uOmnispace is a software solution intended to be used for viewing, manipulation, communication and storage of medical images. It allows processing and filming of multimodality DICOM images.

It can be used as a stand-alone device or together with a variety of cleared and unmodified software options, and also support to plug in multi-vendor applications which meet interface requirements.

u Omnispace is intended to be used by trained professionals, including but not limited to physicians and medical technicians.

The system is not intended for the displaying of digital mammography images for diagnosis in the U.S.

Device Description

uOmnispace is a software only medical device, the hardware itself is not seen as part of the medical device and therefore not in the scope of this product.

uOmnispace provides 2D and 3D viewing, annotation and measurement tools, manually and automatically segmentation tools (Rib extraction algorithm is based on Machine Learning) and film and report features to cover the radiological tasks reading images and reporting. uOmnispace supports DICOM formatted images and objects, CT, MRI, PET and DR multimodality are supported.

uOmnispace is a software medical device that allows multiple users to remotely access clinical applications from compatible computers on a network. The system allows processing and filming of multimodality DICOM images. This software is for use with off the-shelf PC computer technology that meets defined minimum specifications.

uOmnispace communicates with imaging systems of different modalities and medical information systems of the hospital using the DICOM3.0 standard.

The system is not intended for the displaying of digital mammography images for diagnosis in the U.S.

AI/ML Overview

The acceptance criteria and the study proving the device meets accepted criteria for the uOmnispace Medical Image Post-processing Software are described below.

1. Table of Acceptance Criteria and Reported Device Performance:

Validation Type	Acceptance Criteria	Reported Device Performance
Average DICE	The average dice of testing data is higher than 0.8	The average dice on testing data set is 0.855

2. Sample size used for the test set and data provenance:

Sample Size: 60 chest CTs.
Data Provenance: The data was collected during product development. The document does not specify the country of origin of the data nor explicitly states whether it was retrospective or prospective, though the context of "product development" often implies some level of retrospective analysis of collected data.

3. Number of experts used to establish the ground truth for the test set and their qualifications:

Number of Experts: Not explicitly stated as a specific number, but it involved multiple annotators and a "senior clinical specialist".
Qualifications: "Annotators" and a "senior clinical specialist". Specific details like years of experience or medical certifications (e.g., radiologist) are not provided for the individual annotators or the senior specialist.

4. Adjudication method for the test set:

Adjudication Method: A multi-step process:
1. An initial rib mask was generated using a threshold-based interactive tool.
2. Annotators refined the first-round annotation.
3. Annotators checked each other's annotations.
4. A senior clinical specialist checked and modified annotations to ensure ground truth correctness.

5. If a multi reader multi case (MRMC) comparative effectiveness study was done:

No, a multi-reader multi-case (MRMC) comparative effectiveness study was not explicitly done to measure human reader improvement with AI assistance. The study focused on validating the standalone performance of the ML-based rib segmentation algorithm against ground truth.

6. If a standalone (i.e., algorithm only without human-in-the-loop performance) was done:

Yes, a standalone performance study of the ML-based rib segmentation algorithm was done. The performance was evaluated by comparing its output to established ground truth.

7. The type of ground truth used:

Type of Ground Truth: Expert consensus, established through a multi-step annotation and refinement process involving annotators and a senior clinical specialist.

8. The sample size for the training set:

The document states that the training data used was "independent of the algorithm" but does not specify the sample size for the training set.

9. How the ground truth for the training set was established:

The document does not explicitly describe how the ground truth for the training set was established, only that the training data and testing data were independent.

Summary

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Shanghai United Imaging Healthcare Co., Ltd. % Xin Gao RA Manager No. 2258 Chengbei Rd., Jiading District Jiading Industrial District Shanghai, Shanghai 201807 CHINA

Re: K230039

Trade/Device Name: Medical Image Post-processing Software, Model: uOmnispace Regulation Number: 21 CFR 892.2050 Regulation Name: Medical image management and processing system Regulatory Class: Class II Product Code: QIH Dated: June 20, 2023 Received: June 20, 2023

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 (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 located 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.

