Yes
The document explicitly states that "Three machine learning models are included in RUS." and lists them.
No
The device is medical imaging software intended to aid trained medical professionals in reading, interpreting, reporting, and treatment planning by analyzing medical images. It does not directly treat or provide therapy to patients.
Yes
Explanation: The device is described as "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." It helps clinicians interpret medical images and aids in treatment planning, which are key aspects of diagnostics. While it states it's "not intended for use with or for the primary diagnostic interpretation of Mammography images," it explicitly supports the diagnostic interpretation of other medical images (CT scans of the abdomen).
Yes
The device is described as a "software suite" and its components (h-Server, h-Space, and RUS Stomach Planning) are all described as software. The description focuses on data processing, visualization, and planning tools provided by the software, and there is no mention of accompanying hardware components that are part of the medical device itself. While it interfaces with existing medical imaging devices and PACS, these are external systems, not part of the submitted device.
Based on the provided information, this device is an IVD (In Vitro Diagnostic).
Here's why:
- Intended Use: The intended use explicitly states that the software is intended to provide trained medical professionals with tools to aid them in reading, interpreting, reporting, and treatment planning for patients using medical images. While it doesn't directly analyze patient samples, the interpretation of medical images derived from the patient's body (in this case, CT scans) falls under the scope of in vitro diagnostics as it provides information about the patient's health status.
- Device Description: The device processes and analyzes medical images (DICOM compliant CT images) to create 3D models, segment anatomical structures, and aid in surgical planning. This processing and analysis of data derived from the patient is a key characteristic of IVDs.
- Use of Machine Learning Models: The inclusion of machine learning models for organ, vessel, and pneumoperitoneum detection and labeling further supports its classification as an IVD. These models are used to interpret and provide information based on the patient's imaging data.
- Performance Studies: The performance studies focus on the accuracy of segmentations and measurements derived from the imaging data, which are directly related to providing diagnostic or treatment planning information.
- Predicate and Reference Devices: The predicate and reference devices listed (Visible patient Suite and Synapse 3D Base Tools) are also medical imaging software used for similar purposes, which are typically classified as medical devices, and often as IVDs depending on their specific functions and claims.
While the software doesn't perform tests on biological samples in a lab, the interpretation and analysis of medical images derived from the patient's body for diagnostic and treatment planning purposes is considered an in vitro diagnostic activity by regulatory bodies. The software is providing information about the patient's internal state based on data acquired from their body.
No
The input text explicitly states "Control Plan Authorized (PCCP): Not Found," which directly indicates that no PCCP was authorized or mentioned in the provided clearance letter.
Intended Use / Indications for Use
RUS 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. RUS accepts DICOM compliant medical images acquired from iodine contrast-enhanced abdomen CT.
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, 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 structures, etc.), including interactive segmentation tools, basic image filters, etc.
It also includes detection and labeling tools of organ segments, including path definition through vascular 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.
Product codes (comma separated list FDA assigned to the subject device)
OIH
Device Description
RUS uses DICOM (Digital Imaging and Communications in Medicine) standards to analyze CT images. This software provides trained medical professionals with tools to aid them in reading, interpreting, reporting, and treatment planning. By observing the medical images standard protocol (DICOM standards), this software can receive transmitted images from medical imaging devices through the h-Server and can be interfaced with PACS (Picture Archiving and Communication System).
RUS allows surgical planning by 3D modeling from patient's CT data. Surgical planning in RUS does not replace actual surgery and can only be used as an auxiliary tool.
CT is taken at the hospital, the patient's CT data is obtained from PACS, and the CT data is transferred from PACS to h-Server. When CT data and patient information are registered in the h-Server, the data is pseudonymized and anonymized and safely moved to the h-Space. If you request hu3D production by registering CT data and patient information through h-Server, hu3D will be provided within 72 hours. Then you may download the hu3D model through RUS Stomach Planning and perform Surgical planning.
RUS is a software suite and includes three software components: h-Server, h-Space, and RUS Stomach Planning.
