InferOperate Suite is medical imaging software that is intended to provide trained medical professionals with tools to aid them in reading, interpreting, reporting, and treatment planning for patients, including both preoperative surgical planning and intraoperative image display. InferOperate Suite accepts DICOM compliant medical images acquired from a variety of imaging devices.

This product is not intended for use with or for the primary diagnostic interpretation of Mammography images.

It provides several categories of tools. It includes basic imaging tools for general images, including 2D viewing, volume rendering and 3D volume viewing, orthogonal Multi-Planar Reconstructions (MPR), surface rendering, measurements, surgical planning, reporting, storing, general image management and administration tools, etc.

It includes a basic image processing workflow and a custom UI to segment anatomical structures. The processing may include the generation of preliminary segmentations of anatomy using software that employs machine learning and other computer vision algorithms, as well as interactive segmentation tools, etc.

InferOperate Suite is designed for use by trained professionals and is intended to assist the clinician who is responsible for making all final patient management decisions.

InferOperate Suite utilizes machine learning-based algorithms for adult patients undergoing CT chest, abdominal, or pelvic scans. For image data of other anatomical regions or modalities, patients under 21 years of age, or patients with unknown age, we provide non-ML software functions, such as STL viewer.

Device Description

InferOperate Suite receives medical images in DICOM standard format and utilizes machine learning (ML) and other medical image processing techniques, along with interactive segmentation tools, to segment anatomical structures and target ROIs. InferOperate Suite performs 3D reconstruction and visualization, and provides several tools for surgical planning. The server receives DICOM images, analyzes the images, and provides 3D visualization of the anatomical structures. The system can be deployed on a dedicated on-premise server or a cloud server.

InferOperate Suite provides several categories of tools. It includes basic imaging tools for general image, including 2D viewing, volume rendering and 3D volume viewing, Multi-Planar Reconstructions (MPR), surface rendering, measurements, surgical planning, reporting, storing, general image management and administration tools

AI/ML Overview

Here's an analysis of the acceptance criteria and the study proving the device meets them, based on the provided FDA 510(k) clearance letter for the InferOperate Suite.

1. Table of Acceptance Criteria and Reported Device Performance

The acceptance criteria for segmentation performance are explicitly stated as "Target" values for the Dice coefficient (DSC) and 95% Hausdorff Distance (HD95). The reported device performance is presented as the Mean and 95% Confidence Interval (CI) for these metrics.

No.	Model	Metric	Mean Reported Performance	95% CI Reported Performance	Target Acceptance Criteria	Meets Criteria?
1	Bronchus	Dice	0.87	0.85-0.88	0.79	Yes
		HD95	2.33	2.07-2.59	3.5	Yes
2	Pulmonary artery	Dice	0.87	0.86-0.88	0.76	Yes
		HD95	3.35	2.79-3.90	5.55	Yes
	Pulmonary vein	Dice	0.85	0.84-0.86	0.77	Yes
		HD95	3.19	2.96-3.42	5.55	Yes
3	Pulmonary lobe	Dice	0.98	0.97-0.98	0.88	Yes
		HD95	2.63	2.34-2.91	4.15	Yes
	Pulmonary segment	Dice	0.88	0.88-0.89	0.79	Yes
		HD95	3.42	3.13-3.70	4.15	Yes
4	Liver	Dice	0.98	0.98-0.98	0.87	Yes
		HD95	2.15	2.09-2.22	4.95	Yes
5	Hepatic segment (Couinaud's method)	Dice	0.91	0.89-0.94	0.80	Yes
		HD95	2.54	2.18-2.89	4.95	Yes
	Hepatic segment (Vascular method)	Dice	0.91	0.89-0.94	0.80	Yes
		HD95	3.52	2.90-4.14	4.95	Yes
6	Hepatic artery	Dice	0.89	0.88-0.91	0.80	Yes
		HD95	2.36	1.98-2.74	5.55	Yes
7	Hepatic vein	Dice	0.91	0.90-0.91	0.80	Yes
		HD95	1.86	1.75-1.98	5.55	Yes
	Portal vein	Dice	0.86	0.85-0.86	0.80	Yes
		HD95	2.24	1.65-2.82	5.55	Yes
8	Portal vein segment	Dice	0.85	0.83-0.86	0.75	Yes
		HD95	3.46	2.74-4.18	5.55	Yes
9	Gallbladder	Dice	0.94	0.93-0.96	0.78	Yes
		HD95	2.19	1.74-2.63	3.5	Yes
10	Common hepatic-bile duct	Dice	0.83	0.79-0.88	0.73	Yes
		HD95	3.54	2.05-5.03	5.55	Yes
11	Pancreas	Dice	0.97	0.95-0.98	0.7	Yes
		HD95	2.49	1.23-3.75	10.63	Yes
12	Spleen	Dice	0.97	0.96-0.97	0.84	Yes
		HD95	2.79	1.64-3.94	4.94	Yes
13	Kidney	Dice	0.98	0.98-0.98	0.85	Yes
		HD95	1.79	1.63-2.09	4.86	Yes
	Bladder	Dice	0.98	0.97-0.99	0.80	Yes
		HD95	2.33	0.00-5.33	6.22	Yes
14	Renal vein	Dice	0.86	0.85-0.87	0.80	Yes
		HD95	3.03	2.01-4.13	5.55	Yes
15	Renal artery	Dice	0.85	0.85-0.86	0.80	Yes
		HD95	2.24	2.08-2.76	5.55	Yes
16	Upper urinary tract	Dice	0.84	0.82-0.85	0.70	Yes
		HD95	2.81	2.39-3.53	5.55	Yes
17	Adrenal gland	Dice	0.85	0.82-0.87	0.70	Yes
		HD95	2.69	1.98-3.70	10.63	Yes
18	Bone	Dice	0.97	0.97-0.98	0.80	Yes
		HD95	0.83	0.69-0.97	5.75	Yes
19	Skin	Dice	0.97	0.97-0.98	0.90	Yes
		HD95	0.40	0.32-0.48	10.00	Yes

