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EFAI BONESUITE XR BONE AGE PRO ASSESSMENT SYSTEM (EFAI BAPXR) is designed to view and quantify bone age from 2D Posterior Anterior (PA) view of left-hand radiographs using deep learning techniques to aid in the analysis of bone age assessment of patients between 2 to 16 years old for pediatric radiologists. The results should not be relied upon alone by pediatric radiologists to make diagnostic decisions. The images shall be with left hand and wrist fully visible within the field of view, and shall be without any major bone destruction, deformity, fracture, excessive motion, or other major artifacts.

The device is a software designed to aid the quantification of bone age for patients between 2 to 16 years old. The software uses deep learning techniques to analyze posterior-anterior (PA) radiographs of the left-hand according to the Greulich-Pyle (GP) method.

EFAI ICHCT 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 (ICH). EFAI ICHCT analyzes cases using deep learning algorithms to identify suspected ICH findings. It makes case-level output available to a PACS/workstation for worklist prioritization or triage. EFAI ICHCT is not intended to direct attention to specific portions of an image or to anomalies other than acute ICH. 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 studies.

EFAI NEUROSUITE CT ICH ASSESSMENT SYSTEM (EFAI ICHCT) 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 acute ICH are identified. Through the use of EFAI ICHCT, a radiologist is able to review studies with features suggestive of acute ICH 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.

EFAI HCAPSeg is a software device intended to assist trained radiation oncology professionals, including, but not limited to, radiation oncologists, medical physicists, and dosimetrists, during their clinical workflows of radiation therapy treatment planning by providing initial contours of organs at risk on non-contrast CT images. EFAI HCAPSeg is intended to be used on adult patients only. The contours are generated by deep-learning algorithms and then transferred to radiation therapy treatment planning systems. EFAI HCAPSeg must be used in conjunction with a DICOM-compliant treatment planning system to review and edit results generated. EFAI HCAPSeg is not intended to be used for decision making or to detect lesions. EFAI HCAPSeg is an adjunct tool and is not intended to replace a clinician's judgment and manual contouring of the normal organs on CT. Clinicians must not use the software generated output alone without review as the primary interpretation.

EFAI RTSuite CT HCAP-Segmentation System, herein referred to as EFAI HCAPSeg, is a standalone software that is designed to be used by trained radiation oncology professionals to automatically delineate organs-at-risk (OARs) on CT images. This auto-contouring of OARs is intended to facilitate radiation therapy workflows. The device receives CT images in DICOM format as input and automatically generates the contours of OARs, which are stored in DICOM format and in RTSTRUCT modality. The device does not offer a user interface and must be used in conjunction with a DICOM-compliant treatment planning system to review and edit results. Once data is routed to EFAI HCAPSeg, the data will be processed and no user interaction is required, nor provided. The deployment environment is recommended to be in a local network with an existing hospital-grade IT system in place. EFAI HCAPSeg should be installed on a specialized server supporting deep learning processing. The configurations are only being operated by the manufacturer: - Local network setting of input and output destinations; - Presentation of labels and their color; ● - Processed image management and output (RTSTRUCT) file management. ●

EFAI PACS PRO is intended to be used as a Digital Imaging and Communications in Medicine (DICOM) and non-DICOM information and data management system. The EFAI PACS PRO displays, processes, stores, and transfers medical data from original equipment manufacturers (OEMs) that support the DICOM standard, with the exception of mammography. It provides the capability to store images and patient information from OEM equipment, and perform filtering, digital manipulation and quantitative measurements. The client software is designed to run on standard personal and business computers. The product is intended to be used by trained medical professionals, including but not limited to radiologists, oncologists, and physicians. It is intended to provide image and related information that is interpreted by a trained professional to render findings and/or diagnosis, but it does not directly generate any diagnosis or potential findings.

The software is a stand-alone software as medical device (Stand-alone SaMD) which provides instant services for clinicians able to use web browsers at client stations to search and view medical data of desired patients which is stored in the software. The software also provides the following visualization, annotation and quantification functionalities which can be applied to the images on the web browser at client stations.

EFAI Chestsuite XR Pleural Effusion Assessment System is a software workflow tool designed to aid the clinical assessment of adult (18 years of age or older) Chest X-Ray cases with features suggestive of pleural efflusion in the medical care environment. EFAI Chestsuite XR Pleural Effusion Assessment System analyzes cases using an artificial intelligence algorithm to identify suspected findings on chest x-ray images taken in PA position. It makes case-level output available to a PACS/workstation for worklist prioritization or triage. EFAI Chestsuite XR Pleural Effusion Assessment System is not intended to direct attention to specific portions or 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 pleural effusion or otherwise preclude clinical assessment of X-Ray cases.

EFAI ChestSuite XR Pleural Effusion Assessment System, is a radiological computer-assisted triage and notification software system. The software uses deep learning techniques to automatically analyze PA chest x-rays and sends notification messages to the picture archiving and communication system (PACS)/workstation to allow suspicious findings of pleural effusion to be identified. 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 X-ray. The device aims to aid in prioritization and triage of radiological medical images only. The deployment environment is recommended to be in a local network with an existing hospitalgrade IT system in place. EFAI Chestsuite XR Pleural Effusion Assessment System should be installed on a specialized server supporting deep learning processing. The configurations are only being operated by the manufacturer: - Local network setting of input and output destinations; ● EFAI Chestsuite XR Pleural Effusion Assessment System is a software-only device which operates in four stages - data transfer, data preprocessing. AI inference and data post processing. The workflow of the device begins with applying a number of filtering rules based on image characteristics and DICOM tags to ensure only eligible images are analyzed by the algorithm. The image preprocessing unit ensures that all the input data are normalized (image size, contrast) such that it is ready for the algorithm to conduct the analysis. The AI inference generates an assessment which is then post-processed into a JSON message and transferred to an API server. The software is packaged as a docker container such that it can be installed and deployed to both a physical or virtual machine.

