(253 days)
Yes
The "Device Description" section states that the device introduces a "deep-learning algorithm" in several applications.
No.
The uOmnispace.CT is a software for viewing, manipulating, evaluating and analyzing medical images. Its intended use is diagnostic, providing information for the interpretation and evaluation of examinations, not for therapeutic purposes.
Yes
The device is described as software for "viewing, manipulating, evaluating and analyzing medical images" and explicitly states it "supports interpretation and evaluation of examinations." Many of its applications provide quantitative parameters, analyze functional information, or identify specific medical conditions (e.g., calcium scoring, nodule analysis, stenosis analysis), which are all diagnostic functions.
Yes
The device is explicitly described as "post-processing software" that runs on a "platform for viewing, manipulating, evaluating and analyzing medical images." It processes images from CT scans, but it does not include the CT scanner itself or any hardware components. Its functions are entirely software-based.
No.
This device is for viewing, manipulating, evaluating, and analyzing medical images from CT scans, which are in vivo measurements, not in vitro diagnostic tests.
No
The provided text does not contain any explicit statement that the FDA has reviewed and approved or cleared a PCCP for this specific device.
Intended Use / Indications for Use
uOmnispace.CT is a software for viewing, manipulating, evaluating and analyzing medical images. It supports interpretation and evaluation of examinations within healthcare institutions. It has the following additional indications:
- The uOmnispace.CT Colon Analysis application is intended to provide the user a tool to enable easy visualization and efficient evaluation of CT volume data sets of the colon.
- The uOmnispace.CT Dental Application is intended to provide the user a tool to reconstruct panoramic and paraxial views of jaw.
- The uOmnispace.CT Lung Density Analysis application is intended to segment pulmonary, lobes, and airway, providing the user quantitative parameters, structure information to evaluate the lung and airway.
- The uOmnispace.CT Lung Nodule application is intended to provide the user a tool for the review and analysis of thoracic CT images, providing quantitative and characterizing information about nodules in the lung in a single study, or over the time course of several thoracic studies.
- The Vessel Analysis application is intended to provide a tool for viewing, manipulating, and evaluating CT vascular images.
- The uOmnispace.CT Brain Perfusion application is intended to calculate the parameters such as: CBV, CBF, etc. in order to analyze functional blood flow information about a region of interest (ROI) in brain.
- The uOmnispace.CT Heart application is intended to segment heart and extract coronary artery. It also provides analysis of vascular stenosis, plaque and heart function.
- The uOmnispace.CT Calcium Scoring application is intended to identify calcifications and calculate the calcium score.
- The uOmnispace.CT Dynamic Analysis application is intended to support visualization of the CT datasets over time with the 3D/4D display modes.
- The uOmnispace.CT Bone Structure Analysis application is intended to provide visualization and labels for the ribs and spine, and support batch function for intervertebral disk.
- The uOmnispace.CT Liver Evaluation application is intended to provide processing and visualization for liver segmentation and vessel extraction. It also provides a tool for the user to perform liver separation and residual liver segments evaluation.
- The uOmnispace.CT Dual Energy is a post-processing software package that accepts UIH CT images acquired using different tube voltages and/or tube currents of the same anatomical location. The Dual Energy application is intended to provide information on the chemical composition of the scanned body materials and/or contrast agents. Additionally, it enables images to be generated at multiple energies within the available spectrum.
- The uOmnispace.CT Cardiovascular Combined Analysis is an image analysis software package for evaluating contrast enhanced CT images. The CT Cardiovascular Combined Analysis is intended to analyze vascular and cardiac structures.It can be used in the qualitative and quantitative for the analysis of head-neck, abdomen, multi-body part combined, TAVR (Transcatheter Aortic Valve Replacement) CT data as input for the planning of cardiovascular procedures.
- The uOmnispace.CT Body Perfusion is intended to analyze blood flow information of dynamic CTimages, by providing various perfusion-related parameters of the body parts.
Product codes
QIH
Device Description
The uOmnispace.CT is a post-processing software based on the uOmnispace platform for viewing, manipulating, evaluating and analyzing medical images, can run alone or with other advanced commercially cleared applications.
uOmnispace.CT contains the following applications:
- uOmnispace.CT Calcium Scoring
- uOmnispace.CT Lung Nodule
- uOmnispace.CT Colon Analysis
- uOmnispace.CT Lung Density Analysis
- uOmnispace.CT Dental Application
- uOmnispace.CT Bone Structure Analysis
- uOmnispace.CT Dual Energy
- uOmnispace.CT Vessel Analysis
- uOmnispace.CT Heart
- uOmnispace.CT Brain Perfusion
- uOmnispace.CT Dynamic Analysis
- uOmnispace.CT Liver Evaluation
- uOmnispace.CT Cardiovascular Combined Analysis
- uOmnispace.CT Body Perfusion
The modifications performed on the uOmnispace.CT (K233209) in this submission is due to the following changes that include:
- Add new application of Body Perfusion.
- Extend intended patient population for some applications
- Introduce deep-learning algorithm in applications of Lung Density Analysis, Vessel Analysis, Heart, Liver Evaluation and Cardiovascular Combined Analysis.
These modifications do not affect the intended use or alter the fundamental scientific technology of the device
Mentions image processing
Yes
Mentions AI, DNN, or ML
Yes
Input Imaging Modality
CT
Anatomical Site
Colon, Jaw, Lung, Thorax, Brain, Heart, Ribs, Spine, Liver, Head-Neck, Abdomen, Multi-body part combined
Indicated Patient Age Range
The proposed device intends to extend the patient population for some applications, but the specific age ranges for these extensions are not explicitly stated. However, testing data information for various AI-based algorithms provides age ranges for the subjects included in those tests:
- Lung Density Analysis: 1 month to 91 years old.
- Vessel Analysis: 13 to 85 years old.
- Heart: 12 to 92 years old.
- Liver Evaluation (first dataset): 12 to 100 years old.
- Liver Evaluation (second dataset): 12 to 80 years old.
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 Lung segmentation algorithm is independent of the data used to test the algorithm.
For ground truth annotations, all ground truth are annotated by well-trained annotators. Finally, a senior clinical specialist will check and modify annotations to make sure the ground truth is correct.
The training data used for the training of the Lung segmentation algorithm is independent of the data used to test the algorithm.
For ground truth annotations, all ground truth are annotated by well-trained annotators. Finally, a senior clinical specialist will check and modify annotations to make sure the ground truth is correct.
The training data used for the training of the Lung segmentation algorithm is independent of the data used to test the algorithm.
For ground truth annotations, all ground truth are annotated by well-trained annotators. Finally, a senior clinical specialist will check and modify annotations to make sure the ground truth is correct.
