(83 days)
Yes
The document explicitly mentions "deep learning" and describes the training and testing of "Spine labeling Algorithm" and "Rib labeling Algorithm," which are characteristic of ML model development.
No.
The device is a software solution for viewing, processing, evaluating, and analyzing medical images for interpretation and evaluation within healthcare institutions. It is for diagnostic purposes, not for providing therapy or treatment.
Yes
Explanation: The device is described as "intended to be used for viewing, processing, evaluating and analyzing of PET, CT, MR, SPECT images" and supports "interpretation and evaluation of examinations within healthcare institutions." It also specifies applications like "analyzing and reporting PET, CT, MR, SPECT data... on oncology, neurology, cardiology," "display and analyze the follow-up PET, CT, MR data, with which users can do image registration, lesion segmentation, and statistical analysis," and "analyze the brain PET scan, give quantitative results of the relative activity of different brain regions." These functions directly support medical diagnosis.
Yes
The device is explicitly described as a "software solution" and "post-processing software" that runs on a platform. There is no mention of accompanying hardware components that are part of the device itself.
Based on the provided information, this device is not an In Vitro Diagnostic (IVD).
Here's why:
- IVDs analyze samples taken from the human body. The intended use and device description clearly state that this software is for viewing, processing, evaluating, and analyzing medical images (PET, CT, MR, SPECT). These images are generated by imaging equipment that visualizes structures within the body, not by analyzing biological samples taken from the body (like blood, urine, tissue, etc.).
- The functions described are image processing and analysis. The applications listed (MM Fusion, MM Oncology, Dynamic Analysis, NeuroQ, Emory Cardiac Toolbox) all focus on manipulating, analyzing, and quantifying information derived from medical images. This is distinct from the types of tests performed by IVDs, which typically involve chemical, biological, or immunological assays on biological specimens.
Therefore, while this device is a medical device used in healthcare, it falls under the category of medical imaging software rather than an In Vitro Diagnostic device.
No
The letter does not state that the FDA has reviewed and approved or cleared a PCCP for this specific device.
Intended Use / Indications for Use
uOmnispace.MI is a software solution intended to be used for viewing, processing, evaluating and analyzing of PET, CT, MR, SPECT images. It supports interpretation and evaluation of examinations within healthcare institutions. It has the following additional indications:
u uOmnispace.MI MM Fusion application is intended to provide tools for viewing, analyzing and reporting PET, CT, MR, SPECT data with its flexible workflow and optimized layout protocols for dedicated reporting purposes on oncology, neurology, cardiology.
u uOmnispace.MI MM Oncology application is intended to provide tools to display and analyze the follow-up PET, CT, MR data, with which users can do image registration, lesion segmentation, and statistical analysis.
· uOmnispace.MI Dynamic Analysis application is intended to display PET data and anatomical data such as CT or MR, and supports to do lesion segmentation and output associated time activity curve.
u uOmnispace.MI NeuroQ application is intended to analyze the brain PET scan, give quantitative results of the relative activity of different brain regions, and make comparison of activity of normal brain regions in AC database or between two studies from the same patient, as well as provide analysis of amyloid uptake levels in the brain.
u uOmnispace.MI Emory Cardiac Toolbox application is intended to provide cardiac short axis reconstruction, browsing function. And it also performs perfusion analysis, activity analysis and cardiac function analysis of the cardiac short axis.
Product codes
LLZ
Device Description
The uOmnispace.MI is a post-processing software based on the uOmnispace platform (cleared in K230039) for viewing, manipulating, evaluating and analyzing PET, CT, MR, SPECT images, can run alone or with other advanced commercially cleared applications.
This proposed device contains the following applications:
- uOmnispace.MI MM Fusion
- uOmnispace.MI MM Oncology
- . uOmnispace.MI Dynamic Analysis
Additionally, uOmnispace.MI offers the users the options to run the following third-party applications in uOmnispace.MI:
- uOmnispace.MI NeuroQ ●
- uOmnispace.MI Emory Cardiac Toolbox ●
Mentions image processing
Yes
Mentions AI, DNN, or ML
Yes
Input Imaging Modality
PET, CT, MR, SPECT
Anatomical Site
Brain, Cardiac, Spine, Rib, Lung, Liver, Lymph node
Indicated Patient Age Range
(10, 100]
Intended User / Care Setting
Not Found
Description of the training set, sample size, data source, and annotation protocol
The training data used for the training of the spine labeling algorithm is independent of the data used to test the algorithm.
The training data used for the training of the rib labeling algorithm is independent of the data used to test the algorithm.
No other information is available.
Description of the test set, sample size, data source, and annotation protocol
Spine labeling Algorithm:
Testing Data Information:
- Sample Size: 267 Patients, 286 Samples. One patient may have multiple samples, because of the differences in scanning protocols, such as slice thickness, in-plane spacing.
- Equipments and Protocols: CT data were acquired with different multidetector (16, 64, 160, 256, 320-slice) CT scanners from five major manufacturers (GE, Phillips, Siemens, Toshiba, UIH), and with tube voltage of 110 - 140 kVp. tube current of 275-1000mA. slice thickness of 0.625-2.0 mm, in-plane spacing of 0.656 - 1.172 mm.
