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QP-Prostate® CAD is a Computed Aided Detection and Diagnosis (CADe/CADx) image processing software that automatically detects and identifies suspected lesions in the prostate gland based on bi-parametric prostate MRI. The software is intended to be used as a concurrent read by physicians with proper training in a clinical setting as an aid for interpreting prostate MRI studies. The results can be displayed in a variety of DICOM outputs, including identified suspected lesions marked as an overlay onto source MR images. The output can be displayed on third-party DICOM workstations and Picture Archive and Communication Systems (PACS). Patient management decisions should not be based solely on the results of QP-Prostate® CAD.

QP-Prostate® CAD is an artificial intelligence-based Computed Aided Detection and Diagnosis (CADe/CADx) image processing software. QP-Prostate® CAD uses Al-based algorithms trained with pathology data to detect suspicious lesions for clinically significant prostate cancer. The device automatically detects and identifies suspected lesions in the prostate gland based on bi-parametric prostate MRI and provides marks over regions of the images that may contain suspected lesions. There are two possible markers that are provided in different colors suggesting different levels of suspicion of clinically significant prostate cancer (moderate or high suspicion level). The software is intended to be used as a concurrent read by physicians with proper training in a clinical setting as an aid for interpreting prostate MRI studies. The results can be displayed in a variety of DICOM outputs, including identified suspected lesions marked as an overlay onto source MR images. The output can be displayed on third-party DICOM workstations and Picture Archive and Communication Systems (PACS). Based on biparametric input consisting of T2W and DWI series, the analysis is run automatically, and the output in standard DICOM formats is returned to PACS.

Prostate MR AI is a plug-in Radiological Computer Assisted Detection and Diagnosis Software device intended to be used · with a separate hosting application · as a concurrent reading aid to assist radiologists in the interpretation of a prostate MRI examination acquired according to the PI-RADS standard · in adult men (40 years and older) with suspected cancer in treatment naïve prostate glands The plug-in software analyzes non-contrast T2 weighted (T2W) and diffusion weighted image (DWI) series to segment the prostate gland and to provide an automatic detection and segmentation of regions suspicious for cancer. For each suspicious region detected, the algorithm moreover provides a lesion Score, by way of PI-RADS interpretation suggestion. Outputs of the device should be interpreted consistently with ACR recommendations using all available MR data (e.g., dynamic contrast enhanced images [if available]). Patient management decisions should not be made solely based on analysis by the Prostate MR AI algorithm.

This premarket notification addresses the Siemens Healthineers Prostate MR AI (VA10A) Radiological Computer Assisted Detection and Diagnosis Software (CADe/CADx). Prostate MR AI is a Computer Assisted Detection and Diagnosis algorithm designed to plug into a hosting workflow that assists radiologists in the detection of suspicious lesions and their classification. It is used as a concurrent reading aid to assist radiologists in the interpretation of a prostate MRI examination acquired according to the PI-RADS standard. The automatic lesion detection requires transversal T2W and DWI series as inputs. The device automatically exports a list of detected prostate regions that are suspicious for cancer (each list entry consists of contours and a classification by Score and Level of Suspicion (LoS)), a computed suspicion map, and a per-case LoS. The results of the Prostate MR AI plug-in (with the case-level LoS, lesion center points, lesion diameters, lesion ADC median, lesion 10th percentile, suspicion map, and non-PZ segmentation considered optional) are to be shown in a hosting application that allows the radiologist to view the original case, as well as confirm, reject, or edit lesion candidates with their contours and Scores as generated by the Prostate MR AI plug-in. Moreover, the radiologist can add lesions with contours and PI-RADS scores and finalize the case. In addition, the outputs include an automatically computed prostate segmentation, as well as sub-segmentations of the peripheral zone and the rest of the prostate (non-PZ). The algorithm will augment the prostate workflow of currently cleared syngo.MR General Engine if activated via a separate license on the General Engine.