The training data used for the training of the Lung segmentation algorithm is independent of the data used to test the algorithm.
For ground truth annotations, all ground truth are annotated by well-trained annotators. Finally, a senior clinical specialist will check and modify annotations to make sure the ground truth is correct.
Description of the test set, sample size, data source, and annotation protocol
Lung density Analysis:
Test dataset: 100 cases of Chest CT scans, which covered different gender, age (ages ranging from 1 month to 91 years old) and anatomical variants.
Annotation Process: For ground truth annotations, all ground truth are annotated by well-trained annotators. Finally, a senior clinical specialist will check and modify annotations to make sure the ground truth is correct.
Vessel Analysis:
Test dataset: 156 cases of CTA scans, which covered different gender, age (ages ranging from 13 to 85 years old) and anatomical variants.
Annotation Process: For ground truth annotations, all ground truth are annotated by well-trained annotators. Finally, a senior clinical specialist will check and modify annotations to make sure the ground truth is correct.
Heart:
Test dataset: 72 cases of CCTA scans, which covered different gender, age (ages ranging from 12 to 92 years old) and anatomical variants.
Annotation Process: For ground truth annotations, all ground truth are annotated by well-trained annotators. Finally, a senior clinical specialist will check and modify annotations to make sure the ground truth is correct.
Liver Evaluation:
Test dataset:
For liver segmentation algorithm and hepatic artery segmentation algorithm: 74 cases of Chest CT scans, which covered different gender, age (ages ranging from 12 to 100 years old) and anatomical variants.
For hepatic portal vein segmentation algorithm and hepatic vein segmentation algorithm: 80 cases of Chest CT scans, which covered different gender, age (ages ranging from 12 to 80 years old) and anatomical variants.
Annotation Process: For ground truth annotations, all ground truth are annotated by well-trained annotators. Finally, a senior clinical specialist will check and modify annotations to make sure the ground truth is correct.
Summary of Performance Studies (study type, sample size, AUC, MRMC, standalone performance, key results)
Performance Verification of AI-based algorithms was conducted through testing on various subjects. The key results are summarized below:
-
Lung Density Analysis:
- Study Type: Performance testing of AI-based lung segmentation and airway segmentation algorithms.
- Sample Size: 100 subjects.
- Standalone Performance / Key Metrics: Global performance (Dice Similarity Coefficient):
- Lung segmentation: 0.9801 (Acceptance criteria: 0.97)
- Airway segmentation: 0.8954 (Acceptance criteria: 0.86)
- Key Results: The algorithms demonstrated good generality across different subgroups (sex, age, anatomical variants), with Dice coefficients higher than the acceptance criteria.
-
Vessel Analysis:
- Study Type: Performance testing of AI-based bone removal algorithms.
- Sample Size: 156 data subjects.
- Standalone Performance / Key Metrics: Global performance (Dice Similarity Coefficient):
- Bone removal (Abdomen & Limbs): 0.96957 (Acceptance criteria: 0.9)
- Bone removal (Head & Neck): 0.955 (Acceptance criteria: 0.93)
- Key Results: The algorithms demonstrated good generality across different subgroups (sex, age, anatomical variants), with Dice coefficients higher than the acceptance criteria.
-
Heart:
- Study Type: Performance testing of AI-based coronary artery extraction and heart chamber segmentation algorithms.
- Sample Size: 72 subjects.
- Standalone Performance / Key Metrics: Global performance (Dice Similarity Coefficient):
- Coronary artery extraction: 0.916 (Acceptance criteria: 0.870)
- Heart chamber segmentation: 0.970 (Acceptance criteria: 0.910)
- Key Results: The algorithms demonstrated good generality across different subgroups (sex, age, anatomical variants), with Dice coefficients higher than the acceptance criteria.
-
Liver Evaluation:
- Study Type: Performance testing of AI-based liver segmentation, hepatic artery segmentation, hepatic portal vein segmentation, and hepatic vein segmentation algorithms.
- Sample Size: 74 subjects for liver segmentation and hepatic artery segmentation; 80 subjects for hepatic portal vein segmentation and hepatic vein segmentation.
- Standalone Performance / Key Metrics: Global performance (Dice Similarity Coefficient):
- Liver segmentation: 0.981 (Acceptance criteria: 0.97)
- Hepatic artery segmentation: 0.927 (Acceptance criteria: 0.85)
- Hepatic portal vein segmentation: 0.933 (Acceptance criteria: 0.89)
- Hepatic vein segmentation: 0.914 (Acceptance criteria: 0.86)
- Key Results: The algorithms demonstrated good generality across different subgroups (sex, age, anatomical variants), with Dice coefficients higher than the acceptance criteria.
Key Metrics (Sensitivity, Specificity, PPV, NPV, etc.)
Dice Similarity Coefficient
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).
FDA 510(k) Clearance Letter - Medical Image Post-processing Software (uOmnispace.CT)
Page 1
U.S. Food & Drug Administration
10903 New Hampshire Avenue
Silver Spring, MD 20993
www.fda.gov
Doc ID # 04017.07.05
May 14, 2025
Shanghai United Imaging Healthcare Co., Ltd.
Xin Gao
RA Manager
No. 2258 Chengbei Rd. Jiading District
Shanghai, 201807
China
Re: K242624
Trade/Device Name: Medical Image Post-processing Software (uOmnispace.CT)
Regulation Number: 21 CFR 892.2050
Regulation Name: Medical image management and processing system
Regulatory Class: Class II
Product Code: QIH
Dated: April 3, 2025
Received: April 3, 2025
Dear Xin Gao:
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.
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K242624 - Xin Gao Page 2
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).
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 (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-reporting-combination-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.
All medical devices, including Class I and unclassified devices and combination product device constituent parts are required to be in compliance with the final Unique Device Identification System rule ("UDI Rule"). The UDI Rule requires, among other things, that a device bear a unique device identifier (UDI) on its label and package (21 CFR 801.20(a)) unless an exception or alternative applies (21 CFR 801.20(b)) and that the dates on the device label be formatted in accordance with 21 CFR 801.18. The UDI Rule (21 CFR 830.300(a) and 830.320(b)) also requires that certain information be submitted to the Global Unique Device Identification Database (GUDID) (21 CFR Part 830 Subpart E). For additional information on these requirements, please see the UDI System webpage at https://www.fda.gov/medical-devices/device-advice-comprehensive-regulatory-assistance/unique-device-identification-system-udi-system.
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-devices/medical-device-safety/medical-device-reporting-mdr-how-report-medical-device-problems.