- Clinical Subgroup Information:
- Gender: Male: 68, Female: 55, Unknown: 163
- Age: (10, 25]: 4, (25, 40]: 16, (40, 60]: 45, (60, 75]: 47, (75, 100]: 14, Unknown: 160
- Ethnicity: Asian(Chinese) data: 106, European data: 160, The United States data: 20
- Standard Annotation Process: For ground truth annotations, all ground truth are annotated by well-trained annotators. The annotators use an interactive tool to observe the image, and then set annotation points near the center of vertebral body and assign anatomical labels. Finally, a senior clinical specialist will check and modify annotations to make sure the ground truth correct.
Rib labeling Algorithm:
Testing Data Information:
- Sample Size: 156 Patients, 160 Samples. One patient may have multiple samples, because of the differences in scanning protocols, such as slice thickness, in-plane spacing.
- Equipments and Protocols: CT data were acquired with different multidetector (16, 64, 160, 256, 320-slice) CT scanners from five major manufacturers (GE, Phillips, Siemens, Toshiba, UIH), and with tube voltage of 100-140 kVp, tube current of 275-1000 mA, slice thickness of 0.625-3.0 mm, in-plane spacing of 0.531-1.955 mm.
- Clinical subgroups:
- Gender: Male: 110, Female: 39, Unknown: 11
- Age: (10, 25]: 2, (25, 40]: 14, (40, 60]: 51, (60, 75]: 69, (75, 100]: 24
- Ethnicity: Asian(Chinese) data: 80, The United States data: 80
- Standard Annotation Process: For ground truth annotations, all ground truth are annotated by well-trained annotators. A threshold based interactive tool is used to generate initial rib mask, then annotators will refine the rib mask and assign anatomical labels. After the first round annotation, they will check each other's annotation. At last, a senior clinical specialist will check and modify annotations to make sure the ground truth correct.
Summary of Performance Studies (study type, sample size, AUC, MRMC, standalone performance, key results)
Spine labeling Algorithm:
- Study type: Performance verification test.
- Sample size: 286 CT scans.
- Standalone performance:
- Performance Testing Summary: For testing dataset, the average score of the proposed device results to be validated is 4.951 points and greater than 4 points (acceptance criteria was average score > 4 points).
- Subgroup analysis shows that the proposed device algorithm has good generalization in different subgroups (age, gender, ethnicity, all average scores were 4.913-5.000).
- Key results: The average score of the proposed device results is 4.951 points, which is higher than the acceptance criteria of 4 points. This is equivalent to a mean identification rate of spine labeling greater than 92% (>83.3%, correctly labeled vertebrae number >=23, total vertebrae number=25, 23/25=92%).
Rib labeling Algorithm:
- Study type: Performance verification test.
- Sample size: 160 CT scans.
- Standalone performance:
- Performance Testing Summary: For the testing dataset, the average score of the proposed device results to be validated is 5 points and greater than 4 points (acceptance criteria was average score > 4 points).
- Subgroup analysis shows that the proposed device algorithm has good generalization in different subgroups (age, gender, ethnicity, all average scores were 5.00).
- Key results: The average score of the proposed device results is 5 points, which is higher than the acceptance criteria of 4 points. This is equivalent to a mean identification rate of rib labeling greater than 91.7% (>91.5% , correctly labeled rib number >=22, total rib number=24, 22/24~91.7%).
Key Metrics (Sensitivity, Specificity, PPV, NPV, etc.)
Not Found. Scoring based on a 5-point criteria for identification rate.
Predicate Device(s)
Reference Device(s)
Predetermined Change Control Plan (PCCP) - All Relevant Information
Not Found
§ 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).
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Image /page/0/Picture/0 description: The image shows the logo of the U.S. Food and Drug Administration (FDA). On the left is the Department of Health & Human Services logo. To the right of that is the FDA logo, which is a blue square with the letters "FDA" in white. To the right of the blue square is the text "U.S. FOOD & DRUG ADMINISTRATION" in blue.
December 20, 2023
Shanghai United Imaging Healthcare Co., Ltd. Gao Xin Official Correspondent No.2258 Chengbei Road, Jiading District Shanghai, Shanghai 201807 China
Re: K233176
Trade/Device Name: uOmnispace.MI Regulation Number: 21 CFR 892.2050 Regulation Name: Medical Image Management And Processing System Regulatory Class: Class II Product Code: LLZ Dated: September 26, 2023 Received: September 28, 2023
Dear Gao Xin:
We have reviewed your section 510(k) premarket notification of intent to market the device referenced above and have determined the device is substantially equivalent (for the indications for use stated in the enclosure) to legally marketed predicate devices marketed in interstate commerce prior to May 28, 1976, the enactment date of the Medical Device Amendments, or to devices that have been reclassified in accordance with the provisions of the Federal Food, Drug, and Cosmetic Act (the Act) that do not require approval of a premarket approval application (PMA). You may, therefore, market the device, subject to the general controls provisions of the Act. Although this letter refers to your product as a device, please be aware that some cleared products may instead be combination products. The 510(k) Premarket Notification Database available at https://www.accessdata.fda.gov/scripts/cdrh/cfdocs/cfpmn/pmn.cfm identifies combination product submissions. The general controls provisions of the Act include requirements for annual registration, listing of devices, good manufacturing practice, labeling, and prohibitions against misbranding and adulteration. Please note: CDRH does not evaluate information related to contract liability warranties. We remind you, however, that device labeling must be truthful and not misleading.
