Effective diagnosis of Alzheimer's disease by means of large margin-based methodology.

BACKGROUND Functional brain images such as Single-Photon Emission Computed Tomography (SPECT) and Positron Emission Tomography (PET) have been widely used to guide the clinicians in the Alzheimer's Disease (AD) diagnosis. However, the subjectivity involved in their evaluation has favoured the development of Computer Aided Diagnosis (CAD) Systems. METHODS It is proposed a novel combination of feature extraction techniques to improve the diagnosis of AD. Firstly, Regions of Interest (ROIs) are selected by means of a t-test carried out on 3D Normalised Mean Square Error (NMSE) features restricted to be located within a predefined brain activation mask. In order to address the small sample-size problem, the dimension of the feature space was further reduced by: Large Margin Nearest Neighbours using a rectangular matrix (LMNN-RECT), Principal Component Analysis (PCA) or Partial Least Squares (PLS) (the two latter also analysed with a LMNN transformation). Regarding the classifiers, kernel Support Vector Machines (SVMs) and LMNN using Euclidean, Mahalanobis and Energy-based metrics were compared. RESULTS Several experiments were conducted in order to evaluate the proposed LMNN-based feature extraction algorithms and its benefits as: i) linear transformation of the PLS or PCA reduced data, ii) feature reduction technique, and iii) classifier (with Euclidean, Mahalanobis or Energy-based methodology). The system was evaluated by means of k-fold cross-validation yielding accuracy, sensitivity and specificity values of 92.78%, 91.07% and 95.12% (for SPECT) and 90.67%, 88% and 93.33% (for PET), respectively, when a NMSE-PLS-LMNN feature extraction method was used in combination with a SVM classifier, thus outperforming recently reported baseline methods. CONCLUSIONS All the proposed methods turned out to be a valid solution for the presented problem. One of the advances is the robustness of the LMNN algorithm that not only provides higher separation rate between the classes but it also makes (in combination with NMSE and PLS) this rate variation more stable. In addition, their generalization ability is another advance since several experiments were performed on two image modalities (SPECT and PET).

Telehealth system (e-CUIDATE) to improve quality of life in breast cancer survivors: rationale and study protocol for a randomized clinical trial.

BACKGROUND Breast cancer survivors suffer physical impairment after oncology treatment. This impairment reduces quality of life (QoL) and increase the prevalence of handicaps associated to unhealthy lifestyle (for example, decreased aerobic capacity and strength, weight gain, and fatigue). Recent work has shown that exercise adapted to individual characteristics of patients is related to improved overall and disease-free survival. Nowadays, technological support using telerehabilitation systems is a promising strategy with great advantage of a quick and efficient contact with the health professional. It is not known the role of telerehabilitation through therapeutic exercise as a support tool to implement an active lifestyle which has been shown as an effective resource to improve fitness and reduce musculoskeletal disorders of these women. METHODS / DESIGN This study will use a two-arm, assessor blinded, parallel randomized controlled trial design. People will be eligible if: their diagnosis is of stages I, II, or IIIA breast cancer; they are without chronic disease or orthopedic issues that would interfere with ability to participate in a physical activity program; they had access to the Internet and basic knowledge of computer use or living with a relative who has this knowledge; they had completed adjuvant therapy except for hormone therapy and not have a history of cancer recurrence; and they have an interest in improving lifestyle. Participants will be randomized into e-CUIDATE or usual care groups. E-CUIDATE give participants access to a range of contents: planning exercise arranged in series with breathing exercises, mobility, strength, and stretching. All of these exercises will be assigned to women in the telerehabilitation group according to perceived needs. The control group will be asked to maintain their usual routine. Study endpoints will be assessed after 8 weeks (immediate effects) and after 6 months. The primary outcome will be QoL measured by The European Organization for Research and Treatment of Cancer Quality of Life Questionnaire Core 30 version 3.0 and breast module called The European Organization for Research and Treatment of Cancer Breast Cancer-Specific Quality of Life questionnaire. The secondary outcomes: pain (algometry, Visual Analogue Scale, Brief Pain Inventory short form); body composition; physical measurement (abdominal test, handgrip strength, back muscle strength, and multiple sit-to-stand test); cardiorespiratory fitness (International Fitness Scale, 6-minute walk test, International Physical Activity Questionnaire-Short Form); fatigue (Piper Fatigue Scale and Borg Fatigue Scale); anxiety and depression (Hospital Anxiety and Depression Scale); cognitive function (Trail Making Test and Auditory Consonant Trigram); accelerometry; lymphedema; and anthropometric perimeters. DISCUSSION This study investigates the feasibility and effectiveness of a telerehabilitation system during adjuvant treatment of patients with breast cancer. If this treatment option is effective, telehealth systems could offer a choice of supportive care to cancer patients during the survivorship phase. TRIAL REGISTRATION ClinicalTrials.gov Identifier: NCT01801527.