For comprehensive regulatory information about medical devices and radiation-emitting products, including information about labeling regulations, please see Device Advice (https://www.fda.gov/medical-devices/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
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K242624 - Xin Gao Page 3
the DICE website (https://www.fda.gov/medical-devices/device-advice-comprehensive-regulatory-assistance/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,
Jessica Lamb, Ph.D.
Assistant Director
Imaging Software Team
DHT8B: Division of Radiological Imaging
Devices and Electronic Products
OHT8: Office of Radiological Health
Office of Product Evaluation and Quality
Center for Devices and Radiological Health
Enclosure
Page 4
DEPARTMENT OF HEALTH AND HUMAN SERVICES
Food and Drug Administration
Form Approved: OMB No. 0910-0120
Expiration Date: 07/31/2026
See PRA Statement below.
Indications for Use
Submission Number (if known)
K242624
Device Name
Medical Image Post-processing Software (uOmnispace.CT)
Indications for Use (Describe)
uOmnispace.CT is a software for viewing, manipulating, evaluating and analyzing medical images. It supports interpretation and evaluation of examinations within healthcare institutions. It has the following additional indications:
- The uOmnispace.CT Colon Analysis application is intended to provide the user a tool to enable easy visualization and efficient evaluation of CT volume data sets of the colon.
- The uOmnispace.CT Dental Application is intended to provide the user a tool to reconstruct panoramic and paraxial views of jaw.
- The uOmnispace.CT Lung Density Analysis application is intended to segment pulmonary, lobes, and airway, providing the user quantitative parameters, structure information to evaluate the lung and airway.
- The uOmnispace.CT Lung Nodule application is intended to provide the user a tool for the review and analysis of thoracic CT images, providing quantitative and characterizing information about nodules in the lung in a single study, or over the time course of several thoracic studies.
- The Vessel Analysis application is intended to provide a tool for viewing, manipulating, and evaluating CT vascular images.
- The uOmnispace.CT Brain Perfusion application is intended to calculate the parameters such as: CBV, CBF, etc. in order to analyze functional blood flow information about a region of interest (ROI) in brain.
- The uOmnispace.CT Heart application is intended to segment heart and extract coronary artery. It also provides analysis of vascular stenosis, plaque and heart function.
- The uOmnispace.CT Calcium Scoring application is intended to identify calcifications and calculate the calcium score.
- The uOmnispace.CT Dynamic Analysis application is intended to support visualization of the CT datasets over time with the 3D/4D display modes.
- The uOmnispace.CT Bone Structure Analysis application is intended to provide visualization and labels for the ribs and spine, and support batch function for intervertebral disk.
- The uOmnispace.CT Liver Evaluation application is intended to provide processing and visualization for liver segmentation and vessel extraction. It also provides a tool for the user to perform liver separation and residual liver segments evaluation.
- The uOmnispace.CT Dual Energy is a post-processing software package that accepts UIH CT images acquired using different tube voltages and/or tube currents of the same anatomical location. The Dual Energy application is intended to provide information on the chemical composition of the scanned body materials and/or contrast agents. Additionally, it enables images to be generated at multiple energies within the available spectrum.
- The uOmnispace.CT Cardiovascular Combined Analysis is an image analysis software package for evaluating contrast enhanced CT images. The CT Cardiovascular Combined Analysis is intended to analyze vascular and cardiac structures.It can be used in the qualitative and quantitative for the analysis of head-neck, abdomen, multi-body part combined, TAVR (Transcatheter Aortic Valve Replacement) CT data as input for the planning of cardiovascular procedures.
- The uOmnispace.CT Body Perfusion is intended to analyze blood flow information of dynamic CT
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images, by providing various perfusion-related parameters of the body parts.
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)
CONTINUE ON A SEPARATE PAGE IF NEEDED.
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DO NOT SEND YOUR COMPLETED FORM TO THE PRA STAFF EMAIL ADDRESS BELOW.
The burden time for this collection of information is estimated to average 79 hours per response, including the time to review instructions, search existing data sources, gather and maintain the data needed and complete and review the collection of information. Send comments regarding this burden estimate or any other aspect of this information collection, including suggestions for reducing this burden, to:
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Shanghai United Imaging Healthcare Co., Ltd.
Tel: +86 (21) 67076888 Fax：+86 (21) 67076889
www.united-imaging.com
Page 1 of 26
510 (k) SUMMARY
1. Date of Preparation:
August 30, 2024
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 (uOmnispace.CT)
Common Name: Medical image management and processing system
Model(s): uOmnispace.CT
Regulatory Information
Classification Name: Medical image management and processing system
Classification: II
Product Code: QIH
Regulation Number: 21 CFR 892.2050
Review Panel: Radiology
4. Identification of Predicate Device(s)
Predicate Device
510(k) Number: K233209
Device Name: uOmnispace.CT
Reference Device
510(k) Number: K193289
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Shanghai United Imaging Healthcare Co., Ltd.
Tel: +86 (21) 67076888 Fax：+86 (21) 67076889
www.united-imaging.com
Page 2 of 26
Device Name: FastStroke, CT Perfusion 4D
5. Device Description
The uOmnispace.CT is a post-processing software based on the uOmnispace platform for viewing, manipulating, evaluating and analyzing medical images, can run alone or with other advanced commercially cleared applications.
uOmnispace.CT contains the following applications:
- uOmnispace.CT Calcium Scoring
- uOmnispace.CT Lung Nodule
- uOmnispace.CT Colon Analysis
- uOmnispace.CT Lung Density Analysis
- uOmnispace.CT Dental Application
- uOmnispace.CT Bone Structure Analysis
- uOmnispace.CT Dual Energy
- uOmnispace.CT Vessel Analysis
- uOmnispace.CT Heart
- uOmnispace.CT Brain Perfusion
- uOmnispace.CT Dynamic Analysis
- uOmnispace.CT Liver Evaluation
- uOmnispace.CT Cardiovascular Combined Analysis
- uOmnispace.CT Body Perfusion
The modifications performed on the uOmnispace.CT (K233209) in this submission is due to the following changes that include:
- Add new application of Body Perfusion.
- Extend intended patient population for some applications
- Introduce deep-learning algorithm in applications of Lung Density Analysis, Vessel Analysis, Heart, Liver Evaluation and Cardiovascular Combined Analysis.
These modifications do not affect the intended use or alter the fundamental scientific technology of the device
6. Indications for use
uOmnispace.CT is a software for viewing, manipulating, evaluating and analyzing medical images. It supports interpretation and evaluation of examinations within healthcare institutions. It has the following additional indications:
- The uOmnispace.CT Colon Analysis application is intended to provide the user a tool to enable easy visualization and efficient evaluation of CT volume data sets of the colon.
- The uOmnispace.CT Dental Application is intended to provide the user a tool to reconstruct panoramic and paraxial views of jaw.