TechCare Trauma is intended to analyze 2D X ray radiographs using techniques to aid in the detection, localization, and characterization of fractures and/or elbow joint effusion during the review of commonly acquired radiographs of: Ankle, Foot, Knee, Leg (includes Tibia/Fibula), Femur, Wrist, Hand/Finger, Elbow, Forearm, Arm (includes Humerus), Shoulder, Clavicle, Pelvis, Hip, Thorax (includes ribs). TechCare Trauma can provide results for fracture in neonates and infants (from birth to less than 2 years), children and adolescents (aged 2 to less than 22 years) and adults (aged 22 years and over). TechCare Trauma can provide results for elbow joint effusions in children and adolescents (aged 2 to less than 22 years) and adults (aged 22 years and over). The intended users of TechCare Trauma are clinicians with the authority to diagnose fractures and/or elbow joint effusions in various settings including primary care (e. g., family practice, internal medicine), emergency medicine, urgent care, and specialty care (e. g. orthopedics), as well as radiologists who review radiographs across settings. TechCare Trauma results are not intended to be used on a stand-alone basis for clinical decision-making. Primary diagnostic and patient management decisions are made by the clinical user.

The TechCare Trauma device is a software as Medical Device (SaMD). More specifically it is defined as a "radiological computer assisted detection and diagnostic software for suspected fractures". As a CADe/x software, TechCare Trauma is an image processing device intended to aid in the detection and localization of fractures and elbow joint effusions on acquired medical images (2D X-ray radiographs). TechCare Trauma uses an artificial intelligence algorithm to analyze acquired medical images (2D X-ray radiographs) for features suggestive of fractures and elbow joint effusions. TechCare Trauma can provide results for fractures in neonates and infants (from birth to less than 2 years), children and adolescents (aged 2 to less than 22 years) and adults (aged 22 years and over) regardless of their condition. TechCare Trauma can provide results for elbow joint effusions in children and adolescents (aged 2 to less than 22 years) and adults (aged 22 years and over).The device detects and identifies fractures and elbow joint effusions based on a visual model's analysis of images and provides information about the presence and location of these prespecified findings to the user. It relies solely on images provided by DICOM sources. Once integrated into existing networks, TechCare Trauma automatically receives and processes these images without any manual intervention. The processed results, which consist of one or more images derived from the original inputs, are then sent to specified DICOM destinations. This ensures that the results can be seamlessly viewed on any compatible DICOM viewer, allowing smooth into medical imaging workflows. TechCare Trauma can be deployed on-premises or on cloud and be connected to multiple DICOM sources / destinations (including but not limited to DICOM storage platform, PACS, VNA and radiological equipment, such as X-ray systems), ensuring easy integration into existing clinical workflows.

Saige-Dx analyzes digital breast tomosynthesis (DBT) mammograms to identify the presence or absence of soft tissue lesions and calcifications that may be indicative of cancer. For a given DBT mammogram, Saige-Dx analyzes the DBT image stacks and the accompanying 2D images, including full field digital mammography and/or synthetic images. The system assigns a Suspicion Level, indicating the strength of suspicion that cancer may be present, for each detected finding and for the entire case. The outputs of Saige-Dx are intended to be used as a concurrent reading aid for interpreting physicians on screening mammograms with compatible DBT hardware.

Saige-Dx is a software device that processes screening mammograms using artificial intelligence to aid interpreting radiologists. By automatically detecting the presence or absence of soft tissue lesions and calcifications in mammography images, Saige-Dx can help improve reader performance, while also reducing time. The software takes as input a set of x-ray mammogram DICOM files from a single digital breast tomosynthesis (DBT) study and generates finding-level outputs for each image analyzed, as well as an aggregate case-level assessment. Saige-Dx processes both the DBT image stacks and the associated 2D images (full-field digital mammography (FFDM) and/or synthetic 2D images) in a DBT study. For each image, Saige-Dx outputs bounding boxes circumscribing any detected findings and assigns a Finding Suspicion Level to each finding, indicating the degree of suspicion that the finding is malignant. Saige-Dx uses the results of the finding-level analysis to generate a Case Suspicion Level, indicating the degree of suspicion for malignancy across the case. Saige-Dx encapsulates the finding and case-level results into a DICOM Structured Report (SR) object containing markings that can be overlaid on the original mammogram images using a viewing workstation and a DICOM Secondary Capture (SC) object containing a summary report of the Saige-Dx results.

Transpara software is intended for use as a concurrent reading aid for physicians interpreting screening full-field digital mammography exams and digital breast tomosynthesis exams from compatible FFDM and DBT systems, to identify regions suspicious for breast cancer and assess their likelihood of malignancy. Output of the device includes locations of calcifications groups and soft-tissue regions, with scores indicating the likelihood that cancer is present, and an exam score indicating the likelihood that cancer is present in the exam. Patient management decisions should not be made solely on the basis of analysis by Transpara.

