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The Avenda Health Unfold AI Software is an artificial intelligence (AI)-based decision support software, indicated as an adjunct to the review of magnetic resonance (MR) prostate images and biopsy findings in the prostate oncological workflow. The Avenda Health Unfold AI Software is designed to support the prostate oncological workflow by helping the user with the segmentation of MR image features, including the prostate; in the evaluation, quantification, and documentation of lesions; and in pre-planning for diagnostic and interventional procedures such as biopsy and/or soft tissue ablation. The device is intended to be used by physicians trained in the oncological workflow in a clinical setting for planning and guidance for clinical, interventional, diagnostic, and/or treatment procedures of the prostate.

The Avenda Health Unfold AI Software is an artificial intelligence (AI)-based decision support software, indicated as an adjunct to the review of magnetic resonance (MR) prostate images and biopsy findings in the prostate oncological workflow. The Avenda Health Unfold AI Software is designed to support the prostate oncological workflow by helping the user with the segmentation of MR image features, including the prostate; in the evaluation, quantification, and documentation of lesions; and in pre-planning for diagnostic and interventional procedures such as biopsy and/or soft tissue ablation. The device is intended to be used by physicians trained in the oncological workflow in a clinical setting for planning and guidance for clinical, interventional, diagnostic, and/or treatment procedures of the prostate.

The Avenda Health Unfold AI Software is an artificial intelligence (AI)-based decision support software, indicated as an adjunct to the review of magnetic resonance (MR) prostate images and biopsy findings in the prostate oncological workflow. The Avenda Health Unfold AI Software is designed to support the prostate oncological workflow by helping the user with the segmentation of MR image features, including the prostate; in the evaluation, quantification, and documentation of lesions; and in pre-planning for diagnostic and interventional procedures such as biopsy and/or soft tissue ablation. The device is intended to be used by physicians trained in the oncological workflow in a clinical setting for planning and guidance for clinical, interventional, diagnostic, and/or treatment procedures of the prostate.

The Avenda Health Unfold AI Software is an artificial intelligence (AI)-based decision support software, indicated as an adjunct to the review of magnetic resonance (MR) prostate images and biopsy findings in the prostate oncological workflow. The Avenda Health Unfold AI Software is designed to support the prostate oncological workflow by helping the user with the segmentation of MR image features, including the prostate; in the evaluation, quantification, and documentation of lesions; and in pre-planning for diagnostic and interventional procedures such as biopsy and/or soft tissue ablation. The device is intended to be used by physicians trained in the oncological workflow in a clinical setting for planning and guidance for clinical, interventional, diagnostic, and/or treatment procedures of the prostate.

The CogNet AI-MT is a standalone software medical device for screening FFDM mammograms. This software is a triage device that allows the result to be prioritized for viewing by a qualified radiologist.

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AI Metrics is a thin client software application used with general purpose computing hardware for the display and visualization of medical image data, supporting images and anatomical datasets, such as CT and MR. AI Metrics is designed as a workflow automation application with analytical tools to help the user assess, categorize and document the extent of a disease and/or tumor response to therapy in accordance with user selected standards (e.g. RECIST 1.1) and assess changes in imaging findings over multiple time-points. AI Metrics supports the interpretation and evaluation of examinations and follow up documentation of findings within healthcare institutions, for example, in Radiology, Oncology, and other Medical Imaging environments. AI Metrics functionality provides for communication, storage, processing, rendering, and display of DICOM compliant image data derived from various sources, navigation through images, selection of regions of interest, generation of information from those regions, evaluation in accordance with user selected standards, and generation of a structured report. The user controls these functions with a system of interactive menus and semi-automated or manual workflow automation tools, including: • Manual annotation tools for users to select regions of interest (ROIs), • Semi-automatic lesion segmentation suggestions for user-selected ROIs, • Automatic measurement and display of long and short axis of segmented lesions, • Automatic tabulation and summation of measurements, • Semi-automatic lesion labelling suggestions for anatomical location (organ, body region, and laterality), • Automatic calculation of quantitative and qualitative metrics using annotation data in accordance with the selected criteria, and • Automatic generation of a structured report that includes the annotation data and calculated quantitative and qualitative metrics presented in a graph, table, key images, and structured text report.

PYLARIFY AI is intended to be used by healthcare professionals and researchers for acceptance, transfer, storage, image display, manipulation, quantification and reporting of digital medical images. The system is intended to be used with images acquired using nuclear medicine (NM) imaging, using PSMA PET/CT. The device provides general Picture Archiving and Communications System (PACS) tools as well as a clinical application for oncology including marking of regions of interest and quantitative analysis.

PYLARIFY AI is a software only medical device that provides quantitative assessments of standardized uptake values (SUVs) for patients that are subject to examination by nuclear medicine (NM) imaging, using PSMA PET/CT, within the clinical application of oncology. The device performs by automatic organ and bone segmentation and lesions, selected by the user, will automatically be calculated into a lesion index (LI) and intensity-weighted total lesion volume (ITLV). The user of this product is typically a health-care professional using the software to review patient images and analyze results.