If your device is classified (see above) into either class II (Special Controls) or class III (PMA), it may be subject to additional controls. Existing major regulations affecting your device can be found in the Code of Federal Regulations, Title 21, Parts 800 to 898. In addition, FDA may publish further announcements concerning your device in the Federal Register.
Additional information about changes that may require a new premarket notification are provided in the FDA guidance documents entitled "Deciding When to Submit a 510(k) for a Change to an Existing Device" (https://www.fda.gov/media/99812/download) and "Deciding When to Submit a 510(k) for a Software Change to an Existing Device" (https://www.fda.gov/media/99785/download).
1
Your device is also subject to, among other requirements, the Quality System (QS) regulation (21 CFR Part 820), which includes, but is not limited to, 21 CFR 820.30, Design controls; 21 CFR 820.90, Nonconforming product; and 21 CFR 820.100, Corrective and preventive action. Please note that regardless of whether a change requires premarket review, the QS regulation requires device manufacturers to review and approve changes to device design and production (21 CFR 820.30 and 21 CFR 820.70) and document changes and approvals in the device master record (21 CFR 820.181).
Please be advised that FDA's issuance of a substantial equivalence determination does not mean that FDA has made a determination that your device complies with other requirements of the Act or any Federal statutes and regulations administered by other Federal agencies. You must comply with all the Act's requirements, including, but not limited to: registration and listing (21 CFR Part 807); labeling (21 CFR Part 801); medical device reporting of medical device-related adverse events) (21 CFR Part 803) for devices or postmarketing safety reporting (21 CFR Part 4, Subpart B) for combination products (see https://www.fda.gov/combination-products/guidance-regulatory-information/postmarketing-safety-reportingcombination-products); good manufacturing practice requirements as set forth in the quality systems (QS) regulation (21 CFR Part 820) for devices or current good manufacturing practices (21 CFR Part 4, Subpart A) for combination products; and, if applicable, the electronic product radiation control provisions (Sections 531-542 of the Act); 21 CFR Parts 1000-1050.
Also, please note the regulation entitled, "Misbranding by reference to premarket notification" (21 CFR 807.97). For questions regarding the reporting of adverse events under the MDR regulation (21 CFR Part 803), please go to https://www.fda.gov/medical-device-safety/medical-device-reportingmdr-how-report-medical-device-problems.
For comprehensive regulatory information about mediation-emitting products, including information about labeling regulations, please see Device Advice (https://www.fda.gov/medicaldevices/device-advice-comprehensive-regulatory-assistance) and CDRH Learn (https://www.fda.gov/training-and-continuing-education/cdrh-learn). Additionally, you may contact the Division of Industry and Consumer Education (DICE) to ask a question about a specific regulatory topic. See the DICE website (https://www.fda.gov/medical-device-advice-comprehensive-regulatoryassistance/contact-us-division-industry-and-consumer-education-dice) for more information or contact DICE by email (DICE@fda.hhs.gov) or phone (1-800-638-2041 or 301-796-7100).
Sincerely,
Daniel M. Krainak, Ph.D. Assistant Director DHT8C: Division of Radiological Imaging and Radiation Therapy Devices OHT8: Office of Radiological Health Office of Product Evaluation and Quality Center for Devices and Radiological Health
Enclosure
2
Indications for Use
Form Approved: OMB No. 0910-0120 Expiration Date: 07/31/2026 See PRA Statement below.
Submission Number (if known)
33176 Device Name
uOmnispace.MI
Indications for Use (Describe)
uOmnispace.MI is a software solution intended to be used for viewing, processing, evaluating and analyzing of PET, CT, MR, SPECT images. It supports interpretation and evaluation of examinations within healthcare institutions. It has the following additional indications:
u uOmnispace.MI MM Fusion application is intended to provide tools for viewing, analyzing and reporting PET, CT, MR, SPECT data with its flexible workflow and optimized layout protocols for dedicated reporting purposes on oncology, neurology, cardiology.
u uOmnispace.MI MM Oncology application is intended to provide tools to display and analyze the follow-up PET, CT, MR data, with which users can do image registration, lesion segmentation, and statistical analysis.
· uOmnispace.MI Dynamic Analysis application is intended to display PET data and anatomical data such as CT or MR, and supports to do lesion segmentation and output associated time activity curve.
u uOmnispace.MI NeuroQ application is intended to analyze the brain PET scan, give quantitative results of the relative activity of different brain regions, and make comparison of activity of normal brain regions in AC database or between two studies from the same patient, as well as provide analysis of amyloid uptake levels in the brain.
u uOmnispace.MI Emory Cardiac Toolbox application is intended to provide cardiac short axis reconstruction, browsing function. And it also performs perfusion analysis, activity analysis and cardiac function analysis of the cardiac short axis.
