Pulmonary/Respiratory Diseases


AI Shows Promise in Detecting Lung Cancer

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In the latest research, a new AI-based instrument has been revealed by Google and medical associates including Northwestern University that can produce a better model of a patient's lung from CT scan pictures. This 3-D image provides better projections of tumour malignancy and includes learning from prior scans, allowing the AI to help clinicians spot lung cancer in earlier phases when it is much more treatable. While the disease can be quashed if discovered early enough, Google states that a small portion of the eligible U.S. population is tested for lung cancer.AI support in evaluating medical imaging and constructing a statistical model lifts some strain off the shoulders of clinicians, along with the tremendous job and opaque results a human-only evaluation can generate. The rise of data across the healthcare industry implies that all kinds of professionals are inundated with information they need to review. As companies start to invest more in population health, having the capacity to optimize and enhance projections and diagnoses through AI-assisted imaging means leveraging technology to allow suppliers to perform duties they are better adapted to.


AI-Smartphone App 'Listens' to Cough to Diagnose Disease - Docwire News

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A group of Australian researchers have recently developed an AI-powered smartphone app that can diagnose respiratory disorders by "listening" to the user's cough. This technology was developed by researchers at Curtin University and The University of Queensland, Australia, whose findings were published June 6 in the journal Respiratory Research. The researchers created an algorithm that can analyze coughs for features that are unique to five different diseases. This technique is similar to speech recognition technologies in that the software examines the auditory cough for characteristics specific to these conditions. This is typically done by a physician during a clinical exam, with a stethoscope being used to listen to sound produced while breathing or coughing (auscultation).


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

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We had to share this one, AI crossing boundaries for the good of the human race. How many more applications of AI will there be in medicine. I am looking forward to the advances in the coming years and hope this sort of technology will be available for the many and not just the few. "Computers were as good or better than doctors at detecting tiny lung cancers on CT scans, in a study by researchers from Google and several medical centers." "The technology is a work in progress, not ready for widespread use, but the new report, published Monday in the journal Nature Medicine, offers a glimpse of the future of artificial intelligence in medicine."


Artificial intelligence better than humans at spotting lung cancer

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The condition is the leading cause of cancer-related death in the U.S., and early detection is crucial for both stopping the spread of tumors and improving patient outcomes. As an alternative to chest X-rays, healthcare professionals have recently been using computed tomography (CT) scans to screen for lung cancer. In fact, some scientists argue that CT scans are superior to X-rays for lung cancer detection, and research has shown that low-dose CT (LDCT) in particular has reduced lung cancer deaths by 20%. These errors typically delay the diagnosis of lung cancer until the disease has reached an advanced stage when it becomes too difficult to treat. New research may safeguard against these errors.


The Challenge of Crafting Intelligible Intelligence

Communications of the ACM

While there are anemones in those images, it also seems that the system is recognizing a clownfish.


Google's lung cancer detection AI outperforms 6 human radiologists

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Google AI researchers working with Northwestern Medicine created an AI model capable of detecting lung cancer from screening tests better than human radiologists with an average of eight years experience. When analyzing a single CT scan, the model detected cancer 5% more often on average than a group of six human experts and was 11% more likely to reduce false positives. Humans and AI achieved similar results when radiologists were able to view prior CT scans. When it came to predicting the risk of cancer two years after a screening, the model was able to find cancer 9.5% more often compared to estimated radiologist performance laid out in the National Lung Screening Test (NLST) study. Detailed in research published today in Nature Medicine, the end-to-end deep learning model was used to predict whether a patient has lung cancer, generating a patient lung cancer malignancy risk score and identifying the location of the malignant tissue in the lungs.


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.


End-to-end lung cancer screening with three-dimensional deep learning on low-dose chest computed tomography

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D.A., A.P.K., S.B. and B.C. developed the network architecture and data/modeling infrastructure, training and testing setup. D.A. and A.P.K. created the figures, wrote the methods and performed additional analysis requested in the review process. D.P.N. and J.J.R. provided clinical expertise and guidance on the study design. G.C and S.S. advised on the modeling techniques. M.E., S.S., J.J.R., B.C., W.Y. and D.A. created the datasets, interpreted the data and defined the clinical labels.


Aidoc gets FDA nod for AI pulmonary embolism screening tool - MedCity News

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Israeli radiology startup Aidoc has received FDA clearance for its AI-based product meant to help identify potential cases of pulmonary embolism in chest CT scans. Pulmonary embolism (PE) – which occurs when a blood clot gets lodged in the lung – is considered a silent killer that causes up to 200,000 deaths a year in the United States. The condition often strikes with little to no warning and diagnosis of a case can be extremely time-sensitive. Aidoc's technology doesn't require dedicated hardware and runs continuously on hospital systems, automatically ingesting radiological images. The 70-person company focuses on workflow optimization in radiology to help triage high risk patients for additional and faster review.


AI Accelerates Innovation

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The deep learning algorithms of artificial intelligence can identify patterns that help inventors think laterally, make connections between nonobvious ideas, pinpoint hidden invention features, and exploit new science and technology-based opportunities. "To invent, you need a good imagination and a pile of junk." So said Thomas Edison, America's most prolific inventor. Yet the march of technology is now changing the great man's inventive equation: powerful algorithmic advisory systems are now giving inventors far more fertile imaginations, even if they don't have very much of one themselves. After being fed vast datasets of information on a field of inventive endeavor, deep learning algorithms identify patterns that help inventors think laterally, make connections between nonobvious ideas, pinpoint hidden invention features that rivals have missed, and exploit new science and technology-based opportunities from, say, patents and journals.