News | Coronavirus (COVID-19) | March 20, 2020

Researchers Use AI to Detect COVID-19

#COVID19 #Coronavirus #2019nCoV #Wuhanvirus #SARScov2

Representative examples of the attention heatmaps generated using Grad-CAM method for (a) COVID-19, (b) CAP, and (c) Non-Pneumonia. The heatmaps are standard Jet colormap and overlapped on the original image, the red color highlights the activation region associated with the predicted class. COVID-19 = coronavirus disease 2019, CAP = community acquired pneumonia. Image courtesy of the journal Radiology

March 20, 2020 — An artificial intelligence deep learning model can accurately detect COVID-19 and differentiate it from community acquired pneumonia and other lung diseases, according to this multi-center study published in the journal Radiology.

COVID-19 has widely spread all over the world since the first case was detected at the end of 2019. Early diagnosis of the disease is important for treatment and the isolation of the patients to prevent the virus spread.

A deep learning model named COVID-19 detection neural network (COVNet), was developed to extract visual features from 4,356 computed tomography (CT) exams from 3,322 patients for the detection of COVID-19. Community acquired pneumonia (CAP) and non-pneumonia CT exams were included to test the robustness of the model.

The per-exam sensitivity and specificity for detecting COVID-19 in the independent test set was 90 percent and 96 percent, respectively.

“We were able to collect a large number of CT exams from multiple hospitals, which included 1,296 COVID-19 CT exams,” the authors wrote. “More importantly, 1,735 CAP and 1,325 non-pneumonia CT exams were also collected as the control groups in this study in order to ensure the detection robustness considering that certain similar imaging features may be observed in COVID-19 and other types of lung diseases.”

Read the study, Artificial Intelligence Distinguishes COVID-19 from Community Acquired Pneumonia on Chest CT.

Read the latest Radiology and Radiology: Cardiothoracic Imaging COVID-19 research at Special Focus: COVID-19.

Related Coronavirus Content:

Study Looks at CT Findings of COVID-19 Through Recovery

VIDEO: Imaging COVID-19 With Point-of-Care Ultrasound (POCUS)

The Cardiac Implications of Novel Coronavirus

CT Provides Best Diagnosis for Novel Coronavirus (COVID-19)

Radiology Lessons for Coronavirus From the SARS and MERS Epidemics

Deployment of Health IT in China’s Fight Against the COVID-19 Epidemic

Emerging Technologies Proving Value in Chinese Coronavirus Fight

Radiologists Describe Coronavirus CT Imaging Features

Coronavirus Update from the FDA

CT Imaging of the 2019 Novel Coronavirus (2019-nCoV) Pneumonia

CT Imaging Features of 2019 Novel Coronavirus (2019-nCoV)

Chest CT Findings of Patients Infected With Novel Coronavirus 2019-nCoV Pneumonia 

Find more related clinical content Coronavirus (COVID-19)

Related Content

A, Initial conventional axial CT image shows no noticeable lung damage (within red box) in right upper lobe. B, Electron density spectral CT image obtained at same time as image in A shows lesions (within red box) in right upper lobe. C, Follow-up conventional axial chest CT image obtained 5 days after images in A and B confirm presence of lesions (within red box) in right upper lobe.

A, Initial conventional axial CT image shows no noticeable lung damage (within red box) in right upper lobe. B, Electron density spectral CT image obtained at same time as image in A shows lesions (within red box) in right upper lobe. C, Follow-up conventional axial chest CT image obtained 5 days after images in A and B confirm presence of lesions (within red box) in right upper lobe. Image courtesy of the American Roentgen Ray Society (ARRS), American Journal of Roentgenology (AJR)

News | Coronavirus (COVID-19) | October 22, 2020
October 22, 2020 — According to an open-...
The fMRI hyperscanning environment.

(A) The fMRI hyperscanning environment. The clinician (1) and patient (2) were positioned in two different 3T MRI scanners. An audio-video link enabled online communication between the two scanners (3), and video images were used to extract frame-by-frame facial expression metrics. During simultaneous acquisition of blood oxygen level–dependent (BOLD)–fMRI data, the clinician used a button box (4) to apply electroacupuncture (EA) treatment (real/sham, double-blind) to the patient (5) to alleviate evoked pressure pain to the leg (6; Hokanson cuff inflation). Pain and affect related to the treatment were rated after each trial. (B) Study overview. After an initial behavioral visit, each individual participated in a Clinical-Interaction (hyperscan preceded by a clinical intake) and No-Interaction condition (hyperscan without a preceding intake), in a counterbalanced order, with two different partners. (C) Experimental protocol. Each hyperscan was composed of 12 repeated trials (four verum EA, four sham EA, and four no treatment) in a pseudo-randomized order. After a resting period (far left), both participants were shown a visual cue to indicate whether the next pain stimulus would be treated (green frame) or not treated (red frame) by the clinician. These cues prompted clinicians prepare to either apply or not apply treatment while evoking corresponding anticipation for the patient. Following the anticipation cue, moderately painful pressure pain was applied to the patient’s left leg, while the clinician applied or did not apply treatment, respectively. After another resting period, participants rated pain (patients), vicarious pain (clinicians), and affect (both) using a visual analog scale (VAS).

