Video-Based Depression Detection Using Local Curvelet Binary Patterns in Pairwise Orthogonal Planes

International audienceDepression is an increasingly prevalent mood disorder. This is the reason why the field of computer-based depression assessment has been gaining the attention of the research community during the past couple of years. The present work proposes two algorithms for depression detection, one Frame-based and the second Video-based, both employing Curvelet transform and Local Binary Patterns. The main advantage of these methods is that they have significantly lower computational requirements, as the extracted features are of very low dimensionality. This is achieved by modifying the previously proposed algorithm which considers Three-Orthogonal-Planes, to only Pairwise-Orthogonal-Planes. Performance of the algorithms was tested on the benchmark dataset provided by the Audio/Visual Emotion Challenge 2014, with the person-specific system achieving 97.6% classification accuracy, and the person-independed one yielding promising preliminary results of 74.5% accuracy. The paper concludes with open issues, proposed solutions, and future plans

participatory aspects of ict infrastructures for cancer management

Significant improvements in cancer research have led to more cancer patients being cured, and many more enabled to live with their cancer. As the disease is now managed as a chronic illness, it requires long-term surveillance and maintenance treatment. This requires a transformation in the nature of healthcare from reactive to preventive, personalized and participatory. To this direction, in this chapter we present relevant approaches developed within five European funded projects and we report on experiences and lessons learnt. More specifically, we describe the eHealth solutions developed, enabling patients to actively participate in their disease management, the results out of those projects towards the P5 vision and more especially on the participatory aspect and we present a set of requirements and guidelines for future technological solutions

Challenges and Promises of Radiomics for Rectal Cancer

Moreira, J. M., Santiago, I., Santinha, J., Figueiredo, N., Marias, K., Figueiredo, M., ... Papanikolaou, N. (2019). Challenges and Promises of Radiomics for Rectal Cancer. Current Colorectal Cancer Reports, 15(6), 175-180. https://doi.org/10.1007/s11888-019-00446-yPurpose of Review: This literature review aims to gather the relevant works published on the topic of Radiomics in Rectal Cancer. Research on this topic has focused on finding predictors of rectal cancer staging and chemoradiation treatment response from medical images. The methods presented may, in principle, aid clinicians with the appropriate treatment planning options. Finding appropriate automatic tools to help in this task is very important, since rectal cancer has been considered one of the most challenging oncological pathologies in recent years. Recent Findings: Radiomics is a class of methods based on the extraction of mineable, high-dimensional data/features from the routine, standard-of-care medical imaging. This data is then fed to machine learning algorithms, with the goal of automatically obtaining predictions regarding disease stage and therapeutic response. Summary: The literature reviewed suggests that Radiomics will continue to be a part of the body of research in oncology in the upcoming years. However, and excluding very few studies, proper validation on the performance of the methods (mainly with external datasets) is still one of the main limitations of the field, which strongly limits their clinical applicability. Progress will only occur if the community opens itself to collaborate with different groups, as data availability and limited shareability continues to be the barrier for its development. Nowadays, Radiomics is used for nearly every type of cancer. In particular, for rectal cancer, the need for predicting treatment response will continue to demand and boost research in this field.authorsversionpublishe

Correction to: Pattern recognition and pharmacokinetic methods on DCE-MRI data for tumor hypoxia mapping in sarcoma

The article Pattern recognition and pharmacokinetic methods on DCE-MRI data for tumor hypoxia mapping in sarcoma, written by M. Venianaki, O. Salvetti, E. de Bree, T. Maris, A. Karantanas, E. Kontopodis, K. Nikiforaki, K. Marias, was originally published electronically without open access

Pattern recognition and pharmacokinetic methods on DCE-MRI data for tumor hypoxia mapping in sarcoma

The main purpose of this study is to analyze the intrinsic tumor physiologic characteristics in patients with sarcoma through model-free analysis of dynamic contrast enhanced MR imaging data (DCE-MRI). Clinical data were collected from three patients with two different types of histologically proven sarcomas who underwent conventional and advanced MRI examination prior to excision. An advanced matrix factorization algorithm has been applied to the data, resulting in the identification of the principal time-signal uptake curves of DCE-MRI data, which were used to characterize the physiology of the tumor area, described by three different perfusion patterns i.e. hypoxic, well-perfused and necrotic one. The performance of the algorithm was tested by applying different initialization approaches with subsequent comparison of their results. The algorithm was proven to be robust and led to the consistent segmentation of the tumor area in three regions of different perfusion, i.e. well- perfused, hypoxic and necrotic. Results from the model-free approach were compared with a widely used pharmacokinetic (PK) model revealing significant correlations

Investigating the value of radiomics stemming from DSC quantitative biomarkers in IDH mutation prediction in gliomas

ObjectiveThis study aims to assess the value of biomarker based radiomics to predict IDH mutation in gliomas. The patient cohort consists of 160 patients histopathologicaly proven of primary glioma (WHO grades 2–4) from 3 different centers.MethodsTo quantify the DSC perfusion signal two different mathematical modeling methods were used (Gamma fitting, leakage correction algorithms) considering the assumptions about the compartments contributing in the blood flow between the extra- and intra vascular space.ResultsThe Mean slope of increase (MSI) and the K1 parameter of the bidirectional exchange model exhibited the highest performance with (ACC 74.3% AUROC 74.2%) and (ACC 75% AUROC 70.5%) respectively.ConclusionThe proposed framework on DSC-MRI radiogenomics in gliomas has the potential of becoming a reliable diagnostic support tool exploiting the mathematical modeling of the DSC signal to characterize IDH mutation status through a more reproducible and standardized signal analysis scheme for facilitating clinical translation

Deep learning for diabetic retinopathy detection and classification based on fundus images: A review.

