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Annalise Enterprise is a device designed to be used in the medical care environment to aid in triage and prioritization of studies with features suggestive of the following finding: mass effect . The device analyzes studies using an artificial intelligence algorithm to identify the finding. It makes study-level output available to an order and imaging management system for worklist prioritization or triage. The device is not intended to direct attention to specific portions of an image and only provides notification for the suspected finding. Its results are not intended: to be used on a standalone basis for clinical decision making ● ● to rule out specific findings, or otherwise preclude clinical assessment of CTB studies

Annalise Enterprise CTB Triage Trauma is a software workflow tool which uses an artificial intelligence (AI) algorithm to identify suspected findings on non-contrast brain CT studies in the medical care environment. The findings identified by the device include mass effect. Radiological findings are identified by the device using an AI algorithm - a convolutional neural network trained using deep-learning techniques. Images used to train the algorithm were sourced from datasets that included a range of equipment manufacturers and models. The performance of the device's AI algorithm was validated in a standalone performance evaluation, in which the case-level output from the device was compared with a reference standard ('ground truth'). This was determined by two ground truthers, with a third truther used in the event of disagreement. All truthers were US board-certified neuroradiologists. The device interfaces with image and order management systems (such as PACS/RIS) to obtain noncontrast brain CT studies for processing by the AI algorithm. Following processing, if any of the radiological findings of interest are identified in a non-contrast brain CT study, the device provides a notification to the image and order management system for prioritization of that study in the worklist. This enables users to review the studies containing features suggestive of these radiological findings earlier than in the standard clinical workflow. It is important to note that the device will never decrease a study's existing priority in the worklist. This ensures that worklist items will never have their priorities downgraded based on AI results. The device workflow is performed parallel to and in conjunction with the standard clinical workflow for interpretation of non-contrast brain CTs. The device is intended to aid in prioritization and triage of radiological medical images only.

Annalise Enterprise is a device designed to be used in the medical care environment to aid in triage and prioritization of studies with features suggestive of the following finding: · vasogenic edema The device analyzes studies using an artificial intelligence algorithm to identify the finding. It makes study-level output available to an order and imaging management system for worklist prioritization or triage. The device is not intended to direct attention to specific portions of an image and only provides notification for the suspected finding. Its results are not intended: - · to be used on a standalone basis for clinical decision making - · to rule out a specific finding, or otherwise preclude clinical assessment of CTB studies Intended modality: Annalise Enterprise identifies the suspected finding in non-contrast brain CT studies. Intended user: The device is intended to be used by trained clinicians who, as part of their scope of practice, are qualified to interpret brain CT studies. Intended patient population: The intended population is patients who are 22 years or older.

Annalise Enterprise CTB Triage Trauma is a software workflow tool which uses an artificial intelligence (AI) algorithm to identify suspected findings on non-contrast brain CT studies in the medical care environment. The findings identified by the device include vasogenic edema. Radiological findings are identified by the device using an AI algorithm - a convolutional neural network trained using deep-learning techniques. Images used to train the algorithm were sourced from datasets that included a range of equipment manufacturers and models. The performance of the device's AI algorithm was validated in a standalone performance evaluation, in which the case-level output from the device was compared with a reference standard ('ground truth'). This was determined by two ground truthers, with a third truther used in the event of disagreement. All truthers were US board-certified neuroradiologists. The device interfaces with image and order management systems (such as PACS/RIS) to obtain noncontrast brain CT studies for processing by the AI algorithm. Following processing, if any of the radiological findings of interest are identified in a non-contrast brain CT study, the device provides a notification to the image and order management system for prioritization of that study in the worklist. This enables users to review the studies containing features suggestive of these radiological findings earlier than in the standard clinical workflow. It is important to note that the device will never decrease a study's existing priority in the worklist. This ensures that worklist items will never have their priorities downgraded based on AI results. The device workflow is performed parallel to and in conjunction with the standard clinical workflow for interpretation of non-contrast brain CTs. The device is intended to aid in prioritization and triage of radiological medical images only.

Annalise Enterprise is a device designed to be used in the medical care environment to aid in triage and prioritization of studies with features suggestive of the following finding: · obstructive hydrocephalus The device analyzes studies using an artificial intelligence algorithm to identify the finding. It makes study-level output available to an order and imaging management system for worklist prioritization or triage. The device is not intended to direct attention to specific portions of an image and only provides notification for the suspected finding. Its results are not intended: - · to be used on a standalone basis for clinical decision making - · to rule out a specific finding, or otherwise preclude clinical assessment of CTB studies Intended modality: Annalise Enterprise identifies the suspected finding in non-contrast brain CT studies. Intended user: The device is intended to be used by trained clinicians who, as part of their scope of practice, are qualified to interpret brain CT studies. Intended patient population: The intended population is patients who are 22 years or older.

