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qCT LN Quant is a software device used in the tracking, assessment, and quantitative characterization of detected pulmonary nodules. This automatically analyzes user-selected regions within lung CT to provide volumetric, diameter and computer analysis based on morphological characteristics in a single study, or over the time course of several thoracic studies. The system performs the measurements, allowing the preview of lung nodules in 2D and 3D reconstructed views and the respective measurements to be displayed. It is indicated for the evaluation of user detected solid pulmonary nodules.

Qure.ai's computed tomography (CT) scan software, the qCT LN Quant, is a deep-learning-based device that can process non-contract CT (NCCT) scans and assists in quantitative characterization of solid lung nodules of size ≥ 6mm on Chest CTs. qCT LN Quant consists of a cloud module that can interacts with DICOM modality or the user's picture archiving and communication system (PACS) to receive de-identified scans and returns the results to the same destination. In addition, solid nodules are segmented by the user semi-automatically using double seed points on the nodule, followed by interactive fine-tuning of the boundaries. The segmented region is quantitatively characterized by qCT LN Quant and presented to users as an additional overlay by highlighting and labelling respectively. User-assisted segmentation generated by qCT LN Quant can be presented in two ways to the users: a. PACS-based mode: As a new series (secondary capture) which are returned to the originating PACS system with segmentation burnt on the series. This can be done only at PACS which supports GSPS Output. b. Web-based mode: On Qure's web application where the segmentation is overlaid on top of the original scan. qCT LN Quant deep learning algorithm has been trained to quantify the target structures on CT scans and is coupled with pre-and post-processing functionality that allows the device to work directly with the radiology workflow. The user is presented with 2D view and 3D reconstructed view of solid nodule images labelling the quantitative characteristics based on the user-segmented structures. The output consists of information on average diameter and volumes of user identified solid nodules. The additional features include long axis diameter (mm), short axis diameter (mm), Effective diameter (mm), and Mean/Minimum/Max HU (HU) and volume change overtime with matched nodules. In addition, qCT LN Quant consists of a Brock Score - Risk Calculator that uses diameter of the nodule and clinician's input. The Lung-RADS™ calculator feature is based on ACR guideline, which can assist the physician in decision making. qCT LN Quant also provides recommendations based on Fleischner's Society guideline. Thus, qCT LN Quant offers functionality to calculate Brock and LungRADS score as part of integrated or cleared devices with capability to display such output. qCT LN Quant is limited to analysis of imaging data and results generated are meant for information purposes only. The device is not intended for clinical diagnosis of any disease. It does not replace the role of physician or of other testing in the standard of care for lung abnormalities.

The qXR-LN (qXR Lung nodule) is computer-aided detection software to identify and mark regions in relation to suspected pulmonary nodules from 6 to 30 mm in size. The device is intended to be used in the incidental adult population. It is designed to aid the physician to review the frontal (AP/PA) chest radiographs of adults acquired on digital radiographic systems as a second reader and be used with any DICOM viewer or PACS . qXR-LN provides adjunctive information only and is not a substitute for the original chest radiographic image.

qXR-LN is a Computer-Aided Detection (CADe) device that is designed to perform CAD processing in frontal (PA or AP view) Chest X-ray images for indication of locations for high nodule probability, which has an effective detection size from 6 mm to 30 mm. The device is intended to be a secondary aid to the qualified intended user to identify incidental pulmonary lung nodules chest x-ray images. The device utilizes a deep learning algorithm. The qXR-LN was trained on a large and diverse dataset of 2.5million scans from 5 countries across the world. The training dataset was from more than 25 manufacturers. Chest X-rays are sent to qXR-LN by the means of transmission functions within the user's image storage system (e.g., Picture Archiving and Communication System (PACS)) or other radiological imaging equipment (e.g., X-ray systems) and processed by the qXR-LN to detect and localise lung nodules. Following receipt of chest radiographs, the software device automatically analyses each image to detect and localise lung nodules. qXR-LN receives chest x-ray images in digital imaging and communications in medicine (DICOM) as input. The qXR-LN device produces DICOM format outputs that enable users to view the presence and location of lung nodules. This device is intended to aid the intended user in review of chest x-rays and detect and localise lung nodules as a secondary reader. The results are not intended to be used on a standalone basis for clinical decision-making nor is it intended to rule out the target conditions or otherwise preclude clinical assessment of X-ray cases.

