Search Results

Synapse 3D Optional Tools is medical imaging software used with Synapse 3D Base Tools that is intended to provide trained medical professionals, with tools to aid them in reading, interpreting, reporting, and treatment planning. Synapse 3D Optional Tools accepts DICOM compliant medical images acquired from a variety of imaging devices including CT, MR. This product is not intended for use with or for the primary diagnostic interpretation of Mammography images. Addition to the tools in Synapse 3D Base Tools, Synapse 3D Optional Tools provides:

· Imaging tools for CT images including virtual endoscopic viewing.

· Imaging tools for MR images including delayed enhancement image viewing and diffusion-weighted MRI data analysis.

Synapse 3D Optional Tools is an optional software module that works with Synapse 3D Base Tools (cleared by via K120361 on 04/06/2012). Synapse 3D Base Tools is connected through DICOM standard to medical devices such as CT, MR, CR, US, NM, PT, XA, etc. and to a PACS system storing data generated by these medical devices, and retrieves image data via network communication based on the DICOM standard. The retrieved image data are stored on the local disk managed by Synapse 3D Base Tools, and the associated information of the image data is registered in the database and used for display, image processing, analysis, etc.

Synapse 3D Optional Tools provides imaging tools for CT and MR images such as virtual endoscopic simulator (CT) (referred collectively as "Endoscopic Simulator"), diffusion-weighted MRI data analysis (MR) (referred collectively as "IVIM"), and delayed enhancement image viewing (MR) (referred collectively as "Delayed Enhancement"). The software can display the images on a display monitor, or printed them on a hardcopy using a DICOM printer or a Windows printer.

Synapse 3D Optional Tools runs on Windows standalone and server/client configuration installed on a commercial general-purpose Windows-compatible computer. It offers software tools which can be used by trained professionals, such as radiologists, clinicians or general practitioners to interpret medical images obtained from various medical devices to create reports or develop treatment plans.

Here's an analysis of the provided text regarding the acceptance criteria and study for the device:

The provided document (K181773 for Synapse 3D Optional Tools) does not contain a detailed table of acceptance criteria or comprehensive study results for specific performance metrics in the way one might expect for a new AI/CAD device. Instead, it leverages its classification as a "Picture Archiving And Communications System" (PACS) and positions itself as substantially equivalent to predicate devices. This typically means that formal performance studies with detailed acceptance criteria and reported metrics demonstrating specific diagnostic accuracy are not required in the same way as a de novo device or a device making a new diagnostic claim.

The focus is on demonstrating that the features and technical characteristics are similar to existing cleared devices, and that the software development process and risk management ensure safety and effectiveness.

Here's a breakdown of the requested information based on the provided text:

1. Table of acceptance criteria and the reported device performance

As mentioned above, the document does not present a table of quantitative acceptance criteria for performance metrics (e.g., sensitivity, specificity, AUC) and corresponding reported performance values for the Synapse 3D Optional Tools. The "acceptance criteria" are implied to be fulfilled by following software development processes, risk management, and successful functional and system-level testing, which are designed to ensure the device operates as intended and is substantially equivalent to predicate devices.

The "reported device performance" is described qualitatively as:
"Test results showed that all tests passed successfully according to the design specifications. All of the different components of the Synapse 3D Optional Tools software have been stress tested to ensure that the system as a whole provides all the capabilities necessary to operate according to its intended use and in a manner substantially equivalent to the predicate devices."

2. Sample size used for the test set and the data provenance (e.g. country of origin of the data, retrospective or prospective)

The document states:
"benchmark performance testing was conducted using actual clinical images to help demonstrate that the semi-automatic segmentation, detection, and registration functions implemented in Synapse 3D Optional Tools achieved the expected accuracy performance."

However, it does not specify the sample size of the clinical images used for this benchmark performance testing. It also does not specify the data provenance (e.g., country of origin, retrospective or prospective nature of the data).

3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts (e.g. radiologist with 10 years of experience)

The document does not provide information on how ground truth was established for the "actual clinical images" used in benchmark performance testing, nor does it mention the number or qualifications of experts involved in this process.

4. Adjudication method (e.g. 2+1, 3+1, none) for the test set

The document does not specify any adjudication method for establishing ground truth or evaluating the test set.

5. If a multi reader multi case (MRMC) comparative effectiveness study was done, If so, what was the effect size of how much human readers improve with AI vs without AI assistance

The document does not mention a multi-reader, multi-case (MRMC) comparative effectiveness study. It explicitly states: "The subject of this 510(k) notification, Synapse 3D Optional Tools did not require clinical studies to support safety and effectiveness of the software." This reinforces the idea that the submission relies on substantial equivalence and non-clinical testing rather than new clinical effectiveness studies involving human readers.

6. If a standalone (i.e. algorithm only without human-in-the-loop performance) was done

The document notes that "benchmark performance testing was conducted using actual clinical images to help demonstrate that the semi-automatic segmentation, detection, and registration functions implemented in Synapse 3D Optional Tools achieved the expected accuracy performance." This implies some form of standalone evaluation of these specific functions' accuracy. However, "standalone performance" in the context of diagnostic accuracy (e.g., sensitivity/specificity of an AI model) is not explicitly detailed or quantified. The device is described as providing "tools to aid them [trained medical professionals] in reading, interpreting, reporting, and treatment planning," indicating it's an assistive tool, not a standalone diagnostic AI.

7. The type of ground truth used (expert consensus, pathology, outcomes data, etc.)

The document does not explicitly state the type of ground truth used for the "actual clinical images" in the benchmark testing. Given the general nature of the tools (segmentation, detection, registration), the ground truth for "accuracy performance" would likely involve expert-defined annotations or measurements on the images themselves, rather than pathology or outcomes data. However, this is an inference, not a stated fact in the document.