Transpara is a software only application designed to be used by physicians to improve interpretation of full-field digital mammography (FFMD) and digital breast tomosynthesis (DBT). Deep learning algorithms are applied to images for recognition of suspicious calcifications and soft tissue lesions (including densities, masses, architectural distortions, and asymmetries). Algorithms are trained with a large database of biopsy-proven examples of breast cancer, benign abnormalities, and examples of normal tissue. Transpara offers the following functions which may be used at any time in the reading process, to improve detection and characterization of abnormalities and enhance workflow: - AI findings for display in the images to highlight locations where the device detects suspicious calcifications or soft tissue lesions, along with region scores per finding on a scale ranging from 1-100, with higher scores indicating a higher level of suspicion. - Links between corresponding regions in different views of the breast, which may be utilized to enhance user interfaces and workflow. - An exam-based score which categorizes exams with increasing likelihood of cancer on a scale of 1-10 or in three risk categories labeled as 'low', 'intermediate' or 'elevated'. The concurrent use indication implies that it is up to the users to decide how to use Transpara in the reading process. Transpara functions can be used before, during or after visual interpretation of an exam by a user. Results of Transpara are computed in a standalone processing appliance which accepts mammograms in DICOM format as input, processes them, and sends the processing output to a destination using the DICOM protocol in a standardized mammography CAD DICOM format. Common destinations are medical workstations, PACS and RIS. The system can be configured using a service interface. Implementation of a user interface for end users in a medical workstation is to be provided by third parties.

Saige-Dx analyzes digital breast tomosynthesis (DBT) mammograms to identify the presence or absence of soft tissue lesions and calcifications that may be indicative of cancer. For a given DBT mammogram, Saige-Dx analyzes the DBT image stacks and the accompanying 2D images, including full field digital mammography and/or synthetic images. The system assigns a Suspicion Level, indicating the strength of suspicion that cancer may be present, for each detected finding and for the entire case. The outputs of Saige-Dx are intended to be used as a concurrent reading aid for interpreting physicians on screening mammograms with compatible DBT hardware.

Saige-Dx is a software device that processes screening mammograms using artificial intelligence to aid interpreting radiologists. By automatically detecting the presence or absence of soft tissue lesions and calcifications in mammography images, Saige-Dx can help improve reader performance, while also reducing time. The software takes as input a set of x-ray mammogram DICOM files from a single digital breast tomosynthesis (DBT) study and generates finding-level outputs for each image analyzed, as well as an aggregate case-level assessment. Saige-Dx processes both the DBT image stacks and the associated 2D images (full-field digital mammography (FFDM) and/or synthetic 2D images) in a DBT study. For each image, Saige-Dx outputs bounding boxes circumscribing any detected findings and assigns a Finding Suspicion Level to each finding, indicating the degree of suspicion that the finding is malignant. Saige-Dx uses the results of the finding-level analysis to generate a Case Suspicion Level, indicating the degree of suspicion for malignancy across the case. Saige-Dx encapsulates the finding and case-level results into a DICOM Structured Report (SR) object containing markings that can be overlaid on the original mammogram images using a viewing workstation and a DICOM Secondary Capture (SC) object containing a summary report of the Saige-Dx results.

ProFound Detection V4.0 is a computer-assisted detection and diagnosis (CAD) software device intended to be used concurrently by interpreting physicians while reading digital breast tomosynthesis (DBT) exams from compatible DBT system detects soft tissue densities (masses, architectural distortions and asymmetries) and calcifications in the 3D DBT slices. The detections and Certainty of Finding and Case Scores assist interpreting physicians in identifying soft tissue densities and calcifications that may be confirmed or dismissed by the interpreting Physician.

ProFound Detection V4.0 is a computer-assisted detection and diagnosis (CAD) software device that detects malignant soft-tissue densities and calcifications in digital breast tomosynthesis (DBT) images. The ProFound Detection V4.0 software allows an interpreting physician to quickly identify suspicious soft tissue densities and calcifications by marking the detected areas in the tomosynthesis images. When the ProFound Detection V4.0 marks are displayed by a user, the marks will appear as overlays on the tomosynthesis images. Each detected finding will also be assigned a "score" that corresponds to the ProFound Detection V4.0 algorithm's confidence that the detected finding is a cancer (Certainty of Finding). Certainty of Finding scores are a percentage in range of 0% to 100% to indicate CAD's confidence that the finding is malignant. ProFound Detection V4.0 also assigns a score to each case (Case Score) as a percentage in range of 0% to 100% to indicate CAD's confidence that the case has malignant findings. The higher the Certainty of Finding or Case Score, the higher the confidence that the detected finding is a cancer or that the case has malignant findings.

