AI for X-Ray Interpretation

CAD4COVID-XRay- Artificial Intelligence System For X-Ray Interpretation. CAD4COVID-XRay is a deep-learning based AI system for the detection of COVID-19 characteristics on frontal chest radiographs. CAD4COVID-Xray is based on the CAD4TB v6 software, which is a commercial deep-learning system for the detection of tuberculosis on chest radiographs. As pre-processing steps, the system uses image normalisation and lung segmentation using a U-net. The system was re-trained, firstly on a pneumonia dataset acquired prior to the COVID-19 outbreak. This data is publicly available and has been fully anonymised. It is known to come from a single centre but details of the X-ray system are not available. This dataset includes 22,184 images of which 7,851 were labelled normal and 5,012 were labelled as pneumonia.  The remainder had other abnormalities inconsistent with pneumonia. A validation set of 1500 images (500 per label, equally split between PA and AP images) was held out and used to measure performance during the training process. The purpose of re-training using this data was to make the system sensitive and specific to pneumonia in general, since large numbers of COVID-19 images are difficult to acquire at present. To fine-tune the system for detection of COVID-19 specifically, an additional training set of anonymised CXR images was acquired from Bernhoven Hospital comprising 416 images from RT-PCR positive subjects and 191 images from RT-PCR negative subjects. These were combined with 96 COVID-19 images from other institutes and public sources and 291 images from Radboud University Medical Center from the pre-COVID-19 era (used to increase numbers of negative samples). This dataset of 994 images was used to re-train the system a final time, holding 40 images out for validation (all from Bernhoven Hospital, equally split between positive and negative and PA/AP).  This dataset comprised all RT-PCR confirmed data available to us (excluding the test set) with the addition of negative data to balance the class sizes. The system takes approximately 15 seconds to analyse an image on a standard PC.


Mediastinal widening Bronchopneumonia Evaluate for bacterial or viral etiology. See mediastinal window as well.

Segmental magnification in this CAD4COVID 19 study is helpful modality to diagnose earliest As present scan shows of typical covid19 pneumonitis patches

Very nice update @Prashant Ved

Bilateral segmental and lobar consolidation

Left sided extensive opacification Right middle zone opacification Broncho-pneumonia

It's a bacterial or viral etiology Or fibrosis sometime

Viral Pneumonitis

Diseases Related to Discussion