A novel computer-aided diagnosis system for the early detection of hypertension based on cerebrovascular alterations

Using Mean Arterial Pressure in Hypertension Diagnosis versus Using Either Systolic or Diastolic Blood Pressure Measurements

Hypertension is a severe and highly prevalent disease. It is considered a leading contributor to mortality worldwide. Diagnosis guidelines for hypertension use systolic and diastolic blood pressure (BP) together. Mean arterial pressure (MAP), which refers to the average of the arterial blood pressure through a single cardiac cycle, can be an alternative index that may capture the overall exposure of the person to a heightened pressure. A clinical hypothesis, however, suggests that in patients over 50 years old in age, systolic BP may be more predictive of adverse events, while in patients under 50 years old, diastolic BP may be slightly more predictive. In this study, we investigated the correlation between cerebrovascular changes, (impacted by hypertension), and MAP, systolic BP, and diastolic BP separately. Several experiments were conducted using real and synthetic magnetic resonance angiography (MRA) data, along with corresponding BP measurements. Each experiment employs the following methodology: First, MRA data were processed to remove noise, bias, or inhomogeneity. Second, the cerebrovasculature was delineated for MRA subjects using a 3D adaptive region growing connected components algorithm. Third, vascular features (changes in blood vessel’s diameters and tortuosity) that describe cerebrovascular alterations that occur prior to and during the development of hypertension were extracted. Finally, feature vectors were constructed, and data were classified using different classifiers, such as SVM, KNN, linear discriminant, and logistic regression, into either normotensives or hypertensives according to the cerebral vascular alterations and the BP measurements. The initial results showed that MAP would be more beneficial and accurate in identifying the cerebrovascular impact of hypertension (accuracy up to 95.2%) than just using either systolic BP (accuracy up to 89.3%) or diastolic BP (accuracy up to 88.9%). This result emphasizes the pathophysiological significance of MAP and supports prior views that this simple measure may be a superior index for the definition of hypertension and research on hypertension.

Positive Association between Hemoglobin Concentration and Blood Pressure in Adults: A Cross-Sectional Study Based on Rafsanjan Cohort Study

Introduction

Identification of factors associated with blood pressure (BP), including hemoglobin, can be used in diagnosing, controlling, and predicting the prognosis of patients. This study aims to investigate the cross-sectional association between hemoglobin concentration and BP in people aged 35-70 years in a cohort study of Rafsanjan, Iran.

Method

This cross-sectional study was conducted on 9398 urban and rural population of Rafsanjan adult cohort study as a part of the prospective epidemiological research studies in Iran (PERSIAN). Demographic information, medical history, history of smoking and alcohol intake, systolic and diastolic BP, and hemoglobin concentration were collected. A logistic regression test was performed to evaluate the relationship between hemoglobin concentration and BP in 4 unadjusted and adjusted models based on demographic indicators, clinical and laboratory findings using SPSS.24 software and SAS software version 9.2.

Results

The mean age of the participants was 49.78 ± 9.53 years, and 53.2% (5002 people) were women. Adjusted models 3 and 4 showed a positive association between BP and hemoglobin. For each unit increase in hemoglobin, the odds ratio (OR) of BP in the adjusted model 3 was 1.062 (95% CI: 1.005-1.121), and in the adjusted model 4, it was 1.090 (95% CI: 1.031-1.153).

Conclusion

Based on the results, the positive trend of BP and hemoglobin levels may indicate the need to pay more attention to these people as higher-risk groups for hypertension.

