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Related Concept Videos

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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...
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Ultrasonography is an imaging technique that uses high-frequency sound waves to visualize the body's internal structures. It is a non-invasive and safe procedure that does not involve the use of ionizing radiation, making it widely used in various medical fields. Ultrasonography is used to study heart function, blood flow in the neck or extremities, certain conditions such as gallbladder disease, and fetal growth and development.
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Updated: Jan 17, 2026

Determining Gender-Based Differences in Retinal and Choroidal Thickness in Underweight Individuals via Swept-Source Optical Coherence Tomography
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Automated Assessment of Choroidal Mass Dimensions Using Static and Dynamic Ultrasonographic Imaging.

Noah Emmert1,2, Gideon Wall1, Amin Nabavi3

  • 1Department of Ophthalmology, University of Tennessee Health Science Center, Memphis, TN, USA.

Translational Vision Science & Technology
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Summary
This summary is machine-generated.

An artificial intelligence (AI) model accurately measures choroidal mass dimensions from ophthalmic ultrasound images. This deep learning approach offers reproducible, millimeter-level precision for monitoring choroidal tumors.

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Area of Science:

  • Ophthalmology
  • Medical Imaging
  • Artificial Intelligence

Background:

  • Choroidal masses require accurate dimension measurement for monitoring.
  • Ophthalmic ultrasound is a key imaging modality for choroidal tumors.
  • Manual measurement of choroidal masses can be subjective and time-consuming.

Purpose of the Study:

  • To develop and validate an artificial intelligence (AI) model for detecting and measuring choroidal mass dimensions.
  • To assess the accuracy and reproducibility of AI-based measurements on B-scan ophthalmic ultrasound images.
  • To evaluate the model's performance on both still images and cine loops.

Main Methods:

  • A U-Net-based deep learning architecture was employed.
  • The model was trained on 1822 still images and 130 cine loops of choroidal masses.
  • External validation included 180 still images and 374 control images.

Main Results:

  • Internal validation showed 94.5% detection accuracy with millimeter-level precision for mass dimensions.
  • External validation achieved 83.9% detection accuracy with consistent millimeter-level precision.
  • The AI model demonstrated high accuracy in detecting masses and measuring dimensions from both still images and cine loops.

Conclusions:

  • Deep learning enables reproducible, millimeter-level measurements of choroidal mass dimensions.
  • The AI model supports potential clinical use for monitoring choroidal tumors.
  • AI facilitates precise, clinically actionable monitoring of choroidal tumors using ophthalmic ultrasound.