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

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801); medical device reporting of medical device-related adverse events) (21 CFR 803) for devices or postmarketing safety reporting (21 CFR 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 4, Subpart A) for combination products; and, if applicable, the electronic product radiation control provisions (Sections 531-542 of the Act); 21 CFR 1000-1050.

Also, please note the regulation entitled, "Misbranding by reference to premarket notification" (21 CFR Part 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,

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

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Indications for Use

Submission Number (if known)

K230039

Device Name

uOmnispace

Indications for Use (Describe)

uOmnispace is a software solution intended to be used for viewing, manipulation, communication and storage of medical images. It allows processing and filming of multimodality DICOM images.

It can be used as a stand-alone device or together with a variety of cleared and unmodified software options, and also support to plug in multi-vendor applications which meet interface requirements.

u Omnispace is intended to be used by trained professionals, including but not limited to physicians and medical technicians.

The system is not intended for the displaying of digital mammography images for diagnosis in the U.S.

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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510 (k) SUMMARY

1. Date of Preparation: June 20, 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 Manager 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

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: K191040 Device Name: syngo.via Classification: II Product Code: LLZ

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Reference Device 510(k) Number: K183170 Device Name: uWS-CT Classification: II Product Code: LLZ

5. Device Description

uOmnispace is a software only medical device, the hardware itself is not seen as part of the medical device and therefore not in the scope of this product.

uOmnispace communicates with imaging systems of different modalities and medical information systems of the hospital using the DICOM3.0 standard.

The system is not intended for the displaying of digital mammography images for diagnosis in the U.S.

6. Indications for use

uOmnispace is a software solution intended to be used for viewing, manipulation, communication and storage of medical images.

It can be used as a stand-alone device or together with a variety of cleared and unmodified software options, and also support to plug in multi-vendor applications which meet interface requirements.

uOmnispace is intended to be used by trained professionals, including but not limited to physicians and medical technicians.

The system is not intended for the displaying of digital mammography images for diagnosis in the U.S.

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Image /page/5/Picture/1 description: The image contains the logo for United Imaging. The text "UNITED IMAGING" is displayed in a bold, sans-serif font. To the right of the text is a stylized "U" symbol, which is dark gray with a white vertical line running through the center. The logo appears to be clean and modern.

7. Summary of Technological Characteristics

The technology characteristics of uOmnispace, reflected in this 510(k) submission is substantially equivalent to those of the predicate device.

The following tables compare the technology and intended use of uOmnispace when compared to the predicate devices.

