Deep into the medical industry, the UK develops AI models to improve the accuracy of early cancer identification

An artificial intelligence system designed by experts from The Royal Marsden NHS Foundation Trust, The Institute of Cancer Research London and Imperial College London claims to have been able to identify tumor abnormalities found in CT scans Will the growth become cancerous? The findings were published in eBioMedicine, a sub-journal of The Lancet.

The researchers said that this new calculation method is more efficient than the current calculation method and is expected to improve the early screening of tumors in the future and improve the quality of life through the screening, fast tracking and early intervention of high-risk patients. Tumor treatment success rate.

According to the World Health Organization, cancer kills about 10 million people every year, that is, nearly one in six deaths is related to cancer. However, in many cases, if cancer is detected early and treated quickly, it is curable.

The research team took CT scans of approximately 500 patients with large lung nodules and developed an AI algorithm using radiomics, a technology that can extract data from medical images that are not easily visible to the human eye. Extract important information. The researchers then began testing the AI ​​model to see if it could accurately identify cancerous nodules.

By using a method called AUC, the researchers looked at how effective the model was at predicting cancer. An AUC of 1 means the model is perfect. The results showed that the average AUC value of the AI ​​model identifying the cancer risk of each nodule was 0.87.

Bitnet believes that whether it is technology or industry application, artificial intelligence is in the process of continuous development, especially in the medical industry. Artificial intelligence will continue to evolve from auxiliary medicine. In this process, we should also pay attention to Artificial intelligence ethics supports the development of artificial intelligence on the premise of complying with laws and regulations.

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