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Related Concept Videos

Imaging Studies II: Ultrasonography01:24

Imaging Studies II: Ultrasonography

IntroductionUltrasonography, or renal ultrasound, is a noninvasive medical imaging technique that uses high-frequency sound waves to visualize the kidneys, ureters, bladder, and surrounding tissues.Indications for Urinary System UltrasonographyUrinary system ultrasonography is indicated in various clinical scenarios, such as:Kidney Stones (Urolithiasis): To detect and monitor the size and presence of kidney or urinary tract stones.Hydronephrosis: To assess the dilation of the renal pelvis and...
Ultrasound I: Abdominal Ultrasonography01:20

Ultrasound I: Abdominal Ultrasonography

Introduction:
Abdominal ultrasonography, commonly known as abdominal ultrasound, is a vital, non-invasive medical imaging technique widely used in healthcare.
Procedure:
This diagnostic tool allows the clinician to visually inspect internal structures within the abdomen, including vital organs such as the liver, gallbladder, pancreas, kidneys, and spleen.
The abdominal ultrasound process begins with applying a special gel to the patient's skin over the abdomen. This gel enhances the...

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Breast Ultrasound AI Under Dataset Shift: A Patient-Leakage-Aware Benchmark.

Lulu Wang1

  • 1Department of Engineering, Reykjavik University, 102 Reykjavik, Iceland.

Diagnostics (Basel, Switzerland)
|May 27, 2026
PubMed
Summary

This study introduces a reliable benchmark for artificial intelligence (AI) in breast ultrasound imaging, focusing on external generalization and performance under varied data conditions. Results show region-of-interest input enhances AI accuracy, improving clinical translation potential.

Keywords:
artificial intelligencebreast ultrasoundcalibrationdataset shiftdiagnostic imaging

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Machine Learning

Background:

  • Artificial intelligence (AI) shows potential in breast ultrasound analysis but lacks validation across diverse datasets.
  • Clinical application of AI requires robust evaluation under heterogeneous acquisition and curation conditions.

Purpose of the Study:

  • To establish a patient-leakage-aware, reproducible benchmark for breast ultrasound AI.
  • To evaluate AI performance under dataset shift, focusing on external generalization, calibration, and confidence.

Main Methods:

  • Developed a benchmark framework with patient-level splitting and cross-dataset evaluation.
  • Compared whole-image versus region-of-interest (ROI) input strategies for AI models.
  • Assessed calibration using temperature scaling and analyzed confidence-based behavior.

Main Results:

  • Cross-dataset testing yielded lower performance (AUROC 0.719) than internal testing (AUROC 0.801).
  • ROI input significantly improved external generalization, boosting mean external AUROC from 0.666 to 0.805.
  • Temperature scaling enhanced model calibration, reducing expected calibration error.

Conclusions:

  • A reproducible benchmark for breast ultrasound AI under dataset shift was established.
  • Patient-level leakage control and external validity are critical for reliable AI evaluation.
  • The findings emphasize the importance of ROI input and calibration for trustworthy AI in clinical settings.