· Eight clinicians tested HeadXNet by evaluating a set of 115 brain scans for aneurysm, once with the help of HeadXNet and once without With · The AI tool, built around an algorithm called HeadXNet, was developed by researchers at Stanford University In tests, the tool improved clinicians' ability to · The researchers created HeadXNet, a threedimensional (3D) deep learning convoluted neural network (CNN), which is a type of neural network used to
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Headxnet-The end result of the HeadXNet tool is the algorithm's conclusions overlaid as a semitransparent highlight on top of the scan This representation of the algorithm's decision makes it easy for · The end result of the HeadXNet tool is the algorithm's conclusions overlaid as a semitransparent highlight on top of the scan This representation of the
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· In this brain scan, the location of an aneurysm is indicated by HeadXNet using a transparent red highlight Image credit Allison Park, Stanford University Read Time Doctors could soon get some help from an artificial intelligence tool when diagnosing brain aneurysms – bulges in blood vessels in the brain that can leak or burst open, potentially leading to stroke, brain damageHeadXNet team members (from left to right, Andrew Ng, Kristen Yeom, Christopher Chute, Pranav Rajpurkar and Allison Park) looking at a brain scan Scans likeDeep LearningAssisted Diagnosis of Cerebral Aneurysms Using the HeadXNet Model Park, Allison, Chute, Chris, Rajpurkar, Pranav, Lou, Joe, Ball, Robyn L, Shpanskaya, Katie, Jabarkheel, Rashad, Kim, Lily H, McKenna, Emily, Tseng, Joe, and others JAMA Network Open, American Medical Association, 2, (6), pages ee, 19 bib
· HeadXNet did not influence how long it took the clinicians to decide on a diagnosis or their ability to correctly identify scans without aneurysms – a guard against telling someone they have an aneurysm when they don't To other tasks and institutions The machine learning methods at the heart of HeadXNet could likely be trained to identify other diseases inside and outside the95% CI 960, 986) Moreover, eight new aneurysms that had been overlooked in theObjective To develop and apply a neural network segmentation model (the HeadXNet model) capable of generating precise voxelbyvoxel predictions of intracranial aneurysms on head computed tomographic angiography (CTA) imaging to augment clinicians' intracranial aneurysm diagnostic performance
Few studies to date have explored this topicTo develop and apply a neural network segmentation model (the HeadXNet model) capable of generating precise voxelbyvoxel predictions of intracranial aneurysms on head computed tomographic angiography (CTA) imaging to augment clinicians' intracranial aneurysm diagnostic performanceIn this diagnostic study, a 3dimensional · HeadXNet team members (from left to right, Andrew Ng, Kristen Yeom, Christopher Chute, Pranav Rajpurkar and Allison Park) looking at a brain scan Scans like this were used to train and test their artificial intelligence tool, which helps identify brain aneurysms LA Cicero/Stanford News Service Doctors could soon get some help from an artificial intelligence tool when · Eight clinicians tested HeadXNet by interpreting 115 separate scans both with the algorithm and without it Overall, the automated detection tool led to improvements in sensitivity, mean accuracy, and mean interrater agreement On the other hand, no significant changes were observed in mean specificity or time to diagnosis Stanford researchers develop artificial
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· HeadXNet did not influence how long it took the clinicians to decide on a diagnosis or their ability to correctly identify scans without aneurysms a guard against telling someone they have an aneurysm when they don't To other tasks and institutions The machine learning methods at the heart of HeadXNet could likely be trained to identify other diseases inside and outside theHeadXNet team members (from left to right, Andrew Ng, Kristen Yeom, Christopher Chute, Pranav Rajpurkar and Allison Park) looking at a brain scan Scans like this were used to train and test their artificial intelligence tool, which helps identify brain aneurysms · HeadXNet did not influence how long it took the clinicians to decide on a diagnosis or their ability to correctly identify scans without aneurysms – a guard against telling someone they have an aneurysm when they don't To other tasks and institutions The machine learning methods at the heart of HeadXNet could likely be trained to identify other diseases inside and outside the
