2023-08-21 12:47:53
New AI Model Uses Chest X-rays to Accurately Estimate and Detect Diseases
Scientists at Osaka Metropolitan University in Japan have made a significant breakthrough in medical imaging with the development of an artificial intelligence (AI) model capable of estimating a person’s age using chest X-rays. The AI system accurately determines a person’s chronological age, and it also identifies even the slightest deviations from healthy images, which may be early indicators of chronic diseases.
The researchers from the Department of Diagnostic and Interventional Radiology at the Graduate School of Medicine trained the AI model using over 67,000 chest X-rays from approximately 36,000 healthy individuals. The model showed a remarkable correlation coefficient of 0.95, indicating a high level of agreement between the estimated age by the AI and the actual age.
In the subsequent step, the AI model was applied to more than 34,000 chest X-rays of patients with pre-existing conditions such as high blood pressure, high uric acid levels, and chronic lung disease. The results demonstrated that the higher the estimated age by the AI compared to the chronological age, the greater the likelihood of patients having these diseases.
The implications of this AI model are profound in the field of medicine. By accurately estimating a person’s age and identifying potential health conditions, this technology could significantly improve early detection and control of diseases. It may also facilitate the identification of biomarkers associated with the aging process, which can contribute to enhanced preventive care.
“We believe that apparent age based on chest X-rays can accurately reflect health conditions,” said Yasuhito Mitsuyama, one of the study authors. “Our goal is to further develop this research and apply it to assess the severity of chronic diseases, predict life expectancy, and anticipate possible surgical complications.”
The study, published in The Lancet Healthy Longevity journal, highlights the potential of AI in transforming medical imaging and improving patient outcomes. With further advancements, this technology could revolutionize disease screening and lead to more personalized and effective treatment strategies.]
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