All reported device performance metrics (Mean Dice and Mean HD95) meet or exceed their respective target acceptance criteria.

2. Sample Size Used for the Test Set and Data Provenance

Sample Size: A total of 188 cases were used for algorithm performance testing, broken down as:
- 70 cases of the chest
- 61 cases of the abdomen
- 57 cases of the pelvis
- Some individual segmentations (e.g., Gallbladder, Bladder, Spleen, Bone, Skin) had slightly fewer cases than the overall anatomical region, indicating not all structures were present or analyzed in every case (e.g., 56 for Gallbladder out of 61 abdominal cases).
Data Provenance: The dataset was composed of predominantly U.S. subjects. The letter specifies that the data for performance validation was independent of the training set, with no overlap in data sources. The imaging devices mainly included Siemens, GE, Philips, and Toshiba. The cases included a mix of contrast-enhanced and non-contrast enhanced CT scans for chest. All abdominal and pelvic cases were contrast-enhanced CT. The study design is retrospective, as cases were "collected" and analyzed, and the ground truth established post-acquisition.

3. Number of Experts Used to Establish the Ground Truth for the Test Set and Qualifications

Number of Experts: Three experts were used.
- Two Chinese radiologists: Their specific qualifications (e.g., years of experience, subspecialty) are not explicitly stated beyond "radiologists."
- One American board-certified radiologist: This expert served as an arbitrator. Their specific years of experience or subspecialty are not explicitly stated, but "board-certified" indicates a recognized standard of expertise in the U.S.

4. Adjudication Method for the Test Set

The adjudication method used was a 2+1 consensus with arbitration.

Two Chinese radiologists independently annotated the organs and anatomical structures.
An American board-certified radiologist served as an arbitrator.
If there were disagreements between the two initial radiologists' annotations, the arbitrator was responsible for resolving the discrepancies by either selecting the more accurate segmentation as the final ground truth or making any necessary modification.

5. Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study

No, a Multi-Reader Multi-Case (MRMC) comparative effectiveness study was not explicitly detailed in the provided document. The performance evaluation focused solely on the standalone performance of the AI algorithm in segmenting anatomical structures against expert-established ground truth. There is no information provided regarding human readers improving with AI assistance vs. without AI assistance.

6. Standalone Performance

Yes, a standalone (algorithm only without human-in-the-loop performance) study was done. The entire "Performance testing" section quantifies the segmentation accuracy of the InferOperate Suite's machine learning algorithms directly against the established ground truth, using Dice coefficient and Hausdorff distance. This specifically measures the intrinsic performance of the algorithm.

7. Type of Ground Truth Used

The ground truth used was expert consensus with arbitration. This means the ground truth was established by human experts (radiologists) with a formal process for resolving disagreements.

8. Sample Size for the Training Set

The document explicitly states that the dataset for performance validation was "independent of the training set" and had "no overlap in data sources." However, the sample size for the training set is not provided in the given text.

9. How the Ground Truth for the Training Set Was Established

The document mentions that the ground truth for the test set was established by annotators independent of the algorithm development annotators, implying that ground truth was also established for the training set by human annotators. However, the specifics of how the ground truth for the training set was established (e.g., number of experts, qualifications, adjudication method) are not provided in the given text.

