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Methinks NCCT Stroke is a radiological computer aided triage and notification software indicated for use in the analysis of (1) non-contrast head CT (NCCT) images. The device is intended to assist hospital networks and trained physicians in workflow triage by flagging and communicating suspected positive findings of (1) Intracranial Hemorrhage (ICH) and (2) Large Vessel Occlusion (LVO) of the ICA, MCA-M1 and MCA-M2. Methinks NCCT Stroke uses an artificial intelligence algorithm to analyze images and highlight cases with suspected (1) ICH and (2) LVO in the cloud in parallel to the ongoing standard of care image interpretation. The user is presented with notifications for cases with suspected ICH or LVO findings via PACS and/or notifications. Notifications include preview images that are meant for informational purposes only, and are not intended for diagnostic use beyond notification. The device does not alter the original medical image, and it is not intended to be used as a primary diagnostic device. The results of Methinks NCCT Stroke are intended to be used in conjunction with other patient information and based on professional judgment to assist with triage/prioritization of medical images. Notified clinicians are ultimately responsible for reviewing full images per the standard of care. Methinks NCCT Stroke is for adults only.

Methinks NCCT Stroke is a radiological computer-assisted triage and notification software device. The device receives Non-Contrast Computed Tomography (NCCT) images and processes them to provide triage and notification prioritization of suspected Intracranial Hemorrhage (ICH) and Large Vessel Occlusion (LVO) of the ICA, MCA-M1 and MCA-M2. The Methinks NCCT Stroke device is an AI/ML Software as a Medical Device. The outputs of the device are intended to be used by trained clinicians in the prioritization of patients with suspected ICH and/or LVO.

BriefCase-Triage is a radiological computer aided triage and notification software indicated for use in the analysis of CT chest, abdomen, or chest/abdomen exams with contrast (CTA and CT with contrast) in adults or transitional adolescents aged 18 and older. The device is intended to assist hospital networks and appropriately trained medical specialists in workflow triage by flagging and communication of suspected positive findings of Aortic Dissection (AD) pathology. BriefCase-Triage uses an artificial intelligence algorithm to analyze images and highlight cases with detected findings on a standalone desktop application in parallel to the ongoing standard of care image interpretation. The user is presented with notifications for cases with suspected findings. Notifications include compressed preview images that are meant for informational purposes only and not intended for diagnostic use beyond notification. The device does not alter the original medical image and is not intended to be used as a diagnostic device. The results of BriefCase-Triage are intended to be used in conjunction with other patient information and based on their professional judgment, to assist with triage/ prioritization.

Briefcase-Triage is a radiological computer-assisted triage and notification software device. The software is based on an algorithm programmed component and is intended to run on a linux-based server in a cloud environment. The Briefcase-Triage receives filtered DICOM Images, and processes them chronologically by running the algorithms on each series to detect suspected cases. Following the AI processing, the output of the algorithm analysis is transferred to an image review software (desktop application). When a suspected case is detected, the user receives a pop-up notification and is presented with a compressed, low-quality, grayscale image that is captioned "not for diagnostic use, for prioritization only" which is displayed as a preview function. This preview is meant for informational purposes only, does not contain any marking of the findings, and is not intended for primary diagnosis beyond notification. Presenting the users with worklist prioritization facilitates efficient triage by prompting the user to assess the relevant original images in the PACS. Thus, the suspect case receives attention earlier than would have been the case in the standard of care practice alone.

syngo.CT LVO Detection is a radiological post-processing application for the analysis of CT angiography (CTA) head images. syngo.CT LVO Detection supports computer-aided triage, and it addresses vascular abortions in the CTA of the brain, commonly referred to as large vessel occlusion (LVO), in the ICA, M1, and M2 segment. It is intended for all patient populations of age ≥ 22 years, without any of the following contraindications: old infarcts or other diseases impacting the brain vasculature (for example, brain tumors), metal artifacts (for example, coils), surgical signs in the images. The output for triage is intended for informational purposes only. It is not intended for diagnostic use and does not alter the original medical image.