News | Clinical Trials | October 22, 2020
October 22, 2020 — The potential impact of the patient-clinician relationship on a patient's response to treatment is
The FDA clearance, Quantib’s 6th to date, marks the first time a comprehensive AI prostate solution will be available to radiologists in the United States
News | Prostate Cancer | October 21, 2020
October 21, 2020 — Quantib, a market leader in...
Lesion was originally reported as indeterminate enhancing mass, and outside report recommended biopsy. Classic features of benign hemangioma are shown. Error was attributed to faulty reasoning. A, Axial MR image obtained 5 minutes after contrast agent administration shows peripheral nodular discontinuous enhancement. B, Axial MR image obtained 10 minutes after contrast agent administration shows centripetal progression of enhancement (arrow). C, Axial fast imaging employing steady-state acquisition (FIESTA)

Lesion was originally reported as indeterminate enhancing mass, and outside report recommended biopsy. Classic features of benign hemangioma are shown. Error was attributed to faulty reasoning. A, Axial MR image obtained 5 minutes after contrast agent administration shows peripheral nodular discontinuous enhancement. B, Axial MR image obtained 10 minutes after contrast agent administration shows centripetal progression of enhancement (arrow). C, Axial fast imaging employing steady-state acquisition (FIESTA) MR image shows lesion is homogeneously hyperintense compared with liver parenchyma. Image courtesy of American Roentgen Ray Society (ARRS), American Journal of Roentgenology (AJR)

News | Magnetic Resonance Imaging (MRI) | October 21, 2020
October 21, 2020 — According to an artic...
According to an inquest, a man with a heart disorder and chest pain died two days after a doctor viewed the wrong scan and sent him home
News | Computed Tomography (CT) | October 21, 2020
October 21, 2020 — The BBC News
Flowchart of patient inclusion and exclusion.

Figure 1. Flowchart of patient inclusion and exclusion.

News | Coronavirus (COVID-19) | October 20, 2020
October 20, 2020 — A new multi-institutional study published in the journal ...
Rensselaer, First-Imaging, and GE Global researchers develop a deep neural network to perform nearly as well as more complex dual-energy CT imaging technology
News | Computed Tomography (CT) | October 20, 2020
October 20, 2020 — Bioimaging technologies are the eyes that allow doctors to see inside the body in order to diagnos
Ezra, a NY-based startup transforming early cancer screening using magnetic resonance imaging (MRI), announced that it has received FDA 510(k) premarket authorization for its Artificial Intelligence, designed to decrease the cost of MRI-based cancer screening, assisting radiologists in their analysis of prostate MRI scans. It is the first prostate AI to be cleared by the FDA.
News | Artificial Intelligence | October 20, 2020
October 20, 2020 — Ezra, a NY-based startup transforming early cancer screening using...
Lesion was originally reported as indeterminate enhancing mass, and outside report recommended biopsy. Classic features of benign hemangioma are shown. Error was attributed to faulty reasoning. A, Axial MR image obtained 5 minutes after contrast agent administration shows peripheral nodular discontinuous enhancement. B, Axial MR image obtained 10 minutes after contrast agent administration shows centripetal progression of enhancement (arrow). C, Axial fast imaging employing steady-state acquisition (FIESTA)

56-Year-Old Woman With Benign Hemangioma: Lesion was originally reported as indeterminate enhancing mass, and outside report recommended biopsy. Classic features of benign hemangioma are shown. Error was attributed to faulty reasoning. A, Axial MR image obtained 5 minutes after contrast agent administration shows peripheral nodular discontinuous enhancement. B, Axial MR image obtained 10 minutes after contrast agent administration shows centripetal progression of enhancement (arrow). C, Axial fast imaging employing steady-state acquisition (FIESTA) MR image shows lesion is homogeneously hyperintense compared with liver parenchyma.

News | Magnetic Resonance Imaging (MRI) | October 16, 2020
October 16, 2020 —