Diagnostic Medicine


Medical Image Analysis with Deep Learning , Part 4

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Medical Images have 4 key constituents -- Pixel Depth, Photometric Interpretation,Metadata and Pixel data. There are 6 predominant formats for radiology images -- DICOM (Digital Imaging and Communications in Medicine), NIFTI (Neuroimaging Informatics Technology Initiative), PAR/REC (Philips MRI scanner formats), ANALYZE (Mayo Medical Imaging), NRRD (Nearly Raw Raster Data) and MNIC. A Nrrd header accurately represents N-dimensional raster information for scientific visualization and medical image processing. National Alliance for Medical Image Computing (NA-MIC) has developed a way of using the Nrrd format to represent Diffusion Weighted Images (DWI) volumes and Diffusion Tensor Images (DTI).Nrrd DWI and Nrrd DTI data can be read into 3D Slicer, to visually confirm that the orientation of the tensors is consistent with expected neuroanatomy [link] MINC stands for Medical Imaging NetCDF Toolkit.


Machine Learning & Radiology – CancerGeek – Medium

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He happens to be dual board certified, fellowship trained, has multiple decades of experience in reading images, curating information that makes up a patient's medical history, has an amazing "sense" for what is relevant versus not relevant, and is a great teacher. It is to provide radiologists more time to spend with patients directly, having more time to educate their medical community on how to correctly order the right imaging exam to answer the right question for their patients, and to have more time to show how radiology (along with pathology) are not about cost centers, but are about how to deliver care at the N of 1. Radiologists earn trust at the N of 1. As always you can feel free to email me at cancergeek@gmail.com or follow me on Twitter @cancergeek You can read past issues of my weekly newsletter by clicking this link: newsletter and feel free to signup using the subscribe tab at the top.


The Future of Radiology and Artificial Intelligence

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By the early 2000s, cardiac MRI, body MRI, fetal imaging, functional MR imaging became routine exams in many imaging centers. When we can train algorithms to spot and detect many types of abnormalities based on radiology images, why wouldn't we let it do the time-consuming job so we can let radiologists dedicate their precious focus to the hardest issues? By analyzing CT scans from 48 patients, the deep learning algorithms could predict whether they'd die within five years with 69 percent accuracy. Vital also has a similar work-in-progress predictive analytics software for imaging equipment utilization.


The Future of Radiology and Artificial Intelligence - The Medical Futurist

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By the early 2000s, cardiac MRI, body MRI, fetal imaging, functional MR imaging became routine exams in many imaging centers. When we can train algorithms to spot and detect many types of abnormalities based on radiology images, why wouldn't we let it do the time-consuming job so we can let radiologists dedicate their precious focus to the hardest issues? By analyzing CT scans from 48 patients, the deep learning algorithms could predict whether they'd die within five years with 69 percent accuracy. IBM's flagship AI analytics platform, Watson is also utilized in the field of radiology.


AI to drive GDP gains of $15.7 trillion with productivity, personalisation improvements

