Friday November 16, 2018

AI-System to Detect Specks of Lung Cancer

The approach is similar to the algorithms that facial-recognition software uses. It scans thousands of faces looking for a particular pattern to find its match

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Cancer
Cancer Ribbon. Pixabay
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Researchers from University of Central Florida in the US have taught a computer how to detect tiny specks of lung cancer in computed tomography (CT) scans, which radiologists often have a difficult time identifying.

The Artificial Intelligence (AI) system is about 95 per cent accurate, compared to 65 per cent when done by human eyes, the team said.

“We used the brain as a model to create our system,” one of the researchers Rodney LaLonde said in a statement released by the university.

The approach is similar to the algorithms that facial-recognition software uses. It scans thousands of faces looking for a particular pattern to find its match.

Cancer
Cancer Ribbon. Pixabay

The group fed more than 1,000 CT scans into the software they developed to help the computer learn to look for the tumours, according to the research to be presented at the MICCAI 2018 conference in Spain in September.

“I believe this will have a very big impact,” said Assistant Professor Ulas Bagci.

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“Lung cancer is the number one cancer killer in the US and if detected in late stages, the survival rate is only 17 percent. By finding ways to help identify earlier, I think we can help increase survival rates,” Bagci added.

The next step is to move the research project into a hospital setting. After that, the technology could be a year or two away from the marketplace, Bagci said. (IANS)

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AI Technique to Improve Brain Scans to Predict Alzheimer’s Early

"If we diagnose Alzheimer's disease when all the symptoms have manifested, the brain volume loss is so significant that it's too late to intervene," Sohn said

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AI technique can boost brain scans to predict Alzheimer early. Pixabay

Artificial intelligence (AI) can help improve the ability of brain imaging techniques to predict Alzheimer’s disease early, according to a study.

Researchers from the University of California in San Francisco (UCSF) trained a deep learning algorithm on a special imaging technology known as 18-F-fluorodeoxyglucose positron emission tomography (FDG-PET).

They included more than 2,100 FDG-PET brain images from 1,002 patients and on an independent set of 40 imaging exams from 40 patients.

The results showed that the algorithm was able to teach itself metabolic patterns that corresponded to Alzheimer’s disease.

It also achieved 100 per cent sensitivity at detecting the disease an average of more than six years prior to the final diagnosis.

“We were very pleased with the algorithm’s performance. It was able to predict every single case that advanced to Alzheimer’s disease,” said Jae Ho Sohn, from UCSF’s Radiology and Biomedical Imaging Department.

“If FDG-PET with AI can predict Alzheimer’s disease this early, beta-amyloid plaque and tau protein PET imaging can possibly add another dimension of important predictive power,” he added, in the paper detailed in the journal Radiology.

While early diagnosis of Alzheimer’s is extremely important for the treatment, it has proven to be challenging.

"The question for us now is not how to eliminate cholesterol from the brain, but about how to control cholesterol's role in Alzheimer's disease through the regulation of its interaction with amyloid-beta," Vendruscolo said.
The results showed that the algorithm was able to teach itself metabolic patterns that corresponded to Alzheimer’s disease, Pixabay

Although the cause behind the progressive brain disorder remains unconfimed yet, various research has linked the disease process to changes in metabolism, as shown by glucose uptake in certain regions of the brain.

These changes can be difficult to recognise.

“If we diagnose Alzheimer’s disease when all the symptoms have manifested, the brain volume loss is so significant that it’s too late to intervene,” Sohn said.

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“If we can detect it earlier, that’s an opportunity for investigators to potentially find better ways to slow down or even halt the disease process,” he noted.

Sohn explained that the algorithm could be a useful tool to complement the work of radiologists — especially in conjunction with other biochemical and imaging tests — in providing an opportunity for early therapeutic intervention. (IANS)