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Mashup Score: 7Artificial intelligence and deep learning: Wittgenstein beats Plato - 8 month(s) ago
In the 7th book of Politeia, Plato asked Socrates to recount the Allegory of the Cave:Forsee men as in a subterranean, cave-like dwelling, which has an entrance
Source: academic.oup.comCategories: Cardiologists, Latest HeadlinesTweet
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Mashup Score: 15A Novel ECG-Based Deep Learning Algorithm to Predict Cardiomyopathy in Patients With Premature Ventricular Complexes: - 8 month(s) ago
Abstract Background Premature ventricular complexes (PVCs) are prevalent and, although often benign, they may lead to PVC-induced cardiomyopathy. We created a deep-learning algorithm to predict lef…
Source: www.jacc.orgCategories: Latest Headlines, Partners & KOLsTweet
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Mashup Score: 0Spatial cellular architecture predicts prognosis in glioblastoma - 8 month(s) ago
Nature Communications – Intra-tumoral heterogeneity and cell-state plasticity contribute to the development of therapeutic resistance in glioblastoma (GBM). Here the authors use two deep learning…
Source: www.nature.comCategories: Latest Headlines, Oncologists1Tweet
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Mashup Score: 12Zero-shot visual reasoning through probabilistic analogical mapping - 8 month(s) ago
Nature Communications – Inspired by human analogical reasoning in cognitive science, the authors propose an approach combining deep learning systems with an analogical reasoning mechanism, to…
Source: www.nature.comCategories: General Medicine News, Latest HeadlinesTweet
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Mashup Score: 0Deep Learning Models to Classify and Monitor Scoliosis Using a Single Smartphone Photograph - 8 month(s) ago
This diagnostic study assesses the ability of an open platform application using a validated deep learning model to classify and monitor idiopathic scoliosis in adolescents using a smartphone photograph.
Source: jamanetwork.comCategories: General Medicine Journals and Societies, Latest HeadlinesTweet
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Mashup Score: 4Data Consistent Deep Rigid MRI Motion Correction - 9 month(s) ago
Motion artifacts are a pervasive problem in MRI, leading to misdiagnosis or mischaracterization in population-level imaging studies. Current retrospective rigid intra-slice motion correction techniques jointly optimize estimates of the image and the motion parameters. In this paper, we use a deep network to reduce the joint image-motion parameter search to a search over rigid motion parameters…
Source: arXiv.orgCategories: General Medicine News, Latest HeadlinesTweet
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Mashup Score: 11Deep Learning on Bone Scintigraphy to Detect Abnormal Cardiac Uptake at Risk of Cardiac Amyloidosis: - 9 month(s) ago
Abstract Background Cardiac uptake on technetium-99m whole-body scintigraphy (WBS) is almost pathognomonic of transthyretin cardiac amyloidosis. The rare false positives are often related to light-…
Source: www.jacc.orgCategories: Latest Headlines, Partners & KOLsTweet
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Mashup Score: 3Deep Learning-Based Prediction of Right Ventricular Ejection Fraction Using 2D Echocardiograms: - 9 month(s) ago
Abstract Background Evidence has shown the independent prognostic value of right ventricular (RV) function, even in patients with left-sided heart disease. The most widely used imaging technique to…
Source: www.jacc.orgCategories: Latest Headlines, Partners & KOLsTweet
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Mashup Score: 7Deep learning applications in visual data for benign and malignant hematologic conditions: a systematic review and visual glossary - 9 month(s) ago
Deep learning (DL) is a subdomain of artificial intelligence algorithms capable of automatically evaluating subtle graphical features to make highly accurate predictions, which was recently popularized in multiple imaging-related tasks. Because of its capabilities to analyze medical imaging such as radiology scans and digitized pathology specimens, DL has significant clinical potential as a diagnostic or prognostic tool. Coupled with rapidly increasing quantities of digital medical data, numerous novel research questions and clinical applications of DL within medicine have already been explored. Similarly, DL research and applications within hematology are rapidly emerging, although these are still largely in their infancy. Given the exponential rise of DL research for hematologic conditions, it is essential for the practising hematologist to be familiar with the broad concepts and pitfalls related to these new computational techniques. This narrative review provides a visual glossary
Source: haematologica.orgCategories: Hem/Onc News and Journals, Latest HeadlinesTweet
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Mashup Score: 4Heart age gap estimated by explainable advanced electrocardiography is associated with cardiovascular risk factors and survival - 9 month(s) ago
AbstractBackground. Deep neural network artificial intelligence (DNN-AI)-based Heart Age estimations have been presented and used to show that the difference be
Source: academic.oup.comCategories: Cardiology News and Journals, Latest HeadlinesTweet
RT @wenzl_florian: Interested in #ArtificialInteligence and #DeepLearning in #medicine? Read our perspective: 👉🏻 https://t.co/VQe10vpZ71…