In the United States, breast cancer accounts for more deaths in women than from any other form of cancer. In 2019, it is estimated that over 250,000 new cases of invasive breast cancer will be diagnosed.
However, breast cancer, like most other types of cancer, can be difficult to diagnose. This is evident from the fact that one in every 10 cancer positive cases are misdiagnosed as negative resulting in loss of critical time for the patient and her family. There are many instances of women having diagnosed incorrectly as cancer positive in mammogram tests.
What Is Breast Ultrasound Elastography Test
One of the more reliable ways of diagnosing breast cancer is the Breast Ultrasound Elastography test. It works by detecting a lesion in the breast by evaluating the stiffness. This method has clearly proved to be a more accurate way of either confirming or ruling out breast cancer in women when compared to traditional imaging procedures.
However, there is a complex computational issue involved in the Breast Ultrasound Elastography method which can be cumbersome to deal with. It also results in extending the time frame for results to be analyzed and declared. This is where Artificial Intelligence can help.
The Role Of AI In Breast Cancer Diagnosis
AI can be used to train a machine and streamline the steps to diagnosis so that faster and more accurate results can be consistently achieved.
To create and operate such a machine it is important to understand how the breast ultrasound elastography system works.
The image of the affected area is taken and analyzed to determine the displacements happening inside the tissue. This data is used to determine aspects such as stiffness. Next comes the crucial step of identifying and quantifying the features which can help determine whether the condition is benign or malignant. There are computational challenges involved here.
According to the world’s leading experts on the subject, cancer affected breast tissues have two properties:
Based on these properties, a model was developed based on the laws of physics. Varying levels of the properties of breasts were studied and the data input derived from the research was used to train the machine. Through repeated input using thousands of synthetic images, an algorithm was developed to collect various features that can make an accurate distinction between a malignant and benign tumor.
After testing thousands of synthetic images, the machine ran tests on real-world images. The level of accuracy was determined by measuring these results against diagnoses that were confirmed through biopsy.
The Benefits of Using A Machine
There are some vital benefits of using machine learning algorithms for making a breast cancer diagnosis.
However, it would be extremely far-fetched to conclude that the machine can replace a radiologist in making a diagnosis? An algorithm simply cannot and should not be the only way of arriving at cancer diagnosis. However, it can work as a powerful tool that can guide radiologists in making the right conclusions.