Health
New AI model diagnoses diseases by drawing visual maps
NEW DELHI: Scientists have developed a new artificial intelligence (AI) model that accurately identifies tumors and diseases in medical images by explaining each diagnosis with a visual map.
The unique transparency of models published in magazines IEEE Transactions on Medical Imagingthe researchers said, allows doctors to easily follow that line of reasoning, double-check accuracy, and explain the results to patients.
Sourya Sengupta, a graduate student at the Beckman Institute for Advanced Science and Technology, said, “The goal is to detect cancers and diseases at an early stage, like an X on a map, and to understand how those decisions are made.'' “This will help us understand what's going on.” we.
“Our model helps streamline that process and make it easier for doctors and patients alike,” said Sengupta, the study's lead author.
The process of decoding medical images varies depending on the region of the world.
“Many developing countries have a shortage of doctors and long lines of patients. AI could be helpful in these situations,” Sengupta said.
Sengupta said automated medical image screening can be deployed as an adjunct tool when time and human resources are in high demand, but it can never replace a doctor's skills and expertise.
Instead, AI models can proactively scan medical images and flag images containing anything unusual (such as tumors or early signs of disease called biomarkers) for review by a doctor. This method saves time and can also improve the performance of those tasked with reading the scans.
These models work well, but there is room for improvement in many ways, for example when a patient asks why the AI system flagged an image as containing (or not containing) a tumor.
The new AI model interprets itself every time, Sengupta said, explaining each decision rather than blandly reporting a binary “tumor or non-tumor.”
The researchers trained the model on three different disease diagnosis tasks involving more than 20,000 images.
First, the model reviewed simulated mammograms and learned how to indicate early signs of tumors. They then practiced analyzing optical coherence tomography (OCT) images of the retina and identifying deposits called drusen, which can be early signs of macular degeneration.
OCT is a non-invasive imaging test that uses light waves to take cross-sectional pictures of the retina.
Third, the model studied chest X-rays and learned how to detect cardiomegaly, a condition of heart enlargement that can cause disease.
Once the cartographic model was trained, the researchers compared its performance to existing AI systems (systems without self-interpretation settings).
The model performed as well as its counterpart in all three categories, with an accuracy of 77.8 percent for mammography, 99.1 percent for retinal OCT images, and 83 percent for chest X-ray images, the researchers said. These high precisions are an achievement. The nonlinear layers of AI's deep neural networks mimic the nuances of human neurons in decision-making, they added.
(issued March 5, 2024, 08:56 teeth)
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