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Healthcare: Data-driven diagnostic support: Pandemic identification: Imaging diagnostics (radiology, pathology) Automotive: Autonomous fleets for ride sharing; Smart cars/driver assist; Predictive and autonomous maintenance Financial services: Personalised financial planning; Fraud detection and anti-money laundering; Transaction automation Retail: Personalised design and production; Customer insights generation; Inventory and delivery management Technology, communications and entertainment: Media archiving and search; Content creation (marketing, film, music, etc.); Personalized marketing and advertising Manufacturing; Enhanced monitoring and auto-correction; Supply chain and production optimisation; On-demand production Energy: Smart metering; More efficient grid operation and storage; Intelligent infrastructure maintenance Transport and logistics; Autonomous trucking and delivery: Traffic control and reduced congestion; Enhanced security Methodology: To estimate AI impact, our team conducted a dual-phased top-down and bottom-up analysis combining a detailed assessment of the current and future use of AI and an exploration of the economic impact in terms of new jobs, new products, and other secondary effects. Healthcare: Data-driven diagnostic support: Pandemic identification: Imaging diagnostics (radiology, pathology) Automotive: Autonomous fleets for ride sharing; Smart cars/driver assist; Predictive and autonomous maintenance Financial services: Personalised financial planning; Fraud detection and anti-money laundering; Transaction automation Retail: Personalised design and production; Customer insights generation; Inventory and delivery management Technology, communications and entertainment: Media archiving and search; Content creation (marketing, film, music, etc.); Personalized marketing and advertising Manufacturing; Enhanced monitoring and auto-correction; Supply chain and production optimisation; On-demand production Energy: Smart metering; More efficient grid operation and storage; Intelligent infrastructure maintenance Transport and logistics; Autonomous trucking and delivery: Traffic control and reduced congestion; Enhanced security Healthcare: Data-driven diagnostic support: Pandemic identification: Imaging diagnostics (radiology, pathology) Automotive: Autonomous fleets for ride sharing; Smart cars/driver assist; Predictive and autonomous maintenance Financial services: Personalised financial planning; Fraud detection and anti-money laundering; Transaction automation Retail: Personalised design and production; Customer insights generation; Inventory and delivery management Technology, communications and entertainment: Media archiving and search; Content creation (marketing, film, music, etc.


AI could predict how much time people have left to live by analyzing body scans

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For the study, researchers used a machine learning algorithm to analyze routine chest CT scans from 48 adults, all of whom were over 60 years of age. The most immediate application of this AI technology is that it could theoretically analyze more routine chest CT scan data and provide risk calculations without a human expert taking the time to go through each scan. "Our research opens new avenues for the application of artificial intelligence technology in medical image analysis, and could offer new hope for the early detection of serious illness, requiring specific medical interventions." The basic idea behind precision medicine is that large quantities of health data can be analyzed to determine how small differences between people affect their health outcomes.


By scanning CT scans, this AI can predict who will die in the next 5 years

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Deep learning AI could one day work as an early warning system to allow earlier medical intervention to patients. That's because scientists from the University of Adelaide in Australia have used deep learning technology to analyze the computerized tomography (CT) scans of patient organs, in what could one day serve as an early warning system to catch heart disease, cancer, and other diseases early so that intervention can take place. So we can take a known outcome, like death, and look back in time at the patient's medical scans to find patterns that relate to undetected disease. The AI analyzes CT scans to make its decisions.


Artificial Intelligence to scan organs, predict death

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By analysing CT scans from 48 patients, the deep learning algorithms could predict whether they would die within five years with 69% accuracy, which is broadly similar to the scores from human diagnosticians, the paper says. It will open up new avenues for the application of AI in medical image analysis, offering hope for early detection of serious illness that requires specific medical interventions. In the study, the goal was not to build a grim diagnostic system and the AI only analysed retrospective patient data. The research's finding says machine learning, a future frontier for AI, can predict with 80- 90% accuracy whether someone will attempt suicide as far off as two years into the future.


Artificial Intelligence Systems Can Now Predict When You Will Die

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Scientists at the University Of Adelaide in Australia have developed an Artificial Intelligence system that can accurately predict a human's life expectancy. Over 16,000 image features can be analyzed by the deep learning system that give indicators of a possible disease. The use of Artificial Intelligence in medical research and diagnostics is a rapidly growing field. The ability for deep learning computers to rapidly analyze data has the potential to revolutionize diagnostics.


AI Will Predict Your Life Expectancy By Looking At CT scans

International Business Times

But, one field that could benefit largely from AI is medicine and early disease detection. Computers, using artificial intelligence, will examine images of a patient's organs and analyze them to determine their lifespan. It could also incorporate large volumes of data and detect patters that originate over them. According to a study published in the Science Magazine in April, machine-learning capable computers can diagnose heart attacks better than standard medical guidelines as it would incorporate factors such as other diseases and lifestyle factors.