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510(k) Data Aggregation
(202 days)
uOmnispace.MR is a software solution intended to be used for viewing, manipulating and analyzing medical images. It supports interpretation and evaluation of examinations within healthcare institutions. It has the following additional indications:
The uOmnispace.MR Stitching is intended to create full-format images from overlapping MR volume data sets acquired at multiple stages.
The uOmnispace.MR Dynamic application is intended to provide a general postprocessing tool for time course studies.
The uOmnispace.MR MRS (MR Spectroscopy) is intended to evaluate the molecule constitution and spatial distribution of cell metabolism. It provides a set of tools to view, process, and analyze the complex MRS data. This application supports the analysis for both SVS (Single Voxel Spectroscopy) and CSI (Chemical Shift Imaging) data.
The uOmnispace.MR MAPs application is intended to provide a number of arithmetic and statistical functions for evaluating dynamic processes and images. These functions are applied to the grayscale values of medical images.
The uOmnispace.MR Breast Evaluation application provides the user a tool to calculate parameter maps from contrast-enhanced time-course images.
The uOmnispace.MR Brain Perfusion application is intended to allow the visualization of temporal variations in the dynamic susceptibility time series of MR datasets.
· MR uOmnispace.MR Vessel Analysis is intended to provide a tool for viewing, manipulating, and evaluating MR vascular images.
The uOmnispace.MR DCE analysis is intended to view, manipulate, and evaluate dynamic contrast-enhanced MRI images.
The uOmnispace.MR United Neuro is intended to view, manipulate MR neurological images.
■ The uOmnispace.MR Cardiac Function is intended to view, evaluate functional analysis of cardiac MR images.
The uOmnispace.MR Flow Analysis is intended to view, evaluate flow analysis of flow MR images.
The uOmnispace.MR is a post-processing software based on the uOmnispace platform (cleared in K230039) for viewing, manipulating, evaluating and analyzing MR images, can run alone or with other advanced commercially cleared applications.
This proposed device contains the following applications:
- uOmnispace.MR Stitching
- uOmnispace.MR Dynamic
- uOmnispace.MR MRS
- uOmnispace.MR MAPs
- uOmnispace.MR Breast Evaluation
- . uOmnispace.MR Brain Perfusion
- uOmnispace.MR Vessel Analysis
- uOmnispace.MR DCE Analysis
- uOmnispace.MR United Neuro
- uOmnispace.MR Cardiac Analysis
- uOmnispace.MR Flow Analysis
Here's a breakdown of the acceptance criteria and the study proving the device meets them, based on the provided text:
1. Table of Acceptance Criteria and Reported Device Performance
| Validation Type | Acceptance Criteria | Reported Device Performance |
|---|---|---|
| Dice | To evaluate the proposed device of automatic ventricular segmentation, we compared the results with those of the cardiac function application of predicate device. The Sørensen-Dice coefficient is used to evaluate consistency. If dice > 0.95, it is considered consistent between the two devices. | 1.00 |
2. Sample Size Used for the Test Set and Data Provenance
- Sample Size: 114 samples from 114 different patients.
- Data Provenance: The data includes patients of various genders (35 Male, 20 Female, 59 Unknown), ages (5 between 14-25, 12 between 25-40, 22 between 40-60, 13 between 60-79, 62 Unknown), and ethnicities (50 Europe, 53 Asia, 11 USA). The data was acquired using MR scanners from various manufacturers: UIH (58), GE (2), Philips (2), Siemens (52), and with different magnetic field strengths: 1.5T (23), 3.0T (41), 50 Unknown. The text does not explicitly state if the data was retrospective or prospective, but the mention of a "deep learning-based Automatic ventricular segmentation Algorithm for the LV&RV Contour Segmentation feature" and "The performance testing for deep learning-based Automatic ventricular segmentation Algorithm was performed on 114 subjects...during the product development" implies a retrospective study using existing data to validate the developed algorithm.