Type of Use (Select one or both, as applicable)
Prescription Use (Part 21 CFR 801 Subpart D)
Over-The-Counter Use (21 CFR 801 Subpart C)
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Image /page/3/Picture/1 description: The image contains the logo for United Imaging. The logo consists of the words "UNITED IMAGING" stacked on top of each other in a bold, sans-serif font. To the right of the text is a stylized "U" symbol, which is dark blue with a white vertical line running through the center. The logo is simple and modern in design.
510 (k) SUMMARY
-
- Date of Preparation: September 26, 2023
2. Sponsor Identification
Shanghai United Imaging Healthcare Co.,Ltd. No.2258 Chengbei Rd. Jiading District, 201807, Shanghai, China
Establishment Registration Number: 3011015597
Contact Person: Xin GAO Position: Regulatory Affairs Specialist Tel: +86-021-67076888-5386 Fax: +86-021-67076889 Email: xin.gao@united-imaging.com
3. Identification of Proposed Device
Trade Name: Medical Image Post-processing Software Common Name: Medical image management and processing system Model(s): uOmnispace.MI
Regulatory Information Classification Name: Medical image management and processing system Classification: II Product Code: LLZ Regulation Number: 21 CFR 892.2050 Review Panel: Radiology
4. Identification of Predicate Device(s)
Predicate Device 510(k) Number: K192630 Device Name: uWS-MI
Reference Device#1 510(k) Number: K173897
4
Image /page/4/Picture/1 description: The image contains the logo for United Imaging. The text "UNITED IMAGING" is stacked on top of each other and is in a bold, sans-serif font. To the right of the text is a stylized "U" symbol, which is also in a bold font. The logo is simple and modern, and the use of a bold font gives it a strong and confident look.
Device Name: syngo.via MI Workflows
Reference Device#2 510(k) Number: K183170 Device Name: uWS-CT
5. Device Description
The uOmnispace.MI is a post-processing software based on the uOmnispace platform (cleared in K230039) for viewing, manipulating, evaluating and analyzing PET, CT, MR, SPECT images, can run alone or with other advanced commercially cleared applications.
This proposed device contains the following applications:
- uOmnispace.MI MM Fusion
- uOmnispace.MI MM Oncology
- . uOmnispace.MI Dynamic Analysis
Additionally, uOmnispace.MI offers the users the options to run the following third-party applications in uOmnispace.MI:
- uOmnispace.MI NeuroQ ●
- uOmnispace.MI Emory Cardiac Toolbox ●
6. Indications for use
uOmnispace.MI is a software solution intended to be used for viewing, processing, evaluating and analyzing of PET, CT, MR, SPECT images. It supports interpretation and evaluation of examinations within healthcare institutions. It has the following additional indications:
- uOmnispace.MI MM Fusion is intended to provide tools for viewing, analyzing and reporting PET, CT, MR, SPECT data with its flexible workflow and optimized layout protocols for dedicated reporting purposes on oncology, neurology, and cardiology.
- . uOmnispace.MI MM Oncology application is intended to provide tools to display and analyze the follow-up PET, CT, and MR data, with which users can do image registration, lesion segmentation, and statistical analysis.
- . uOmnispace.MI Dynamic Analysis application is intended to display PET data and anatomical data such as CT or MR, and supports to do lesion segmentation and output associated time activity curve.
- . uOmnispace.MI NeuroQ application is intended to analyze the brain PET scan, give quantitative results of the relative activity of different brain regions, and make comparison of activity of normal brain regions in AC database or between two studies from the same patient, as well as provide analysis of amyloid uptake levels in the brain.
5
Image /page/5/Picture/1 description: The image shows the logo for United Imaging. The logo consists of the words "UNITED IMAGING" in a bold, sans-serif font, stacked on top of each other. To the right of the words is a stylized "U" shape, which is also in a bold font. The logo is simple and modern, and the use of a bold font gives it a strong and confident look.
- uOmnispace.MI Emory Cardiac Toolbox application is intended to provide cardiac . short axis reconstruction, browsing function. And it also performs perfusion analysis, activity analysis and cardiac function analysis of the cardiac short axis.
7. Summary of Technological Characteristics
The technology characteristics of the uOmnispace.MI, reflected in this 510(k) submission are substantially equivalent to those of the predicate devices.
The following tables compare the features, principles of operation, fundamental scientific technology and intended use of uOmnispace.MI when compared to the predicate devices.