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ClariPlatform (Ver. ClariSuite View) is a medical imaging platform that hosts clinical AI solutions and provides user interface in an on-premise environment. The system facilitates the networking, filtering, routing, and optional de-identification of medical image data between imaging systems, allowing users to easily execute AI solutions on-demand or integrate them into their clinical workflow. Additionally the system allows users to interact with the AI analysis results including confirming, rejecting, and selecting other category than provided by AI. While ClariPlatform (Ver.1.0) operates as a cloud-based system, ClariPlatform (Ver. ClariSuite View) is specifically designed for on-premise deployment, ensuring compliance with institutional security policies, regulatory requirements, and data privacy concerns. ClariPlatform (Ver. ClariSuite View) is deployed within anorganization’s internal infrastructure, allowing healthcare providers to maintain full control over medical imaging data while leveraging advanced AI-assisted visualization tools. Clinical AI solutions refer to separate medical device software and are not included within the scope of this system.

ClariPlatform (Ver. 1.0) is a medical imaging platform that hosts clinical AI solutions in a cloud-based SaaS format. The system facilitates the networking, filtering, routing, and de-identification of medical image data between imaging systems, allowing users to easily execute AI solutions on-demand or integrate them into their clinical workflow. ClariPlatform is only deployed on the cloud, ensuring accessibility and compliance with regulations. Clinical AI solutions refer to separate medical device software and are not included within the scope of this system.

An application software program designed to function as a portal to allow software providers to make available radiological interpretive software [typically artificial intelligence (AI) based] for radiology professionals to access, use, evaluate, and compare. It is intended to facilitate distribution and hospital implementation of algorithms designed to assist image interpretation within a healthcare facility.

EchoConfidence is Software as a Medical Device (SaMD) that displays images from a Transthoracic Echocardiogram, and assists the user in reviewing the images, making measurements and writing a report. This includes automatic view classification; segmentation of cardiac structures including the left and right ventricle, chamber walls, left and right atria and great vessels; measures of cardiac function; and Doppler assessments.

1CMR Pro is software that displays, analyses and transfers DICOM cardiovascular images acquired in Cardiovascular Magnetic Resonance (CMR) scanners, specifically structure, function and flow in the heart and major vessels using multi-slice, multi-phase, multi-parametric and velocity encoded CMR images. It is compatible with 1.5T and 3T CMR acquisitions

A digital epilepsy care management web portal for HCP.

A digital epilepsy care management platform application for iOS.

A digital epilepsy care management platform application for Android.

Contour+ (MVision AI Segmentation) provides a software interface to image analysis algorithms to be used in clinical workflows including, but not limited to, radiation therapy treatment planning workflows. The system includes processing tools for automatically contouring of imaging data of supported modalities CT and MRI using machine learning based algorithms. The produced segmentation templates for regions of interest are intended to be transferred to appropriate image visualization systems to be used as a template for a medical professional to visualize, modify and approve prior to further use in clinical workflows. The system supports automatic contouring of pre-defined structures from common anatomical sites, i.e. Head and Neck, Brain, Breast, Lung and Abdomen, Male Pelvis, and Female Pelvis. Contour+ (MVision AI Segmentation) is not intended to detect lesions or tumors. The device is not intended for use with real-time adaptive planning workflows.

AccipioIx is a software workflow tool designed to aid in prioritizing the clinical assessment of adult non-contrast head CT cases with features suggestive of acute intracranial hemorrhage in the acute care environment. AccipioIx analyzes cases using an artificial intelligence algorithm to identify suspected findings. It makes case-level output available to a PACS/workstation for worklist prioritization or triage. AccipioIx is not intended to direct attention to specific portions of an image or to anomalies other than acute intracranial hemorrhage. Its results are not intended to be used on a stand-alone basis for clinical decision-making nor is it intended to rule out hemorrhage or otherwise preclude clinical assessment of CT cases.

AccipioIx is a software workflow tool designed to aid in prioritizing the clinical assessment of adult non-contrast head CT cases with features suggestive of acute intracranial hemorrhage in the acute care environment. AccipioIx analyzes cases using an artificial intelligence algorithm to identify suspected findings. It makes case-level output available to a PACS/workstation for worklist prioritization or triage. AccipioIx is not intended to direct attention to specific portions of an image or to anomalies other than acute intracranial hemorrhage. Its results are not intended to be used on a stand-alone basis for clinical decision-making nor is it intended to rule out hemorrhage or otherwise preclude clinical assessment of CT cases.

Software application that uses machine learning to assess breast tissue composition.

Tempus ECG-AF is a cardiovascular machine learning-based notification software intended to analyze recordings of 12-lead ECG devices from patients 65 years of age and older. The software employs machine learning techniques to analyze ECG recordings and detect signs associated with a patient experiencing atrial fibrillation and/or atrial flutter (collectively referred to as AF) within the next 12 months. The device is designed to extract otherwise unavailable information from ECGs conducted under the standard of care, to help health care providers better identify patients who may be at risk for undiagnosed AF in order to evaluate them for referral of further diagnostic follow up and address the unmet need of reducing the number of undiagnosed AF patients.