Diabetic Retinopathy is a retina disease caused by diabetes mellitus and it is the leading cause of blindness globally. Early detection and treatment are necessary in order to delay or avoid vision deterioration and vision loss. To that end, many artificial-intelligence-powered methods have been proposed by the research community for the detection and classification of diabetic retinopathy on fundus retina images. This review article provides a thorough analysis of the use of deep learning methods at the various steps of the diabetic retinopathy detection pipeline based on fundus images. We discuss several aspects of that pipeline, ranging from the datasets that are widely used by the research community, the preprocessing techniques employed and how these accelerate and improve the models' performance, to the development of such deep learning models for the diagnosis and grading of the disease as well as the localization of the disease's lesions. We also discuss certain models that have been applied in real clinical settings. Finally, we conclude with some important insights and provide future research directions

iManageMyHealth and iSupportMyPatients: mobile decision support and health management apps for cancer patients and their doctors

Clinical decision support systems can play a crucial role in healthcare delivery as they promise to improve health outcomes and patient safety, reduce medical errors and costs and contribute to patient satisfaction. Used in an optimal way, they increase the quality of healthcare by proposing the right information and intervention to the right person at the right time in the healthcare delivery process. This paper reports on a specific approach to integrated clinical decision support and patient guidance in the cancer domain as proposed by the H2020 iManageCancer project. This project aims at facilitating efficient self-management and management of cancer according to the latest available clinical knowledge and the local healthcare delivery model, supporting patients and their healthcare providers in making informed decisions on treatment choices and in managing the side effects of their therapy. The iManageCancer platform is a comprehensive platform of interconnected mobile tools to empower cancer patients and to support them in the management of their disease in collaboration with their doctors. The backbone of the iManageCancer platform comprises a personal health record and the central decision support unit (CDSU). The latter offers dedicated services to the end users in combination with the apps iManageMyHealth and iSupportMyPatients. The CDSU itself is composed of the so-called Care Flow Engine (CFE) and the model repository framework (MRF). The CFE executes personalised and workflow oriented formal disease management diagrams (Care Flows). In decision points of such a Care Flow, rules that operate on actual health information of the patient decide on the treatment path that the system follows. Alternatively, the system can also invoke a predictive model of the MRF to proceed with the best treatment path in the diagram. Care Flow diagrams are designed by clinical experts with a specific graphical tool that also deploys these diagrams as executable workflows in the CFE following the Business Process Model and Notation (BPMN) standard. They are exposed as services that patients or their doctors can use in their apps in order to manage certain aspects of the cancer disease like pain, fatigue or the monitoring of chemotherapies at home. The mHealth platform for cancer patients is currently being assessed in clinical pilots in Italy and Germany and in several end-user workshops

Status and recommendations of technological and data-driven innovations in cancer care:Focus group study

Background: The status of the data-driven management of cancer care as well as the challenges, opportunities, and recommendations aimed at accelerating the rate of progress in this field are topics of great interest. Two international workshops, one conducted in June 2019 in Cordoba, Spain, and one in October 2019 in Athens, Greece, were organized by four Horizon 2020 (H2020) European Union (EU)-funded projects: BOUNCE, CATCH ITN, DESIREE, and MyPal. The issues covered included patient engagement, knowledge and data-driven decision support systems, patient journey, rehabilitation, personalized diagnosis, trust, assessment of guidelines, and interoperability of information and communication technology (ICT) platforms. A series of recommendations was provided as the complex landscape of data-driven technical innovation in cancer care was portrayed. Objective: This study aims to provide information on the current state of the art of technology and data-driven innovations for the management of cancer care through the work of four EU H2020-funded projects. Methods: Two international workshops on ICT in the management of cancer care were held, and several topics were identified through discussion among the participants. A focus group was formulated after the second workshop, in which the status of technological and data-driven cancer management as well as the challenges, opportunities, and recommendations in this area were collected and analyzed. Results: Technical and data-driven innovations provide promising tools for the management of cancer care. However, several challenges must be successfully addressed, such as patient engagement, interoperability of ICT-based systems, knowledge management, and trust. This paper analyzes these challenges, which can be opportunities for further research and practical implementation and can provide practical recommendations for future work. Conclusions: Technology and data-driven innovations are becoming an integral part of cancer care management. In this process, specific challenges need to be addressed, such as increasing trust and engaging the whole stakeholder ecosystem, to fully benefit from these innovations