3. Number of Experts Used to Establish the Ground Truth for the Test Set and Qualifications of Those Experts
The test set's ground truth was established by comparing the proposed device's results with those of the predicate device. The text does not explicitly state that human experts established the ground truth for the test set by manually segmenting the images for direct comparison against the algorithm's output. Instead, it seems the predicate device's output serves as the "ground truth" for the comparison of the new device's algorithm.
However, for the training ground truth, the following was stated:
- Number of Experts: Two cardiologists.
- Qualifications: Both cardiologists had "more than 10 years of experience each."
4. Adjudication Method for the Test Set
The study does not describe an adjudication method for the test set in the conventional sense of multiple human readers independently assessing the cases. Instead, the comparison is made between the proposed device's algorithm output and the predicate device's output.
For the training ground truth, the following adjudication method was used:
- Manual tracing was performed by an experienced user.
- Validation of these contours was done by two independent experts (more than 10 years experience).
- If there was a disagreement, a consensus between the experts was reached.
5. If a Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study Was Done, and the Effect Size
No MRMC comparative effectiveness study was done to assess how much human readers improve with AI vs without AI assistance. The study focuses on comparing the proposed device's algorithm performance directly against a predicate device's cardiac function application based on the Dice coefficient.
6. If a Standalone (i.e., algorithm only without human-in-the-loop performance) Was Done
Yes, a standalone performance study was done for the "deep learning-based Automatic ventricular segmentation Algorithm" for the LV&RV Contour Segmentation feature. The device's algorithm output was directly compared to the output of the predicate device's cardiac function application using the Dice coefficient.
7. The Type of Ground Truth Used
For the test set, the "ground truth" for comparison was the output of the cardiac function application of the predicate device.
For the training set, the ground truth was expert consensus based on manual tracing by an experienced user and validated by two independent cardiologists with over 10 years of experience.
8. The Sample Size for the Training Set
The document states: "The training data used for the training of the cardiac ventricular segmentation algorithm is independent of the data used to test the algorithm." However, it does not provide the specific sample size for the training set.
9. How the Ground Truth for the Training Set Was Established
The ground truth for the training set was established through manual annotation and expert consensus:
- It was "manually drawn on short axis slices in diastole and systole by two cardiologists with more than 10 years of experience each."
- "Manual tracing of the cardiac was performed by an experienced user."
- "The validation of these contours was done by two independent expert (more than 10 years) in this domain."
- "If there is a disagreement, a consensus between the experts was done."
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(493 days)
QIR Suite is intended to be used for viewing, post-processing, and quantitative evaluation of cardiovascular Magnetic Resonance (MR) images in a DICOM (Digital Imaging and Communication in Medicine) Standard format. The software has been validated for use on adult patients.
QIR Suite comprises QIR-MR for analysis of MR images. QIR-MR is composed of a viewer and analysis modules, and uses user inputs, standard algorithms, and/or automated deep learning detection algorithms.
QIR Suite support the following functionalities:
· Receive, store, transmit, post-process, display, and manipulate medical MR/CT images in the DICOM format (all transfer syntaxes supported including JPEG2000).
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· Client/server functionalities to connect to a PACS (Picture Archiving and Communication System), to a HL7 server.
· Visualization of 2D and 2D + time of single or multiple MR datasets. -
· Segmentation of regions of interest.
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· Measurement of distances and areas.
· Cardiac function MR analyses for the four chambers, including ejection assessment, local myocardial mass, diastolic function, thickness and thickening.
• 2D Flow studies.
Each module generates an automated report of the analysis. QIR Suite allows connection and storage of analyses on a PACS and on a HL7 server.
The software is not intended for use by patients, but rather by qualified medical professionals, experienced in examining and interpreting cardiovascular MR images to obtain diagnostic information as part of a comprehensive diagnostic decision-making process. OIR Suite cannot replace the diagnosis of a qualified practitioner and cannot be regarded as a sole medical point-of-view. The final diagnosis is the sole responsibility of the practitioner.