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Item	Proposed Device	Predicate Device	Reference Device	Comparison
	uOmnispace	syngo.via	uWS-CT
		(K191040)	(K183170)
Device ClassificationName	Medical imagemanagement andprocessing system	Medical imagemanagement andprocessing system	/	Same
Product Code	LLZ, QIH	LLZ	/	Same
Regulation Number	21 CFR 892.2050	21 CFR 892.2050	/	Same
Device Class	II	II	/	Same
Classification Panel	Radiology	Radiology	/	Same
Intended Use	uOmnispace is a softwaresolution intended to beused for viewing,manipulation,communication and storageof medical images. Itallows processing andfilming of multimodalityDICOM images.It can be used as a stand-alone device or togetherwith a variety of clearedand unmodified softwareoptions, and also support	syngo.via is a softwaresolution intended to beused for viewing,manipulation,communication, andstorage of medicalimages.It can be used as a stand-alone device or togetherwith a variety of clearedand unmodifiedsyngo based softwareoptions.	/	Substantial EquivalenceThis difference between the predicatedevice and the proposed device is aboutdescription, it doesn't impact the safetyand effectiveness of the subject device.
	to plug in multi-vendorapplications which meetinterface requirements.uOmnispace is intended tobe used by trainedprofessionals, including butnot limited to physiciansand medical technicians.The system is not intendedfor the displaying of digitalmammography images fordiagnosis in the U.S.	syngo.via supportsinterpretation andevaluation ofexaminations withinhealthcare institutions,For example, inRadiology, NuclearMedicine and Cardiologyenvironments.The system is notintended for thedisplaying of digitalmammography imagesfor diagnosis in the U.S.
Client-ServerArchitecture and Multi-User Access	YesBased on client-serverarchitecture and supportsmulti-user access.	YesBased on client-serverarchitecture and supportsmulti-user access.	/ Same
Image communicationand storage	YesCommunicate and storemedical images based onstandard communicationprotocol DICOM.	YesCommunicate and storemedical images based onstandard communicationprotocol DICOM.	/ Same
Hardware /OS	YesClient:Microsoft Windows 7 orMicrosoft	YesClient:Microsoft Windows 7 SP1or Microsoft	/ Functional Substantially EquivalentThe client and server operating systems ofthe proposed device are Microsoft Windowsand Linux respectively, while those of the
	Windows 10 or compatibleversionsServer:Linux Core CentOS7.7 orcompatible versions	Windows 8.1 or MicrosoftWindows 10Server:Microsoft Windows Server2008 R2 orMicrosoft Windows Server2012 R2.		predicate device are Microsoft Windows andMicrosoft Windows Server respectively.This difference doesn't impact the safetyand effectiveness of the subject device.
		or Microsoft Server 2016
Workflow control	YesPre-processing: Autoprocess images beforeloading into post-processingapplications.The client workflowsupports both single monitorand dual monitors.	YesWorkflows support theuser in preparing imagesfor examination.Supports various monitorsetups workflow.	/	Functional Substantially EquivalentPre-processing is equivalent to part of the"preparing images for examination", thepredicate device also assigns theexamination to an application. Thisdifference doesn't impact the safety andeffectiveness of the subject device.
Patient Administration	YesPatient administrationdisplays the patient data andoffers the function ofsearching, sorting, andediting of patient data andimage preview.	YesWith simplified searchfunctionality, clearerstructure of search results,unlimited search results,periodic updates of searchresults, image preview andflexible floating patientbrowser window.	/	Functional Substantially EquivalentThe proposed device does not supportflexible floating patient browser window.This difference doesn't impact the safetyand effectiveness of the subject device.
Review 2D	Yes	/	Yes	Same

	2D image viewing, Textualand graphical annotations,distance, angle, ROI, imageaddition and subtraction,image filter are supported.	2D image viewing, Textualand graphicalannotations, distance,angle, ROI, image additionand subtraction, imagefilter are supported.
Review 3D	Yes3D image viewing, Save 3Dimages in batches.Segmentation functions suchas CT bone removal tools,CT/MR tissue growing tools,VOI, cut.	Yes3D image viewing.Save 3D images inbatches.Segmentation functionssuch as CT bone removaltools, CT tissue growingtools, VOI, cut.	Same
Review 3D algorithm	YesVolume rendering (VR) withHyper Realistic Rendering(HRR), Multi-PlanarReconstruction (MPR),Maximum IntensityProjection (MIP), MinimumIntensity Projection (MinIP),Curved Planar Reformation(CPR), Surface-shadedDisplay (SSD).Automatic body boneremoval algorithm	YesVolume rendering (VR),Multi-PlanarReconstruction (MPR),Maximum IntensityProjection (MIP),Minimum IntensityProjection (MinIP),Curved PlanarReformation (CPR),Surface-shaded Display(SSD).Automatic body boneremoval algorithm	Functional Substantially EquivalentHRR is an extension to the standard VRrendering algorithm to visualize thephotorealistic images.This difference between the proposed deviceand the reference device doesn't impact thesafety and effectiveness of the subject deviceas the necessary measures taken for thesafety and effectiveness of the proposeddevice.
	Automatic head boneremoval algorithm		Automatic head boneremoval algorithm
Rib segmentation	YesMachine learning basedalgorithm	/	YesThreshold basedinteractive algorithm	Functional Substantially EquivalentBoth are used for rib segmentation, thereference device requires interaction,while the proposed device does not.This difference between the proposed deviceand the reference device doesn't impact thesafety and effectiveness of the subject deviceas the necessary measures taken for thesafety and effectiveness of the proposeddevice
Inner view	Yes3D virtual endoscopy viewand extract the centerline ofvessel, airway and colon.Imaging algorithms:CT colon Inner ViewalgorithmCT vessel Inner ViewalgorithmCT lung trachea InnerView algorithmMR vessel Inner Viewalgorithm	/	Yes3D virtual endoscopy viewand extract the centerlineof vessel, airway andcolon.Imaging algorithms:CT colon Inner Viewalgorithm, CT vessel InnerView algorithmCT lung trachea InnerView algorithm	Functional Substantially EquivalentThe MR vessel Inner View algorithm issame with CT vessel Inner View algorithm,the difference of input modality does notaffects the output.This difference between the proposed deviceand the reference device doesn't impact thesafety and effectiveness of the subject deviceas the necessary measures taken for thesafety and effectiveness of the proposeddevice.
Filming	Yes	/	Yes	Same