Summary

FDA 510(k) Clearance Letter - InferOperate Suite

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U.S. Food & Drug Administration
10903 New Hampshire Avenue
Silver Spring, MD 20993
www.fda.gov

Doc ID # 04017.08.00

September 15, 2025

Beijing Infervision Healthcare Medical Technology Co., Ltd.
Matt Deng
Official Correspondent
Room B403, 4th Floor, Building 1, No.12, Shangdi Information
Haidian District
Beijing, 100085
China

Re: K250237
Trade/Device Name: InferOperate Suite
Regulation Number: 21 CFR 892.2050
Regulation Name: Medical Image Management And Processing System
Regulatory Class: Class II
Product Code: QIH
Dated: August 15, 2025
Received: August 15, 2025

Dear Matt Deng:

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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2K250237 - Matt Deng

FDA's substantial equivalence determination also included the review and clearance of your Predetermined Change Control Plan (PCCP). Under section 515C(b)(1) of the Act, a new premarket notification is not required for a change to a device cleared under section 510(k) of the Act, if such change is consistent with an established PCCP granted pursuant to section 515C(b)(2) of the Act. Under 21 CFR 807.81(a)(3), a new premarket notification is required if there is a major change or modification in the intended use of a device, or if there is a change or modification in a device that could significantly affect the safety or effectiveness of the device, e.g., a significant change or modification in design, material, chemical composition, energy source, or manufacturing process. Accordingly, if deviations from the established PCCP result in a major change or modification in the intended use of the device, or result in a change or modification in the device that could significantly affect the safety or effectiveness of the device, then a new premarket notification would be required consistent with section 515C(b)(1) of the Act and 21 CFR 807.81(a)(3). Failure to submit such a premarket submission would constitute adulteration and misbranding under sections 501(f)(1)(B) and 502(o) of the Act, respectively.

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

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3K250237 - Matt Deng

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 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
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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FORM FDA 3881 (8/23)
DEPARTMENT OF HEALTH AND HUMAN SERVICES
Food and Drug Administration

Indications for Use

Form Approved: OMB No. 0910-0120
Expiration Date: 07/31/2026
See PRA Statement below.

510(k) Number (if known): K250237
Device Name: InferOperate Suite

Indications for Use (Describe)

This product is not intended for use with or for the primary diagnostic interpretation of Mammography images.

InferOperate Suite is designed for use by trained professionals and is intended to assist the clinician who is responsible for making all final patient management decisions.

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.

This section applies only to requirements of the Paperwork Reduction Act of 1995.
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:

Department of Health and Human Services
Food and Drug Administration
Office of Chief Information Officer
Paperwork Reduction Act (PRA) Staff
PRAStaff@fda.hhs.gov

"An agency may not conduct or sponsor, and a person is not required to respond to, a collection of information unless it displays a currently valid OMB number."

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510(k) Summary- K250237

InferOperate Suite

Submitter: Beijing Infervision Healthcare Medical Technology Co., Ltd.

Address: Room B403, 4th Floor, Building 1, No.12,
Shangdi Information Road, Haidian District,
Beijing 100085, China.
Phone: +86 10-86462323

Contact: Mr. Matt Deng
Email: matt.deng@infervision.ai
Phone: 929-335-4879

Date Prepared: September 12, 2025

Device Name and Classification

Trade Name: InferOperate Suite
Common Name: Automated Radiological Image Processing Software
Classification: Class II
Regulation Number: 21 CFR 892.2050
Classification Panel: Radiology
Product Code: QIH
510(k) Number: K250237

Predicate Device:

Trade Name: Visible patient Suite
Manufacturer: Visible Patient, SAS
Common Name: system, image processing, radiological
Classification: Class II
510(k) Number: K212896
Regulation Number: 21 CFR 892.2050
Classification Panel: Radiology
Product Code: LLZ

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Device Description

Indications for Use

This product is not intended for use with or for the primary diagnostic interpretation of Mammography images.

InferOperate Suite is designed for use by trained professionals and is intended to assist the clinician who is responsible for making all final patient management decisions.

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Substantial Equivalence

InferOperate Suite is substantially equivalent in both intended use and technical characteristics to the predicate devices. The variations in design and performance compared to the predicate devices do not impact the safety or effectiveness of InferOperate Suite for its intended use. Table below lists the predicate device alongside the subject device, including their respective Product Codes and Indications for Use.