QIR Suite is a software for quantitative analyses of cardiovascular magnetic resonance images in the DICOM format. Analyses are performed using standardized and deep-learning algorithms. QIR Suite has been validated for adult patients. QIR Suite is intended to be used by qualified medical professionals, experienced in examining and evaluating cardiovascular MR images for the purpose of obtaining diagnostic information, as part of a comprehensive diagnostic decision-making process. QIR Suite cannot replace the diagnosis of a qualified practitioner and cannot be regarded as a sole medical point-of-view.
Acceptance Criteria and Device Performance Study for QIR Suite
1. Table of Acceptance Criteria and Reported Device Performance
The performance testing for QIR Suite focused on demonstrating substantial equivalence to predicate devices (Segment CMR and CVI42) by comparing quantitative measurements and evaluating deep learning algorithm performance. The acceptance criteria and reported device performance are summarized below:
| Feature/Parameter Tested | Acceptance Criteria | Reported Device Performance |
|---|---|---|
| Quantitative Parameters (Comparison with Predicate Devices) | ||
| Correlation Coefficient (R²) for all measurements (QIR Suite vs. Predicate) | R² > 0.95 | Systematically above 0.97, with an average correlation above 0.99 for all comparisons. Specifically, for cardiac function parameters, the minimum R² was 0.9792, and most were above 0.99, with an average of 0.9954. For 2D flow parameters, the minimum R² was 0.9590, and most were above 0.99, with an average of 0.9907. |
| Absolute Mean Difference for all measurements (QIR Suite vs. Predicate) |
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(200 days)
Myomics Q is intended to be used for viewing, post-processing and analysis of cardiac magnetic resonance (MR) images in a Digital Imaging and Communications in Medicine (DICOM) Standard format. It enables:
- Importing cardiac MR images in DICOM format.
- Supporting clinical diagnostics by analysis of cardiac MR images using display functionality such as panning, windowing, zooming through series/slices of the images.
- Supporting clinical diagnostics analysis of the heart in cardiac MR images and signal intensity.
- Software package is designed to support the physician compliance assessment, document and follow up heart disease by cardiac MRI.
It shall be used by qualified medical professionals, experienced in examining cardiovascular MR images, for the purpose of obtaining diagnostic information as part of a comprehensive diagnostic decision-making process. This device is a software application that can be used as a stand-alone product or in a network environment.
The target population for the device is not restricted, however the image acquisition by a cardiac MR scanner may limit the use of the device for certain sectors or the public.
Myomics Q is software application for evaluating cardiovascular images in a DICOM Standard format. The software can be used as a stand-alone product that can be integrated into a hospital or private practice environment. This device has a graphical user interface which allows users to analyze cardiac MR Images qualitatively and quantitatively.
Here's a breakdown of the acceptance criteria and study details for the Myomics Q device, based on the provided text:
1. Table of Acceptance Criteria and Reported Device Performance
The document does not explicitly state formal acceptance criteria with specific thresholds for each performance metric. Instead, the performance tests verify the proper functioning of features and quantitative comparisons against a reference device within a certain margin. The implicit acceptance criterion for the quantitative comparisons is that the results should be "very similar" and fall within a ±5% deviation from the reference device.
| Acceptance Criteria (Implicit) | Reported Device Performance |
|---|---|
| Functional Verification | |
| Proper installation of Myomics Q on appropriate OS (Window) | Passed (SPPT001) |
| Import cardiac MR Images function working properly | Passed (SPPT002) |
| Export cardiac MR Images function working properly | Passed (SPPT003) |
| Patient information function working properly | Passed (SPPT004) |
| Series overview function working properly | Passed (SPPT005) |
| Contour drawing functions (Endocardium, Epicardium, Move, Pinch, Nudge, Curved Line, Free Hand, Smoothing, Undo, Redo, Restart, Delete, Confirm, Zooming, Panning, Windowing) working properly | Passed (SPPT006) |
| T1 analysis function working properly (T1 Image or T1 Map display) | Passed (SPPT007) |
| T2 analysis function working properly (T2 Image or T2 Map display) | Passed (SPPT008) |
| LGE analysis function working properly (LGE Image display) | Passed (SPPT009) |
| Quantitative Comparison (Implicit Acceptance Threshold: ≤ ±5% deviation from cvi42) | |
| Results of Myomics Q are very similar to cvi42 in polar map report in Native T1 analysis | The results of Myomics Q did not show a difference of more than ±5% compared to the results of cvi42 (95% of cvi42 results |
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(255 days)
uWS-MR is a software solution intended to be used for viewing, manipulation, and storage of medical images. It supports interpretation and evaluation of examinations within healthcare institutions. It has the following additional indications:
The MR Stitching is intended to create full-format images from overlapping MR volume data sets acquired at multiple stages.
