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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 AI predicts hospital inpatient death risks with 95% accuracy

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Using raw data from the entirety of a patient's electronic health record, Google researchers have developed an artificial intelligence network capable of predicting the course of their disease and risk of death during a hospital stay, with much more accuracy than previous methods. The deep learning models were trained on over 216,000 deidentified EHRs from more than 114,000 adult patients, who had been hospitalized for at least one day at either the University of California, San Francisco or the University of Chicago. For those two academic medical centers, the AI predicted the risks of mortality, readmission and prolonged stays, as well as discharge diagnoses, by ICD-9 code. The network was 95% accurate in predicting a patient's risk of dying while in the hospital--with a much lower rate of false alerts--than the traditional regressive model--the augmented Early Warning Score--which measures 28 factors and was about 85% accurate at the two centers. The researchers' findings were published last month in the Nature journal npj Digital Medicine.


Machine Learning To Help Physicians

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Physicians have long used visual judgment of medical images to determine the course of cancer treatment. A new program package from Fraunhofer researchers reveals changes in images and facilitates this task using deep learning. The experts will demonstrate this software in Chicago from November 27 to December 2 at RSNA, the world's largest radiology meeting. Has a tumor shrunk during the course of treatment over several months, or have new tumors developed? To answer questions like these, physicians often perform CT and MRI scans.