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Related Concept Videos

Assessing Blood pressure using a doppler ultrasound01:19

Assessing Blood pressure using a doppler ultrasound

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:
Peripheral Arterial Disease II: Clinical Manifestations and Diagnostic Evaluation01:21

Peripheral Arterial Disease II: Clinical Manifestations and Diagnostic Evaluation

Clinical manifestationsPeripheral Arterial Disease (PAD) manifests through a range of symptoms, from the characteristic intermittent claudication to atypical presentations and severe complications in advanced stages. Intermittent claudication, a hallmark symptom of PAD, presents as exercise-induced muscle pain that typically resolves within minutes of rest. This pain is reproducible and stems from inadequate blood flow, leading to the accumulation of lactic acid produced during anaerobic...

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Diagnostic Performance of a Deep Learning-Powered Application for Aortic Dissection Triage Prioritization and

Vladimir Laletin1, Angela Ayobi1, Peter D Chang2,3

  • 1Avicenna.AI, 375 Avenue du Mistral, 13600 La Ciotat, France.

Diagnostics (Basel, Switzerland)
|September 14, 2024
PubMed
Summary
This summary is machine-generated.

A deep learning tool accurately detects and classifies aortic dissections (ADs) on CT angiography scans. This AI application shows high sensitivity and specificity, potentially speeding up diagnosis for urgent cases.

Keywords:
AI-based solution for radiologyaortic dissectiondeep learningemergency radiologymachine learning diagnostic performancemedical and biomedical image processingmedical imaging automated analysis

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Area of Science:

  • Radiology
  • Artificial Intelligence
  • Cardiovascular Imaging

Background:

  • Aortic dissection (AD) is a life-threatening condition requiring rapid diagnosis.
  • CT angiography (CTA) is a primary imaging modality for AD detection.
  • Deep learning (DL) offers potential for improving diagnostic accuracy and efficiency in medical imaging.

Purpose of the Study:

  • To evaluate the diagnostic performance of a DL-based application (CINA-CHEST (AD)) for detecting and classifying aortic dissections on chest and thoraco-abdominal CTA scans.
  • To compare the DL application's performance against a radiologist-adjudicated ground truth.
  • To assess the clinical effectiveness by measuring the DL algorithm's time to notification.

Main Methods:

  • A multicenter retrospective study analyzed 1303 CTA scans from over 200 cities.
  • CTA scans were processed by the CINA-CHEST (AD) deep learning device.
  • Diagnostic performance was benchmarked against a consensus of three U.S. board-certified radiologists.

Main Results:

  • The DL application achieved a sensitivity of 94.2% and specificity of 97.3% for detecting AD.
  • Classification accuracy for AD types was 99.5% for Type A and 97.5% for Type B.
  • The mean time for processing and notifying potential AD cases was 27.9 seconds.

Conclusions:

  • The deep learning application demonstrates strong diagnostic performance in detecting and classifying aortic dissections.
  • The AI tool shows potential for enabling faster triage of urgent aortic dissection cases in clinical practice.
  • The application's speed and accuracy suggest a valuable role in improving patient outcomes for AD.