Annalise Enterprise CTB Triage - OH is a software workflow tool which uses an artificial intelligence (AI) algorithm to identify suspected findings on non-contrast brain CT studies in the medical care environment. The findings identified by the device include obstructive hydrocephalus. Radiological findings are identified by the device using an AI algorithm - a convolutional neural network trained using deep-learning techniques. Images used to train the algorithm were sourced from datasets that included a range of equipment manufacturers including Toshiba, GE Medical Systems, Siemens. Philips, and Canon Medical Systems. This dataset, which contained over 200.000 CT brain imaging studies, was annotated by qualified and trained radiologists. The performance of the device's AI algorithm was validated in a standalone performance evaluation, in which the case-level output from the device was compared with a reference standard ('ground truth'). This was determined by two ground truthers, with a third truther used in the event of disagreement. All truthers were US board-certified neuroradiologists. The device interfaces with image and order management systems (such as PACS/RIS) to obtain noncontrast brain CT studies for processing by the AI algorithm. Following processing, if any of the radiological findings of interest are identified in a non-contrast brain CT study, the device provides a notification to the image and order management system for prioritization of that study in the worklist. This enables users to review the studies containing features suggestive of these radiological findings earlier than in the standard clinical workflow. It is important to note that the device will never decrease a study's existing priority in the worklist. This ensures that worklist items will never have their priorities downgraded based on AI results. The device workflow is performed parallel to and in conjunction with the standard clinical workflow for interpretation of non-contrast brain CTs. The device is intended to aid in prioritization and triage of radiological medical images only.

Annalise Enterprise is a device designed to be used in the medical care environment to aid in triage and prioritization of studies with features suggestive of the following findings: - · acute subdural/epidural hematoma* · acute subarachnoid hemorrhage * · intra-axial hemorrhage* · intraventricular hemorrhage* *These findings are intended to be used together as one device. The device analyzes studies using an artificial intelligence algorithm to identify findings. It makes study-level output available to an order and imaging management system for worklist prioritization or triage. The device is not intended to direct attention to specific portions of an image and only provides notification for suspected findings. Its results are not intended: · to be used on a standalone basis for clinical decision making · to rule out specific findings, or otherwise preclude clinical assessment of CTB studies Intended modality: Annalise Enterprise identifies suspected findings in non-contrast brain CT studies. Intended user: The device is intended to be used by trained clinicians who, as part of their scope of practice, are qualified to interpret brain CT studies. Intended patient population: The intended population is patients who are 22 years or older.

Annalise Enterprise CTB Triage Trauma is a software workflow tool which uses an artificial intelligence (Al) algorithm to identify suspected findings on non-contrast brain CT studies in the medical care environment. The findings identified by the device include acute subdural/ epidural hematoma, acute subarachnoid hemorrhage, intra-axial hemorrhage, and intraventricular hemorrhage. Radiological findings are identified by the device using an AI algorithm - a convolutional neural network trained using deep-learning techniques. Images used to train the algorithm were sourced from datasets that included a range of equipment manufacturers including Toshiba, GE Medical Systems, Siemens, Philips, and Canon Medical Systems. This dataset, which contained over 200,000 CT brain imaging studies, was labelled by trained radiologists regarding the presence of the four findings of interest. The performance of the device's AI algorithm was validated in a standalone performance evaluation, in which the case-level output from the device was compared with a reference standard ('ground truth'). This was determined by two ground truthers, with a third truther used in the event of disagreement. All truthers were US board-certified neuroradiologists. The device interfaces with image and order management systems (such as PACS/RIS) to obtain noncontrast brain CT studies for processing by the AI algorithm. Following processing, if any of the clinical findings of interest are identified in a non-contrast brain CT study, the device provides a notification to the image and order management system for prioritization of that study in the worklist. This enables users to review the studies containing features suggestive of these clinical findings earlier than in the standard clinical workflow. It is important to note that the device will never decrease a study's existing priority in the worklist. This ensures that worklist items will never have their priorities downgraded based on AI results. The device workflow is performed parallel to and in conjunction with the standard clinical workflow for interpretation of non-contrast brain CTs. The device is intended to aid in prioritization and triage of radiological medical images only.

