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510(k) Data Aggregation
(33 days)
MIRADA RTX
RTx is intended to be used by trained medical professionals including, but not limited to, radiologists, nuclear medicine physicians, radiation oncologists, dosimetrists and physicists.
RTx is a software application intended to display and visualize 2D & 3D multi-modal medical image data. The user may process, render, review, store, print and distribute DICOM 3.0 compliant datasets within the system and/or across computer networks. Supported modalities include static and gated CT, PET, MR, SPECT and planar NM. The user may also create, display, print, store and distribute reports resulting from interpretation of the datasets.
RTx allows the user to register combinations of anatomical and functional images and display them with fused and non-fused displays to facilitate the comparison of image data by the user. The result of the registration operation can assist the user in assessing changes in image data, either within or between examinations and aims to help the user obtain a better understanding of the combined information that would otherwise have to be visually compared disjointedly.
RTx provides a number of tools such as rulers and region of interests, which are intended to be used for the assessment of regions of an image to support a clinical workflow. Examples of such workflows include, but are not limited to, the evaluation of the presence or absence of lesions, determination of treatment response and follow-up.
RTx supports the loading and saving of DICOM RT objects and allows the user to define, import, display, transform, store and export such objects including regions of interest structures and dose volumes to radiation therapy planning systems. RTx allows the user to transform regions of interest associated with a particular imaging dataset to another, supporting atlas-based contouring and rapid re-contouring of the same patient.
RTx is a software application for displaying and visualizing 2D & 3D multi-modal medical image data such as static and gated CT, PET, MR, SPECT and planar NM. RTx runs on a workstation with color monitor(s), keyboard, mouse and optional CD-RW or may be deployed on a server. RTx is designed to enable rendering, reviewing, storing, printing and distribution of DICOM 3.0 compliant datasets and reports within the system and/or across computer networks.
RTx enables automatic and manual registration of combinations of anatomical and functional images that can be displayed with fused and non-fused displays to facilitate the comparison of image data by the user.
RTx provides a number of tools such as rulers and semi-automated and manual regions of interest for the assessment of regions of an image to support a clinical workflow. RTx supports the loading and saving of DICOM RT objects and allows the user to define, import, display, transform and store and export such objects including regions of interest structures and dose volumes to radiation therapy planning systems.
Here's an analysis of the provided text regarding the acceptance criteria and supporting study for the RTx device:
The provided document describes the RTx device as a software application for displaying and visualizing 2D & 3D multi-modal medical image data, with tools for image registration and assessment. However, it does not contain specific, quantitative acceptance criteria for performance metrics such as accuracy, sensitivity, or specificity, nor does it detail a specific study with a test set, ground truth, or expert involvement to prove these criteria.
Instead, the submission for K130393 relies on a general statement of verification and validation. It asserts that RTx meets user needs, requirements, and demonstrates substantial equivalence to predicate devices.
Here's a breakdown of the requested information based on the provided text, highlighting what is present and what is absent:
Acceptance Criteria and Study Details for RTx (K130393)
1. Table of Acceptance Criteria and Reported Device Performance
| Acceptance Criterion | Reported Device Performance |
|---|---|
| Specific quantitative performance metrics (e.g., accuracy, sensitivity, specificity for image registration or lesion detection) are NOT provided in the document. | Specific quantitative performance metrics are NOT provided in the document. The document generally states: "The results of performance, functional and algorithmic testing demonstrate that RTx meets the user needs and requirements of the device, which are demonstrated to be substantially equivalent to those of the listed predicate devices." |
| Meeting user needs and requirements | "RTx meets the user needs and requirements of the device..." |
| Substantial equivalence to predicate devices (K102687, K091373, K093982, K081076) | "...demonstrated to be substantially equivalent to those of the listed predicate devices." |
| Compliance with ISO 13485:2003, CFR 21 Part 820, and DICOM standard | "Verification and Validation for RTx has been carried out in compliance with the requirements of ISO 13485:2003, CFR 21 Part 820 and in adherence to the DICOM standard." |
| No new potential safety risks | "RTx meets requirements for safety and effectiveness and does not introduce any new potential safety risks." |
2. Sample Size Used for the Test Set and Data Provenance
- Sample Size: Not specified. The document refers generally to "performance, functional and algorithmic testing" but does not detail the size or nature of any test dataset(s) used.