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Shanghai United Imaging Healthcare Co., Ltd.
Tel: +86 (21) 67076888 Fax：+86 (21) 67076889
www.united-imaging.com
Page 3 of 26
- The uOmnispace.CT Lung Density Analysis application is intended to segment pulmonary, lobes, and airway, providing the user quantitative parameters, structure information to evaluate the lung and airway.
- The uOmnispace.CT Lung Nodule application is intended to provide the user a tool for the review and analysis of thoracic CT images, providing quantitative and characterizing information about nodules in the lung in a single study, or over the time course of several thoracic studies.
- The uOmnispace.CT Vessel Analysis application is intended to provide a tool for viewing, manipulating, and evaluating CT vascular images.
- The uOmnispace.CT Brain Perfusion application is intended to calculate the parameters such as: CBV, CBF, etc. in order to analyze functional blood flow information about a region of interest (ROI) in brain.
- The uOmnispace.CT Heart application is intended to segment heart and extract coronary artery. It also provides analysis of vascular stenosis, plaque and heart function.
- The uOmnispace.CT Calcium Scoring application is intended to identify calcifications and calculate the calcium score.
- The uOmnispace.CT Dynamic Analysis application is intended to support visualization of the CT datasets over time with the 3D/4D display modes.
- The uOmnispace.CT Bone Structure Analysis application is intended to provide visualization and labels for the ribs and spine, and support batch function for intervertebral disk.
- The uOmnispace.CT Liver Evaluation application is intended to provide processing and visualization for liver segmentation and vessel extraction. It also provides a tool for the user to perform liver separation and residual liver segments evaluation.
- The uOmnispace.CT Dual Energy is a post-processing software package that accepts UIH CT images acquired using different tube voltages and/or tube currents of the same anatomical location. The Dual Energy application is intended to provide information on the chemical composition of the scanned body materials and/or contrast agents. Additionally, it enables images to be generated at multiple energies within the available spectrum.
- The uOmnispace.CT Cardiovascular Combined Analysis is an image analysis software package for evaluating contrast enhanced CT images. The CT Cardiovascular Combined Analysis is intended to analyze vascular and cardiac structures.It can be used in the qualitative and quantitative for the analysis of head-neck, abdomen, multi-body part combined, TAVR (Transcatheter Aortic Valve Replacement) CT data as input for the planning of cardiovascular procedures.
- The uOmnispace.CT Body Perfusion is intended to analyze blood flow information of dynamic CT images, by providing various perfusion-related parameters of the body parts.
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Shanghai United Imaging Healthcare Co., Ltd.
Tel: +86 (21) 67076888 Fax：+86 (21) 67076889
www.united-imaging.com
Page 4 of 26
7. Summary of Technological Characteristics
The technology characteristics of uOmnispace.CT, reflected in this 510(k) submission are substantially equivalent to those of the predicate devices.
The following tables compare the main features, principles of operation, fundamental scientific technology and intended use of uOmnispace.CT when compared to the predicate devices.
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Shanghai United Imaging Healthcare Co., Ltd.
Tel: +86 (21) 67076888 Fax：+86 (21) 67076889
www.united-imaging.com
Page 5 of 26
| Item | Proposed Device uOmnispace.CT | Predicate Device uOmnispace.CT (K233209) |
|---|---|---|
| General | ||
| Device Classification Name | Medical image management and processing system | Medical image management and processing system |
| Product Code | QIH | QIH |
| Regulation Number | 21 CFR 892.2050 | 21 CFR 892.2050 |
| Device Class | II | II |
| Classification Panel | Radiology | Radiology |
| Intended Use | uOmnispace.CT is a software for viewing, manipulating, evaluating and analyzing medical images. It supports interpretation and evaluation of examinations within healthcare institutions. It has the following additional indications: • The uOmnispace.CT Colon Analysis application is intended to provide the user a tool to enable easy visualization and efficient evaluation of CT volume data sets of the colon. • The uOmnispace.CT Dental application is intended to provide the user a tool to reconstruct panoramic and paraxial views of jaw. • The uOmnispace.CT Lung Density Analysis application is intended to segment pulmonary, lobes, and airway, providing the user quantitative parameters, structure | uOmnispace.CT is a software for viewing, manipulating, evaluating and analyzing medical images. It supports interpretation and evaluation of examinations within healthcare institutions. It has the following additional indications: • The uOmnispace.CT Colon Analysis application is intended to provide the user a tool to enable easy visualization and efficient evaluation of CT volume data sets of the colon. • The uOmnispace.CT Dental application is intended to provide the user a tool to reconstruct panoramic and paraxial views of jaw. • The uOmnispace.CT Lung Density Analysis application is intended to segment pulmonary, lobes, and airway, providing the user quantitative parameters, structure |
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Shanghai United Imaging Healthcare Co., Ltd.
Tel: +86 (21) 67076888 Fax：+86 (21) 67076889
www.united-imaging.com
Page 6 of 26
information to evaluate the lung and airway.
• The uOmnispace.CT Lung Nodule application is intended to provide the user a tool for the review and analysis of thoracic CT images, providing quantitative and characterizing information about nodules in the lung in a single study, or over the time course of several thoracic studies.
• The uOmnispace.CT Vessel Analysis application is intended to provide a tool for viewing, manipulating, and evaluating CT vascular images.
• The uOmnispace.CT Brain Perfusion application is intended to calculate the parameters such as: CBV, CBF, etc. in order to analyze functional blood flow information about a region of interest (ROI) in brain.
• The uOmnispace.CT Heart application is intended to segment heart and extract coronary artery. It also provides analysis of vascular stenosis, plaque and heart function.
• The uOmnispace.CT Calcium Scoring application is intended to identify calcifications and calculate the calcium score.
information to evaluate the lung and airway.
• The uOmnispace.CT Lung Nodule application is intended to provide the user a tool for the review and analysis of thoracic CT images, providing quantitative and characterizing information about nodules in the lung in a single study, or over the time course of several thoracic studies.
• The uOmnispace.CT Vessel Analysis application is intended to provide a tool for viewing, manipulating, and evaluating CT vascular images.
• The uOmnispace.CT Brain Perfusion application is intended to calculate the parameters such as: CBV, CBF, etc. in order to analyze functional blood flow information about a region of interest (ROI) in brain.
• The uOmnispace.CT Heart application is intended to segment heart and extract coronary artery. It also provides analysis of vascular stenosis, plaque and heart function.
• The uOmnispace.CT Calcium Scoring application is intended to identify calcifications and calculate the calcium score.
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• The uOmnispace.CT Dynamic Analysis application is intended to support visualization of the CT datasets over time with the 3D/4D display modes.
