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Related Concept Videos

Flail Chest-II01:26

Flail Chest-II

Managing flail chest, a condition characterized by a segment of the chest wall moving independently from the rest of the thoracic cage, requires a comprehensive approach. It includes a thorough assessment of the patient's condition, a diagnostic evaluation to determine the extent of the injury, and the implementation of appropriate medical interventions tailored to the individual's needs.
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Spinal Cord Injury ll: Pathophysiology01:14

Spinal Cord Injury ll: Pathophysiology

Spinal cord injury progresses through two interconnected phases: primary injury and secondary injury.Primary InjuryPrimary injury happens at the moment of trauma and involves immediate mechanical damage to the spinal cord.Compression happens when broken vertebrae, herniated discs, or accumulating blood (such as a hematoma) press directly against the spinal cord, distorting its normal shape and function. In cases of contusion, the cord is bruised by a blunt force (like penetrating injuries or...
Secondary Spinal Cord Injury llI: Pathophysiology01:25

Secondary Spinal Cord Injury llI: Pathophysiology

Early Ischemia and Ionic ImbalanceWithin minutes of spinal cord injury, a secondary cascade begins, progressing over hours to weeks. Vascular damage reduces blood flow, causing ischemia and mitochondrial dysfunction. ATP depletion leads to ion pump failure, membrane depolarization, sodium influx, potassium efflux, and water accumulation, resulting in cellular swelling. Increased intracellular calcium further disrupts mitochondria and accelerates cellular injury.Excitotoxicity and Neuronal...

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Related Experiment Video

Updated: May 12, 2026

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma
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OSAIRIS: Lessons Learned From the Hospital-Based Implementation and Evaluation of an Open-Source Deep-Learning Model

A D Constantinou1, A Hoole1, D C Wong2

  • 1Medical Physics, Cambridge University Hospitals NHS Foundation Trust, Cambridge, UK.

Clinical Oncology (Royal College of Radiologists (Great Britain))
|November 10, 2024
PubMed
Summary
This summary is machine-generated.

This study shows that the OSAIRIS AI model significantly reduces contouring time in radiotherapy planning, improving workflow efficiency. Evaluations must include clinical utility and safety, not just accuracy metrics, for reliable AI assessment.

Keywords:
AIAccuracy metricsAutosegmentationHuman-centricMachine-learningMedical device

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Area of Science:

  • Medical Physics
  • Radiotherapy
  • Artificial Intelligence

Background:

  • Autosegmentation for organs at risk (OAR) outlining in radiotherapy is widely studied for accuracy.
  • Existing evaluations often overlook perceived utility and safety, focusing solely on metrics.
  • The OSAIRIS open-source AI model was developed as a medical device for OAR contouring.

Purpose of the Study:

  • To clinically evaluate the OSAIRIS AI model for radiotherapy treatment planning.
  • To assess tangible benefits, quantify utility, and evaluate risks within a clinical workflow.
  • To compare OSAIRIS's performance against traditional methods and commercial AI devices.

Main Methods:

  • Clinical evaluation of OSAIRIS for head and neck and male pelvis (prostate) OAR contouring.
  • Quantification of time-saving benefits for clinicians.
  • Correlation analysis between accuracy metrics (Dice, Hausdorff) and editing time.
  • Safety evaluation focusing on error detection and correction by clinicians.
  • Blinded clinician contour rating exercise and 'Mystery Shopping' experiment for risk assessment.

Main Results:

  • OSAIRIS achieved significant time savings: 36% for prostate and 67% for head and neck contouring.
  • Hausdorff distance showed the best correlation with editing time (Spearman 0.70, Kendall 0.52), outperforming Dice.
  • Clinicians corrected most errors (72% head and neck, 81% prostate).
  • Anchoring bias was observed during AI contour editing.
  • Blinded ratings indicated AI contours were not rated lower than clinician contours.

Conclusions:

  • Clinical evaluation of AI in radiotherapy must extend beyond accuracy metrics to include workflow, utility, and safety.
  • Human-AI interaction effects are crucial for assessing practical usability and technology adoption.
  • Comprehensive risk assessment and mitigation strategies are essential for AI medical devices.