(56 days)
Microsoft Radiomics App v1.0 is a software-only medical device intended for use by trained radiation oncologists, dosimetrists and physicists to derive optimal organ and tumor contours for input to radiation treatment planning. Supported image modalities are Computed Tomography and Magnetic Resonance. Radiomics App assists in the following scenarios:
· Load, save and display of medical images and contours for treatment evaluation and treatment planning.
· Creation, transformation, and modification of contours for applications including, but not limited to: transferring contours to radiotherapy treatment planning systems, aiding adaptive therapy, and archiving contours for patient followup.
· Localization and definition of both solid tumors and healthy anatomical structures.
- · Fusion display of compatible images for treatment planning.
· Three-dimensional rendering of medical images and the segmented contours.
Images reviewed using the Radiomics App software should not be used for primary image interpretations. Radiomics App is not for use with digital mammography.
Microsoft Radiomics App v1.0 is a software-only medical device intended for use by trained radiation oncologists, dosimetrists and medical physicists for radiation treatment planning.
Radiomics App stems from more than eight years of research in computerized medical image analysis, computer vision and machine learning. It applies well tested, state-of-the-art algorithms for the assisted delineation of anatomical structures of interest in three-dimensional, clinical radiological scans.
Radiomics App works on computed tomography (CT) and magnetic resonance (MR) scans, and is designed to contour/delineate both healthy anatomical structures as well as lesions such as solid tumors.
Radiomics App integrates into the clinical data network of radiation therapy treatment centers, receiving data from imaging devices such as CT and MR scanners. The purpose of the tool is to assist the expert user in producing segmentations (three-dimensional contours) of anatomical structures, for both solid tumors and healthy tissue structures. The following segmentation tools are provided:
- Assisted Contouring. This module allows for the manual, user-guided segmentation of . structures of interest in both CT and MR images.
- . Machine-learning based contouring. This module uses machine learning algorithms (ML) to provide an initial segmentation of certain structures of interest automatically. The user has the option to accept this initial segmentation or edit and refine it.
- . Contour refinement. This module allows the user to edit and improve segmentations created by either the machine learning or the assisted contouring algorithms.
These segmentations are then exported back into the clinical data network, and subsequently utilized in a radiotherapy treatment planning system to generate a treatment plan for a patient.
The provided document is a 510(k) Summary for the Microsoft Radiomics App v1.0. It describes the device, its intended use, and compares it to a predicate device (MIM 5.2). However, it does not contain detailed acceptance criteria or a specific study proving the device meets those criteria with performance metrics, sample sizes, expert qualifications, or ground truth details.
The document primarily focuses on software verification and validation, asserting that the software performs in accordance with specifications and that its performance is comparable to the predicate device, but without providing quantitative results from validation of specific features.
Therefore, for many of your requested items, the information is explicitly stated as "Not applicable" or is not provided in detail.
Here's a breakdown of the available information:
1. Table of Acceptance Criteria and Reported Device Performance
This information is not provided in this document. The document states that "Validation testing of the following functions of the Radiomics App demonstrated that the software meets user needs and intended uses and to support substantial equivalence," and lists functions like measurements, volumetric rendering, and contouring. However, it does not specify quantitative acceptance criteria (e.g., "accuracy > X%", "Dice score > Y%") nor does it report specific performance metrics against such criteria.
2. Sample size used for the test set and the data provenance
This information is not provided in this document. The document mentions "Validation Testing" but does not detail the number of cases or images used for these tests, nor the origin (country, retrospective/prospective) of any data used.
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 this document. While the device is intended for use by "trained radiation oncologists, dosimetrists and physicists," there is no mention of experts being used to establish ground truth for testing purposes.
4. Adjudication method for the test set
This information is not provided in this 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
No, a multi-reader multi-case (MRMC) comparative effectiveness study was not done. The document explicitly states under "Clinical Study": "Not applicable. Clinical studies are not necessary to establish the substantial equivalence of this device." This device is an AI-assisted contouring tool, but the submission does not include a study on its comparative effectiveness with human readers.
6. If a standalone (i.e., algorithm only without human-in-the-loop performance) was done
The document mentions "Automatic Contouring - Validation Test" as one of the validation tests performed. This implies some level of standalone algorithm performance was assessed. However, no specific performance metrics or acceptance criteria for this standalone performance are provided. The device description also states: "Machine-learning based contouring. This module uses machine learning algorithms (ML) to provide an initial segmentation of certain structures of interest automatically. The user has the option to accept this initial segmentation or edit and refine it." This confirms a standalone component, but performance details are absent.
7. The type of ground truth used (expert consensus, pathology, outcomes data, etc.)
This information is not explicitly detailed for the validation testing. Given the context of contouring for radiation treatment planning, it's highly probable that expert-generated contours would serve as ground truth, but the document does not confirm this or specify the method of ground truth establishment.
8. The sample size for the training set
This information is not provided in this document. The document mentions "Machine-learning based contouring" and that "Radiomics App stems from more than eight years of research in computerized medical image analysis, computer vision and machine learning," implying a training phase, but the sample size used for training is not disclosed.
9. How the ground truth for the training set was established
This information is not provided in this document.
§ 892.2050 Medical image management and processing system.
(a)
Identification. A medical image management and processing system is a device that provides one or more capabilities relating to the review and digital processing of medical images for the purposes of interpretation by a trained practitioner of disease detection, diagnosis, or patient management. The software components may provide advanced or complex image processing functions for image manipulation, enhancement, or quantification that are intended for use in the interpretation and analysis of medical images. Advanced image manipulation functions may include image segmentation, multimodality image registration, or 3D visualization. Complex quantitative functions may include semi-automated measurements or time-series measurements.(b)
Classification. Class II (special controls; voluntary standards—Digital Imaging and Communications in Medicine (DICOM) Std., Joint Photographic Experts Group (JPEG) Std., Society of Motion Picture and Television Engineers (SMPTE) Test Pattern).