-
h-Server
h-Server includes modules dedicated to data management and data gateway. The software is a simple tool either to anonymize or pseudonymize multidimensional digital images acquired from a variety of medical imaging modalities (DICOM images). There is no 3D data volume interpretation in this software. -
h-Space
h-Space includes data management (except for DICOM files anonymization/pseudonymization module) and 3D reconstruction. This software offers a flexible solution to help trained medical professionals with image processing knowledge (usually radiologists or radiologist technicians) in (1) the evaluation of patient's anatomy, and (2) in the creation of a 3D model of the patient's anatomy. This software proposes flexible workflow options: visualization of patient's anatomy from medical images; creation a 3D model of the patient's anatomical structures, organ segments and volumetric data; creation of an anatomical atlas (a colored image where each color represents a structure); and exports these medical data to be analyzed or reviewed later. -
RUS Stomach Planning
RUS Stomach Planning includes modules dedicated to patient & data management and surgical planning. This software offers a flexible visualization solution to help trained medical professionals (clinicians) in the evaluation of patient's anatomy to plan therapy or surgery.
Mentions image processing
Yes
Mentions AI, DNN, or ML
Three machine learning models are included in RUS.
Input Imaging Modality
iodine contrast-enhanced abdomen CT
Anatomical Site
bones, organs, vascular structures, etc. (specifically liver, stomach, spleen, gallbladder, and pancreas for organ segmentation)
Indicated Patient Age Range
Not Found
Intended User / Care Setting
Trained professionals (including physicians, surgeons and technicians) / Hospital
Description of the training set, sample size, data source, and annotation protocol
A total of 60 imaging studies were used to evaluated the device. No imaging study used to verify performance was used for training; independence of training and testing data were enforced at the level of the scanning institution, namely, studies sourced from a specific institution were used for either training or testing but could not be used for both. The data used in the device validation ensured diversity in patient population and CT system manufacturer. The data acquired from different CT systems and acquisition condition to reflect the intended use environment and the recommended CT settings. The data includes patients with and without disease.
Performance was verified by comparing segmentations and pneumoperitoneum generated by the machine learning models against segmentations generated by medical professionals and 3D scan data form the same imaging study.
Description of the test set, sample size, data source, and annotation protocol
A total of 60 imaging studies were used to evaluated the device. No dataset contained more than one imaging study from any particular patient. No imaging study used to verify performance was used for training; independence of training and testing data were enforced at the level of the scanning institution, namely, studies sourced from a specific institution were used for either training or testing but could not be used for both. The data used in the device validation ensured diversity in patient population and CT system manufacturer. The data acquired from different CT systems and acquisition condition to reflect the intended use environment and the recommended CT settings. The data includes patients with and without disease.
Performance was verified by comparing segmentations and pneumoperitoneum generated by the machine learning models against segmentations generated by medical professionals and 3D scan data form the same imaging study.
Summary of Performance Studies (study type, sample size, AUC, MRMC, standalone performance, key results)
Performance Tests (Segmentation Accuracy, pneumoperitoneum, Length Measurement)
Three machine learning models are included in RUS. (Organ: CADD U-NET, Vessel: 3D U-NET, Pneumoperitoneum: Linear regression). These models were verified with datasets of actual CT imaging studies of patients. A total of 60 imaging studies were used to evaluated the device.
For Organ Segmentation, the target performance is set DSC 0.920. The performance of the machine learning models was 0.927 DSC.
For Vessel Segmentation, we set a target performance of DSC 0.890. The performance of the machine learning models was 0.920 DSC.
For Pneumoperitoneum, the target performance was set to MAE +/- 1.083mm based on the validation data during the model development process. The performance of the machine learning models was +/- 0.972 mm.
The accuracy of length measurement features has been validated on phantom data and hu3D data. The type of measurements verified were distances between two points (Ruler function). The measurements produced by RUS were verified to be accurate within a mean difference of +/- 10%.
Key Metrics (Sensitivity, Specificity, PPV, NPV, etc.)
Dice coefficient Scores (DSC), Mean Absolute Error (MEA), mean difference
Predicate Device(s): If the device was cleared using the 510(k) pathway, identify the Predicate Device(s) K/DEN number used to claim substantial equivalence and list them here in a comma separated list exactly as they appear in the text. List the primary predicate first in the list.
Reference Device(s): Identify the Reference Device(s) K/DEN number and list them here in a comma separated list exactly as they appear in the text.
Predetermined Change Control Plan (PCCP) - All Relevant Information for the subject device only (e.g. presence / absence, what scope was granted / cleared under the PCCP, any restrictions, etc).
Not Found
§ 892.2050 Medical image management and processing system.
