The objective of this study is to develop a robust deep learning-based framework to distinguish COVID-19, Community-Acquired Pneumonia (CAP), and Normal cases based on chest CT scans acquired in different imaging centers using various protocols, and radiation doses. We showed that while our proposed model is trained on a relatively small dataset acquired from only one imaging center using a specific scanning protocol, the model performs well on heterogeneous test sets obtained by multiple scanners using different technical parameters. We also showed that the model can be updated via an unsupervised approach to cope with the data shift between the train and test sets and enhance the robustness of the model upon receiving a new external dataset from a different center. We adopted an ensemble architecture to aggregate the predictions from multiple versions of the model. For initial training and development purposes, an in-house dataset of 171 COVID-19, 60 CAP, and 76 Normal cases was used, which contained volumetric CT scans acquired from one imaging center using a constant standard radiation dose scanning protocol. To evaluate the model, we collected four different test sets retrospectively to investigate the effects of the shifts in the data characteristics on the model's performance. Among the test cases, there were CT scans with similar characteristics as the train set as well as noisy low-dose and ultra-low dose CT scans. In addition, some test CT scans were obtained from patients with a history of cardiovascular diseases or surgeries. The entire test dataset used in this study contained 51 COVID-19, 28 CAP, and 51 Normal cases. Experimental results indicate that our proposed framework performs well on all test sets achieving total accuracy of 96.15% (95%CI: [91.25-98.74]), COVID-19 sensitivity of 96.08% (95%CI: [86.54-99.5]), CAP sensitivity of 92.86% (95%CI: [76.50-99.19]).

Tuberculosis is an issue close to home for Pune, India-based healthcare startup DeepTek. India has the world's highest prevalence of the disease -- accounting for over one-quarter of the 10 million new cases each year. It's a fitting first project for the company, whose founders hope to greatly improve global access to medical imaging diagnostics with an AI-powered radiology platform. India aims to eradicate TB by 2025, five years before the United Nations' global goal to end the epidemic by 2030. Chest X-ray imaging is the most sensitive screening tool for pulmonary TB, helping clinicians determine which patients should be referred for further lab testing.

While advances in hybrid imaging and other cutting-edge modalities seem to get all the attention, digital radiography (DR) maintains its solid foundation of diagnostic support for imaging centers, emergency departments, outpatient clinics, and mobile operations worldwide. Based on the myriad of presentations scheduled for RSNA 2019, DR arguably might be the modality that will benefit most from artificial intelligence (AI) and deep-learning algorithms. As DR-specific applications are tested and validated, radiologists could soon turn to AI to generate clinically relevant x-ray reports, diagnose fractures and other ailments throughout the body, visualize motion, and increase the accuracy of their readings. AI also is expected to significantly reduce the time and effort it takes for some of the tasks that are necessary but tedious and time-consuming with digital radiography, such as double reading and confirming normal results. Beyond AI, researchers continue to explore hardware improvements in DR systems and develop new technologies to sharpen image quality and shorten exam and procedure times.

Subtle Medical today announced its artificial intelligence (AI)-powered image processing software, SubtleMR, received 510(k) clearance from the U.S. Food and Drug Administration (FDA). SubtleMR uses deep learning algorithms, denoising and resolution enhancement to improve the image quality of existing scanners. "We are pleased to received FDA clearance for SubtleMR, and we look forward to helping radiology departments and imaging centers get the most out of their existing MRI scanners," said Enhao Gong, Ph.D., founder and CEO of Subtle Medical. The software, which Subtle Medical said is compatible with any brand of MRI scanner and picture archiving and communication systems (PACS), could be beneficial for patients who have trouble staying still for long periods of time. Reducing the scan time for these patients not only improves the patient experience but could result in fewer artifact-ridden images and the need for physicians to re-scan an individual.

Medical Diagnostic Web has entered the fast-growing AI app store world, as it introduces an new marketplace on its medical imaging blockchain platform. Radiologists will have access to a range of specific AI algorithms that can augment their practices – and will be able to try before they buy, ensuring that they can find the correct solution they need. WHY IT MATTERS Healthcare imaging centers sit on top of massive amounts of data. Analyzing specific results can be time consuming for radiologists and even highly-trained technicians can miss things or make mistakes. AI algorithms help assist practitioners in diagnosis and prediction.

Interest in artificial intelligence (AI) is exploding, with Accenture forecasting that AI in health care will grow to $6.6 billion in a few short years, at a 40% annual compounded growth rate. Accenture also believes this technology will enable an opportunity for $150 billion in industry savings. So, is this hype justified? The short answer is yes, but it belies a much deeper question: How do we weed out the hype and determine exactly what is the most effective role for AI so that we make the rest of 2018 a year for positive change and not disruptive chaos? AI can augment a physician's thought process and how he or she reasons out a problem.