Tuesday, January 18, 2022
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Google’s AI for lung cancer diagnosis proves more accurate

After having experimental and researched with the AI models to detect cardiovascular and eye disorders and predicting patient outcomes in the medical records, internet giant Google is now working on the new AI model that will detect the lung cancer, the company revealed in a report.

As of now, lung cancer claims to be having more than $1.7 Million death every year and is also considered to be the deadliest of all cancers. It is also the sixth most common cause of death globally, according to the report which has been revealed.

While some other techniques such as the lower dose CT screening have been successful in reducing the mortality, Google believes that there are challenges that more often than not lead the unclear diagnosis.

In order to achieve efficiency in Artificial Intelligence, the internet giant has come with a model that can not only generate the overall lung cancer malignancy prediction but also identify subtle malignant tissue in the lungs.

“In late 2017, we began exploring how we could address some of these challenges using AI. Using advances in 3D volumetric modelling alongside datasets from our partners (including Northwestern University), we’ve made progress in modelling lung cancer prediction as well as laying the groundwork for future clinical testing,” Shravya Shetty, technical lead at Google, wrote as part of the blog post, explaining that radiologists typically look through hundreds of 2D images within a single CT scan and cancer can be minuscule and hard to spot.

“We validated the results with a second dataset and also compared our results against six US board-certified radiologists,” Shetty said, adding that when using a single CT scan for diagnosis, Google’s model performed on a par with or better than the six radiologists.

“The model achieved an AUC of 94.4%,” Shetty said. AUC is a common metric used in machine learning and provides an aggregate measure for classification performance.

Google is also working internally with some other divisions as Google Cloud Healthcare and life Sciences to serve his model with its cloud healthcare API.

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