qXR-CTR is a deep-learning based software for use by hospitals and clinics for automated assessment of the CTR on chest X-ray (CXRs) scans. qXR-CTR is designed to measure the ratio of the maximal transverse diameter of the heart (CD) and the maximal inner transverse diameter (TD) of the thoracic cavity and calculate the CTR value on posterior-anterior view chest view using an artificial intelligence algorithm. The intended users of this device are physicians or licensed practitioners in healthcare institutions, such as clinics, hospitals, residential care facilities, long-term care services, and healthcare facilities. The system is suitable for adults > 22 years of age. The device is used to aid the intended users and results are not intended to be used on a stand-alone basis for clinical decision making or otherwise preclude clinical assessment of CTR cases.

The qXR-CTR is a non-invasive software medical device designed to be installed on the computer with specific system requirements. It is a radiological computer-assisted software system that automatically analyzes DICOM chest X-ray images in PA view and outputs the cardiac diameter, thoracic diameter, and CTR through an artificial intelligence algorithm. The structured report includes a preview of the compressed chest X-ray image with the automatically derived CTR result and annotation line, indicating the maximal transverse diameter of heart and maximal inner transverse diameter of thoracic cavity.

qXR-PTX-PE is a radiological computer-assisted triage and notification software that analyzes adult chest X-ray images for the presence of pre-specified suspected critical findings (pleural effusion and/or pneumothorax). qXR-PTX-PE uses an artificial intelligence algorithm to analyze images for features suggestive of critical findings and provides case-level output available in the PACS/workstation for worklist prioritization or triage. As a passive notification for prioritization-only software tool within standard of care workflow, qXR-PTX-PE does not send a proactive alert directly to the appropriately trained medical specialists. qXR-PTX-PE is not intended to direct attention to specific portions of an image or to anomalies other than pleural effusion and/or pneumothorax. Its results are not intended to be used on a stand-alone basis for clinical decision-making.

qXR-PTX-PE is a radiological computer aided triage and notification software that analyses adult frontal (AP or PA views) CXR images for the presence of pre-specified suspected target conditions (pleural effusion and/or pneumothorax). The algorithm was trained on training data from across the world. The training dataset consisted of 74% of the data from India, 20.04% from the EU, 3.9% from the US, 1.4% from Brazil and 0.63% from Vietnam. The input for qXR-PTX-PE is a frontal chest X-ray (AP and PA view) in digital imaging and communications in medicine (DICOM) format Chest X-rays are sent to qXR-PTX-PE by the means of transmission within the user's image storage system (e.g., Picture Archiving and Communication System (PACS)) or other radiological imaging equipment (e.g., X-ray systems) and processed by the qXR-PTX-PE for analysis. Following receipt of chest radiographs, the software device automatically analyses each image to detect features suggestive of pneumothorax and/or pleural effusion. This would allow the appropriately trained medical specialists to group suspicious exams together that may potentially benefit for their prioritization. Chest radiographs without the suspicious findings are placed in the worklist for routine review, which is the standard of care at present. A secondary capture is available for the information on presence of the suspicious findings. qXR-PTX-PE does not provide any proactive alerts. qXR-PTX-PE is not intended to direct attention to specific portions of the image. The results are not intended to be used on a standalone basis for clinical decision-making nor is it intended to rule out the target conditions or otherwise preclude clinical assessment of X-Ray cases.

