Networks (GNNs) for medical image classification. We introduce a novel model that combines GNNs and edge convolution, leveraging the interconnectedness of RGB channel feature values to strongly represent connections between crucial graph nodes. Our proposed model not only performs on par with state-of-the-art Deep Neural Networks (DNNs) but does so with 1000 times fewer parameters, resulting in reduced training time and data requirements. We compare our Graph Convolutional Neural Network (GCNN) to pre-trained DNNs for classifying MedMNIST dataset classes, revealing promising prospects for GNNs in medical image analysis. Our results also encourage further exploration of advanced graph-based models such as Graph Attention Networks (GAT) and Graph Auto-Encoders in the medical imaging domain. The proposed model yields more reliable, interpretable, and accurate outcomes for tasks like semantic segmentation and image classification compared to simpler GCNN

Effect of a task-shared, collaborative care psychosocial intervention to improve depressive symptomatology among older adults in socioeconomically deprived areas of Brazil (PROACTIVE):cluster randomised controlled trial

BACKGROUND: There is an urgent need to reduce the burden of depression among older adults in low-income and middle-income countries (LMICs). We aimed to evaluate the efficacy of a task-shared, collaborative care psychosocial intervention for improving recovery from depression in older adults in Brazil. METHODS: PROACTIVE was a pragmatic, two-arm, parallel-group, cluster-randomised controlled trial conducted in Guarulhos, Brazil. Primary care clinics (clusters) were stratified by educational level and randomly allocated (1:1) to either enhanced usual care alone (control group) or to enhanced usual care plus the psychosocial intervention (intervention group), which involved a 17-week psychosocial programme based on psychoeducation and behavioural activation approaches. Individuals approached for the initial screening assessment were selected randomly from a list of individuals provided by the Health Secretariat of Guarulhos. Face-to-face baseline assessments were conducted among adults aged 60 years or older registered with one of the primary care clinics and identified with clinically significant depressive symptomatology (9-item Patient Health Questionnaire [PHQ-9] score ≥10). Community health workers delivered the programme through home sessions, supported by a dedicated tablet application. Masking of clinic staff and community health workers who delivered the intervention was not feasible; however, research assistants conducting recruitment and follow-up assessments were masked to trial allocation. The primary outcome was recovery from depression (PHQ-9 score <10) at 8-month follow-up. All primary analyses were performed by intention to treat with imputed data. Adaptations to the protocol were made due to the COVID-19 pandemic; recruitment and intervention home sessions were stopped, and follow-up assessments were conducted by telephone. This trial is registered with the ISRCTN registry, ISRCTN57805470. FINDINGS: We identified 24 primary care clinics in Guarulhos that were willing to participate, of which 20 were randomly allocated to either the control group (ten [50%] clusters) or to the intervention group (ten [50%] clusters). The four remaining eligible clusters were kept as reserves. Between May 23, 2019, and Feb 21, 2020, 8146 individuals were assessed for eligibility, of whom 715 (8·8%) participants were recruited: 355 (49·7%) in the control group and 360 (50·3%) in the intervention group. 284 (80·0%) participants in the control group and 253 (70·3%) in the intervention group completed follow-up at 8 months. At 8-month follow-up, 158 (62·5%) participants in the intervention group showed recovery from depression (PHQ-9 score <10) compared with 125 (44·0%) in the control group (adjusted odds ratio 2·16 [95% CI 1·47–3·18]; p<0·0001). These findings were maintained in the complete case analysis. No adverse events related to the intervention were observed. INTERPRETATION: Although the COVID-19 pandemic altered delivery of the intervention, the low-intensity psychosocial intervention delivered mainly by non-mental health professionals was highly efficacious in improving recovery from depression in older adults in Brazil. Our results support a low-resource intervention that could be useful to reduce the treatment gap for depression among older people in other LMICs. FUNDING: São Paulo Research Foundation and Joint Global Health Trials (UK Department for International Development, Medical Research Council, and the Wellcome Trust)

Depressive and subthreshold depressive symptomatology among older adults in a socioeconomically deprived area in Brazil

Depressive and subthreshold depressive symptomatology are common but often neglected in older adults. / Objective: This study aimed to assess rates of depressive and subthreshold depressive symptomatology, and the characteristics associated, among older adults living in a socioeconomically deprived area of Brazil. / Methods: This study is part of the PROACTIVE cluster randomised controlled trial. 3356 adults aged 60+ years and registered in 20 primary health clinics were screened for depressive symptomatology with the Patient Health Questionnaire-9 (PHQ-9). Depressive status was classified according to the total PHQ-9 score and the presence of core depressive symptoms (depressed mood and anhedonia) as follows: no depressive symptomatology (PHQ-9 score 0–4, or 5–9 but with no core depressive symptom); subthreshold depressive symptomatology (PHQ-9 score 5–9 and at least one core depressive symptom); and depressive symptomatology (PHQ-9 score ≥ 10). Sociodemographic information and self-reported chronic conditions were collected. Relative risk ratios and 95% CIs were obtained using a multinomial regression model. / Results: Depressive and subthreshold depressive symptomatology were present in 30% and 14% of the screened sample. Depressive symptomatology was associated with female gender, low socioeconomic conditions and presence of chronic conditions, whereas subthreshold depressive symptomatology was only associated with female gender and having hypertension. / Conclusions: Depressive and subthreshold depressive symptomatology is highly prevalent in this population registered with primary care clinics. Strategies managed by primary care non-mental health specialists can be a first step for improving this alarming and neglected situation among older adults

