Search Results

Multi-Modality Tumor Tracking (MMTT) application is a post processing software application used to display, process, analyze , quantify and manipulate anatomical and functional images, for CT, MR PET/CT and SPECT/CT images and/or multiple time-points. The MMTT application is intended for use on tumors which are known/confirmed to be pathologically diagnosed cancer. The results obtained may be used as a tool by clinicians in determining the diagnosis of patient disease conditions in various organs, tissues, and other anatomical structure.

Philips Medical Systems' Multi-Modality Tumor Tracking (MMTT) application is a post - processing software. It is a non-organ specific, multi-modality application which is intended to function as an advanced visualization application. The MMTT application is intended for displaying, processing, analyzing, quantifying and manipulating anatomical and functional images, from multi-modality of CT ,MR PET/CT and SPECT/CT scans.

The Multi-Modality Tumor Tracking (MMTT) application allows the user to view imaging, perform segmentation and measurements and provides quantitative and characterizing information of oncology lesions, such as solid tumor and lymph node, for a single study or over the time course of several studies (multiple time-points). Based on the measurements, the MMTT application provides an automatic tool which may be used by clinicians in diagnosis, management and surveillance of solid tumors and lymph node, conditions in various organs, tissues, and other anatomical structures, based on different oncology response criteria.

The provided text does not contain detailed information about a study that proves the device meets specific acceptance criteria, nor does it include a table of acceptance criteria and reported device performance.

The submission is a 510(k) premarket notification for the "Multi-Modality Tumor Tracking (MMTT) application." For 510(k) submissions, the primary goal is to demonstrate substantial equivalence to a legally marketed predicate device, rather than proving a device meets specific, pre-defined performance acceptance criteria through a rigorous clinical or non-clinical study that would be typical for a PMA (Premarket Approval) application.

Here's what can be extracted and inferred from the document regarding the device's validation:

Key Information from the Document:

• Study Type: No clinical studies were required or performed to support equivalence. The validation was based on non-clinical performance testing, specifically "Verification and Validation (V&V) activities."
• Demonstration of Compliance: The V&V tests were intended to demonstrate compliance with international and FDA-recognized consensus standards and FDA guidance documents, and that the device "Meets the acceptance criteria and is adequate for its intended use and specifications."
• Acceptance Criteria (Implied): While no quantitative table is provided, the acceptance criteria are implicitly tied to:
  • Compliance with standards: ISO 14971, IEC 62304, IEC 62366-1, DICOM PS 3.1-3.18.
  • Compliance with FDA guidance documents for software in medical devices.
  • Addressing intended use, technological characteristics claims, requirement specifications, and risk management results.
  • Functionality requirements and performance claims as described in the device description (e.g., longitudinal follow-up, multi-modality support, automated/manual registration, segmentation, measurement calculations, support for oncology response criteria, SUV calculations).
• Performance (Implied): "Testing performed demonstrated the Multi-Modality Tumor Tracking (MMTT) meets all defined functionality requirements and performance claims." Specific quantitative performance metrics are not given.

Information NOT present in the document:

The following information, which would typically be found in a detailed study report proving acceptance criteria, is not available in this 510(k) summary:

1. A table of acceptance criteria and the reported device performance: This document states the device "Meets the acceptance criteria and is adequate for its intended use and specifications," but does not list these criteria or the test results.
2. Sample sizes used for the test set and the data provenance: No details on the number of images, patients, or data characteristics used for non-clinical testing.
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): Since it was non-clinical testing, there's no mention of expert involvement in establishing ground truth for a test set.
4. Adjudication method (e.g., 2+1, 3+1, none) for the test set: Not applicable as no expert-adjudicated clinical test set is described.
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: No MRMC study was performed as no clinical studies were undertaken.
6. If a standalone (i.e. algorithm only without human-in-the-loop performance) was done: The V&V activities would have included testing the software's functionality, which could be considered standalone performance testing, but specific metrics are not provided. The device is a "post processing software application" used "by clinicians," implying a human-in-the-loop tool rather than a fully autonomous AI diagnostic device.
7. The type of ground truth used (expert consensus, pathology, outcomes data, etc.): Not detailed for the non-clinical V&V testing. For the intended use, the device is for "tumors which are known/confirmed to be pathologically diagnosed cancer," suggesting that the "ground truth" for the intended use context is pathological diagnosis. However, this is not the ground truth for the V&V testing itself.
8. The sample size for the training set: Not applicable; this is a 510(k) for a software application, not specifically an AI/ML product where a training set size would be relevant for model development. The document does not describe any machine learning model training.
9. How the ground truth for the training set was established: Not applicable for the same reason as above.