The subject device syngo.CT LVO Detection is an image processing software that utilizes artificial intelligence learning algorithms to support qualified clinicians (Radiologists, Neuroradiologists, Neurologists) in prioritization of CT-angiography images by algorithmically identifying findings suspicious of a large vessel occlusion and providing notification to the user. syngo.CT LVO Detection provides a reproducible detection of large vessel occlusions (LVO) on contrast-enhanced CT examinations of the head for detection of ICA, M1, and M2 vessel occlusions in patients suspected of having stroke related circulation occlusion. syngo.CT LVO Detection analyses CT-angiography (CTA) images of the head. The subject device provides a pipeline for the analysis and identification of potential LVO The output which can be send to an external notification device does not highlight or direct attention of the reading physician to any portion of the image.

JLK-SDH is a notification-only, parallel workflow tool that is intended to assist trained radiologists to identify and communicate images of specific patients to a specialist, independent of the standard of care workflow. JLK-SDH uses an artificial intelligence algorithm to analyze images for findings suggestive of a prespecified clinical condition and to notify an appropriate user of these findings in parallel to standard of care image interpretation. Identification of suspected findings is not for diagnostic use beyond notification. Specifically, the device analyzes non-contrast CT images of the head for subdural hemorrhage (SDH) and sends notifications to a clinician that a suspected SDH has been identified and recommends a review of those images. Images can be previewed and compressed through PACS and mobile applications. Notified clinicians are responsible for viewing non-compressed images on a diagnostic viewer and engaging in appropriate patient evaluation and relevant discussion with a treating physician before making care-related decisions or requests. JLK-SDH is limited to the analysis of imaging data and should not be used in lieu of full patient evaluation or relied upon to make or confirm the diagnosis.

JLK-SDH is a radiological computer-assisted triage and notification (CADt) software package compliant with the DICOM standard. The device functions as a Non-Contrast Computed Tomography (NCCT) processing module, providing triage and notification for suspected hemispheric subdural hemorrhage (SDH). It serves as a notification-only, parallel workflow tool for hospital networks and trained clinicians. The device helps to identify and communicate specific patient images to trained radiologists, independent of the standard of care workflow. Utilizing an artificial intelligence algorithm, the system automatically receives and analyzes NCCT studies for image features indicating the presence of SDH and sends a notification to alert a radiologist of the case. This algorithm, hosted on JLK servers, is designed to analyze non-contrast CT images of the head acquired on CT scanners and forwarded to JLK servers. The mobile software module that enables user to receive and toggle notifications for suspected subdural hemorrhages identified by the JLK-SDH Image Analysis Algorithm. Users can view a patient list, and nondiagnostic CT scans through the mobile application. Image viewing through the mobile application interface is for non-diagnostic purposes only.

EFAI CHESTSUITE XR MALPOSITIONED ETT ASSESSMENT SYSTEM (EFAI ETTXR) is a radiological computer-aided triage and notification software indicated for use in the analysis of chest X-ray (CXR) images in adults. The device is intended to assist hospital networks and appropriately trained medical specialists in workflow triage by flagging and communicating suspected positive cases of vertically malpositioned endotracheal tube (ETT) in relation to the carina. Findings are flagged when the ETT distal tip is assessed as being more than 7 cm above the carina, less than 3 cm above the carina, or when it is below the carina (i.e in the right or left mainstem bronchus). The device assesses solely the vertical position of the ETT distal tip relative to the carina, does not factor patient positioning, and cannot detect esophageal intubation. The device is tested in the single lumen endotracheal tube, while it may trigger a false prioritization alert in the case of properly positioned double lumen ETT. EFAI ETTXR analyzes cases using algorithms to identify suspected malpositioned ETT findings. It makes case-level output available to a PACS/workstation for worklist prioritization or triage. EFAI ETTXR is not intended to direct attention to specific portions of an image or to anomalies of an image. Its results are not intended to be used on a stand-alone basis for clinical decision-making nor is it intended to rule out malpositioned ETT or otherwise preclude clinical assessment of chest radiographs.