Annalise Enterprise CXR Triage Trauma is a software workflow tool designed to aid the clinical assessment of adult chest X-ray cases with features suggestive of pleural effusion and pneumoperitoneum in the medical care environment. The device analyzes cases using an artificial intelligence algorithm to identify findings. It makes case-level output available to a PACS or RIS for worklist prioritization or triage. The device is intended to be used by trained clinicians who are qualified to interpret chest X-rays as part of their scope of practice. The device is not intended to direct attention to specific portions of an image or to anomalies other than pleural effusion and pneumoperitoneum. Its results are not intended to be used on a standalone basis for clinical decision making nor is it intended to rule out specific critical findings, or otherwise preclude clinical assessment of X-ray cases. Standalone performance evaluation of the device was performed on a dataset that included supine and erect positioning. Use of this device with prone positioning may result in differences in performance. Standalone performance evaluation of the device was performed on a dataset where most cases were of unilateral right sided and bilateral pneumoperitoneum. Use of this device for unilateral left sided pneumoperitoneum may result in differences in performance. Specificity may be reduced for pleural effusion in the presence of scarring and and/or pleural thickening.

Annalise Enterprise CXR Triage Trauma is a software workflow tool which uses an artificial intelligence (AI) algorithm to identify suspected findings on chest X-ray (CXR) studies in the medical care environment. The findings identified by the device include pleural effusion and pneumoperitoneum. Radiological findings are identified by the device using an AI algorithm - a convolutional neural network trained using deep-learning techniques. Images used to train the algorithm were sourced from datasets across three continents, including a range of equipment manufacturers and models. The performance of the device's AI algorithm was validated in a standalone performance evaluation, in which the case-level output from the device was compared with a reference standard ('ground truth'). This was determined by two ground truthers, with a third truther used in the event of disagreement. All truthers were US board-certified radiologists. The device interfaces with image and order management systems (such as PACS/RIS) to obtain CXR studies for processing by the AI algorithm. Following processing, if any of the clinical findings of interest are identified in a CXR study, the device provides a notification to the image and order management system for prioritization of that study in the worklist. This enables users to review the studies containing features suggestive of these clinical findings earlier than in the standard clinical workflow. It is important to note that the device will never decrease a study's existing priority in the worklist. This ensures that worklist items will never have their priorities downgraded based on AI results. The device workflow is performed parallel to and in conjunction with the standard clinical workflow for interpretation of CXRs. The device is intended to aid in prioritization and triage of radiological medical images only.

Annalise Enterprise CXR Triage Trauma is a software workflow tool designed to aid the clinical assessment of adult chest X-ray cases with features suggestive of vertebral compression fracture in the medical care environment. The device analyzes cases using an artificial intelligence algorithm to identify findings. It makes case-level output available to a PACS or RIS for worklist prioritization or triage intended for clinicians in Bone Health and Fracture Liaison Service programs. The device is intended to be used by trained clinicians who are qualified to interpret chest X-rays as part of their scope of practice. The device is not intended to direct attention to specific portions of an image or to anomalies other than vertebral compression fracture. Its results are not intended to be used on a standalone basis for clinical decision making nor is it intended to rule out specific critical findings, or otherwise preclude clinical assessment of X-ray cases. Standalone performance evaluation of the device was performed on a dataset that included only erect positioning. Use of this device with supine positioning may result in differences in performance.

Annalise Enterprise CXR Triage Trauma is a software workflow tool which uses an artificial intelligence (AI) algorithm to identify suspected findings on chest X-ray (CXR) studies in the medical care environment. The findings identified by the device include vertebral compression fractures. Radiological findings are identified by the device using an AI algorithm - a convolutional neural network trained using deep-learning techniques. Images used to train the algorithm were sourced from datasets across three continents, including a range of equipment manufacturers and models. The performance of the device's AI algorithm was validated in a standalone performance evaluation, in which the case-level output from the device was compared with a reference standard ('ground truth'). This was determined by two ground truthers, with a third truther used in the event of disagreement. All truthers were US board-certified radiologists. The device interfaces with image and order management systems (such as PACS/RIS) to obtain CXR studies for processing by the AI algorithm. Following processing, if the clinical finding of interest is identified in a CXR study, the device provides a notification to the image and order management system for prioritization of that study in the worklist. This enables users to review the studies containing features suggestive of these clinical findings earlier than in the standard clinical workflow. It is important to note that the device will never decrease a study's existing priority in the worklist. This ensures that worklist items will never have their priorities downgraded based on AI results. The device workflow is performed parallel to and in conjunction with the standard clinical workflow for interpretation of CXRs. The device is intended to aid in prioritization and triage of radiological medical images only.