To compare predicted and final infarct lesion volumes determined by processing apparent diffusion coefficient (ADC) maps derived at admission diffusion-weighted (DW) magnetic resonance (MR) imaging in patients with acute stroke and to verify that predicted areas of infarct growth reflect at-risk penumbral regions based on recanalization status. The French legislation waived the requirement for informed patient consent for the described research, which was based on patient medical files. However, patients and/or their relatives were informed that they could decline to participate in the research. Authors tested a semiautomated proprietary image analysis procedure in 98 patients with middle cerebral artery (MCA) stroke by modeling infarct growth on DW imaging-derived ADC maps. Predicted infarct growth (PIG) areas and predicted infarct volumes were correlated with final observed data. In addition, the effect of MCA recanalization on the correlation between predicted and observed infarct growth volumes was qualitatively assessed. Predicted and final infarct volumes (rho = 0.828; 95% confidence interval [CI]: 0.753, 0.882; P < .0001) and infarct growth volumes (rho = 0.506; 95% CI: 0.342, 0.640; P < .0001) were significantly correlated. Visual comparative examination revealed satisfactory qualitative consistency between predicted and follow-up lesion masks. In patients without MCA recanalization, PIG did not differ significantly from final observed infarct growth (median PIG obtained with 0.93 ADC ratio cutoff [PIG(ratio)] of 27.1 cm(3) vs median infarct growth of 19.8 cm(3), P = .17). MCA recanalization revealed an overestimation of PIG (median PIG(ratio) of 24.8 cm(3) vs median infarct growth of 12 cm(3), P = .005), suggesting that the PIG area was part of ischemic penumbra. Data show the feasibility of identifying at-risk ischemic tissue in patients with acute MCA stroke by using semiautomated analysis of ADC maps derived at DW imaging, without intravenous contrast material-enhanced perfusion-weighted imaging.

Motivated by the need of poor and rural Mexico, where the population has limited access to advanced medical technology and services, we have developed a new paradigm for medical diagnostic based on the technology of “Volumetric Electromagnetic Phase Shift Spectroscopy” (VEPS), as an inexpensive partial substitute to medical imaging. VEPS, can detect changes in tissue properties inside the body through non-contact, multi-frequency electromagnetic measurements from the exterior of the body, and thereby provide rapid and inexpensive diagnostics in a way that is amenable for use in economically disadvantaged parts of the world. We describe the technology and report results from a limited pilot study with 46 healthy volunteers and eight patients with CT radiology confirmed brain edema and brain hematoma. Data analysis with a non-parametric statistical Mann-Whitney U test, shows that in the frequency range of from 26 MHz to 39 MHz, VEPS can distinguish non-invasively and without contact, with a statistical significance of p<0.05, between healthy subjects and those with a medical conditions in the brain. In the frequency range of between 153 MHz to 166 MHz it can distinguish with a statistical significance of p<0.05 between subjects with brain edema and those with a hematoma in the brain. A classifier build from measurements in these two frequency ranges can provide instantaneous diagnostic of the medical condition of the brain of a patient, from a single set of measurements. While this is a small-scale pilot study, it illustrates the potential of VEPS to change the paradigm of medical diagnostic of brain injury through a VEPS classifier-based technology. Obviously substantially larger-scale studies are needed to verify and expand on the findings in this small pilot study.

Magnetic Resonance Imaging (MRI) is increasingly used for the diagnosis and monitoring of neurological disorders. In particular Diffusion-Weighted MRI (DWI) is highly sensitive in detecting early cerebral ischemic changes in acute stroke. Cerebral infarction lesion segmentation from DWI is accomplished in this work by applying nonparametric density estimation. The quality of the class boundaries is improved by including an edge confidence map, that is the confidence of truly being in the presence of a border between adjacent regions. The adjacency graph, that is constructed with the label regions, is analyzed and pruned to merge adjacent regions. The method was applied to real images, keeping all parameters constant throughout the process for each data set. The combination of region segmentation and edge detection proved to be a robust automatic technique of segmentation from DWI images of cerebral infarction regions in acute ischemic stroke. In a comparison with the reference infarct lesions segmentation, the automatic segmentation presented a significant correlation (r = 0.935), and an average Tanimoto index of 0.538.

BACKGROUND AND PURPOSE: Previous data have shown the feasibility of identifying ischemic penumbra in patients with acute stroke by using a semiautomated analysis of ADC maps. Here, we investigated whether the fate of ADC-defined penumbra was altered by HG. We also examined the interaction between HG and arterial recanalization on infarct growth. MATERIALS AND METHODS: We examined 94 patients by using MR imaging within 6 hours of stroke onset and a follow-up MR imaging within 7 days. The ADC-defined tissue-at-risk was calculated from the early MR imaging. Patients were classified according to high (>7 mmol/L; n = 34/94, HG) or normal (n = 60/94) baseline SGL. The impact of HG status on infarct growth was assessed by using multiple regression models and analysis of the slopes of regression lines for each group. Interaction between HG status and arterial recanalization on infarct growth was investigated by using multiple regression analysis. RESULTS: The slope of the predicted versus observed infarct growth regression line was steeper in HG than non-HG patients (P = .0008), suggesting that infarct growth within ADC-defined tissue-at-risk was increased in HG patients. The effect was 2.8 times more severe in nonrecanalized patients (P = .01) than in patients with recanalization (P = .001). CONCLUSIONS: ADC-defined tissue-at-risk may represent ischemic penumbra because part of this area may be salvaged in normal SGL patients. The toxicity in HG patients seems to be more related to penumbra-infarction transition than reperfusion injury in humans because the effect was larger in nonrecanalized than in recanalized patients.

Chagas disease, caused by the Trypanosoma cruzi (T. cruzi) parasite, is the third most common parasitosis worldwide. Most of the infected subjects can remain asymptomatic without an opportune and early detection or an objective diagnostic is not conducted. Frequently, the disease manifests itself after a long time, accompanied by severe heart disease or by sudden death. Thus, the diagnosis is a complex and challenging process where several factors must be considered. In this paper, a novel pipeline is presented integrating temporal data from four modalities (electrocardiography signals, echocardiography images, Doppler spectrum, and ELISA antibody titers), multiple features selection analyses by a univariate analysis and a machine learning-based selection. The method includes an automatic dichotomous classification of animal status (control vs. infected) based on Random Forest, Extremely Randomized Trees, Decision Trees, and Support Vector Machine. The most relevant multimodal attributes found were ELISA (IgGT, IgG1, IgG2a), electrocardiography (SR mean, QT and ST intervals), ascending aorta Doppler signals, and echocardiography (left ventricle diameter during diastole). Concerning automatic classification from selected features, the best accuracy of control vs. acute infection groups was 93.3 ± 13.3% for cross-validation and 100% in the final test; for control vs. chronic infection groups, it was 100% and 100%, respectively. We conclude that the proposed machine learning-based approach can be of help to obtain a robust and objective diagnosis in early T. cruzi infection stages.