The model was trained on three disease diagnosis tasks using more than 20,000 images. First, he analyzed simulated mammograms, looking for early signs of tumors. He then examined retinal images using optical coherence tomography and identified signs of retinal degeneration. Finally, the model analyzed chest X-rays to detect heart enlargement that could lead to disease.
A comparison of existing systems showed that the new model showed similar accuracy: 77.8% for mammograms, 99.1% for retinal images, and 83% for chest X-rays. Scientists hope that future models will be able to detect and diagnose abnormalities in various parts of the body, increasing trust and transparency between doctors and patients.
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Source: Ferra
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