The Dynamic application is intended to provide a general post-processing tool for time course studies.
The Image Fusion application is intended to combine two different image series so that the displayed anatomical structures match in both series.
MRS (MR Spectroscopy) is intended to evaluate the molecule constitution and spatial distribution of cell metabolism. It provides a set of tools to view, process, and analyze the complex MRS data. This application supports the analysis for both SVS (Single Voxel Spectroscopy) and CSI (Chemical Shift Imaging) data.
The MAPs application is intended to provide a number of arithmetic and statistical functions for evaluating dynamic processes and images. These functions are applied to the grayscale values of medical images.
The MR Breast Evaluation application provides the user a tool to calculate parameter maps from contrast-enhanced timecourse images.
The Brain Perfusion application is intended to allow the visualizations in the dynamic susceptibility time series of MR datasets.
MR Vessel Analysis is intended to provide a tool for viewing, manipulating MR vascular images.
The Inner view application is intended to perform a virtual camera view through hollow structures (cavities), such as vessels.
The DCE analysis is intended to view, manipulate, and evaluate dynamic contrast-enhanced MRI images.
The United Neuro is intended to view, manipulate, and evaluate MR neurological images.
The MR Cardiac Analysis application is intended to be used for viewing, post-processing and quantitative evaluation of cardiac magnetic resonance data.
uWS-MR is a comprehensive software solution designed to process, review and analyze MR (Magnetic Resonance Imaging) studies. It can be used as a stand-alone SaMD or a post processing application option for cleared UIH (Shanghai United Imaging Healthcare Co.,Ltd.) MR Scanners. It can transfer images in DICOM 3.0 format over a medical imaging network or import images from external storage devices such as CD/DVDs or flash drives. These images can be functional data, as well as anatomical datasets. It can be at one or more time-points or include one or more time-frames. Multiple display formats including MIP and volume rendering and multiple statistical analysis including mean, maximum and minimum over a user-defined region is supported. A trained, licensed physician can interpret these displayed images as well as the statistics as per standard practice.
The provided 510(k) summary for the uWS-MR device from Shanghai United Imaging Healthcare Co., Ltd. does not contain a typical acceptance criteria table with reported device performance metrics in the format usually associated with diagnostic performance studies (e.g., sensitivity, specificity, accuracy).
Instead, this document describes modifications to an already cleared Picture Archiving and Communications System (PACS) named uWS-MR (K183164) and introduces a new advanced application (MR Cardiac Analysis) and a modified existing application (United Neuro). The "acceptance criteria" here are framed around demonstrating substantial equivalence to predicate devices for these functionalities.
Here's a breakdown of the requested information based on the provided text, focusing on the performance verification mentioned, which is the closest to a "study" described.
1. Table of Acceptance Criteria and Reported Device Performance
As noted, a quantitative performance table with metrics like sensitivity or specificity is not present. The "acceptance criteria" are implied by the comparison to predicate devices, focusing on functional parity and safe operation, rather than diagnostic accuracy for a specific disease or condition. The performance is summarized as the device having a "safety and effectiveness profile that is similar to the predicate device and reference devices."