6
| Item | Proposed Device
uOmnispace.MI | Predicate Device
uWS-MI (K192630) | Remark |
|-------------------------------|------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| General | | | |
| Device Classification
Name | Medical image management and processing system | Medical image management and processing system | Same |
| Product Code | LLZ | LLZ | Same |
| Regulation Number | 21 CFR 892.2050 | 21 CFR 892.2050 | Same |
| Device Class | II | II | Same |
| Classification Panel | Radiology | Radiology | Same |
| Advanced
Application | Yes | Yes | Same |
| Indications for use | uOmnispace.MI is a software solution intended to be used for viewing, processing, evaluating and analyzing of PET, CT, MR, SPECT images. It supports interpretation and evaluation of examinations within healthcare institutions. It has the following additional indications:
uOmnispace.MI MM Fusion application is intended to provide tools for viewing, analyzing and reporting PET, CT, MR, SPECT data with its flexible workflow and optimized layout protocols for dedicated reporting purposes on oncology, neurology, cardiology.
uOmnispace.MI The MM Oncology application is intended to provide tools to display and analyze the | uWS-MI is a software solution intended to be used for viewing, manipulation, communication, and storage of medical images. It supports interpretation and evaluation of examinations within healthcare institutions. It has the following additional indications:
The Oncology application is intended to provide tools to display and analyze the follow-up PET/CT data, with which users can do image registration, lesion segmentation, and statistical analysis.
The Dynamic Analysis application is intended to display PET data and anatomical data such as CT or MR, and supports to do lesion segmentation | The indication for use of the proposed device is expanded and replenished. The proposed device includes more applications and functions which are discussed in the following chapters. The difference will not impact the safety and effectiveness of the device. |
| Item | Proposed Device | Predicate Device | Remark |
| | uOmnispace.MI | uWS-MI (K192630) | |
| | follow-up PET, CT, MR data, with which users can
do image registration, lesion segmentation, and
statistical analysis. | and output associated time-activity curve. | |
| | uOmnispace.MI Dynamic Analysis application is
intended to display PET data and anatomical data
such as CT or MR, and supports to do lesion
segmentation and output associated time activity
curve. | NeuroQ application is intended to analyze the
brain PET scan, give quantitative results of the
relative activity of 240 different brain regions, and
make comparison of activity of normal brain
regions in AC database or between two studies
from the same patient, as well as provide analysis
of amyloid uptake levels in the brain. | |
| | uOmnispace.MI NeuroQ application is intended to
analyze the brain PET scan, give quantitative results
of the relative activity of different brain regions, and
make comparison of activity of normal brain
regions in AC database or between two studies from
the same patient, as well as provide analysis of
amyloid uptake levels in the brain. | Emory Cardiac Toolbox application is intended to
provide cardiac short axis reconstruction,
browsing function. And it also performs perfusion
analysis, activity analysis and cardiac function
analysis of the cardiac short axis. | |
| | uOmnispace.MI Emory Cardiac Toolbox
application is intended to provide cardiac short axis
reconstruction, browsing function. And it also
performs perfusion analysis, activity analysis and
cardiac function analysis of the cardiac short axis. | | |
Table 1 Substantial equivalent discussion for hasic functions
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| Table 2 Substantial equivalent discussion for MM Fusion |
|---|
| Application | Function name | Proposed device | Predicate | Reference | Remark |
|---|---|---|---|---|---|
| uOmnispace.MI | device | device#2: |
8
| | | uWS-MI
(K192630) | uWS-CT
(K183170) | |
|------------------------|--------------------------------------|-----------------------|---------------------|-----------------------------------------------|
| MM Fusion | Image Fusion | Yes | / | Same |
| | Study Comparison | Yes | / | Same |
| | 3D Visualization | Yes | / | Same |
| Registration | Manual Registration | Yes | / | Same |
| | Dot Registration | Yes | / | Same |
| | Auto Registration | Yes | / | Same |
| | Default Layout and Customized Layout | Yes | / | Functional Substantial Equivalent
Note1 |
| | Position Correction | Yes | / | Same |
| Lesion
Segmentation | Fix threshold segmentation | Yes | / | Same |
| | Percentage threshold
segmentation | Yes | / | Same |
| | Adaptive threshold
segmentation | Yes | / | Same |
| | | CT Liver Segmentation | / | Yes |
| | CT Lung Segmentation | / | Yes | Same |
| | Rib Labeling | / | Yes | Functional Substantially Equivalent
Note 2 |
| | Spine Labeling | / | Yes | Functional Substantially Equivalent
Note 3 |
| | Save/Film/Report | Yes | / | Same |
9
| Application | Function name | Proposed device
uOmnispace.MI | Predicate
device
uWS-MI
(K192630) | Reference
device#2:
uWS-CT
(K183170) | Reference
device#3:
syngo.via MI
Workflows
(K173897) | Remark | |
|----------------|------------------------|--------------------------------------|--------------------------------------------|-----------------------------------------------|------------------------------------------------------------------|--------|-------------------------------------------------|
| MM
Oncology | Registration | Manual Registration | Yes | Yes | / | / | Same |
| | Registration | Auto Registration | Yes | Yes | / | / | Same |
| | One-Step Evaluation | | Yes | Yes | / | / | Same |
| | Lesion
Segmentation | Fix threshold
segmentation | Yes | Yes | / | / | Same |
| | | Percentage threshold
segmentation | Yes | Yes | / | / | Same |
| | | Adaptive threshold
segmentation | Yes | Yes | / | / | Same |
| | | Lung nodule
segmentation | Yes | Yes | / | / | Same |
| | | Liver tumor
segmentation | Yes | Yes | / | / | Same |
| | | Lymph node
segmentation | Yes | Yes | / | / | Same |
| | | Spread | Yes | Yes | / | / | Same |
| | CT liver Segmentation | | Yes | / | Yes | / | Same |
| | CT lung Segmentation | | Yes | / | Yes | / | Same |
| | Rib Labeling | | Yes | / | Yes | / | Functional Substantial
Equivalent.