• The uOmnispace.CT Bone Structure Analysis application is intended to provide visualization and labels for the ribs and spine, and support batch function for intervertebral disk.
• The uOmnispace.CT Liver Evaluation application is intended to provide processing and visualization for liver segmentation and vessel extraction. It also provides a tool for the user to perform liver separation and residual liver segments evaluation.
• The uOmnispace.CT Dual Energy is a post-processing software package that accepts UIH CT images acquired using different tube voltages and/or tube currents of the same anatomical location. The Dual Energy application is intended to provide information on the chemical composition of the scanned body materials and/or contrast agents. Additionally, it enables images to be generated at
• The uOmnispace.CT Dynamic Analysis application is intended to support visualization of the CT datasets over time with the 3D/4D display modes.
• The uOmnispace.CT Bone Structure Analysis application is intended to provide visualization and labels for the ribs and spine, and support batch function for intervertebral disk.
• The uOmnispace.CT Liver Evaluation application is intended to provide processing and visualization for liver segmentation and vessel extraction. It also provides a tool for the user to perform liver separation and residual liver segments evaluation.
• The uOmnispace.CT Dual Energy is a post-processing software package that accepts UIH CT images acquired using different tube voltages and/or tube currents of the same anatomical location. The uOmnispace.CT Dual Energy application is intended to provide information on the chemical composition of the scanned body materials and/or contrast agents. Additionally, it enables images to
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multiple energies within the available spectrum.
• The uOmnispace.CT Cardiovascular Combined Analysis is an image analysis software package for evaluating contrast enhanced CT images. The CT Cardiovascular Combined Analysis is intended to analyze vascular and cardiac structures.It can be used in the qualitative and quantitative for the analysis of head-neck, abdomen, multi-body part combined, TAVR (Transcatheter Aortic Valve Replacement) CT data as input for the planning of cardiovascular procedures.
• The uOmnispace.CT Body Perfusion is intended to analyze blood flow information of dynamic CT images, by providing various perfusion-related parameters of the body parts.
be generated at multiple energies within the available spectrum.
• The uOmnispace.CT Cardiovascular Combined Analysis is an image analysis software package for evaluating contrast enhanced CT images. The CT Cardiovascular Combined Analysis is intended to analyze vascular and cardiac structures.It can be used in the qualitative and quantitative for the analysis of head-neck, abdomen, multi-body part combined, TAVR (Transcatheter Aortic Valve Replacement) CT data as input for the planning of cardiovascular procedures.
| Application | Function name | Proposed device uOmnispace.CT | Predicate Device uOmnispace.CT (K233209) | Remark |
|---|---|---|---|---|
| Lung Density Analysis | Lung Segmentation | Yes | Yes | Functional Substantially Equivalent (Note 1) |
| Lung Density Analysis | Yes | Yes | Same | |
| Lung Contour Editing | Yes | Yes | Same |
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| Application | Function name | Proposed device uOmnispace.CT | Predicate Device uOmnispace.CT (K233209) | Remark |
|---|---|---|---|---|
| Pulmonary lobes Segmentation | Yes | Yes | Same | |
| Airway Segmentation | Yes | Yes | Functional Substantially Equivalent (Note 1) | |
| Airway Tree Extraction and Editing | Yes | Yes | Same | |
| Airway Contour Editing | Yes | Yes | Same | |
| Statistical Analysis | Yes | Yes | Same | |
| Save, Report, Print | Yes | Yes | Same |
| Application | Function Name | Proposed device uOmnispace.CT | Predicate Device uOmnispace.CT (K233209) | Remark |
|---|---|---|---|---|
| Brain Perfusion | Motion Correction | Yes | Yes | Same |
| Vein and Artery Definition | Yes | Yes | Same | |
| Parameter Map Calculation | Yes | Yes | Same | |
| Time-density curve analysis | Yes | Yes | Same | |
| Tmax | Yes | Yes | Same | |
| Ischemic penumbra analysis | Yes | Yes | Same | |
| Symmetric ROI and ROI Template | Yes | Yes | Same | |
| Save, Report, Print | Yes | Yes | Same |
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| Application | Function name | Proposed device uOmnispace.CT | Predicate Device uOmnispace.CT (K233209) | Remark |
|---|---|---|---|---|
| Heart | Multi-Phase Loading | Yes | Yes | Same |
| Hyper Realistic Rendering | Yes | Yes | Same | |
| Heart Chamber Segmentation | Yes | Yes | Functional Substantially Equivalent (Note 1) | |
| Coronary Artery Extraction | Yes | Yes | Functional Substantially Equivalent (Note 1) | |
| Editing Tools | Yes | Yes | Same | |
| Centerline Extraction | Yes | Yes | Same | |
| Stenosis Analysis | Yes | Yes | Same | |
| Plaque Analysis | Yes | Yes | Same | |
| Cardiac function Assessment | Yes | Yes | Same | |
| Save, Report, Print | Yes | Yes | Same |
| Application | Function name | Proposed device uOmnispace.CT | Predicate Device uOmnispace.CT (K233209) | Remark |
|---|---|---|---|---|
| Calcium Scoring | Calcium sites segmentation | Yes | Yes | Same |
| Calculate Calcium score | Yes | Yes | Same | |
| Save, Report, Print | Yes | Yes | Same |
| Application | Function name | Proposed device uOmnispace.CT | Predicate Device uOmnispace.CT (K233209) | Remark |
|---|---|---|---|---|
| Bone Structure Analysis | Labeling Ribs | Yes | Yes | Same |
| Labeling Spine | Yes | Yes | Same | |
| Batch | Yes | Yes | Same | |
| Save, Report, Print | Yes | Yes | Same |
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| Application | Function name | Proposed device uOmnispace.CT | Predicate Device uOmnispace.CT (K233209) | Remark |
|---|---|---|---|---|
| Dynamic Analysis | Motion correction | Yes | Yes | Same |
| Multiple phase viewing | Yes | Yes | Same | |
| Bone Removal | Yes | Yes | Same | |
| Data Loading and 3D/4D Display | Yes | Yes | Same | |
| Artery and Vein Display | Yes | Yes | Same | |
| Save, Report, Print | Yes | Yes | Same |
| Application | Function name | Proposed device uOmnispace.CT | Predicate Device uOmnispace.CT (K233209) | Remark |
|---|---|---|---|---|
| Liver Evaluation | Phase Selection | Yes | Yes | Same |
| Liver Segmentation | Yes | Yes | Functional Substantially Equivalent (Note 1) | |