Detailed Comparison of the Subject and Predicate Devices:

Item	Subject Device: InferOperate Suite	Primary Predicate: Visible Patient Suite (K212896)	Comparison
Product Code	QIH	LLZ
Classification	Class II	Class II
Indications for Use	InferOperate Suite is medical imaging software that is intended to provide trained medical professionals with tools to aid them in reading, interpreting, reporting, and treatment planning for patients, including both preoperative surgical planning and intraoperative image display. InferOperate Suite accepts DICOM compliant medical images acquired from a variety of imaging devices.This product is not intended for use with or for the primary diagnostic interpretation of Mammography images.It provides several categories of tools. It includes basic imaging tools for general images, including 2D viewing, volume rendering and 3D volume viewing, orthogonal Multi-Planar Reconstructions (MPR), surface rendering, measurements, surgical planning, reporting, storing, general image management	Visible Patient Suite is medical imaging software that is intended to provide trained medical professionals with tools to aid them in reading, interpreting, reporting. and treatment planning for both pediatric and adult patients. Visible Patient Suite accepts DICOM compliant medical images acquired from a variety of imaging devices, including CT, MR.This product is not intended for use with or for the primary diagnostic interpretation of Mammography images.The software provides several categories of tools. It includes basic imaging tools for general images, including 2D viewing, volume rendering and 3D volume viewing. orthogonal Multi-Planar	Same

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Item	Subject Device: InferOperate Suite	Primary Predicate: Visible Patient Suite (K212896)	Comparison
	and administration tools, etc.It includes a basic image processing workflow and a custom UI to segment anatomical structures. The processing may include the generation of preliminary segmentations of anatomy using software that employs machine learning and other computer vision algorithms, as well as interactive segmentation tools, etc.InferOperate Suite is designed for use by trained professionals and is intended to assist the clinician who is responsible for making all final patient management decisions.InferOperate Suite utilizes machine learning-based algorithms for adult patients undergoing CT chest, abdominal, or pelvic scans. For image data of other anatomical regions or modalities, patients under 21 years of age, or patients with unknown age, we provide non-ML software functions, such as STL viewer.	Reconstructions (MPR), image fusion, surface rendering. measurements, reporting, storing, general image management and administration tools, etc.It includes a basic image processing workflow and a custom UI to segment anatomical structures, which are visible in the image data (bones, organs, vascular/airway structures. etc.), including interactive segmentation tools, basic image filters, etc.It also includes detection and labeling tools of organ segments (liver, lungs and kidneys), including path definition through vascular/airway, approximation of vascular/airway territories from tubular structures and interactive labeling.The software is designed to be used by trained professionals (including physicians, surgeons and technicians) and is intended to assist the clinician who is solely responsible for making all final patient management decisions.
Intended User	Trained professionals	Trained professionals	Same
Image Input	DICOM	DICOM	Same
2D viewing	Yes	Yes	Same
3D volume viewing	Yes	Yes	Same

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Item	Subject Device: InferOperate Suite	Primary Predicate: Visible Patient Suite (K212896)	Comparison
Orthogonal Multi-Planar Reconstructions (MPR)	Yes	Yes	Same
Surface rendering	Yes	Yes	Same
Measurements	Yes	Yes	Same
Surgical planning	Yes	Yes	Same
Anatomical Region	Thorax, abdomen, pelvis, etc.	Thorax, abdomen, pelvis, etc.	Same
Preoperative viewing of 3D images	Yes	Yes	Same
Intraoperative viewing of 3D images	Yes	Yes	Same
Storing	Yes	Yes	Same
General image data management and administration tools	Yes	Yes	Same
Segmentation	Yes	Yes	Same
Product Availability	Software product	Software product	Same
Modifies the Original DICOM data	No	No	Same

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Performance testing:

InferOperate Suite has been evaluated and verified in accordance with software specifications and applicable performance standards through software verification and validation testing. Additionally, the software validation activities were performed in accordance with IEC 62304 - Medical device software – Software life cycle processes, in addition to the FDA Guidance documents, "Content of Premarket Submissions for Device Software Functions" and "Cybersecurity in Medical Devices: Quality System Considerations and Content of Premarket Submissions."

Software Verification/Validation Tests
Performance Tests
Cybersecurity Testing and Analysis

Segmentation performance was validated on a dataset which was composed of predominantly U.S. subjects. The dataset for performance validation was independent of the training set, with no overlap in data sources.

A total of 188 cases were collected for algorithm performance testing (70 cases of the chest, 61 cases of the abdomen, and 57 cases of the pelvic). The imaging devices mainly included Siemens, GE, Philips and Toshiba, others. Among the chest CT cases, 46 were contrast-enhanced CT scans and the other 24 were non-contrast enhanced CT. All abdominal and pelvic cases were contrast-enhanced CT.