Note 4 |
| | Spine Labeling | | Yes | / | Yes | | Functional Substantial
Equivalent |
Table 3 Substantial equivalent discussion for MM Oncology
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| Note 5 | |||||
|---|---|---|---|---|---|
| Statistical Analysis | Yes | Yes | / | Same | |
| Reference VOI | Yes | Yes | / | Same | |
| Response | |||||
| Assessment | PERCIST | Yes | Yes | / | Same |
| RECIST1.0 | Yes | Yes | / | Same | |
| RECIST1.1 | Yes | / | Yes | Same | |
| Deauville Score | Yes | / | Yes | Same | |
| Save | Yes | Yes | / | Same |
Table 4 Substantial equivalent discussion for Dynamic Analysis
| Application | Function name | Proposed device | Predicate device | Remark |
|---|---|---|---|---|
| uOmnispace.MI | uWS-MI (K192630) | |||
| Dynamic Analysis | Automatic cine | Yes | Yes | Same |
| ROI Drawing | Yes | Yes | Same | |
| Data Recombination | Yes | Yes | Same | |
| Curve Analysis | Yes | Yes | Same | |
| Table Quantization Statistics | Yes | Yes | Same | |
| Save | Yes | Yes | Same | |
| Print (Filming) | Yes | Yes | Same | |
| Registration | Manual Registration | Yes | Yes | Same |
| Dot Registration | Yes | Yes | Same | |
| Auto Registration | Yes | Yes | Same | |
| Lesion | ||||
| Segmentation | Fix threshold segmentation | Yes | Yes | Same |
| Percentage threshold | ||||
| segmentation | Yes | Yes | Same | |
| Adaptive threshold | ||||
| segmentation | Yes | Yes | Same |
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Note 1: Compared to predicate device optimizes image layout that adds more default layout and adjusts the interaction of customized layout. This difference between the reference device doesn't impact the safety and effectiveness of the subject device.
Note 2: Compared to predicate device optimizes a new algorithm based on deep learing. This algorithm supports the same function as uWS-CT (K183170 cleared). This difference between the reference device device doesn't impact the safety and effectiveness of the subject device.
Note 3: Compared to predicate device optimizes a new algorithm based on deep learing. This algorithm supports the same function as uWS-CT (K183170 cleared). This difference between the reference device doesn't impact the safety and effectiveness of the subject device.
Note 4: Compared to predicate device optimizes a new algorithm based on deep learning. This algorithm suports the same finction as uWS-CT (K183170 cleared). This difference between the reference device device doesn't impact the safety and effectiveness of the subject device.
Note 5: Compared to predicate device optimizes a new algorithm based on deep learning This algorithm supports the same function as uWS-CT (K183170 cleared). This difference between the reference device and the reference device doesn't impact the safety and effectiveness of the subject device.
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Image /page/12/Picture/1 description: The image contains the logo for United Imaging. The logo consists of the words "UNITED IMAGING" stacked on top of each other in a bold, sans-serif font. To the right of the text is a stylized symbol that resembles the letter "U" with a vertical line running through the center, creating a negative space that suggests the letter "I". The color of the text and symbol is a dark teal.
Performance Data 8.
The following performance data were provided in support of the substantial equivalence determination.
Biocompatibility
Not Applicable to the proposed device, because the device is stand-alone software.
Electrical Safety and Electromagnetic Compatibility (EMC)
Not Applicable to the proposed device, because the device is stand-alone software.
Software Verification and Validation
Software verification and validation testing was provided to demonstrate safety and efficacy of the proposed device. This includes a hazard analysis, and the potential hazards have been classified as a moderate level of concern (LOC). Those documentations include:
- · Software Description
- · Device Hazard Analysis
- · Software Requirements Specification (SRS)
- · Software Architecture Design Chart
- · Software Development Environment Description
- · Software Verification and Validation
- · Cybersecurity Documents
Animal Study
No animal study was required.
Clinical Studies
No clinical study was required.
Performance Verification
● Design of the performance verification test
The identification rate is a commonly used evaluation metric to evaluate the labelling performance [1-3], which is defined as the ratio of correctly identified vertebrae/rib to the total number of vertebrae/rib.
