Goto

Collaborating Authors

Imaging


Toward Reduction in False-Positive Thyroid Nodule Biopsies with a Deep Learning–based Risk Stratification System Using US Cine-Clip Images

#artificialintelligence

The Cine-CNNTrans achieved an average AUC of 0.88 0.10 for classifying benign versus malignant thyroid nodules. The Cine-CNNTrans showed higher AUC than the Static-2DCNN (P .03). For aggregating framewise outputs into nodulewise scores, the Cine-CNNTrans tended toward higher AUC compared with the Cine-CNNAvePool (P .17). Our system tended toward higher AUC than the Cine-Radiomics and the ACR TI-RADS level, though the difference did not achieve statistical significance (P .16


In the future, these five jobs will be replaced by Artificial Intelligence

#artificialintelligence

When we think about artificial intelligence taking over human activities, this is the first thing that springs to mind. Artificial intelligence is now widely regarded as one of the most transformative technologies of our time. Technology offers immense potential for driving corporate growth, automating industrial processes, providing insightful results, and using targeted ads, among other things. Artificial intelligence's practical applications are no laughing matter. Many people have painted AI in a bad light because of the level of automation it causes.


How Do Patients Benefit From Artificial Intelligence

#artificialintelligence

Artificial intelligence in healthcare has come a long way. The use of computers has advanced significantly over the past few years. Today, sophisticated machines have been developed to perform human tasks like analyzing and interpreting data and assisting with problem-solving. While machine learning (ML) has been widely used in many industries, the use and application of Artificial Intelligence (AI) in healthcare is still relatively new. It is only recently that we have seen AI move from the world of academics and research laboratories to hospitals.


Aidoc and Gleamer Partner To Expand the Use of AI in Medical Imaging

#artificialintelligence

This partnership will help health systems address the increasing volume of medical images and the worldwide radiologist labor shortage. Integration of Boneview into Aidoc's AI platform will give many more clinicians access to a tool to help them identify fractures in limbs, pelvis, thoracic and lumbar spine, and rib cage. Aidoc's end-to-end AI platform already includes numerous third-party AI vendors including Imbio, Riverain, Subtle, Icometrix and ScreenPoint. Over 152 million X-rays are performed every year in the US. Although there are about 37,000 radiologists in the US, they are not evenly distributed.


Disease Classification using Medical MNIST

#artificialintelligence

The objective of this study is to classify medical images using the Convolutional Neural Network(CNN) Model. Here, I trained a CNN model with a well-processed dataset of medical images. This model can be used to classify medical images based on categories provided as per the training dataset. This dataset was developed in 2017 by Arturo Polanco Lozano. It is also known as the MedNIST dataset for radiology and medical imaging. For the preparation of this dataset, images have been gathered from several datasets, namely, TCIA, the RSNA Bone Age Challange, and the NIH Chest X-ray dataset.


Davos 2022: Artificial intelligence is vital in the race to meet the SDGs

#artificialintelligence

The computer algorithm, which was trained using mammography images from almost 29,000 women, was shown to be as effective as human radiologists in spotting cancer. At a time when health services around the world are stretched as they deal with long backlogs of patients following the pandemic, this sort of technology can help ease bottlenecks and improve treatment. For malaria, a handheld lab-on-a-chip molecular diagnostics systems developed with AI could revolutionize how the disease is detected in remote parts of Africa. The project, which is led by the Digital Diagnostics for Africa Network, brings together collaborators such as MinoHealth AI Labs in Ghana and Imperial's Global Development Hub. This technology could help pave the way for universal health coverage and push us towards achieving SDG3.


Artificial Intelligence and the Future of Medical Imaging - DataScienceCentral.com

#artificialintelligence

Artificial intelligence (AI) is the imitation of human intelligence progressions by machines, mainly computer systems. Artificial intelligence has extensive applications in the healthcare sector. AI solutions assist healthcare providers in several aspects of patient care and administrative processes. Medical imaging can be defined as the diagnostic procedure that encompasses the formation of visual assistance and image representations of the human body and includes the monitoring of the execution and working of the organs of the human body. Artificial intelligence primarily consists of two types, machine learning and robots.


Artificial Intelligence in Veterinary Radiology with Seth Wallack

#artificialintelligence

Subscribe to The Vet Blast Podcast on Apple Podcasts, Spotify, or wherever you get your podcasts. The applications of artificial intelligence (AI) in veterinary radiology are the subject of this episode of The Vet Blast Podcast with Seth Wallack, DVM, DACVR. Wallack explains how advances in AI are changing the game in radiology by improving efficiency with no changes in the clinician's workflow. Below is a partial transcript. Listen to the full podcast for more.


AI recognition of patient race in medical imaging: a modelling study

#artificialintelligence

Previous studies in medical imaging have shown disparate abilities of artificial intelligence (AI) to detect a person's race, yet there is no known correlation for race on medical imaging that would be obvious to human experts when interpreting the images. We aimed to conduct a comprehensive evaluation of the ability of AI to recognise a patient's racial identity from medical images. Using private (Emory CXR, Emory Chest CT, Emory Cervical Spine, and Emory Mammogram) and public (MIMIC-CXR, CheXpert, National Lung Cancer Screening Trial, RSNA Pulmonary Embolism CT, and Digital Hand Atlas) datasets, we evaluated, first, performance quantification of deep learning models in detecting race from medical images, including the ability of these models to generalise to external environments and across multiple imaging modalities. Second, we assessed possible confounding of anatomic and phenotypic population features by assessing the ability of these hypothesised confounders to detect race in isolation using regression models, and by re-evaluating the deep learning models by testing them on datasets stratified by these hypothesised confounding variables. Last, by exploring the effect of image corruptions on model performance, we investigated the underlying mechanism by which AI models can recognise race.


Deep Learning: Types and Applications in Healthcare

#artificialintelligence

Deep learning (DL), also known as deep structured learning or hierarchical learning, is a subset of machine learning. It is loosely based on the way neurons connect to each other to process information in animal brains. To mimic these connections, DL uses a layered algorithmic architecture known as artificial neural networks (ANNs) to analyze the data. By analyzing how data is filtered through the layers of the ANN and how the layers interact with each other, a DL algorithm can'learn' to make correlations and connections in the data. These capabilities make DL algorithms an innovative tool with the potential to transform healthcare.