| Lesion Segmentation | Yes | Yes | Same | |
| Rib Segmentation and manual correction | Yes | Yes | Same | |
| Vessel Extraction | Yes | Yes | Functional Substantially Equivalent (Note 1) | |
| Vascular Editing | Yes | Yes | Same | |
| Liver Segments | Yes | Yes | Same | |
| Virtual Planning | Yes | Yes | Same | |
| RFA | Yes | Yes | Same | |
| Vascular Territories Computation and visualization | Yes | Yes | Same | |
| Measurement | Yes | Yes | Same | |
| Save, Report, Print | Yes | Yes | Same |
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| Application | Function name | Proposed device uOmnispace.CT | Predicate Device uOmnispace.CT (K233209) | Remark |
|---|---|---|---|---|
| Dual Energy | Mono Energetic Image | Yes | Yes | Same |
| Mixed Enhanced Image | Yes | Yes | Same | |
| CNR(Contrast Noise Ratio) Image | Yes | Yes | Same | |
| Base Material Images: Including Water-Iodine, Water-Calcium, Calcium-Iodine, Uric acid-Calcium, Water-HAP, Liver-Fat Base Material Pair image. | Yes | Yes | Same | |
| Image Registration | Yes | Yes | Same | |
| Effective Atomic Number Images • Component analysis of kidney stones, uric acid stones or non-uric acid stones • Component analysis of joint gout, uric acid gout or non-uric acid gout | Yes | Yes | Same | |
| Electron Density Images | Yes | Yes | Same | |
| Virtual Non contrast Images | Yes | Yes | Same | |
| Save. Report, Print | Yes | Yes | Same |
| Application | Function name | Proposed device uOmnispace.CT | Predicate Device uOmnispace.CT (K233209) | Remark |
|---|---|---|---|---|
| Dental Application | Defining the Reference Plane | Yes | Yes | Same |
| Plotting Panoramic Curve | Yes | Yes | Same | |
| Marking the Nerve Canals | Yes | Yes | Same | |
| Cross Sectional Operations | Yes | Yes | Same | |
| Dental VRT Display | Yes | Yes | Same | |
| Save, Report, Print(True size printing) | Yes | Yes | Same |
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| Application | Function name | Proposed device uOmnispace.CT | Predicate Device uOmnispace.CT (K233209) | Remark |
|---|---|---|---|---|
| Colon Analysis | Colon Segmentation and Centerline Calculate | Yes | Yes | Same |
| Electronic Colon Cleansing | Yes | Yes | Same | |
| Manual Polyps Marking | Yes | Yes | Same | |
| Colon editing and Center Line editing | Yes | Yes | Same | |
| Polyps' Quantitative Calculation and Analysis | Yes | Yes | Same | |
| Virtual Endoscopy | Yes | Yes | Same | |
| Save, Report, Print | Yes | Yes | Same |
| Application | Function name | Proposed device uOmnispace.CT | Predicate Device uOmnispace.CT (K233209) | Remark |
|---|---|---|---|---|
| Vessel Analysis | Bone removal | Yes | Yes | Functional Substantially Equivalent (Note 1) |
| Vessel and centerlines Extraction | Yes | Yes | Same | |
| Semi-automatic vessel extraction | Yes | Yes | Same | |
| Vascular Measurement and vascular stenosis analysis | Yes | Yes | Same | |
| Save, Report, Print | Yes | Yes | Same | |
| Hyper Realistic Rendering | Yes | Yes | Same |
| Application | Function name | Proposed device uOmnispace.CT | Predicate Device uOmnispace.CT (K233209) | Remark |
|---|---|---|---|---|
| Lung Nodule | Marking Nodules | Yes | Yes | Same |
| Follow-up Analysis | Yes | Yes | Same | |
| Lung Segmentation | Yes | Yes | Same | |
| Nodule Segmentation | Yes | Yes | Same | |
| Measurement for the segmented nodule | Yes | Yes | Same | |
| Save, Report, Print | Yes | Yes | Same |
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| Application | Function name | Proposed device uOmnispace.CT | Predicate Device uOmnispace.CT (K233209) | Remark |
|---|---|---|---|---|
| Cardiovascular Combined Analysis | Vessel analysis: • Bone removal • Vessel and centerlines Extraction • Semi-automatic vessel extraction • Vascular Measurement and vascular stenosis analysis • Hyper Realistic Rendering | Yes | Yes | Functional Substantially Equivalent (Note 1) |
| Heart Analysis: • Multi-Phase Loading • Hyper Realistic Rendering • Heart Chamber Segmentation • Coronary Artery Extraction • Editing Tools • Centerline Extraction • Stenosis Analysis • Plaque Analysis • Cardiac function Assessment | Yes | Yes | Functional Substantially Equivalent (Note 1) | |
| Fusion Review: • The Combined Display of Heart and Vessels tissue | Yes | Yes | Same | |
| TAVR Evaluation: • Automatic Aortic annulus location and manually correction • Automatic Coronary ostia Location and manually correction • Multi-parameter Calculation | Yes | Yes | Same | |
| Save, Report, Print | Yes | Yes | Same |
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| Application | Function Name | Proposed device uOmnispace.CT | Predicate Device uOmnispace.CT (K233209) | Reference Device CT Perfusion 4D (K193289) | Remark |
|---|---|---|---|---|---|
| Body Perfusion | Motion Correction | Yes | Yes | / | Functional Substantially Equivalent (Note 2) |
| Segmentation | Yes | Yes | / | Functional Substantially Equivalent (Note 3) | |
| Vessel Definition | Yes | / | Yes | Functional Substantially Equivalent (Note 4) | |
| Parameter Map Calculation | Yes | / | Yes | Functional Substantially Equivalent (Note 4) | |
| ROI Tools | Yes | Yes | / | Functional Substantially Equivalent (Note 5) | |
| Time-density curve analysis | Yes | Yes | / | Functional Substantially Equivalent (Note 5) | |
| Save, Report, Print | Yes | Yes | / | Same |
Note 1 Compared to predicate device, the proposed device introduces deep-learning algorithm. This algorithm supports the substantively equivalent functions as those in uOmnispace.CT (K233209). This difference between the proposed device and the predicate device doesn't impact the safety and effectiveness of the proposed device.
Note 2 Compared to predicate device, the proposed device integrates both rigid and non-rigid motion correction algorithms, which is substantively equivalent as those in Brain Perfusion and Liver Evaluation in uOmnispace.CT (K233209). This difference between the proposed device and the predicate device doesn't impact the safety and effectiveness of the proposed device.
Note 3
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Compared to predicate device, the proposed device integrates both HU segmentation and auto liver segmentation algorithms, which is substantively equivalent as those in Brain Perfusion and Liver Evaluation in uOmnispace.CT (K233209). This difference between the proposed device and the predicate device doesn't impact the safety and effectiveness of the proposed device.