Report	Support to print image and selection of printer.YesSupport to create reports, text editing and image inserting, customize report template, exporting and printing of report.	/	Support to print image and selection of printer.YesSupport to create reports, text editing and image inserting, customize report template, exporting and printing of report.	Same
Archiving	YesImport of DICOM images from configured network nodes (modalities, medical imaging process software, PACS, etc) or local and network drives or DVD/CD.Export (archive) DICOM images to network nodes, or local and network drives or DVD/CD.	YesImport of DICOM data from network nodes or external media, and of DICOM-compliant or non DICOM compliant data from external media and Windows file system.Export to CD/DVD, Windows file system, or other DICOM nodes.	/	Functional Substantially EquivalentThe proposed product does not support to import non DICOM compliant data from external media and Windows file system.This difference between the proposed device and the reference device doesn't impact the safety and effectiveness of the subject device as the necessary measures taken for the safety and effectiveness of the proposed device

Table 1 Substantial equivalent discussion for basic functions

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Image /page/8/Picture/1 description: The image contains the logo for United Imaging. The words "UNITED IMAGING" are stacked on top of each other and are in a bold, sans-serif font. To the right of the words is a stylized "U" symbol. The symbol is a dark gray color and has a vertical line running through the center of the "U".

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Image /page/10/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 formed by two vertical bars connected by a horizontal bar in the middle. The logo is simple and modern in design.

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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 a shield or a stylized letter "U" with a horizontal line through the middle. The color of the text and symbol is a dark teal or blue-gray.

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 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
Software Architecture Design Chart
· Software Development Environment Description
Software Verification and Validation
· Cybersecurity Documents

Animal Study

No animal study was required.

Clinical Studies

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No clinical study was required.

Performance Verification

To validate the uOmnispace software from a clinical perspective, the ML-based rib segmentation algorithm contained in the product underwent a scientific evaluation. The results of clinical data-based software validation for the subject device demonstrated equivalent performance in comparison to the reference device.

The performance testing for ML-based rib segmentation algorithm was performed on 60 subjects (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:

Table 8-1. Validation type and acceptance criteria

Validation Type	Acceptance Criteria
Average DICE	The average dice of testing data is higher than 0.8

Testing Data Information .
Table 8-2. Testing data information

Information of data	60 chest CTs
Sex	Male 37Female 23
Age	[14, 35] : 5[36, 69] : 41[70, 86] : 14

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Performance Testing Summary: .

The average dice on testing data set is 0.855, which is higher than 0.8. Meanwhile, the subgroup analysis shows that (Table 8-3) the performance of algorithm are consist in different subgroups.

Age	DICE
[14,35]	0.848
[36,69]	0.856
[70,86]	0.856
Gender	DICE
Female	0.856
Male	0.855

Toblo 0 2 Subaraum norfam

Standard Annotation Process .

For ground truth annotations, all ground truth are annotators. A threshold based interactive tool is used to generate initial rib mask, then annotators will refine 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 post-processing algorithm is independent of the digorithm

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

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Image /page/15/Picture/1 description: The image shows the logo for United Imaging. The words "UNITED IMAGING" are stacked on top of each other in a bold, sans-serif font. To the right of the words is a stylized "U" symbol, which is made up of two vertical lines and a horizontal line. The logo is simple and modern, and the colors are muted.

The features described in this premarket submission are supported with the results of the testing mentioned above; the uOmnispace was found to have a safety and effectiveness profile that is similar to the predicate device and reference devices.

9. Substantially Equivalent 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 devices.

Regulation Number and Section

§ 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).