Incremental prognostic value of hybrid [15O]H2O positron emission tomography-computed tomography: combining myocardial blood flow, coronary stenosis severity, and high-risk plaque morphology

AimsThis study sought to determine the prognostic value of combined functional testing using positron emission tomography (PET) perfusion imaging and anatomical testing using coronary computed tomography angiography (CCTA)-derived stenosis severity and plaque morphology in patients with suspected coronary artery disease (CAD).Methods and resultsIn this retrospective study, 539 patients referred for hybrid [15O]H2O PET-CT imaging because of suspected CAD were investigated. PET was used to determine myocardial blood flow (MBF), whereas CCTA images were evaluated for obstructive stenoses and high-risk plaque (HRP) morphology. Patients were followed up for the occurrence of all-cause death and non-fatal myocardial infarction (MI). During a median follow-up of 6.8 (interquartile range 4.8–7.8) years, 42 (7.8%) patients experienced events, including 23 (4.3%) deaths, and 19 (3.5%) MIs. Annualized event rates for normal vs. abnormal results of PET MBF, CCTA-derived stenosis, and HRP morphology were 0.6 vs. 2.1%, 0.4 vs. 2.1%, and 0.8 vs. 2.8%, respectively (P ConclusionPET-derived MBF, CCTA-derived stenosis severity, and HRP morphology were univariably associated with death and MI, whereas only stenosis severity and HRP morphology provided independent prognostic value.</div

Functional stress imaging to predict abnormal coronary fractional flow reserve: the PACIFIC 2 study

AimsThe diagnostic performance of non-invasive imaging in patients with prior coronary artery disease (CAD) has not been tested in prospective head-to-head comparative studies. The aim of this study was to compare the diagnostic performance of qualitative single-photon emission computed tomography (SPECT), quantitative positron emission tomography (PET), and qualitative magnetic resonance imaging (MRI) in patients with a prior myocardial infarction (MI) or percutaneous coronary intervention (PCI).Methods and resultsIn this prospective clinical study, all patients with prior MI and/or PCI and new symptoms of ischaemic CAD underwent 99mTc-tetrofosmin SPECT, [15O]H2O PET, and MRI, followed by invasive coronary angiography with fractional flow reserve (FFR) in all coronary arteries. All modalities were interpreted by core laboratories. Haemodynamically significant CAD was defined by at least one coronary artery with an FFR ≤0.80. Among the 189 enrolled patients, 63% had significant CAD. Sensitivity was 67% (95% confidence interval 58–76%) for SPECT, 81% (72–87%) for PET, and 66% (56–75%) for MRI. Specificity was 61% (48–72%) for SPECT, 65% (53–76%) for PET, and 62% (49–74%) for MRI. Sensitivity of PET was higher than SPECT (P = 0.016) and MRI (P = 0.014), whereas specificity did not differ among the modalities. Diagnostic accuracy for PET (75%, 68–81%) did not statistically differ from SPECT (65%, 58–72%, P = 0.03) and MRI (64%, 57–72%, P = 0.052). Using FFR ConclusionIn this prospective head-to-head comparative study, SPECT, PET, and MRI did not show a significantly different accuracy for diagnosing FFR defined significant CAD in patients with prior PCI and/or MI. Overall diagnostic performances, however, were discouraging and the additive value of non-invasive imaging in this high-risk population is questionable.</p

Identification and control of marine vehicles using artificial intelligence techniques

peer-reviewedIn this thesis a novel approach to the identification of marine craft dynamics using neural networks is described. From a literature review it emerged that augmented controllers, in which a conventional controller is augmenter with neural network, which accounts for unmodelled phenomena and/or unmodelled operation regions, are most likely to be used for future neural controller architectures. Such controllers are appealing, as neural networks can be used to identify the unknown phenomena with a high accuracy. However, at th ecurrent time, neural networks are predominantlz used to identify unknown phenomena in a lumped way. As a result, it is difficult, or even impossible, to use these neural networks in a conventional controller. A novel approach, involving the use of several neural networks for the identification of individual model parameters, is presented. The new approach is tested, first in simulations and consecutively in an experiment, and found to offer increased accuracy compared to a benchmark least squares identification method. Additionally, it is demonstrated that the obtained model can easily be reformulated in order to be used in a control scheme. In this control scheme, the learning capabilities of neural networks and the robustness and guaranteed stability of more conventional control schemes, can be combined, thus obtaining the advantages of both approaches