The tables in the document (Table 1 and Table 2) compare the functional features of the proposed device against predicate/reference devices. These tables demonstrate functional equivalence rather than meeting specific quantifiable performance thresholds.
| Item | Acceptance Criterion (Implied) | Reported Device Performance (Implied) |
|---|---|---|
| General Device | Substantial equivalence to predicate device in classification, product code, regulation number, device class, and panel. | All general characteristics are "Same" as the predicate device. |
| Indications for Use | Substantial equivalence in core indications, with additional applications not negatively impacting safety/effectiveness. | The indications for use are supplemented, and additional applications are discussed, with the conclusion that differences "will not impact the safety and effectiveness of the device." |
| Basic Functions | Functional equivalence for image communication, hardware/OS, patient administration, review 2D/3D, filming, fusion, inner view, visibility, ROI/VOI, MIP display, compare, and report. | All basic functions are "Same" as the predicate device, except "Report" which is "Optimized function which will not impact the safety and effectiveness." |
| MR Cardiac Analysis | Functional equivalence to reference devices (cvi42 and Philips IntelliSpace Cardiovascular) for specific cardiac analysis functions (Cardiac Function and Flow Analysis). | All listed functions for Cardiac Analysis ("Type of imaging scans" to "Report") are "Same" as the reference devices. |
| United Neuro | Functional equivalence to predicate device (uWS-MR K183164) for neurological image processing functions, with acceptable modification to MR Segmentation. | Most listed functions for United Neuro are "Same" as the predicate. "MR Segmentation" is a new feature, allowing manual user segmentation, with the note, "does not affect safety and effectiveness." |
| Software V&V | Demonstration of safety and efficacy through software verification and validation. | Software V&V, hazard analysis (moderate LOC), and various software documentation were provided. |
| Other Standards/Guidance | Compliance with relevant standards (DICOM, ISO 14971, IEC 62304). | Compliance with these standards is implicitly claimed by their listing under "Other Standards and Guidance." |
2. Sample Size Used for the Test Set and Data Provenance
The document states "No clinical study was required" and "No animal study was required." The performance verification relies on "Software Verification and Validation" and a "Performance Evaluation Report for MR Cardiac Analysis." Details regarding the specific datasets used for these evaluations (sample size, data provenance like country of origin, or retrospective/prospective nature) are not provided in this summary.
3. Number of Experts Used to Establish the Ground Truth for the Test Set and Qualifications of Those Experts
Given that "No clinical study was required," the direct involvement of medical experts for establishing ground truth on a specific test set (for diagnostic performance) is not explicitly mentioned or detailed. The evaluation appears to be primarily focused on software functionality and engineering verification rather than a human reading study to assess diagnostic accuracy.
4. Adjudication Method for the Test Set
Since no clinical study or human reading study is mentioned, there is no information provided regarding an adjudication method for a test set.
5. Multi Reader Multi Case (MRMC) Comparative Effectiveness Study
A multi-reader, multi-case (MRMC) comparative effectiveness study is not mentioned in the document. Therefore, no effect size for human readers improving with or without AI assistance is provided.
6. Standalone Performance Study (Algorithm Only Without Human-in-the-Loop Performance)
The device is described as "a stand-alone SaMD" (Software as a Medical Device) or a "post processing application option." While the device itself is standalone software, the performance verification details do not explicitly detail a standalone performance study (e.g., diagnostic accuracy metrics) for its advanced applications. The evaluation is implicitly focused on the software's functional correctness and safety, rather than its diagnostic performance in a clinical scenario without human interaction to interpret results. The "Performance Evaluation Report for MR Cardiac Analysis" would be the closest to this, but its contents (e.g., metrics, sample size) are not detailed.
7. Type of Ground Truth Used
Given the lack of a clinical study, specific "ground truth" (such as pathology, outcomes data, or expert consensus on clinical cases) for diagnostic performance evaluation is not explicitly stated or detailed in the document. The "ground truth" for the software verification and validation would likely be defined by functional requirements specifications and adherence to design documents. For the cardiac and neuro applications, the "truth" could be defined by expected mathematical outcomes or standardized rendering accuracies.
8. Sample Size for the Training Set
The document does not provide any information regarding a training set sample size. This is typical for submissions focused on software modifications and functional equivalence rather than de novo AI algorithm development requiring extensive data for training and validation.
9. How the Ground Truth for the Training Set Was Established
As no training set is mentioned, no information is provided on how its ground truth would have been established.
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