(a)
Identification. A medical image management and processing system is a device that provides one or more capabilities relating to the review and digital processing of medical images for the purposes of interpretation by a trained practitioner of disease detection, diagnosis, or patient management. The software components may provide advanced or complex image processing functions for image manipulation, enhancement, or quantification that are intended for use in the interpretation and analysis of medical images. Advanced image manipulation functions may include image segmentation, multimodality image registration, or 3D visualization. Complex quantitative functions may include semi-automated measurements or time-series measurements.(b)
Classification. Class II (special controls; voluntary standards—Digital Imaging and Communications in Medicine (DICOM) Std., Joint Photographic Experts Group (JPEG) Std., Society of Motion Picture and Television Engineers (SMPTE) Test Pattern).
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July 12, 2024
Image /page/0/Picture/1 description: The image shows the logo of the U.S. Food and Drug Administration (FDA). On the left is the Department of Health & Human Services logo. To the right of that is the FDA logo, which is a blue square with the letters "FDA" in white. To the right of the blue square is the text "U.S. FOOD & DRUG ADMINISTRATION" in blue.
Hutom Inc. % Priscilla Chung Regulatory Affairs Consultant LK Consulting Group USA, Inc. 18881 Von Karman Ave. STE 160 IRVINE CA 92612
Re: K233457
Trade/Device Name: RUS Regulation Number: 21 CFR 892.2050 Regulation Name: Medical Image Management And Processing System Regulatory Class: Class II Product Code: OIH Dated: June 14, 2024 Received: June 14, 2024
Dear Priscilla Chung:
We have reviewed your section 510(k) premarket notification of intent to market the device referenced above and have determined the device is substantially equivalent (for the indications for use stated in the enclosure) to legally marketed predicate devices marketed in interstate commerce prior to May 28, 1976, the enactment date of the Medical Device Amendments, or to devices that have been reclassified in accordance with the provisions of the Federal Food, Drug, and Cosmetic Act (the Act) that do not require approval of a premarket approval application (PMA). You may, therefore, market the device, subject to the general controls provisions of the Act. Although this letter refers to your product as a device, please be aware that some cleared products may instead be combination products. The 510(k) Premarket Notification Database available at https://www.accessdata.fda.gov/scripts/cdrh/cfdocs/cfpmn/pmn.cfm identifies combination product submissions. The general controls provisions of the Act include requirements for annual registration, listing of devices, good manufacturing practice, labeling, and prohibitions against misbranding and adulteration. Please note: CDRH does not evaluate information related to contract liability warranties. We remind you, however, that device labeling must be truthful and not misleading.
If your device is classified (see above) into either class II (Special Controls) or class III (PMA), it may be subject to additional controls. Existing major regulations affecting your device can be found in the Code of Federal Regulations, Title 21, Parts 800 to 898. In addition, FDA may publish further announcements concerning your device in the Federal Register.
Additional information about changes that may require a new premarket notification are provided in the FDA guidance documents entitled "Deciding When to Submit a 510(k) for a Change to an Existing Device" (https://www.fda.gov/media/99812/download) and "Deciding When to Submit a 510(k) for a Software Change to an Existing Device" (https://www.fda.gov/media/99785/download).
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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 OS regulation requires device manufacturers to review and approve changes to device design and production (21 CFR 820.30 and 21 CFR 820.70) and document changes and approvals in the device master record (21 CFR 820.181).
Please be advised that FDA's issuance of a substantial equivalence determination does not mean that FDA has made a determination that your device complies with other requirements of the Act or any Federal statutes and regulations administered by other Federal agencies. You must comply with all the Act's requirements, including, but not limited to: registration and listing (21 CFR Part 807); labeling (21 CFR Part 801); medical device reporting of medical device-related adverse events) (21 CFR Part 803) for devices or postmarketing safety reporting (21 CFR Part 4, Subpart B) for combination products (see https://www.fda.gov/combination-products/guidance-regulatory-information/postmarketing-safety-reportingcombination-products); good manufacturing practice requirements as set forth in the quality systems (QS) regulation (21 CFR Part 820) for devices or current good manufacturing practices (21 CFR Part 4, Subpart A) for combination products; and, if applicable, the electronic product radiation control provisions (Sections 531-542 of the Act); 21 CFR Parts 1000-1050.