In summary, this 510(k) submission relies on a demonstration of substantial equivalence to existing predicate devices and internal non-clinical verification and validation testing, rather than a clinical study with specific, quantifiable performance metrics against an established ground truth.

Ask a specific question about this device

Keosys Medical Imaging Suite (KSWVWR) is intended to be used by trained medical professionals included, but not limited to, radiologists, nuclear medicine physicians and physicists.

Keosys Medical Imaging Suite is a software application intended to aid in diagnostic and evaluation of medical image data. Although this device allows the visualization of mammography images, it is not intended as a tool for primary diagnosis in mammography.

Keosys Medical Imaging Suite can be used for display, process, temporarily store, print and also create and print reports from 2D and 3D multimodal DICOM medical image data. The imaging data can be Computed Tomography (CT), Magnetic Resonance (MR), Radiography X (CR, DX, XRF, MG), Nuclear Medecine (NM) including planar imaging (Static, Whole body, Dynamic, Gated) and tomographic imaging (SPECT, Gated SPECT), Positon Emission Tomography (PT), Ultrasound (US).

Keosys Medical Imaging suite provides tools like rulers, markers or region of interests (e.g. it can be used in an oncology clinical workflow for tumor burden assessment or therapeutic response evaluation).

It is the user responsibility to check that the ambient luminosity conditions, the images compression ratio and the interpretation monitor specifications are consistent with a clinical diagnostic use of the data.

Keosys' Advanced Medical Imaging Software Suite (aka Viewer, aka KSWVWR) is a multimodality diagnostic workstation for visualization and 3D post-processing of Radiological and Nuclear Medicine medical images. It includes dedicated applications for the therapeutic response evaluation process in a multi-vendor, multi-modal and multi time-points context. The solution also includes the latest recommendations for SUV calculation.

Here's an analysis of the provided text to extract the acceptance criteria and details about the study, as requested.

Note: The provided document is a 510(k) summary for the "Advanced Medical Imaging Software Suite (KSWVWR)". It outlines the device's indications for use and compares it to predicate devices. However, it does not contain detailed acceptance criteria, specific study results, or information about sample sizes, ground truth establishment methods, or expert qualifications for a performance study. The document primarily focuses on demonstrating substantial equivalence to previously cleared devices rather than providing a standalone performance study report. Therefore, many of your requested points cannot be directly addressed from this text.

1. Table of Acceptance Criteria and Reported Device Performance

As noted, the document does not explicitly state quantitative acceptance criteria or detailed reported device performance in a study. The focus is on functionality and equivalence.

2. Sample size used for the test set and the data provenance

The document does not provide details on a specific "test set" with sample sizes or data provenance (e.g., country of origin, retrospective/prospective) for a performance study. The testing mentioned is part of the general software development process.

3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts

This information is not provided in the document.

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

This information is not provided in the document.

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 describe an MRMC comparative effectiveness study where human readers used the AI. The device is a "viewer" and "software application intended to aid in diagnostic and evaluation." It provides tools but is not explicitly an "AI" in the sense of providing automated diagnostic suggestions or classifications to be compared with human performance with/without its assistance. It enables the display and manipulation of images and provides measurement tools.

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

The document describes the device as a "software application intended to aid in diagnostic and evaluation of medical image data" and that it "provides tools like rulers, markers or region of interests." It is a diagnostic workstation for visualization and 3D post-processing, and its use is by "trained medical professionals." This implies a human-in-the-loop device. There is no mention of a standalone algorithm performance study.

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

This information is not provided in the document.

8. The sample size for the training set

The document does not describe any machine learning or AI components that would historically require a "training set" in the context of supervised learning, nor does it mention a sample size for such.

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

Not applicable, as no training set is described.

Ask a specific question about this device