Researchers have developed a machine learning software that accurately diagnoses the risk of breast cancer in patients 30 times faster than doctors based on breast film results and personal medical history.
The system can help doctors better diagnose breast cancer, which means fewer times of mammogram callbacks and false positives. Stephen Wong, from the Houston Methodist Institute, is one of the researchers. He said: "This software can intelligently check millions of cases in a short period of time, which allows us to use the patient's breasts more effectively. Tablet to determine the risk of breast cancer. It can avoid unnecessary biopsy."
Mammogram refers to mammogram, which is to find potential cancer cells before any symptoms appear. In many countries, women over the age of 50 are advised to do preventive screening every two years. Although such preventive screening is good, there are 50% of the tests in the United States that have false positive results. In other words, one in every two healthy women was wrongly told that they might have cancer.
In addition, “suspicious†breast radiographs also have a large gray area with a cancer risk of between 3 and 95 percent. These patients are generally recommended for subsequent biopsies. Researchers say that about two percent of current biopsies in the United States are unnecessary. The new AI is able to produce accurate results on the first screening, greatly reducing this number.
Computer software has been used by doctors to take images of the mammogram, but this system goes a step further by analyzing the doctor's report and the patient's complete medical history during the scan to more accurately determine the probability of breast cancer.
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