EFAI CARDIOSUITE SPECT MYOCARDIAL PERFUSION AGILE WORKFLOWS is an image processing software that provides analysis on DICOM images acquired from GE Medical Systems Nuclear Quantitative Perfusion SPECT software to support appropriately trained healthcare professionals in the evaluation and assessment of myocardial perfusions. It provides the following functionality: - Segmentation of the Bull's Eye images from the original DICOM - Analysis of the Bull's Eye images to help assess perfusion - Custom settings to generate text reports The results of this processing may be used to aid in evaluating and assessing myocardial perfusions. The system is an adjunct tool for GE Medical Systems Nuclear Quantitative Perfusion SPECT software.

The device allows users to interact with the software application via a web interface to upload, inspect, assess myocardial perfusion from Bull's Eye images. The user can change the quantitative settings to correct for numerical calculations and clinical adjustments. The device is designed to take images produced by GE Medical Systems Nuclear Quantitative Perfusion SPECT software and process the data to provide both numerical analysis of the Bull's Eye images to help assess for myocardial perfusions, and generate a report based on the users report settings and preference. The algorithm segments bull's eye images from SPECT images generated by GE's workstation and conducts quantitative analysis based on the color settings set by the user. The color scale is designed to follow GE's design convention, where the color red is indicative of a normal condition and blue representing severe perfusion. Each of the 17 segments would produce a quantitative evaluation under rest and stress conditions based on the color scale. The clinician would then design and fill in diagnosis terminologies that is best suited for each associated numerical results of each segment and generate a template report documenting the patient's condition.

EFAI HNSeg is a software device intended to assist trained radiation oncology professionals, including, but not limited to, radiation oncologists, medical physicists, and dosimetrists, during their clinical workflows of radiation therapy treatment planning by providing initial contours of organs at risk in the head and neck region on non-contrast CT images. EFAI HNSeg is intended to be used on adult patients only. The contours are generated by deep-learning algorithms and then transferred to radiation therapy treatment planning systems. EFAI HNSeg must be used in conjunction with a DICOM-compliant treatment planning system to review and edit results generated. EFAI HNSeg is not intended to be used for decision making or to detect lesions. EFAI HNSeg is an adjunct tool and is not intended to replace a clinician's judgment and manual contouring of the normal organs on CT. Clinicians must not use the software generated output alone without review as the primary interpretation.

EFAI RTSuite CT HN-Segmentation System, herein referred to as EFAI HNSeg, is a standalone software that is designed to be used by trained radiation oncology professionals to automatically delineate head-and-neck organs-at-risk (OARs) on CT images. This auto-contouring of OARs is intended to facilitate radiation therapy workflows. The device receives CT images in DICOM format as input and automatically generates the contours of OARs, which are stored in DICOM format and in RTSTRUCT modality. The device does not offer a user interface and must be used in conjunction with a DICOM-compliant treatment planning system to review and edit results. Once data is routed to EFAI HNSeg, the data will be processed and no user interaction is required, nor provided. The deployment environment is recommended to be in a local network with an existing hospitalgrade IT system in place. EFAI HNSeg should be installed on a specialized server supporting deep learning processing. The configurations are only being operated by the manufacturer: - Local network setting of input and output destinations; - Presentation of labels and their color; - Processed image management and output (RTSTRUCT) file management.

EFAI PACS PICTURE ARCHIVING AND COMMUNICATION SYSTEM is intended to be used as a Digital Imaging and Communications in Medicine (DICOM) and non-DICOM information and data management system. The EFAI PACS PICTURE ARCHIVING AND COMMUNICATION SYSTEM displays, processes, stores, and ransfers medical data from original equipment manufacturers (OEMs) that support the DICOM standard, with the exception of mammography. It provides the capability to store images and patient information from OEM equipment, and perform filtering, digital manipulation and quantitative measurements. The client software is designed to run on standard personal and business computers. The product is intended to be used by trained medical professionals, including but not limited to radiologists. oncologists, and physicians. It is intended to provide image and related information that is interpreted by a trained professional to render findings and/or diagnosis, but it does not diagnosis or potential findings.

The software is a stand-alone software as medical device (Stand-alone SaMD) which provide to instant services for clinician can able to use web browser at client stations to search and view medical data of desired patient which is stored in the software also provides the following visualization, annotation and quantification functionalities which can be applied to the images on the web browser at client stations: Visualization Functionalities: - a). 2D image view - b). Zoom ratio adjust - c). Pan - d). Window Level and Window Width adjust - e). Positive & Negative film effect - f). Mirror Flip - g). Multi-screen display - h). Cine Loop Play Annotation Functionality: - Point Annotation Quantification Functionalities: - a). Distance measurement - b). Area measurements - c). Angle measurement - d). Single Point measurement