- Data Provenance: Not specified. The country of origin or whether the data was retrospective or prospective is not mentioned.
3. Number of Experts Used to Establish Ground Truth and Qualifications
- Number of Experts: Not specified. The document does not describe the use of experts to establish ground truth for testing.
- Qualifications of Experts: Not specified.
4. Adjudication Method for the Test Set
- Adjudication Method: Not applicable/Not specified. Since no expert-adjudicated test set is described, there is no mention of an adjudication method (e.g., 2+1, 3+1).
5. Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study
- MRMC Study: No. An MRMC comparative effectiveness study is not mentioned or described in the provided text.
- Effect Size of Human Reader Improvement with AI vs. without AI: Not applicable, as no MRMC study or AI assistance comparison is described. The device is a viewing and processing tool, not explicitly an AI-assisted diagnostic device in the context of human reader performance improvement.
6. Standalone (Algorithm Only) Performance Study
- Standalone Performance Study: No specific standalone performance study with quantitative results (e.g., for algorithms like image registration) is detailed in the submission. The "algorithmic testing" mentioned is general and no metrics are provided.
7. Type of Ground Truth Used
- Type of Ground Truth: Not specified. The document does not describe how ground truth was established for any performance evaluations.
8. Sample Size for the Training Set
- Sample Size: Not applicable/Not specified. The document does not refer to a "training set" in the context of machine learning. RTx is described as a software application providing tools for display, visualization, and processing, rather than an AI/ML model that would typically require a training set.
9. How the Ground Truth for the Training Set was Established
- Ground Truth for Training Set: Not applicable/Not specified, as no training set for a machine learning model is mentioned.
Summary Observation:
The submission for RTx 510(k) K130393 follows a traditional approach for medical image display and processing software. It focuses on functional verification and validation, adherence to standards, and substantial equivalence to existing predicate devices, rather than establishing quantitative performance metrics through specific clinical studies with expert-adjudicated ground truth, as would be expected for an AI/ML-based diagnostic device. The "testing" mentioned is broad and refers to meeting user needs and requirements through performance, functional, and algorithmic testing, without providing specific details on the tests or their outcomes in a quantitative manner.
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(17 days)
MIRADA RT
Mirada RT is intended to be used by trained medical professionals including, but not limited to, radiologists, nuclear medicine physicians, and physicists.
Mirada RT is a software application intended to display and visualize 2D & 3D multi-modal medical image data. The user may process, render, review, store, print and distribute DICOM 3.0 compliant datasets within the system and/or across computer networks. Supported modalities include, static and gated CT and PET, and static MR, SPECT and planar NM. The user may also create, display, print, store and distribute reports resulting from interpretation of the datasets.
Mirada RT allows the user to register combinations of anatomical and functional images and display them with fused and non-fused displays to facilitate the comparison of image data by the user. The result of the registration operation can assist the user in assessing changes in image data, either within or between examinations and aims to help the user obtain a better understanding of the combined information that would otherwise have to be visually compared disjointedly.
Mirada RT provides a number of tools such as rulers and region of interests, which are intended to be used for the assessment of regions of an image to support a clinical workflow. Examples of such workflows include, but are not limited to, the evaluation of the presence or absence of lesions, determination of treatment response and follow-up.
Mirada RT allows the user to define, import, transform and store and export regions of interest structures and dose volumes in DICOM RT format for use in radiation therapy planning systems.
Mirada RT is a software application for displaying and visualizing 2D & 3D multi-modal medical image data such as static and gated CT and PET, and static MR, SPECT and planar NM. Mirada RT runs on a workstation with color monitor(s), keyboard, mouse and optional CD-RW. Mirada RT is designed to enable rendering, reviewing, storing, printing and distribution of DICOM 3.0 compliant datasets and reports within the system and/or across computer networks.
Mirada RT enables automatic and manual registration of combinations of anatomical and functional images that can be displayed with fused and non-fused displays to facilitate the comparison of image data by the user.