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Collection convenience kit to collect, contain, and ship a single human blood specimen to a Renalytix laboratory for kidneyintelX.dkd testing

Test Report for the kidneyintelX.dkd test.

Collection kit used to collect, contain and ship human urine to a Renalytix clinical laboratory for uACR testing

Collection convenience kit used to collect, contain and ship human blood to a Renalytix clinical laboratory for KidneyIntelX testing.

The STAR Apollo Mapping System is a cardiac mapping method that is based on proprietary algorithms developed to identify localized sources of atrial activation during AF. The STAR Apollo Mapping System does not aim to elucidate the mechanism of AF, rather it seeks to reveal the anatomical location of regions where activation may be originating from, regardless of mechanism. This is based on a very simple and fundamental principle that the source of activation will have activation emerging from it in multiple directions, but not progressing towards it.

The STAR Apollo Mapping System is a cardiac mapping method that is based on proprietary algorithms developed to identify localized sources of atrial activation during AF. The STAR Apollo Mapping System does not aim to elucidate the mechanism of AF, rather it seeks to reveal the anatomical location of regions where activation may be originating from, regardless of mechanism. This is based on a very simple and fundamental principle that the source of activation will have activation emerging from it in multiple directions, but not progressing towards it.

Limbus Contour is a stand-alone software medical device. It is a single purposes cross-platform application for automatic contouring (segmentation) of CT/MRI DICOM images via pre-trained and expert curated machine learning models. The software is intended to be used by trained medical professionals to derive contours for input to radiation treatment planning. The Limbus Contour software segments normal tissues using machine leaming models and further post-processing on machine learning model prediction outputs. Limbus Contour does not display or store DICOM images and relies on existing radiotherapy treatment planning systems (TPS) and DICOM image viewers for display and modification of generated segmentations. Limbus Contour interfaces with the user's operating system file system (importing DICOM image.dcm files and exporting segmented DICOM RT-Structure Set .dcm files).

Limbus Contour is a stand-alone software medical device. It is a single purposes cross-platform application for automatic contouring (segmentation) of CT/MRI DICOM images via pre-trained and expert curated machine learning models. The software is intended to be used by trained medical professionals to derive contours for input to radiation treatment planning. The Limbus Contour software segments normal tissues using machine learning models and further post-processing on machine learning model prediction outputs. Limbus Contour does not display or store DICOM images and relies on existing radiotherapy treatment planning systems (TPS) and DICOM image viewers for display and modification of generated segmentations. Limbus Contour interfaces with the user’s operating system file system (importing DICOM image .dcm files and exporting segmented DICOM RT-Structure Set .dcm files).

Limbus Contour is a stand-alone software medical device. It is a single purposes cross-platform application for automatic contouring (segmentation) of CT/MRI DICOM images via pre-trained and expert curated machine learning models. The software is intended to be used by trained medical professionals to derive contours for input to radiation treatment planning. The Limbus Contour software segments normal tissues using machine learning models and further post-processing on machine learning model prediction outputs. Limbus Contour does not display or store DICOM images and relies on existing radiotherapy treatment planning systems (TPS) and DICOM image viewers for display and modification of generated segmentations. Limbus Contour interfaces with the user’s operating system file system (importing DICOM image .dcm files and exporting segmented DICOM RT-Structure Set .dcm files).

Limbus Contour is a stand-alone software medical device. It is a single purposes cross-platform application for automatic contouring (segmentation) of CT/MRI DICOM images via pre-trained and expert curated machine learning models. The software is intended to be used by trained medical professionals to derive contours for input to radiation treatment planning. The Limbus Contour software segments normal tissues using machine learning models and further post-processing on machine learning model prediction outputs. Limbus Contour does not display or store DICOM images and relies on existing radiotherapy treatment planning systems (TPS) and DICOM image viewers for display and modification of generated segmentations. Limbus Contour interfaces with the user’s operating system file system (importing DICOM image .dcm files and exporting segmented DICOM RT-Structure Set .dcm files).

Limbus Contour is a stand-alone software medical device. It is a single purposes cross-platform application for automatic contouring (segmentation) of CT/MRI DICOM images via pre-trained and expert curated machine learning models. The software is intended to be used by trained medical professionals to derive contours for input to radiation treatment planning. The Limbus Contour software segments normal tissues using machine learning models and further post-processing on machine learning model prediction outputs. Limbus Contour does not display or store DICOM images and relies on existing radiotherapy treatment planning systems (TPS) and DICOM image viewers for display and modification of generated segmentations. Limbus Contour interfaces with the user’s operating system file system (importing DICOM image .dcm files and exporting segmented DICOM RT-Structure Set .dcm files).

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