cancer


'A.I., Captain': The Robotic Navy Ship of the Future

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The swells in the middle of the North Pacific were reaching nine feet when one of two engines on the diesel-powered U.S. naval ship called Sea Hunter shut down. About 1,500 nautical miles from its home base in San Diego, the 132-foot-long craft, which had been cruising at 10 knots, couldn't send a member of its crew to check out the problem--because it didn't have a crew. Sea Hunter's sleek, spiderlike silhouette, with a narrow hull and two outriggers, is a prototype of what could be a new class of autonomous warships for the U.S. Navy. Its artificial intelligence–based controls and navigation system, designed by Leidos Holdings, a defense contractor based in Reston, Va., were seven years in the making. And this maiden voyage--a more than 4,000-mile roundtrip to the giant Pearl Harbor naval station--was its first major proof of concept. Nothing like this had ever been attempted before.


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.


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.


A.I. Took a Test to Detect Lung Cancer. It Got an A.

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The process, known as deep learning, is already being used in many applications, like enabling computers to understand speech and identify objects so that a self-driving car will recognize a stop sign and distinguish a pedestrian from a telephone pole. In medicine, Google has already created systems to help pathologists read microscope slides to diagnose cancer, and to help ophthalmologists detect eye disease in people with diabetes. "We have some of the biggest computers in the world," said Dr. Daniel Tse, a project manager at Google and an author of the journal article. "We started wanting to push the boundaries of basic science to find interesting and cool applications to work on." In the new study, the researchers applied artificial intelligence to CT scans used to screen people for lung cancer, which caused 160,000 deaths in the United States last year, and 1.7 million worldwide.


Genome sequencing can provide the key to cancer prevention

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A genome is the body's instruction manual. It's made of DNA and there is a copy in almost every cell. Through genome sequencing and genomics, clinicians can better understand how cancer cells might evolve and what treatments will be most responsive, known as precision and personalised medicine. Furthermore, genomics combined with technologies such as machine-learning and artificial intelligence (AI) has huge, as yet untapped, potential for determining a healthy person's future risk of cancer. To sequence the first genome cost $3 billion and took 13 years.


Using AI to predict breast cancer and personalize care

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Despite major advances in genetics and modern imaging, the diagnosis catches most breast cancer patients by surprise. For some, it comes too late. Later diagnosis means aggressive treatments, uncertain outcomes, and more medical expenses. As a result, identifying patients has been a central pillar of breast cancer research and effective early detection. With that in mind, a team from MIT's Computer Science and Artificial Intelligence Laboratory (CSAIL) and Massachusetts General Hospital (MGH) has created a new deep-learning model that can predict from a mammogram if a patient is likely to develop breast cancer as much as five years in the future.


We don't see AI opportunity

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If a picture tells a thousand words, these are the two jostling foremost in a patient's mind when a radiologist scans their body for a better image of that suspicious lump or mass. But there is so much more a picture can tell us about cancer, particularly if we consider the possibilities of artificial intelligence. In 2017, US scientists announced they had developed an algorithm, or a computerised tool, to identify skin cancers through analysis of photographs. The algorithm scans a photo of a patch of skin to look for common forms of skin cancer, performing on par with board-certified dermatologists in identifying malignant melanomas (the third most common cancer in Australia) and keratinocyte carcinoma. This technology might enable skin cancer detection in country clinics and suburban GPs' offices at the highest accuracy available.


AI is already changing how cancer is diagnosed

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Cancer is a worldwide issue. Statistics show that 17 million cases of the disease were diagnosed across the globe last year alone. Depressingly, the same research suggests there will be 27.5 million new cancer cases diagnosed each year by 2040. Although the stats don't necessarily spell good news, it's important to note that diagnosis, treatment, and in turn, patient outcomes have improved significantly. If we look back at the 1970s, less than a quarter of people with the disease survived.