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Related Concept Videos

Local Anesthetics: Clinical Application as Epidural Anesthesia01:29

Local Anesthetics: Clinical Application as Epidural Anesthesia

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Epidural anesthetics are administered in the fat-filled epidural space, the outermost part of the spinal canal. This technique is commonly employed for pain management and anesthesia during lower abdomen and pelvis surgeries or labor and delivery.
Since epidural anesthetics can be infused through an epidural catheter, all types of drugs, including short-acting ones, can be administered. Chloroprocaine and lidocaine are examples of short and long-duration anesthetics, respectively. Bupivacaine...
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Local Anesthetics: Clinical Application as Spinal Anesthesia01:11

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Spinal anesthetics are given during lower abdomen and limb surgeries to block sensory and motor neurons. They are administered in the mid to low lumbar regions, primarily acting on the cauda equina's nerve roots. The blockade level depends on the local anesthetic (LA) concentration. Usually, low LA concentrations are sufficient to block sensory fibers, while only high LA concentrations block motor fibers. Other factors like injection volume and speed, the patient's posture, and the drug...
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Detection of Critical Spinal Epidural Lesions on CT Using Machine Learning.

Robert J Harris1, Scott G Baginski, Yulia Bronstein

  • 1Virtual Radiologic, Eden Prairie, MN.

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|July 29, 2022
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Summary
This summary is machine-generated.

A machine learning model can identify critical spinal epidural lesions on CT scans, aiding in faster diagnosis and reducing missed findings in teleradiology. This tool helps prioritize urgent cases and improves patient outcomes.

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Area of Science:

  • Radiology
  • Artificial Intelligence
  • Medical Imaging

Background:

  • Critical spinal epidural pathologies pose significant risks, including paralysis and death.
  • Computed tomography (CT) is more frequently used than magnetic resonance imaging (MRI) for spinal imaging.
  • Early detection of epidural lesions is crucial for timely intervention.

Purpose of the Study:

  • To develop and evaluate a machine learning model for screening critical epidural lesions on CT images.
  • To assess the model's utility in prioritizing emergent teleradiology studies.
  • To identify missed epidural findings in clinical practice.

Main Methods:

  • A machine learning model was trained on 153 segmented epidural lesions from CT studies.
  • A test dataset included previously missed epidural lesions.
  • The model was integrated into a 90-day teleradiology workflow for prospective evaluation.

Main Results:

  • The model achieved 50.0% sensitivity and 99.0% specificity in identifying epidural lesions on test data.
  • In prospective use, the model prioritized 66.7% of initially diagnosed epidural lesions with 98.9% specificity.
  • Approximately 2.0 studies per day were flagged, leading to the discovery of 17 missed epidural lesions over 90 days.

Conclusions:

  • Machine learning models can effectively identify spinal epidural hematomas and abscesses on CT.
  • Clinical implementation of this AI tool is feasible within a teleradiology workflow.
  • The findings suggest a substantial rate of missed critical spinal epidural lesions on initial CT reads.