EFAI CHESTSUITE XR MALPOSITIONED ETT ASSESSMENT SYSTEM (EFAI ETTXR) is a radiological computer-assisted triage and notification software system. The software uses deep learning techniques to automatically analyze chest radiographs and alerts the PACS/RIS workstation once images with features suggestive of malpositioned ETT are identified. Through the use of EFAI ETTXR, a radiologist is able to review studies with features suggestive of malpositioned ETT earlier than in standard of care workflow. The device is intended to provide a passive notification through the PACS/workstation to the radiologists indicating the existence of a case that may potentially benefit from the prioritization. It does not mark, highlight, or direct users' attention to a specific location on the original chest radiographs. The device aims to aid in prioritization and triage of radiological medical images only.

BriefCase-Triage is a radiological computer aided triage and notification software indicated for use in the analysis of contrast-enhanced images that include the lungs in adults or transitional adolescents age 18 and older. The device is intended to assist hospital networks and appropriately trained medical specialists in workflow triage by flagging and communication of suspect cases of incidental Pulmonary Embolism (iPE) pathologies. BriefCase-Triage uses an artificial intelligence algorithm to analyze images and highlight cases with detected findings in parallel to the ongoing standard of care image interpretation. The user is presented with notifications for suspect cases. Notifications include compressed preview images that are meant for informational purposes only and not intended for diagnostic use beyond notification. The device does not alter the original medical image and is not intended to be used as a diagnostic device. The results of BriefCase-Triage are intended to be used in conjunction with other patient information and based on their professional judgment, to assist with triage/prioritization of medical images. Notified clinicians are responsible for viewing full images per the standard of care.

Briefcase-Triage is a radiological computer-assisted triage and notification software device. The software is based on an algorithm programmed component and is intended to run on a linux-based server in a cloud environment. The Briefcase-Triage receives filtered DICOM Images, and processes them chronologically by running the algorithms on each series to detect suspected cases. Following the AI processing, the output of the algorithm analysis is transferred to an image review software (desktop application). When a suspected case is detected, the user receives a pop-up notification and is presented with a compressed, low-quality, grayscale image that is captioned "not for diagnostic use, for prioritization only" which is displayed as a preview function. This preview is meant for informational purposes only, does not contain any marking of the findings, and is not intended for primary diagnosis beyond notification. Presenting the users with worklist prioritization facilitates efficient triage by prompting the user to assess the relevant original images in the PACS. Thus, the suspect case receives attention earlier than would have been the case in the standard of care practice alone.

JLK-ICH is a radiological computer-aided triage and notification software indicated for use in the analysis of non-contrast CT images. JLK-ICH is a notification-only, parallel workflow tool that is intended to assist hospital networks and trained clinicians to identify and communicate images of specific patients to specialists, independent of the standard of care workflow. JLK-ICH uses an artificial intelligence algorithm to analyze images for findings suggestive of pre-specified clinical conditions and promptly notifies the appropriate medical specialists of these findings in parallel with the standard of care image interpretation. Identification of suspected findings is not for diagnostic use beyond notification. Specifically, the device analyzes non-contrast CT images of the head to detect intracranial hemorrhage (ICH). The system sends a notification to a clinician that a suspected ICH has been identified and recommends a review of those images. Images can be previewed and compressed through a mobile application. Notified clinicians are responsible for viewing non-compressed images on a diagnostic viewer and engaging in appropriate patient evaluation and relevant discussion with a treating clinician before making care-related decisions or requests. JLK-ICH is limited to the analysis of imaging data and should not be used in lieu of full patient evaluation or relied upon to make or confirm the diagnosis.

