Artificial intelligence system spots lung cancer before radiologists

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CHICAGO --- Deep learning - a form of artificial intelligence - was able to detect malignant lung nodules on low-dose chest computed tomography (LDCT) scans with a performance meeting or exceeding that of expert radiologists, reports a new study from Google and Northwestern Medicine. This deep-learning system provides an automated image evaluation system to enhance the accuracy of early lung cancer diagnosis that could lead to earlier treatment. The deep-learning system was compared against radiologists on LDCTs for patients, some of whom had biopsy confirmed cancer within a year. In most comparisons, the model performed at or better than radiologists. Deep learning is a technique that teaches computers to learn by example.


Artificial intelligence system spots lung cancer before radiologists

#artificialintelligence

CHICAGO --- Deep learning - a form of artificial intelligence - was able to detect malignant lung nodules on low-dose chest computed tomography (LDCT) scans with a performance meeting or exceeding that of expert radiologists, reports a new study from Google and Northwestern Medicine. This deep-learning system provides an automated image evaluation system to enhance the accuracy of early lung cancer diagnosis that could lead to earlier treatment. The deep-learning system was compared against radiologists on LDCTs for patients, some of whom had biopsy confirmed cancer within a year. In most comparisons, the model performed at or better than radiologists. Deep learning is a technique that teaches computers to learn by example.


Google's AI boosts accuracy of lung cancer diagnosis, study shows - STAT

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One of lung cancer's most lethal attributes is its ability to trick radiologists. Some nodules appear threatening but turn out to be false positives. Others escape notice entirely, and then spiral without symptoms into metastatic disease. On Monday, however, Google unveiled an artificial intelligence system that -- in early testing -- demonstrated a remarkable talent for seeing through lung cancer's disguises. A study published in Nature Medicine reported that the algorithm, trained on 42,000 patient CT scans taken during a National Institutes of Health clinical trial, outperformed six radiologists in determining whether patients had cancer.


NYU open-sources breast cancer screening model trained on over 200,000 mammography exams

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Breast cancer is the second leading cancer-related cause of death among women in the U.S. It's estimated that in 2015, 232,000 women were diagnosed with the disease and approximately 40,000 died from it. And while diagnostic exams like mammography have come into wide practice -- in 2014, over 39 million breast cancer screenings were performed in the U.S. alone -- they're not always reliable. About 10 to 15 percent of women who undergo a mammogram are asked to return following an inconclusive analysis. That's why researchers at New York University are investigating an AI-driven technique that promises much higher precision than today's tests. In a newly published paper on Arxiv.org


Google shows how AI might detect lung cancer faster and more reliably

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New research from Google shows how machine learning could one day be used to detect signs of lung cancer earlier than often occurs today. Early warning: Danial Tse, a researcher at Google, developed an algorithm that beat a number of trained radiologists in testing. Tse and colleagues trained a deep-learning algorithm to detect malignant lung nodules in more than 42,000 CT scans. The resulting algorithms turned up 11% fewer false positives and 5% fewer false negatives than their human counterparts. The work is described in a paper published in the journal Nature today.