icobrain aria is a computer-assisted detection (CADe) and diagnosis (CADx) software device to be used as a concurrent reading aid to help trained radiologists in the detection, assessment and characterization of Amyloid Related Imaging Abnormalities (ARIA) from a set of brain MR images. The software provides information about the presence, location, size, severity and changes of ARIA-E (brain edema or sulcal effusions) and ARIA-H (hemosiderin deposition, including microhemorrhage and superficial siderosis). Patient management decisions should not be made solely on the basis of analysis by icobrain aria.

icobrain aria is a software-only device for assisting radiologists with the detection of amyloid-related imaging abnormalities (ARIA) on brain MRI scans of Alzheimer's disease patients under an amyloid beta-directed antibody therapy. The device utilizes 2D fluid-attenuated inversion recovery (FLAR) for the detection of ARIA-E (edema/sulcal effusion) and 2D T2* gradient echo (T2*-GRE) for the detection of ARIA-H (hemosiderin deposition). icobrain aria automatically processes input brain MRI scans in DICOM format from two time points and generates annotated DICOM images and an electronic report.

Lunit INSIGHT DBT is a computer-assisted detection and diagnosis (CADe/x) software intended to be used concurrently by interpreting physicians to aid in the detection and characterization of suspected lesions for breast cancer in digital breast tomosynthesis (DBT) exams from compatible DBT systems. Through the analysis. the regions of soft tissue lesions and calcifications are marked with an abnormality score indicating the likelihood of the presence of malignancy for each lesion. Lunit INSIGHT DBT uses screening mammograms of the female population. Lunit INSIGHT DBT is not intended as a replacement for a complete interpreting physician's review or their clinical judgment that takes into account other relevant information from the image or patient history.

Lunit INSIGHT DBT is a computer-assisted detection/diagnosis (CADe/x) software as a medical device that provides information about the presence, location and characteristics of lesions suspicious for breast cancer to assist interpreting physicians in making diagnostic decisions when reading digital breast tomosynthesis (DBT) images. The software automatically analyzes digital breast tomosynthesis slices via artificial intelligence technology that has been trained via deep learning. For each DBT case, Lunit INSIGHT DBT generates an artificial intelligence analysis results that include the lesion type, location, lesion-level/case-level score, and outline of the regions suspected of breast cancer. This peripheral information intends to augment the physician's workflow to better aid in detection and diagnosis of breast cancer.

See-Mode Augmented Reporting Tool, Thyroid (SMART-T) is a stand-alone reporting software to assist trained medical professionals in analyzing thyroid ultrasound images of adult (>=22 years old) patients who have been referred for an ultrasound examination. Output of the device includes regions of interest (ROIs) placed on the thyroid ultrasound images assisting healthcare professionals to localize nodules in thyroid studies. The device also outputs ultrasonographic lexicon-based descriptors based on ACR TI-RADS. The software generates a report based on the image analysis results to be reviewed and approved by a qualified clinician after performing quality control. SMART-T may also be used as a structured reporting software for further ultrasound studies. The software includes tools for reading measurements and annotations from the images that can be used for generating a structured report. Patient management decisions should not be made solely on the basis of analysis by See-Mode Augmented Reporting Tool, Thyroid.

See-Mode Augmented Reporting Tool, Thyroid (SMART-T) is a stand-alone, web-based image processing and reporting software for localization, characterization and reporting of thyroid ultrasound images. The software analyzes thyroid ultrasound images and uses machine learning algorithms to extract specific information. The algorithms can identify and localize suspicious soft tissue nodules and also generate lexicon-based descriptors, which are classified according to ACR TI-RADS (composition, echogenicity, shape, margin, and echogenic foci) with a calculated TI-RADS level according to the ACR TI-RADS chart. SMART-T may also be used as a structured reporting software for further ultrasound studies. The software includes tools for reading measurements and annotations from the images that can be used for generating a structured report. The software then generates a report based on the image analysis results to be reviewed and approved by a qualified clinician after performing quality control. Any information within this report can be changed and modified by the clinician if needed during quality control and before finalizing the report. The software runs on a standard "off-the-shelf" computer and can be accessed within the client web browser to perform the reporting of ultrasound images. Input data and images for the software are acquired through DICOM-compliant ultrasound imaging devices.