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Artificial intelligence can predict survival of ovarian cancer patients
The artificial intelligence software, created by researchers at Imperial College London and the University of Melbourne, has been able to predict the prognosis of patients with ovarian cancer more accurately than current methods.
They hope that the technology can be used to stratify ovarian cancer patients into groups based on the subtle differences in the texture of their cancer on CT scans rather than classification based on what type of cancer they have, or how advanced it is.
Our technology is able to give clinicians more detailed and accurate information on the how patients are likely to respond to different treatments, which could enable them to make better and more targeted treatment decisions.'
There are 6,000 new cases of ovarian cancer a year in the UK but the long-term survival rate is just 35-40 per cent as the disease is often diagnosed at a much later stage once symptoms such as bloating are noticeable.
Doctors diagnose ovarian cancer in a number of ways including a blood test to look for a substance called CA125 - an indication of cancer - followed by a CT scan that uses x-rays and a computer to create detailed pictures of the ovarian tumour.
Researchers used a mathematical software tool called TEXLab to identify the aggressiveness of tumours in CT scans and tissue samples from 364 women with ovarian cancer between 2004 and 2015.
The software examined four biological characteristics of the tumours which significantly influence overall survival - structure, shape, size and genetic makeup - to assess the patients' prognosis.
Artificial Intelligence Can Help Improve Understanding of Earth Science: Study
Artificial Intelligence (AI) can significantly improve our understanding of the climate and Earth science, says a study by German scientists.
'This is where deep learning techniques become a promising tool, beyond classical machine learning applications such as image recognition, natural language processing or AlphaGo,' added co-author Joachim Denzler, from the Friedrich Schiller University in Jena (FSU).
All data-driven and statistical approaches do not guarantee physical consistency per se, are highly dependent on data quality, and may experience difficulties with extrapolations, according to the study published in the journal Nature.
From predicting and identifying disease to revolutionizing the way we work, the next few decades will tell the story of the rise of machine learning and intelligence.
- On 25. oktober 2021
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