Note 4 Compared to predicate device, the proposed device is substantively equivalent to the predicate device CT Perfusion 4D (K193289). This difference between the proposed device and the predicate device and reference device doesn't impact the safety and effectiveness of the proposed device.
Note 5 Compared to predicate device, the proposed device supports the substantively equivalent function as Brain Perfusion in uOmnispace.CT (K233209). This difference between the proposed device and the predicate device and reference device doesn't impact the safety and effectiveness of the proposed device.
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8. Performance Data
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
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 to satisfy the recommended documentation for basic documentation. These 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
These algorithms based on ML/AI method:
- Lung density Analysis - Lung segmentation algorithm
- Lung density Analysis - Airway segmentation algorithm
- Vessel Analysis - Automatic bone removal (Abdomen & Limbs) algorithm
- Vessel Analysis - Automatic bone removal (Head & Neck) algorithm
- Heart - Coronary artery extraction algorithm
- Heart - Heart chamber segmentation algorithm
- Liver Evaluation - Liver segmentation algorithm
- Liver Evaluation - Hepatic artery segmentation algorithm
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- Liver Evaluation - Portal vein segmentation algorithm
- Liver Evaluation - Hepatic vein segmentation algorithm
To validate the uOmnispace.CT applications from a clinical perspective, the deep learning-based algorithm contained in the product underwent a scientific evaluation. More information about the testing on ML/AI algorithm is as follows:
1. Lung density Analysis:
The performance testing for the AI-based lung segmentation algorithm and airway segmentation algorithm was performed on 100 subjects during product development.
Acceptance Criteria
In order to verify the effectiveness of the algorithm in the proposed device, we compared the segmentation results with the ground truth. The validation type and acceptance criteria is shown below：
| Validation Type | Algorithm | Acceptance Criteria |
|---|---|---|
| Dice | Lung segmentation | 0.97 |
| Dice | Airway segmentation | 0.86 |
Testing Data Information
The test dataset comprises 100 cases of Chest CT scans, which covered different gender, age (ages ranging from 1 month to 91 years old) and anatomical variants. The information of distribution was as follows:
| Subgroup | Distribution | Quantity |
|---|---|---|
| Sex | F | 38 |
| M | 62 | |
| Age | [0.1,12) | 36 |
| [12,22) | 14 | |
| [22,60) | 22 | |
| >=60 | 25 | |
| Unknown | 3 | |
| Anatomical variation | With anatomical variation | 56 |
| Without anatomical variation | 44 |
Performance Testing Summary
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The results output by algorithm were compared with the reference standard, the value of Dice coefficient was shown in the table below, which is higher than the acceptance criteria:
| Algorithm | Global performance (Dice Similarity Coefficient) |
|---|---|
| lung segmentation | 0.9801 |
| airway segmentation | 0.8954 |
The subgroup analysis was conducted based on gender, age and anatomical variants. The results were shown in the table below, demonstrating that the algorithms have good generality across different subgroups:
| Subgroup | Distribution | Lung segmentation | Airway segmentation |
|---|---|---|---|
| Sex | F | 0.9792 | 0.8957 |
| M | 0.9806 | 0.8951 | |
| Age | [0.1,12) | 0.9792 | 0.8875 |
| [12,22) | 0.9805 | 0.8929 | |
| [22,60) | 0.9813 | 0.907 | |
| >=60 | 0.9803 | 0.9023 | |
| Unknown | 0.9779 | 0.9028 | |
| Anatomical variation | With anatomical variation | 0.9797 | 0.8931 |
| Without anatomical variation | 0.9799 | 0.8957 |
Standard Annotation Process
For ground truth annotations, all ground truth are annotated by well-trained annotators. Finally, a senior clinical specialist will check and modify annotations to make sure the ground truth is correct.
Testing & Training Data Independence
The training data used for the training of the Lung segmentation algorithm is independent of the data used to test the algorithm.
2. Vessel Analysis:
The performance testing for the AI-based bone removal algorithm was performed on 156 data subjects during product development.
Acceptance Criteria
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In order to verify the effectiveness of the algorithm in the proposed device, we compared the segmentation results with the ground truth. The validation type and acceptance criteria is shown below：
| Validation Type | Algorithm | Acceptance Criteria |
|---|---|---|
| Dice | Bone removal (Abdomen & Limbs) | 0.9 |
| Dice | Bone removal (Head & Neck) | 0.93 |
Testing Data Information
The test dataset comprises 156 cases of CTA scans, which covered different gender, age (ages ranging from 13 to 85 years old) and anatomical variants. The information of distribution was as follows:
| Subgroup | Distribution | Quantity |
|---|---|---|
| Sex | F | 54 |
| M | 96 | |
| Unknown | 6 | |
| Age | [12,22) | 46 |
| [22,60) | 41 | |
| >=60 | 60 | |
| Unknown | 9 | |
| Anatomical variation | With anatomical variation | 28 |
| Without anatomical variation | 128 | |
| Body parts | Abdomen & Limbs | 98 |
| Head & Neck | 58 |
Performance Testing Summary
The results output by algorithm were compared with the reference standard, the value of Dice coefficient was shown in the table below, which is higher than the acceptance criteria:
| Algorithm | Global performance (Dice Similarity Coefficient) |
|---|---|
| Bone removal (Abdomen & Limbs) | 0.96957 |
| Bone removal (Head & Neck) | 0.955 |
The subgroup analysis was conducted based on gender, age and anatomical variants. The results were shown in the table below, demonstrating that the algorithms have good generality across different subgroups:
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| Subgroup | Distribution | Bone removal (Abdomen & Limbs) | Bone removal (Head & Neck) |
|---|---|---|---|
| Sex | F | 0.9662 | 0.955 |
| M | 0.9718 | 0.955 | |
| Age | [12,22) | 0.9605 | 0.94 |
| [22,60) | 0.9743 | 0.956 | |
| >=60 | 0.9723 | 0.964 | |
| Anatomical variation | With anatomical variation | 0.9683 | 0.966 |
| Without anatomical variation | 0.97 | 0.958 |
Standard Annotation Process
For ground truth annotations, all ground truth are annotated by well-trained annotators. Finally, a senior clinical specialist will check and modify annotations to make sure the ground truth is correct.
Testing & Training Data Independence
The training data used for the training of the Lung segmentation algorithm is independent of the data used to test the algorithm.
3. Heart:
The performance testing for the AI-based coronary artery extraction algorithm and heart chamber segmentation algorithm was performed on 72 subjects during product development.
