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Cancer Nodules Detection

PROBLEM

  1. Lung cancer (both small cell and non-small cell) is the second most common cancer in both men and women.
  2. In most death late diagnose is the major problem faces by people.
  3. We do have DICOM images of early nodules but still the problem is we not able to predict those symptoms.
  4. The American Cancer Society’s estimates for lung cancer in the United States for 2021 are:
  5. About 235,760 new cases of lung cancer (119,100 in men and 116,660 in women)
  6. About 131,880 deaths from lung cancer (69,410 in men and 62,470 in women)

SOLUTION

  1. Early detection of the cancer can allow for early treatment which significantly increases the chances of survival.
  2. This project creates an AI algorithm that automatically detects candidate nodules and predicts the probability that the lung will be diagnosed with cancer within 1 year of the CT scans.  The algorithm is summarized by the following framework:
  3. The LIDC-IDRI dataset is a publicly available dataset for nodules in lung. They have classified nodules as cancerous and non-cancerous.
  4. We can create a neural net to first identify nodules in any DICOM image. Then once the nodule gets detected, we can train an image classification network to identify nodules as cancerous or non-cancerous.
  5. So this Image Processing-based AI algorithm can help us identify the actual problem and predict it before the situation gets worse.

IMPLEMENTATION

  1. We can integrate this with any EMR / EHR through API integration or it can be integrated with our own Meditibb EHR, ezTelemed Telemed Platform or our Cloud-based PACS Tele-radiology integration.
  2. Images and AI results can be viewed using our integrated DICOM viewer.

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