Using Artificial Intelligence in the Women’s Health Space: Managing Breast Imaging

Using Artificial Intelligence in the Women’s Health Space: Managing Breast Imaging

Mammography is an X-ray imaging method used by radiologists to examine the breast for the early detection of cancer. According to MSQA national statistics, there are 39,704,071 mammography procedures done annually in the United States.

The early detection of breast cancer and personalized cancer treatment are two of the most critical strategies in preventing deaths from breast cancer. When breast cancer is found early, it’s easier to treat successfully. Getting regular mammography screening tests is the most reliable way to find breast cancer early. The American Cancer Society screening recommendations for people at average risk of breast cancer include:  

  • Women between 40 and 44 have the option to start yearly mammogram screenings
  • Women 45 to 54 should get mammograms every year
  • Women 55 and older can switch to a mammogram every other year, or they can choose to continue yearly mammograms; screening should continue as long as a woman is in good health and is expected to live at least 10 more years

Radiologists can leverage artificial intelligence (AI) technology to address the complexity of breast imaging and enhance various aspects of the process, including accurate density assessment, risk stratification, lesion detection, and callback rates. This week’s Domain Knowledge explores ways radiologists can incorporate AI in their practice.

Radiologists can Implement solutions to address the full complexity of breast imaging, including:

Density Assessment:

  • Collaborate with AI developers or utilize AI software that can analyze mammograms to accurately quantify breast density. AI algorithms can automatically identify and segment breast tissue, distinguishing between fatty and dense tissue.
  • Integrate AI tools into your existing workflow to streamline the density assessment process. AI can provide consistent and standardized density measurements, reducing inter-observer variability.

Risk Stratification:

  • Incorporate AI models that combine multiple risk factors, including breast density, patient history, genetic data, and other clinical information, to generate personalized risk assessments.
  • Use AI-powered risk prediction tools that can provide individualized risk scores for patients, enabling you to tailor screening and surveillance protocols based on their specific risk levels.

Lesion Detection:

  • Utilize AI algorithms that have been trained on large datasets of mammograms to assist in the detection of suspicious lesions. These algorithms can learn to recognize patterns and anomalies indicative of breast cancer.
  • Integrate AI software or platforms that can automatically analyze mammograms and highlight potentially suspicious areas for your review.
  • Use AI-based computer-aided detection (CAD) systems that can act as a second reader, flagging areas that may require further attention.

Callback Reduction:

  • Implement AI systems that can provide a second opinion to help reduce unnecessary callbacks. These systems can analyze mammograms alongside radiologists’ interpretations and provide additional insights.
  • Evaluate AI algorithms designed to minimize false-negative results by detecting subtle abnormalities that may be missed during the initial interpretation.
  • Consider using AI-powered decision support tools that provide evidence-based recommendations, helping you make informed decisions regarding callbacks.

To incorporate AI effectively, radiologists should:

  • Stay updated with the latest advancements in AI technologies for breast imaging. Engage with AI developers, attend conferences, and collaborate with research institutions to stay at the forefront of the field.
  • Ensure that any AI tools you adopt have undergone rigorous validation, demonstrating their effectiveness and safety in clinical practice.
  • Participate in continuing education and training programs to enhance your understanding of AI concepts and how they can be applied in breast imaging.
  • Continuously evaluate and monitor the performance of AI systems to ensure they align with your clinical needs and provide reliable results.
  • Maintain open communication with patients, explaining the role of AI in their care and addressing any concerns they may have about the integration of technology.

That’s a wrap for this week’s review of news and happenings in the healthcare AI space. In closing, we invite you to learn more about the benefits of using AI to manage the complexities of breast imaging and offer you a personal demo of Ferrum’s platform and growing AI catalog.

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