8. The sample size for the training set

The document does not provide information regarding a training set. This is consistent with a 510(k) for software tools that are substantially equivalent to existing PACS systems, rather than a de novo AI/ML algorithm that requires extensive training data. While it mentions "semi-automatic segmentation, detection, and registration functions," which often involve learned components, the submission focuses on the functionality of these tools as part of a PACS system rather than reporting on the underlying AI model's development details.

9. How the ground truth for the training set was established

Since no training set information is provided, there is no information on how ground truth for a training set was established.

Ask a specific question about this device

REX™ 3.0 is a software package intended for viewing and manipulating DICOM-compliant medical images acquired from CT and MR scanners. REX™ 3.0 can be used for real-time image viewing, image manipulation, 3D volume rendering, virtual endoscopy, and issuance of reports.

REX™ 3.0 is a tool for 3D (three dimensional) and 2D (two dimensional) viewing and manipulation of DICOM compliant CT and MR images. The proposed software provides real-time image viewing, image manipulation, 3D volume rendering, virtual endoscopy, and issuance of reports.

The provided text is a 510(k) Premarket Notification Summary for the REX™ 3.0 PACS / Image Processing Software. It focuses on demonstrating substantial equivalence to predicate devices rather than providing detailed acceptance criteria and a specific study proving the device meets them in the way modern AI/medical device submissions typically do.

Based on the provided text, here's a breakdown of the information requested, with "N/A" where the information is not available in the document:

Acceptance Criteria and Device Performance

1. Table of Acceptance Criteria and Reported Device Performance

The document describes the device's performance through a comparison to predicate devices rather than against pre-defined, quantitative acceptance criteria. The "acceptance criteria" here are implicitly that the REX™ 3.0 software performs all specified functions in line with software requirements and safety standards, and is substantially equivalent to predicate devices.

2. Sample size used for the test set and the data provenance (e.g. country of origin of the data, retrospective or prospective)

The document refers to "Validation testing" but does not specify a separate "test set" with a defined sample size for clinical or image-based performance evaluation. The "test set" is implicitly the DICOM-compliant images used during software validation, but no details are provided about their origin, number, or whether they were retrospective or prospective.

• Sample Size: N/A (Not specified as a distinct clinical test set with a quantifiable size)
• Data Provenance: N/A (Not specified)

3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts (e.g. radiologist with 10 years of experience)

N/A. The submission does not describe a process for establishing ground truth by expert consensus for a test set, as it is a PACS/image processing software focused on viewing and manipulation, not diagnostic interpretation or algorithm-based detection needing labeled ground truth in the context of an AI device. The validation is focused on software functionality and compliance.

4. Adjudication method (e.g. 2+1, 3+1, none) for the test set

N/A. Since no specific test set with expert-established ground truth is described, no adjudication method is mentioned.

5. If a multi-reader multi-case (MRMC) comparative effectiveness study was done, If so, what was the effect size of how much human readers improve with AI vs without AI assistance

N/A. This is not an AI-assisted diagnostic device. It is a PACS/image processing software. Therefore, an MRMC study concerning AI assistance is not relevant or described.

6. If a standalone (i.e. algorithm only without human-in-the-loop performance) was done

The device itself is described as "Standalone" software (meaning it's not embedded within a larger system). However, a "standalone algorithm performance" study related to an AI diagnostic function is not applicable here as it is not an AI diagnostic algorithm. The safety statement explicitly mentions: "Clinician interactive review/editing of data integral to use," indicating human-in-the-loop is part of its intended operational model.

7. The type of ground truth used (expert consensus, pathology, outcomes data, etc.)

N/A. Ground truth in the context of diagnostic accuracy is not discussed because this is an image viewing and manipulation software, not a diagnostic algorithm. The "truth" for its proper functioning is adherence to DICOM standards and its own software requirements specification.

8. The sample size for the training set

N/A. This is not an AI/machine learning device that relies on a "training set" in the context of deep learning models. The software performs deterministic image processing and viewing functions.

9. How the ground truth for the training set was established

N/A. Not applicable, as there is no training set mentioned or implied for an AI/ML model.

Summary of the Study and Device Performance:

The "study" described in K030457 is primarily a software validation and verification process to ensure the REX™ 3.0 software conforms to its design specifications, DICOM standards (Version 3.0), and relevant regulations. It is a non-clinical performance data assessment rather than a clinical trial or performance study involving patient data in a diagnostic context.

The primary method to "prove" the device meets acceptance criteria (which are largely functional and safety-based for this type of software) is through:

1. Conformance to DICOM 3.0: Stated directly in the "Non-Clinical Performance Data" section.
2. Validation and Verification Process: PointDx followed established procedures for software development, validation, and verification which confirm that REX™ 3.0 "performs all input functions, output functions, and all required actions according to the functional requirements specified in the Software Requirements Specification."
3. Hazard Analysis: Potential hazards were identified and controlled through design, protective measures, and user warnings, concluding that REX™ 3.0 "does not result in any new potential safety risks."
4. Substantial Equivalence Comparison: A detailed tabular comparison against predicate devices (REX™ 1.0 and Rapidia® V 2.0) highlights that REX™ 3.0 has similar features and functionalities, with improvements such as MR image support and multi-planar reformatting compared to REX™ 1.0, and overall equivalence in common features to Rapidia® V 2.0. This comparison implicitly serves as evidence that the device meets the "acceptance criteria" of being similar in performance and safety to already cleared devices.

In essence, the submission relies on software engineering best practices and regulatory compliance to demonstrate that the REX™ 3.0 software functions as intended and is safe, rather than a clinical study measuring diagnostic performance or accuracy against ground truth.

Ask a specific question about this device