As for spine labeling, the mean identification rate of the five top-performing algorithms are 83.3% [2]. For rib labeling, the mean identification rate (labelaccuracy) of a SOTA algorithm is 91.5% [3]. If the proposed device algorithm can
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Image /page/13/Picture/1 description: The image contains the logo for United Imaging. The logo consists of the words "UNITED IMAGING" in bold, sans-serif font, stacked on top of each other. To the right of the text is a stylized "U" shape, which is also in bold. The color of the text and the "U" shape is a dark teal.
achieve comparable performance, it is considered that the proposed device algorithm meets the clinical requirements.
We set a 5-point scoring criteria, and the acceptance criteria was that the average score of the proposed device results is higher than 4 points is equivalent to the mean identification rate of spine labeling is greater than 92% (>83.3%, correctly labeled vertebrae number ≥23, total vertebrae number=25, 23/25=92%), and the mean identification rate of rib labeling is greater than 91.7%(>91.5% , correctly labeled rib number ≥22, total rib number=24, 22/24~91.7%). So, the setting of evaluation criteria in this way can meet clinical requirements.
Moreover, scoring according to the number of incorrect label is a common way to evaluate the labeling algorithm [1]. The number of incorrect label is directly related to the extra workload of the doctor, e.g. manual correction. While the purpose of the labeling algorithm is to improve the work efficiency of doctors, a more intuitive way is to score according to the number of errors in automatic label
To validate the uOmnispace.MI software from a clinical perspective, the deep learning-based spine labeling algorithm and rib labeling algorithm contained in the product underwent a scientific evaluation. The validation results for the proposed device demonstrated the good performance, the robustness and good generalization ability among different subgroups.
1. Spine labeling Algorithm
The performance testing for deep learning-based spine labeling algorithm was performed on 286 CT scans (data shown in Table 8-2) during the product development.
. Acceptance Criteria
The validation type and acceptance criteria is shown in the Table 8-1 below:
| Validation Type | Acceptance Criteria |
|---|---|
| Score based on gold standard | The average score of the proposed device |
| results is higher than 4 points. |
Table 8-1 Validation type and acceptance criteria
● Testing Data Information
1) Equipments and Protocols
CT data were acquired with different multidetector (16, 64, 160, 256, 320-slice) CT scanners from five major manufacturers (GE, Phillips, Siemens, Toshiba, UIH), and with tube voltage of 110 - 140 kVp. tube current of 275-1000mA. slice thickness of 0.625-2.0 mm, in-plane spacing of 0.656 - 1.172 mm.
2) Sample Size
| Dataset | Patients Number | Samples Number |
|---|---|---|
| Testing Dataset | 267 | 286 |
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Image /page/14/Picture/1 description: The image contains the logo for United Imaging. The logo consists of the words "UNITED IMAGING" stacked on top of each other in a bold, sans-serif font. To the right of the text is a stylized "U" shape, which is also in a bold font. The color of the text and the "U" shape is a dark gray.
(Note: One patient may have multiple samples, because of the differences in scanning protocols, such as slice thickness, in-plane spacing)
Clinical Subgroup Information 3)
The testing data information is summarized below.
| Table 8-2. Testing data information | |
|---|---|
| Information of data | 286 spine CTs |
| Gender | Male: 68 |
| Female: 55 | |
| Unknown: 163 | |
| Age | (10, 25]: 4 |
| (25, 40]: 16 | |
| (40, 60]: 45 | |
| (60, 75]: 47 | |
| (75, 100]: 14 | |
| Unknown: 160 | |
| Ethnicity | Asian(Chinese) data: 106 |
| European data: 160 | |
| The United States data: 20 |
. Performance Testing Summary
For testing dataset, the average score of the proposed device results to be validated is 4.951 points and greater than 4 points. Meanwhile, the subgroup analysis shows that (Table 8-3) the proposed device algorithm has good generalization in different subgroups.
| Age | Average score of the proposed device |
|---|---|
| (10, 25] | 5.000 |
| (25, 40] | 5.000 |
| (40, 60] | 5.000 |
| (60, 75] | 5.000 |
| (75, 100] | 5.000 |
| Unknown | 4.914 |
| Gender | Average score of the proposed device |
| Female | 5.000 |
| Male | 5.000 |
| Unknown | 4.913 |
| Ethnicity | Average score of the proposed device |
| Asian(Chinese) data | 5.000 |
| European data | 4.913 |
| The United States data | 5.000 |
Table 8-3. Subgroup performance test
- Standard Annotation Process ●
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Image /page/15/Picture/1 description: The image shows the logo for United Imaging. The text "UNITED IMAGING" is displayed in bold, sans-serif font, stacked on top of each other. To the right of the text is a stylized letter "U" symbol, which is dark teal in color. The logo is simple and modern in design.
For ground truth annotations, all ground truth are annotated by well-trained annotators. The annotators use an interactive tool to observe the image, and then set annotation points near the center of vertebral body and assign anatomical labels. Finally, a senior clinical specialist will check and modify annotations to make sure the ground truth correct.
. Testing & Training Data Independence
The training data used for the training of the spine labeling algorithm is independent of the data used to test the algorithm.
Confounders
There are no confounders exist for the training methodology.