Acceptance Criteria
In order to verify the effectiveness of the algorithm in the proposed device, we compared the segmentation results with the ground truth. The validation type and acceptance criteria is shown below：
| Validation Type | Algorithm | Acceptance Criteria |
|---|---|---|
| Dice | Coronary artery extraction | 0.870 |
| Dice | Heart chamber segmentation | 0.910 |
Testing Data Information
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The test dataset comprises 72 cases of CCTA scans, which covered different gender, age (ages ranging from 12 to 92 years old) and anatomical variants. The information of distribution was as follows:
| Subgroup | Distribution | Quantity |
|---|---|---|
| Sex | F | 17 |
| M | 49 | |
| Unknown | 6 | |
| Age | [12,22) | 12 |
| [22,60) | 10 | |
| >=60 | 35 | |
| Unknown | 15 | |
| Anatomical variation | With anatomical variation | 53 |
| Without anatomical variation | 7 | |
| Unknown | 12 |
Performance Testing Summary
The results output by algorithm were compared with the reference standard, the value of Dice coefficient was shown in the table below, which is higher than the acceptance criteria:
| Algorithm | Global performance (Dice Similarity Coefficient) |
|---|---|
| Coronary artery extraction | 0.916 |
| Heart chamber segmentation | 0.970 |
The subgroup analysis was conducted based on gender, age and anatomical variants. The results were shown in the table below, demonstrating that the algorithms have good generality across different subgroups:
| Subgroup | Distribution | Coronary artery extraction | Heart chamber segmentation |
|---|---|---|---|
| Sex | F | 0.915 | 0.969 |
| M | 0.918 | 0.971 | |
| Unknown | 0.912 | 0.963 | |
| Age | [12,22) | 0.908 | 0.962 |
| [22,60) | 0.926 | 0.974 | |
| >=60 | 0.915 | 0.971 | |
| Unknown | 0.920 | 0.971 | |
| Anatomical variation | With anatomical variation | 0.915 | 0.970 |
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| | Without anatomical variation | 0.924 | 0.971 |
| | Unknown | 0.908 | 0.962 |
Standard Annotation Process
For ground truth annotations, all ground truth are annotated by well-trained annotators. Finally, a senior clinical specialist will check and modify annotations to make sure the ground truth is correct.
Testing & Training Data Independence
The training data used for the training of the Lung segmentation algorithm is independent of the data used to test the algorithm.
4. Liver Evaluation:
The performance testing for the AI-based liver segmentation algorithm and hepatic artery segmentation algorithm were performed on 74 subjects during product development. The performance testing for the AI-based hepatic portal vein segmentation algorithm and hepatic vein segmentation algorithm were performed on 80 subjects during product development.
Acceptance Criteria
In order to verify the effectiveness of the algorithm in the proposed device, we compared the segmentation results with the ground truth. The validation type and acceptance criteria is shown below：
| Validation Type | Algorithm | Acceptance Criteria |
|---|---|---|
| Dice | Liver segmentation | 0.97 |
| Dice | Hepatic artery segmentation | 0.85 |
| Dice | Hepatic portal vein segmentation | 0.89 |
| Dice | Hepatic vein segmentation | 0.86 |
Testing Data Information
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The test dataset of liver segmentation algorithm and hepatic artery segmentation algorithm, comprises 74 cases of Chest CT scans, which covered different gender, age (ages ranging from 12 to 100 years old) and anatomical variants. The information of distribution was as follows:
| Subgroup | Distribution | Quantity |
|---|---|---|
| Sex | F | 17 |
| M | 34 | |
| unknown | 23 | |
| Age | [12,22) | 14 |
| [22,60) | 23 | |
| >=60 | 14 | |
| unknown | 23 | |
| Anatomical variation | With anatomical variation | 50 |
| Without anatomical variation | 24 |
The test dataset of hepatic portal vein segmentation algorithm and hepatic vein segmentation algorithm, comprises 80 cases of Chest CT scans, which covered different gender, age (ages ranging from 12 to 80 years old) and anatomical variants. The information of distribution was as follows:
| Subgroup | Distribution | Quantity |
|---|---|---|
| Sex | F | 25 |
| M | 25 | |
| unknown | 30 | |
| Age | [12,22) | 12 |
| [22,60) | 21 | |
| >=60 | 20 | |
| unknown | 30 | |
| Anatomical variation | With anatomical variation | 51 |
| Without anatomical variation | 29 |
Performance Testing Summary
The results output by algorithm were compared with the reference standard, the value of Dice coefficient was shown in the table below, which is higher than the acceptance criteria:
| Algorithm | Global performance (Dice Similarity Coefficient) |
|---|---|
| Liver segmentation | 0.981 |
| Hepatic artery segmentation | 0.927 |
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| Hepatic portal vein segmentation | 0.933 |
| Hepatic vein segmentation | 0.914 |
The subgroup analysis was conducted based on gender, age and anatomical variants. The results were shown in the table below, demonstrating that the algorithms have good generality across different subgroups:
| Subgroup | Distribution | Liver segmentation | Hepatic artery segmentation | Hepatic portal vein segmentation | Hepatic vein segmentation |
|---|---|---|---|---|---|
| Sex | F | 0.981 | 0.928 | 0.932 | 0.924 |
| M | 0.98 | 0.926 | 0.93 | 0.918 | |
| unknown | 0.981 | 0.926 | 0.936 | 0.91 | |
| Age | [12,22) | 0.978 | 0.921 | 0.925 | 0.906 |
| [22,60) | 0.982 | 0.93 | 0.934 | 0.929 | |
| >=60 | 0.98 | 0.926 | 0.933 | 0.928 | |
| unknown | 0.981 | 0.926 | 0.936 | 0.91 | |
| Anatomical variation | With anatomical variation | 0.981 | 0.927 | 0.933 | 0.915 |
| Without anatomical variation | 0.98 | 0.925 | 0.932 | 0.919 |
Standard Annotation Process
For ground truth annotations, all ground truth are annotated by well-trained annotators. Finally, a senior clinical specialist will check and modify annotations to make sure the ground truth is correct.
Testing & Training Data Independence
The training data used for the training of the Lung segmentation algorithm is independent of the data used to test the algorithm.
Other Standards and Guidance
- NEMA PS 3.1 - 3.20 Digital Imaging and Communications in Medicine (DICOM) Set (2023e).
- ISO 14971 Medical devices - Application of risk management to medical devices (Third Edition 2019-12).
- IEC 62304 Medical device software - Software life cycle processes (Edition 1.1, 2015).
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Shanghai United Imaging Healthcare Co., Ltd.
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www.united-imaging.com
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Summary
The features described in this premarket submission are supported with the results of the testing mentioned above; the uOmnispace.CT was found to have a safety and effectiveness profile that is similar to the predicate device and reference devices.
9. Substantially Equivalent (SE) Conclusion
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 device.