JLK-ICH is a radiological computer-assisted triage and notification (CADt) software that adheres to the DICOM standard. The device functions as a Non-Contrast Computed Tomography (NCCT) processing module, providing triage and notification for suspected hemispheric intracranial hemorrhage (ICH). This software acts as a notificationonly, parallel workflow tool for hospital networks and trained clinicians, enabling the identification and communication of suspected patient images to relevant specialists, independent of the standard care workflow. JLK-ICH processes non-contrast computed tomography (NCCT) scans, prioritizing triage and notification for suspected hemispheric intracranial hemorrhaqe (ICH). The system utilizes advanced artificial intelligence to automatically analyze NCCT scans for indicators of ICH and promptly notify appropriate medical specialists of potential cases. JLK-ICH comprises an image analysis algorithm hosted on JLK servers and a mobile application for notification management. The Al/ML-based algorithm is designed to analyze NCCT of the head scans forwarded from CT scanners to the JLK servers. The mobile software module enables users to receive and toggle notifications for suspected ICH cases identified by the JLK-ICH Image Analysis Algorithm. Users can view a patient list and non-diagnostic CT scans through the mobile application. Image viewing through the mobile application interface is for non-diagnostic purposes only.

EFAI NEUROSUITE CT MIDLINE SHIFT ASSESSMENT SYSTEM (EFAI MLSCT) is a software workflow tool designed to aid in prioritizing the clinical assessment of non-contrast head CT cases with features suggestive of midline shift (MLS) in individuals aged 18 years and above. EFAI MLSCT analyzes cases using deep learning algorithms to identify suspected MLS findings. It makes case-level output available to a PACS/workstation for worklist prioritization or triage. EFAI MLSCT is not intended to direct attention to specific portions of an image or to anomalies other than MLS. Its results are not intended to be used on a stand-alone basis for clinical decision-making nor is it intended to rule out MLS or otherwise preclude clinical assessment of CT studies.

EFAI NEUROSUITE CT MIDLINE SHIFT ASSESSMENT SYSTEM (EFAI MLSCT) is a radiological computer-assisted triage and notification software system. The software uses deep learning techniques to automatically analyze non-contrast head CTs and alerts the PACS/RIS workstation once images with features suggestive of MLS are identified. Through the use of EFAI MLSCT, a radiologist is able to review studies with features suggestive of MLS earlier than in standard of care workflow. The device is intended to provide a passive notification through the PACS/workstation to the radiologists indicating the existence of a case that may potentially benefit from the prioritization. It does not mark, highlight, or direct users' attention to a specific location on the original non-contrast head CT. The device aims to aid in prioritization and triage of radiological medical images only.

NeuroICH is a notification-only, parallel workflow tool for use by hospital networks and trained clinicians to identify and communicate images of suspected ICH patients to a specialist, independent of standard of care workflow. The device uses an artificial intelligence algorithm to analyze non-contrast CT images of the head acquired in the acute setting for findings suggestive of intracranial hemorrhage (ICH) in parallel to the ongoing standard of care image interpretation and notify an appropriate clinician of these findings. Notifications include non-diagnostic preview images that are meant for informational purposes only. The device does not alter or remove the original medical image and is not intended to be used as a diagnostic device. Images can be previewed through a mobile application. Notified clinicians are responsible for viewing high quality images on a diagnostic viewer per the standard of care and engaging in appropriate patient evaluation in conjunction with other patient information before making care-related decisions. NeuroICH is limited to analysis of imaging data and should not be used in-lieu of full patient evaluation or relied upon to make or confirm diagnosis.

NeurolCH is a software-only parallel workflow tool designed for use by hospital networks and trained clinicians to identify and communicate prioritized images of specific patients to an appropriate specialist such as neurovascular or neurosurgical specialist independent of the standard of care workflow. NeuroICH mainly consists of an image analysis module hosted on cloud, and a mobile application for preview of notification and non-diagnostic images. The standalone software device automatically receives and analyzes non-contrast head CT (NCCT) studies of patients undergoing stroke protocol, for image features that indicate the presence of an intracranial hemorrhage (ICH) using deep learning artificial intelligence algorithm, and upon detection of a suspected ICH case, sends a notification along with non-diagnostic image on mobile application to alert a specialist clinician.