2. Rib labeling Algorithm
The performance testing for deep learning-based rib labeling algorithm was performed on 160 CT scans (data shown in Table 8-6) during the product development.
. Acceptance Criteria
The validation type and acceptance criteria is shown in the Table 8-4 below: Table8-4. Validation type and acceptance criteria
| Validation Type | Acceptance Criteria |
|---|---|
| Score based on gold standard | The average score of the proposed device |
| results is higher than 4 points. |
Testing Data Information .
1) Equipments and Protocols
CT data were acquired with different multidetector (16, 64, 160, 256, 320-slice) CT scanners from five major manufacturers (GE, Phillips, Siemens, Toshiba, UIH), and with tube voltage of 100-140 kVp, tube current of 275-1000 mA, slice thickness of 0.625-3.0 mm, in-plane spacing of 0.531-1.955 mm.
2) Sample Size
Table 8-5. Sample size information of testing data
| Dataset | Patients Number | Samples Number |
|---|---|---|
| Testing Dataset | 156 | 160 |
(Note: One patient may have multiple samples, because of the differences in scanning protocols, such as slice thickness, in-plane spacing.)
3) Clinical subgroups
The clinical subgroups information of the testing data is summarized below.
Table 8-6. Testing data information
| Information of data | 160 CTs |
|---|---|
| Gender | Male: 110 |
| Female: 39 |
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Image /page/16/Picture/1 description: The image contains the logo for United Imaging. The logo consists of the words "UNITED IMAGING" in a bold, sans-serif font, stacked on top of each other. To the right of the text is a stylized "U" shape, which is formed by two vertical bars and a horizontal bar in the middle. The logo is simple and modern, and the use of bold font and the stylized "U" shape makes it easily recognizable.
| Unknown: 11 | |
|---|---|
| Age | (10, 25]: 2 |
| (25, 40]: 14 | |
| (40, 60]: 51 | |
| (60, 75]: 69 | |
| (75, 100]: 24 | |
| Ethnicity | Asian(Chinese) data: 80 |
| The United States data: 80 |
. Performance Testing Summary
For the testing dataset, the average score of the proposed device results to be validated is 5 points and greater than 4 points. Meanwhile, the subgroup analysis shows that (Table 8-12) the proposed device algorithm has good generalization in different subgroups.
| Age | Average score of the proposed device |
|---|---|
| (10, 25] | 5.00 |
| (25, 40] | 5.00 |
| (40, 60] | 5.00 |
| (60, 75] | 5.00 |
| (75, 100] | 5.00 |
| Gender | Average score of the proposed device |
| Female | 5.00 |
| Male | 5.00 |
| Unknown | 5.00 |
| Ethnicity | Average score of the proposed device |
| Asian(Chinese) data | 5.00 |
| The United States data | 5.00 |
Table 8-7. Subgroup performance test
. Standard Annotation Process
For ground truth annotations, all ground truth are annotated by well-trained annotators. A threshold based interactive tool is used to generate initial rib mask, then annotators will refine the rib mask and assign anatomical labels. After the first round annotation, they will check each other's annotation. At last, a senior clinical specialist will check and modify annotations to make sure the ground truth correct.
. Testing & Training Data Independence
The training data used for the training of the rib labeling algorithm is independent of the data used to test the algorithm.
Confounders
There are no confounders exist for the training methodology.
References:
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Image /page/17/Picture/1 description: The image contains the logo for United Imaging. The logo consists of the words "UNITED IMAGING" in a bold, sans-serif font, stacked on top of each other. To the right of the text is a stylized "U" shape, which is also in a bold font. The color of the logo is a dark teal.
[1]Major, David , et al. "Automated landmarking and labeling of fully and partially scanned spinal columns in CT images." Medical Image Analysis 17.8(2013):1151-1163.
[2]C, Anjany Sekuboyina A B , et al. "VerSe : A Vertebrae Labelling and Segmentation Benchmark for Multi-detector CT Images." Medical Image Analysis (2021).
[3]Jin L, Gu S, Wei D, et al. "RibSeg v2: A Large-scale Benchmark for Rib Labeling and Anatomical Centerline Extraction". IEEE Transactions on Medical Imaging, 2023. DOI: 10.1109/TMI.2023.3313627.
Other Standards and Guidance
- . NEMA PS 3.1 - 3.20 Digital Imaging and Communications in Medicine (DICOM) Set (2016).
- ISO 14971 Medical devices - Application of risk management to medical devices (Edition 2.0, corrected version, 2007).
- IEC 62304 Medical device software - Software life cycle processes (Edition 1.1, 2015).
Summary
The features described in this premarket submission are supported with the results of the testing mentioned above; the uOmnispace.MI was found to have a safety and effectiveness profile that is similar to the predicate device and reference devices.
Substantially Equivalent (SE) Conclusion 9.
The proposed device is equivalent to the predicate device with regard to safety and efficacy. This conclusion is based upon a comparison of intended use, technological characteristics, performance specification, device hazards as well as verification and validation results.
In summary, the proposed device is determined to be Substantially Equivalent (SE) to the predicate device and reference devices.