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Ultrasound I: Abdominal Ultrasonography

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...

Ultrasonography

Ultrasonography

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.
During an ultrasonography procedure, a handheld device called...

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A Swin Transformer-Based Model for Thyroid Nodule Detection in Ultrasound Images

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A Deep Learning Framework for Enhanced Ovarian Adnexal Mass Classification Using Routinely Acquired Ultrasound

Mrinal K Dhar¹, Luigi De Vitis², Adriana V Gregory¹

¹Department of Radiology, Mayo Clinic, Rochester, MN, USA.

Journal of Imaging Informatics in Medicine

|March 20, 2026

Summary

This summary is machine-generated.

This study introduces a deep learning AI for classifying ovarian masses using ultrasound radiomics and substructure analysis. The AI achieves high accuracy, outperforming existing methods for improved clinical decision-making.

Keywords:

Adnexal mass classification Deep learning Multi-modal network Ovarian cancer Radiomics Ultrasound imaging

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Published on: March 3, 2023

Area of Science:

Medical Imaging
Artificial Intelligence
Oncology

Background:

Accurate classification of ovarian masses is vital for effective clinical management.
B-mode ultrasound is a common imaging modality, but differentiating complex adnexal masses can be challenging.

Purpose of the Study:

To develop and evaluate a deep learning framework for enhanced diagnostic accuracy in classifying ovarian masses.
To integrate radiomics and mass substructure analysis for improved differentiation of benign and malignant adnexal masses.

Main Methods:

A retrospective study of 230 patients with adnexal masses imaged via ultrasound.
A deep learning model for automatic mass segmentation, fluid/solid component distinction, and multi-modal classification.
An explainability method using feature embedding similarity to identify similar training cases.

Main Results:

The deep learning framework achieved 90% image-level accuracy and 94% AUC, and 91% patient-level accuracy and 92% AUC.
Performance surpassed existing methods like ADNEX, O-RADS 2019, and O-RADS 2022.
The explainability method enhances clinical interpretability by providing similar historical case visualizations.

Conclusions:

The proposed deep learning framework significantly improves the accuracy of ovarian mass classification compared to current standards.
This AI-assisted approach offers potential for enhanced clinical decision-making by providing malignancy predictions and visual case comparisons.
Further exploration of clinical applications for AI in ovarian mass diagnosis is warranted.