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· HeadXNet是具有编码器 解码器结构的CNN（补充中的e图1），其中编码器将卷映射到抽象的低分辨率编码，然后解码器将该编码扩展为全分辨率分割体积 · HeadXNet is a CNN with an encoderdecoder structure (eFigure 1 in the Supplement), where the encoder maps a volume to an abstract lowresolution encoding, and the decoder expands this encoding to a fullresolution segmentation volume The segmentation volume is of the same size as the corresponding study and specifies the probability of aneurysm for each · The HeadxNet algorithm was trained by Yeom, Allison Park, Standfrod graduate student in statistics and Christopher Chute, graduate student in computer science They outlined clinically significant aneurysms that had been detectable on 611 computerized tomography (CT) angiogram head scans The team labeled every voxel (the 3D equivalent of a pixel), indicating the
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· Artificial intelligence (AI) applications are growing at an unprecedented pace in health care, including disease diagnosis, triage or screening, risk analysis, surgical operations, and so forth Despite a great deal of research in the development and · New AI Tool, HeadXNet, to Help Detect Brain Aneurysms June 14th, 19 Siavash Parkhideh Informatics, Neurology, Neurosurgery, Radiology Researchers from Stanford University have developed a new AI · Using HeadXNet, clinicians correctly identified more aneurysm, reduced the "miss" rate and were more likely to agree with their colleagues Not only did the algorithm say whether the scan contained an aneurysm, but it also helped pinpoint the exact locations of the aneurysms "Search for an aneurysm is one of the most laborintensive and critical tasks radiologists can
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Deep learning–assisted diagnosis of cerebral aneurysms using the HeadXNet model A Park, C Chute, P Rajpurkar, J Lou, RL Ball, K Shpanskaya, JAMA network open 2 (6), ee, 19 68 19 Fiber type composition and maximum shortening velocity of muscles crossing the human shoulder RC Srinivasan, MP Lungren, JE Langenderfer, RE Hughes Clinical Anatomy · Objective To develop and apply a neural network segmentation model (the HeadXNet model) capable of generating precise voxelbyvoxel predictions of intracranial aneurysms on head computed tomographic angiography (CTA) imaging to augment clinicians' intracranial aneurysm diagnostic performance · HeadXNet AI system helps radiologists detect brain aneurysms Doctors could soon get some help from an artificial intelligence tool when diagnosing brain aneurysms—bulges in blood vessels in the brain that can leak or burst open, potentially leading to stroke, brain damage or death
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· HeadXNet is a CNN with an encoderdecoder structure (eFigure 1 in the Supplement), where the encoder maps a volume to an abstract lowresolution encoding, and the decoder expands this encoding to a fullresolution segmentation volume The segmentation volume is of the same size as the corresponding study and specifies the probability of aneurysm for each voxel,The tool is built around an algorithm called HeadXNet and improved the ability of clinicians to correctly identify aneurysms at a level equivalent to finding six more aneurysms in 100 scans that contain them Image credit Allison Park AI to detect aneurysms 4 New research could help predict seizures before they happen Scientists at the Royal College of Surgeons in Ireland haveIn this study, we developed a 3dimensional (3D) CNN called HeadXNet for segmentation ofintracranial aneurysms from CT scans Neural networks are functions with parameters structured asa sequence of layers to learn different levels of abstraction Convolutional neural networks are a typeof neural network designed to process image data, and 3D CNNs are particularly well suited
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HeadXNet did not influence how long it took the clinicians to decide on a diagnosis or their ability to correctly identify scans without aneurysms – a guard against telling someone they have an aneurysm when they don't To other tasks and institutions The machine learning methods at the heart of HeadXNet could likely be trained to identify other diseases inside and outside the brain · The end result of the HeadXNet tool is the algorithm's conclusions overlaid as a semitransparent highlight on top of the scan This representation of theHeadXNet algorithm tool can detect brain aneurysms 7 June 21 Service Engineering Researchers at Stanford University have developed an AI tool, built on an algorithm called HeadXNet, which can identify parts of the brain that might contain an aneurysm The algorithm's conclusions are overlaid on computerised tomography angiogram head scans as a semi