For ground truthing, two Chinese radiologists independently annotated the organs and anatomical structures. Then, an American board-certified radiologist served as an arbitrator. If there were disagreements between the two radiologists' annotations, the arbitrator was responsible for resolving the discrepancies by either selecting the more accurate segmentation as the final ground truth or making any necessary modification as he judged to be accurate. The truthers were independent from algorithm devleopment annotators.

The segmentation performance, characterized by the Dice coefficient (DSC) and 95% Hausdorff Distance (HD95) (mm) is summarized below:

No.	Model	N	Dice			HD95
			Mean	95%CI	Target	Mean	95%CI	Target
1	Bronchus	70	0.87	0.85-0.88	0.79	2.33	2.07-2.59	3.5
2	Pulmonary artery	70	0.87	0.86-0.88	0.76	3.35	2.79-3.90	5.55
	Pulmonary vein		0.85	0.84-0.86	0.77	3.19	2.96-3.42	5.55
3	Pulmonary lobe	70	0.98	0.97-0.98	0.88	2.63	2.34-2.91	4.15

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	Pulmonary segment		0.88	0.88-0.89	0.79	3.42	3.13-3.70	4.15
4	Liver	61	0.98	0.98-0.98	0.87	2.15	2.09-2.22	4.95
5	Hepatic segment (Couinaud's method)	61	0.91	0.89-0.94	0.80	2.54	2.18-2.89	4.95
	Hepatic segment (Vascular method)	61	0.91	0.89-0.94	0.80	3.52	2.90-4.14	4.95
6	Hepatic artery	61	0.89	0.88-0.91	0.80	2.36	1.98-2.74	5.55
7	Hepatic vein	61	0.91	0.90-0.91	0.80	1.86	1.75-1.98	5.55
	Portal vein	61	0.86	0.85-0.86	0.80	2.24	1.65-2.82	5.55
8	Portal vein segment	61	0.85	0.83-0.86	0.75	3.46	2.74-4.18	5.55
9	Gallbladder	56	0.94	0.93-0.96	0.78	2.19	1.74-2.63	3.5
10	Common hepatic-bile duct	61	0.83	0.79-0.88	0.73	3.54	2.05-5.03	5.55
11	Pancreas	61	0.97	0.95-0.98	0.7	2.49	1.23-3.75	10.63
12	Spleen	59	0.97	0.96-0.97	0.84	2.79	1.64-3.94	4.94
13	Kidney	57	0.98	0.98-0.98	0.85	1.79	1.63-2.09	4.86
	Bladder	17	0.98	0.97-0.99	0.80	2.33	0.00-5.33	6.22
14	Renal vein	57	0.86	0.85-0.87	0.80	3.03	2.01-4.13	5.55
15	Renal artery	57	0.85	0.85-0.86	0.80	2.24	2.08-2.76	5.55
16	Upper urinary tract	57	0.84	0.82-0.85	0.70	2.81	2.39-3.53	5.55
17	Adrenal gland	57	0.85	0.82-0.87	0.70	2.69	1.98-3.70	10.63
18	Bone	30	0.97	0.97-0.98	0.80	0.83	0.69-0.97	5.75
19	Skin	30	0.97	0.97-0.98	0.90	0.40	0.32-0.48	10.00

Predetermined Change Control Plan (PCCP)

The InferOperate Suite PCCP outlines two planned modifications to improve ML-based organ segmentation functionality without requiring additional FDA submissions. These include (1) algorithm performance improvement through expansion of the training data, (2) algorithm performance improvement through modification of model architecture, pre/post processing, training methods and parameters. Each proposed change will be implemented according to a

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thorough modification protocol and must satisfy rigorous performance standards. This includes demonstrating non-inferiority to the original model; additionally, the performance metric (DICE) must remain above the predetermined performance target values. Established performance assessment and validation protocols will be used. Acceptance criteria must be fulfilled before any model release. All authorized modifications made under the PCCP will be documented and included in the device's labeling as the modifications are released. In accordance with the PCCP, the modified algorithms will be adequately trained, tuned, tested, and locked before release. Implemented modifications to the InferOperate Suite AI algorithms will be communicated to users via the software update notification and through updated labelling.

Conclusion

The InferOperate Suite is substantially equivalent in intended use, design, principles of operation, and safety features to the predicate device. The minor differences in software do not affect its safety and effectiveness. Performance data demonstrates that the InferOperate Suite is as safe and effective as the predicate, and meets its intended use and specifications. Thus, the InferOperate Suite is substantially equivalent.

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).