The qXR-BT device is intended to generate a secondary digital chest X-ray image that facilitates confirmation of the position of a breathing tube and an anatomical landmark on adult chest X-rays. This device is intended for use by licensed physicians who are trained in the evaluation of breathing tube placement on chest X-rays. The qXR-BT image provides adjunctive information and is not a substitute for the original PA/AP image.

qXR-BT is a standalone image analysis software used during the review of digital chest radiographic images, intended to facilitate determining the position of the breathing tube relative to the carina. Standard of care medical imaging workflows are well established, and include pre-existing software components such as a PACS, DICOM viewer and imaging worklist; qXR-BT is designed to integrate with these components. X-rays are sent to qXR-BT by means of transmission functions within the user's PACS system. Upon completion of processing, the qXR-BT device returns results to the user's PACS or other userspecified radiology software system or database. The input to the qXR-BT device is a chest X-ray (AP and PA, referred to as frontal) in digital imaging and communications in medicine (DICOM) format. The qXR-BT device produces PDF and DICOM format outputs that enable users to view the position of a breathing tube and an anatomical landmark (carina). The PDF format output contains preview images that show segmented structures outlined with a textual report describing the structures detected. The text report is restricted to the presence or absence of the breathing tubes and the carina as detected by the software device. The DICOM format output consists of a single complete additional DICOM series for each input scan. This DICOM output contains labeled overlays indicating the location and extent of the segmentable structures, suitable for viewing in the PACS or radiology viewer. The qXR-BT analysis module consists of a set of pre-trained convolutional neural networks (CNNs), that form the core processing component shown in Figure 1. This core processing component is coupled with a pre-processing module to prepare input DICOMs for processing by the CNNs and a post-processing module to convert the output into visual and tabular format for users.

The qER-Quant device is intended for automatic labeling, visualization of segmentable brain structures from a set of Non-Contrast head CT (NCCT) images. The software is intended to automate the current manual process of identifying, labeling and quantifying the volume of segmentable brain structures identified on NCCT images. qER-Quant provides volumes from NCCT images acquired at a single time point and provides a table with comparative analysis for two or more images that were acquired on the same image acquisition protocol for the same individual at multiple time points. The qER-Quant software is indicated for use in the following structures: Intracranial Hyperdensities, Lateral Ventricles and Midline Shift.

qER-Quant is a standalone software device that processes non-contrast head CT scans to outline and quantify the structures described in the intended use. The qER-Quant software interacts with the user's picture archiving and communication system (PACS) to receive scans and returns the results to the same destination. The analysis module of the qER-Quant software contains of a set of pre-trained convolutional neural networks (CNNs), that form the core processing component shown in Figure 1. This core processing component is coupled with a pre-processing module to prepare input digital imaging and communications in medicine (DICOMs) for processing by the CNNs and a post-processing module to convert the output into visual and tabular output for users.

qER is a radiological computer aided triage and notification software in the analysis of non-contrast head CT images. The device is intended to assist hospital networks and trained medical specialists in workflow triage by flagging the following suspected positive findings of pathologies in head CT images: intracranial hemorrhage, mass effect, midline shift and cranial fracture. qER uses an artificial intelligence algorithm to analyze images on a standalone cloud-based application in parallel to the ongoing standard of care image interpretation. The user is presented with notifications for cases with suspected findings. Notifications include non-diagnostic preview images that are meant for informational purposes only. The device does not alter the original medical image and is not intended to be used as a diagnostic device. The results of the device are intended to be used in conjunction information and based on professional judgment, to assist with triage/prioritization of medical images. Notified clinicians are responsible for viewing full images per the standard of care.

Qure.ai Head CT scan interpretation software, qER, is a deep-learning-based software device that analyses head CT scans for signs of intracranial hemorrhage, midline shift, mass effect or cranial fractures in order to prioritize them for clinical review. The standalone software device consists of an on-premise module and a cloud module. qER accepts non-contrast adult head CT scan DICOM files as input and provides a priority flag indicating critical scans. Additionally, the software has the preview of critical scans to the medical specialist.