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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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Knowledge Tensor-Aided Breast Ultrasound Image Assistant Inference Framework.

Guanghui Li1,2, Lingli Xiao3, Guanying Wang4

  • 1School of Computer Science, Northwestern Polytechnical University, Xi'an 710129, China.

Healthcare (Basel, Switzerland)
|July 29, 2023
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Summary

A novel knowledge tensor model aids breast ultrasound diagnosis by using senior physician BI-RADS scores. This AI tool improves accuracy in distinguishing benign from malignant breast tumors, enhancing early cancer detection.

Keywords:
BI-RADSgeneralization inferenceknowledge tensor

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Oncology

Background:

  • Breast cancer is a leading cause of cancer in women, necessitating early detection for improved patient outcomes.
  • Breast ultrasound (BUS) is crucial for early screening but its accuracy depends on physician expertise.
  • Variability in diagnostic interpretation can impact patient prognosis.

Purpose of the Study:

  • To develop a knowledge tensor-based model to assist in breast ultrasound diagnosis.
  • To leverage senior physician expertise (BI-RADS scores) as a gold standard for training the model.
  • To improve the accuracy of differentiating benign and malignant breast tumors.

Main Methods:

  • A generalized inference model was constructed using a knowledge tensor approach.
  • The model incorporated Breast Imaging Reporting and Data System (BI-RADS) scores from senior radiologists.
  • Diagnostic results were compared against those of junior physicians, with knowledge tensor fusion applied.

Main Results:

  • The knowledge tensor trained on senior radiologist data achieved a high diagnostic AUC of 0.983 for breast cancer.
  • A tensor trained on junior radiologist data showed a lower AUC of 0.849.
  • Knowledge tensor fusion improved the overall AUC to 0.887, reducing misclassification.

Conclusions:

  • The proposed knowledge tensor model effectively aids breast ultrasound diagnosis.
  • The model helps mitigate diagnostic errors by senior radiologists, enhancing accuracy.
  • This approach shows significant potential for improving breast cancer screening and diagnosis.