Mirada RT provides a number of tools such as rulers and region of interests through SUV calculation for the assessment of regions of an image to support a clinical workflow. Mirada RT allows the user to define, import, transform and store and export regions of interest structures and dose volumes in DICOM RT format for use in radiation therapy planning systems.
The provided text describes Mirada RT as a software application for displaying and visualizing 2D & 3D multi-modal medical image data, intended for use by trained medical professionals. The document focuses on showing substantial equivalence to predicate devices rather than providing detailed acceptance criteria or a specific study to prove performance against such criteria.
Here's the information extracted and observations based on your request:
1. Table of Acceptance Criteria and Reported Device Performance
The document does not provide a table of acceptance criteria with specific quantitative targets for performance (e.g., accuracy, sensitivity, specificity) for the Mirada RT device. Instead, it states that:
| Acceptance Criteria | Reported Device Performance |
|---|---|
| Substantial Equivalence to Predicate Devices | "The results of performance, functional and algorithmic testing demonstrate that Mirada RT meets the user needs and requirements of the device, which are demonstrated to be substantially equivalent to those of the listed predicate devices." |
| "In conclusion, performance testing demonstrates that Mirada RT is substantially equivalent to, and performs at least as safely and effectively as the listed predicate devices. Mirada RT meets requirements for safety and effectiveness and does not introduce any new potential safety risks." | |
| Compliance with User Needs and Requirements | "Mirada RT is validated and verified against its user needs and intended use by the successful execution of planned performance, functional and algorithmic testing included in this submission." |
| Compliance with Standards | "Verification and Validation for Mirada RT has been carried out in compliance with the requirements of ISO 13485:2003 and in adherence to the DICOM standard." |
Observation: The document focuses on demonstrating substantial equivalence to predicate devices rather than establishing novel quantitative performance criteria for Mirada RT. The "performance testing" mentioned is to demonstrate this equivalence, not to achieve specific predefined operating characteristics for an AI component in the typical sense of current AI/ML device submissions.
2. Sample Size Used for the Test Set and Data Provenance
The document does not explicitly state the sample size used for any test set or the data provenance (e.g., country of origin, retrospective/prospective). It generally refers to "performance, functional and algorithmic testing."
3. Number of Experts Used to Establish Ground Truth and Qualifications
The document does not specify the number of experts used to establish ground truth or their qualifications.
4. Adjudication Method for the Test Set
The document does not specify any adjudication method (e.g., 2+1, 3+1, none) for a test set.
5. Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study
An MRMC comparative effectiveness study was not mentioned or described in the provided text. The document does not discuss human reader performance with or without AI assistance. Mirada RT is described as a software application with tools for image display, processing, and analysis, not specifically as an AI solution designed to augment human reader performance in the sense of a standalone diagnostic aid.
6. Standalone (Algorithm Only) Performance
The document does not provide details on standalone (algorithm only) performance. While it mentions "algorithmic testing," it does not present specific metrics or results for such testing in isolation from a human user. The device's intended use clearly involves trained medical professionals, suggesting a human-in-the-loop context.
7. Type of Ground Truth Used
The document does not explicitly state the type of ground truth used (e.g., expert consensus, pathology, outcomes data).
8. Sample Size for the Training Set
The document does not mention or specify any training set sample size, as it does not describe the development or evaluation of an AI/ML model in the typical sense that would require a dedicated training set. The descriptions of "algorithmic testing" are generic and do not refer to machine learning model training.
9. How Ground Truth for the Training Set Was Established
As no training set is discussed, the method for establishing ground truth for a training set is not mentioned.
Summary Observation: This 510(k) summary from 2010 predates the heightened focus on specific AI/ML performance metrics and study designs that are common in more recent submissions. The submission frames Mirada RT as a medical image processing and visualization tool that is substantially equivalent to existing predicate devices, rather than an AI-driven diagnostic or assistive device that would require extensive validation against specific ground truths using large, expertly annotated datasets for training and testing. The validation described is primarily focused on demonstrating the software functions as intended and safely, aligning with general medical device regulations and established standards like DICOM and ISO 13485.
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