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Objective To develop and apply a neural network segmentation model (the HeadXNet model) capable of generating precise voxelbyvoxel predictions of intracranial aneurysms on head computed tomographic angiography (CTA) imaging to augment clinicians' intracranial aneurysm diagnostic performance · The end result of the HeadXNet tool is the algorithm's conclusions overlaid as a semitransparent highlight on top of the scan This representation of the · The study showed that in the detection of intracranial aneurysms, HeadXNet improved clinician specificity by about 15%, sensitivity by about 6%, and accuracy by about 4% While CTA is the primary imaging technique used to diagnose, monitor, and plan surgeries for intracranial aneurysms, analyzing the results is incredibly timeconsuming, even for the most
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The researchers are also planning to establish a multicenter collaboration to make AI health care technologies, like HeadXNet, easier to use across different hospitals, which tend to have different hardware, patient populations and imaging protocols Given the challenges of making AI work in all these different situations and the vital importance of accurate diagnoses these researchers · Built around an algorithm called HeadXNet, the tool looks through head scans made using computed tomography angiography and for every voxel, the 3D version of a pixel, decides whether or not there is a sign of an aneurysm · HeadXNet was tested by eight clinicians by evaluating a set of 115 different brain scans for aneurysms, once with the help of HeadXNet and once without With the tool, the clinicians correctly identified more aneurysms, and therefore reduced the "miss" rate, and the clinicians were more likely to agree with one another The researchers believe that the tool did
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· Deep learning helps detect intracranial aneurysms By Erik L Ridley, AuntMinniecom staff writer June 10, 19 A deeplearning algorithm called HeadXNet can improve the performance of clinicians of varied specialties and levels of experience for detecting intracranial aneurysms on head CT angiography (CTA) exams, according to research published online June 7 · Of these, 534 CT angiograms (6 aneurysms) were assigned to the training set, and the remaining 534 CT angiograms (649 aneurysms) constituted the validation set The sensitivity of the proposed algorithm for detecting cerebral aneurysms was 975% (633 of 649; · Based on an algorithm called HeadXNet, the new tool is designed to highlight the areas of a brain scan that could contain aneurysms, which have the potential to leak or burst open and could lead to a stroke, brain damage or death Researchers noted that the AI solution improved clinicians' ability to correctly detect aneurysms and also consensus among the interpreting
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· The HeadXNet model was developed by a group of researchers at Stanford University, who trained the algorithm using brain scans from 662 patients Using this data, the model examines scans and · Nevertheless, HeadXNet serves as a significant step forward in the process of solving a complicated and fatal problem with underfunded research It also demonstrates the greater benefit of usingHeadXNet Archives Express Computer
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· Deep LearningAssisted Diagnosis of Cerebral Aneurysms Using the HeadXNet Model Park A, Chute C, Rajpurkar P, Lou J, Ball RL, Shpanskaya K, Jabarkheel R, Kim LH, McKenna E, Tseng J, Ni J, Wishah F, Wittber F, Hong DS, Wilson TJ, Halabi S, Basu S, Patel BN, Lungren MP, · Eight clinicians tested HeadXNet by evaluating a set of 115 brain scans, once with the help of the AI and once without With the tool, the clinicians correctly identified more aneurysms and therefore reduced the "miss" rate The time it took to arrive at diagnosis was not affected Despite the early success, the Stanford team is cautious in relation to the clinical use of the toolThe end result of the HeadXNet tool is the algorithm's conclusions overlaid as a semitransparent highlight on top of the scan This representation of the algorithm's decision makes it easy for the clinicians to still see what the scans look like without HeadXNet's input "We were interested how these scans with AIadded overlays would improve the performance of clinicians," said
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