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Related Concept Videos

Pre-Procedural Guidelines for Assessing Blood Pressure01:10

Pre-Procedural Guidelines for Assessing Blood Pressure

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Accurate blood pressure assessment is crucial for diagnosing and managing various health conditions. To ensure the reliability of these measurements, healthcare professionals must adhere to standardized pre-procedural guidelines. These guidelines enhance patient safety and improve the overall quality of healthcare. The following steps are essential for obtaining accurate and consistent blood pressure readings, from using the appropriate tools to ensuring effective communication with the...
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Hypertension and Regulation of Blood Pressure01:18

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Hypertension, the most common cardiovascular disease, is diagnosed through repeated measurements of elevated blood pressure. Its risks, including damage to the kidney, heart, and brain, are directly proportional to blood pressure levels. Starting from 115/75 mm Hg, the risk of cardiovascular disease doubles with each increment of 20/10 mm Hg. The diagnosis relies on blood pressure measurements, not on patient symptoms, as hypertension is often asymptomatic until end-organ damage is imminent or...
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Assessing Blood pressure using a doppler ultrasound01:19

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To obtain accurate blood pressure measurements in clinical settings, especially when traditional methods are insufficient, healthcare professionals utilize the Doppler ultrasound technique. This method uses high-frequency sound waves to detect blood flow within the arteries, which is crucial for patients with conditions that complicate circulatory system assessment.
Pre-Procedural Guidelines for Doppler Ultrasound Blood Pressure Assessment:
Preparation of Equipment:
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Assessment of blood pressure in brachial artery(two-step method)01:23

Assessment of blood pressure in brachial artery(two-step method)

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Measuring blood pressure is a fundamental skill in healthcare that aids in diagnosing and monitoring hypertension and other cardiovascular conditions. An aneroid sphygmomanometer, commonly used in clinical settings, offers a manual and precise method for blood pressure measurement. The technique for using this instrument involves specific steps that must be carefully executed to ensure accuracy. The following detailed description outlines a two-step technique for assessing blood pressure using...
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Errors occurring during blood pressure monitoring01:25

Errors occurring during blood pressure monitoring

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Blood pressure monitoring is a crucial clinical procedure in diagnosing and managing various cardiovascular conditions. Despite its significance, the accuracy of blood pressure measurements can be compromised by multiple factors, potentially leading to either falsely high or low readings. These inaccuracies are critical as they can significantly impact patient care. So, it is vital to understand these challenges deeply and adopt strategic approaches to minimize errors.
Several factors...
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Assessment of blood pressure in brachial artery(one-step method)01:15

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This procedural guide systematically measures blood pressure using an oscillometric digital sphygmomanometer, emphasizing accuracy, patient safety, and comfort.
Prepare for the Procedure:
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Hydra, a Computer-Based Platform for Aiding Clinicians in Cardiovascular Analysis and Diagnosis
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HyMNet: A Multimodal Deep Learning System for Hypertension Prediction Using Fundus Images and Cardiometabolic Risk

Mohammed Baharoon1,2, Hessa Almatar1, Reema Alduhayan1

  • 1AI and Bioinformatics Department, King Abdullah International Medical Research Center, King Saud bin Abdulaziz University for Health Sciences (KSAU-HS), Riyadh 11481, Saudi Arabia.

Bioengineering (Basel, Switzerland)
|November 27, 2024
PubMed
Summary
This summary is machine-generated.

A new multimodal deep learning system, HyMNet, improves hypertension detection by combining retinal images with age and sex data. This approach shows superior performance, especially in diabetic patients.

Keywords:
artificial intelligencecardiovascular diseasescomputer visionfundus imageshypertension detectionmachine learningmultimodal deep learning

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Area of Science:

  • Ophthalmology
  • Cardiology
  • Artificial Intelligence

Background:

  • Hypertension (HTN) detection is crucial for cardiovascular health.
  • Current diagnostic methods may benefit from advanced computational approaches.
  • Integrating diverse data sources can potentially improve diagnostic accuracy.

Purpose of the Study:

  • To develop and evaluate HyMNet, a multimodal deep learning (MMDL) system for enhanced hypertension detection.
  • To integrate retinal fundus images with cardiometabolic factors (age, sex) for improved HTN diagnosis.
  • To assess the impact of diabetes on the performance of the MMDL system for HTN detection.

Main Methods:

  • Developed HyMNet, an MMDL system utilizing RETFound for fundus image analysis and a neural network for age/sex data.
  • Employed a fusion network to jointly train and integrate features from both data modalities.
  • Trained and validated the system on a dataset of 5016 retinal images from 1243 individuals.

Main Results:

  • HyMNet achieved a superior F1 score of 0.771, outperforming the unimodal system (0.745).
  • The system demonstrated higher accuracy for diabetic patients (F1 score 0.796) compared to non-diabetic patients (F1 score 0.466).
  • MMDL approach showed improved performance over models using only demographic data (F1 score 0.752).

Conclusions:

  • HyMNet demonstrates the effectiveness of MMDL systems in enhancing hypertension detection accuracy.
  • Diabetes status significantly influences HTN prediction, with improved accuracy in diabetic individuals.
  • Integrating diverse data sources via MMDL holds promise for improved clinical applicability and broader generalization in HTN screening.