Two-step machine learning method for the rapid analysis of microvascular flow in intravital video microscopy

Microvascular blood flow is crucial for tissue and organ function and is often severely affected by diseases. Therefore, investigating the microvasculature under different pathological circumstances is essential to understand the role of the microcirculation in health and sickness. Microvascular blood flow is generally investigated with Intravital Video Microscopy (IVM), and the captured images are stored on a computer for later off-line analysis. The analysis of these images is a manual and challenging process, evaluating experiments very time consuming and susceptible to human error. Since more advanced digital cameras are used in IVM, the experimental data volume will also increase significantly. This study presents a new two-step image processing algorithm that uses a trained Convolutional Neural Network (CNN) to functionally analyze IVM microscopic images without the need for manual analysis. While the first step uses a modified vessel segmentation algorithm to extract the location of vessel-like structures, the second step uses a 3D-CNN to assess whether the vessel-like structures have blood flowing in it or not. We demonstrate that our two-step algorithm can efficiently analyze IVM image data with high accuracy (83%). To our knowledge, this is the first application of machine learning for the functional analysis of microvascular blood flow in vivo.

Adherence to Treatment, Arterial Stiffness and Cognitive Function in Irbesartan- Treated Newly Diagnosed Hypertensive Patients

OBJECTIVE

Non-adherence to antihypertensive agents leads to reduced blood pressure (BP) control. Data supporting the correlation of adherence with arterial stiffness (AS) are few. Furthermore, the causal relationship between AS and cognitive dysfunction (CO/DY) has not been clearly established. It is suggested that angiotensin II receptor blockers (ARBs) exhibit the lowest discontinuation rate among antihypertensive drugs.

DESIGN AND METHODS

We followed up with patients receiving monotherapy with irbesartan. CO/DY was assessed with the Mini-Mental State Examination (MΜSE) and other tests.

RESULTS

Patients [n=77; mean age: 56±11 years; 39 men (50.6%)] were followed-up for 16.1±10.9 months. At the end of follow up, significant reductions were observed in mean peripheral systolic BP (135±117 vs 153±11 mmHg; p<0.005), mean peripheral diastolic BP (85±11 vs 95±10 mmHg; p<0.005), mean central systolic BP (130±11 vs 142±12 mmHg; p<0.005) as well as in mean central diastolic BP (85±8 vs 95±97 mmHg; p<0.005). AS indices [carotid-femoral pulse wave velocity and augmentation index] also improved significantly: 7.7±1.4 vs 8.2±1.4 m/sec (p<0.005), and 29.1±8.3 vs 32.3±9.1 (p<0.005), respectively. At the end of the study, a significant improvement was observed in the MMSE test (29.7±0.7 vs. 29.2±0.9; p<0.02), as well as a significant reduction in 24h urine albumin (94±82 vs. 204±112 mg/24h, p<0.005). The level of adherence was high in 60/77 (77.9%), medium in 9/77 (11.6%) and low in 8/77 (10.38%) patients.

CONCLUSION

Hypertensive patients receiving mono-therapy with an ARB showed reduced AS, cognitive improvement, significant reductions in BP (peripheral and central) and decreased 24h urinary albumin excretion.

Quantitative Analysis of the Cerebral Vasculature on Magnetic Resonance Angiography

The arterial connections in the Circle of Willis are a central source of collateral blood flow and play an important role in pathologies such as stroke and mental illness. Analysis of the Circle of Willis and its variants can shed light on optimal methods of diagnosis, treatment planning, surgery, and quantification of outcomes. We developed an automated, standardized, objective, and high-throughput approach for categorizing and quantifying the Circle of Willis vascular anatomy using magnetic resonance angiography images. This automated algorithm for processing of MRA images isolates and automatically identifies key features of the cerebral vasculature such as branching of the internal intracranial internal carotid artery and the basilar artery. Subsequently, physical features of the segments of the anterior cerebral artery were acquired on a sample and intra-patient comparisons were made. We demonstrate the feasibility of using our approach to automatically classify important structures of the Circle of Willis and extract biomarkers from cerebrovasculature. Automated image analysis can provide clinically-relevant vascular features such as aplastic arteries, stenosis, aneurysms, and vessel caliper for endovascular procedures. The developed algorithm could facilitate clinical studies by supporting high-throughput automated analysis of the cerebral vasculature.