Also, please note the regulation entitled, "Misbranding by reference to premarket notification" (21 CFR 807.97). For questions regarding the reporting of adverse events under the MDR regulation (21 CFR Part 803), please go to https://www.fda.gov/medical-device-safety/medical-device-reportingmdr-how-report-medical-device-problems.
For comprehensive regulatory information about medical devices and radiation-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,
Zhkke
, for
Jessica Lamb Assistant Director DHT8B: Division of Radiologic 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
510(k) Number (if known) K233457
Device Name RUS
Indications for Use (Describe)
RUS 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. RUS accepts DICOM compliant medical images acquired from iodine contrast-enhanced abdomen CT.
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, image fusion, surface rendering, measurements, reporting, storing, 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 structures, etc.), including interactive segmentation tools, basic image filters, etc.
It also includes detection and labeling tools of organ segments, including path definition through vascular 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.
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
(K233457)
This summary of 510(k) information is being submitted in accordance with requirements of 21 CFR Part 807.92.
1. Date: 06/13/2024
2. Applicant / Submitter
Hutom Inc. 6F, 279, Dongmak-ro, Mapo-gu, Seoul Republic of Korea
3. U.S. Designated Agent
Priscilla Chung LK Consulting Group USA, Inc. 18881 Von Karman Ave. STE 160 Irvine, CA 92612 Fax: 714.409.3357 Tel: 714.202.5789 Email: juhee.c@LKconsultingGroup.com
4. Device Information:
- Trade/Device Name: RUS
- Common Name: Automated Radiological Image Processing Software
- Regulation Name: Medical image management and processing system ●
- Regulation Number: 21 CFR 892.2050
- Regulatory Class: II
- Product Code: QIH ●
5. Predicate Device:
- Primary Predicate Device: Visible patient Suite (K212896) by Visible Patient, SAS ●
- Reference Predicate Device: Synapse 3D Base Tools v6.6 (K221677) by FUJIFILM ● Corporation
6. Device Description:
RUS uses DICOM (Digital Imaging and Communications in Medicine) standards to analyze CT images. This software provides trained medical professionals with tools to aid them in
4
reading, interpreting, reporting, and treatment planning. By observing the medical images standard protocol (DICOM standards), this software can receive transmitted images from medical imaging devices through the h-Server and can be interfaced with PACS (Picture Archiving and Communication System).
RUS allows surgical planning by 3D modeling from patient's CT data. Surgical planning in RUS does not replace actual surgery and can only be used as an auxiliary tool.
CT is taken at the hospital, the patient's CT data is obtained from PACS, and the CT data is transferred from PACS to h-Server. When CT data and patient information are registered in the h-Server, the data is pseudonymized and anonymized and safely moved to the h-Space. If you request hu3D production by registering CT data and patient information through h-Server, hu3D will be provided within 72 hours. Then you may download the hu3D model through RUS Stomach Planning and perform Surgical planning.
RUS is a software suite and includes three software components: h-Server, h-Space, and RUS Stomach Planning.
1) h-Server
h-Server includes modules dedicated to data management and data gateway. The software is a simple tool either to anonymize or pseudonymize multidimensional digital images acquired from a variety of medical imaging modalities (DICOM images). There is no 3D data volume interpretation in this software.
2) h-Space
h-Space includes data management (except for DICOM files
anonymization/pseudonymization module) and 3D reconstruction. This software offers a flexible solution to help trained medical professionals with image processing knowledge (usually radiologists or radiologist technicians) in (1) the evaluation of patient's anatomy, and (2) in the creation of a 3D model of the patient's anatomy. This software proposes flexible workflow options: visualization of patient's anatomy from medical images; creation a 3D model of the patient's anatomical structures, organ segments and volumetric data; creation of an anatomical atlas (a colored image where each color represents a structure); and exports these medical data to be analyzed or reviewed later.
3) RUS Stomach Planning
RUS Stomach Planning includes modules dedicated to patient & data management and surgical planning. This software offers a flexible visualization solution to help trained medical professionals (clinicians) in the evaluation of patient's anatomy to plan therapy or surgery.
7. Indication for use:
RUS 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. RUS accepts DICOM compliant medical images acquired from iodine contrast-enhanced abdomen CT.
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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, 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 structures, etc.), including interactive segmentation tools, basic image filters, etc.
It also includes detection and labeling tools of organ segments, including path definition through vascular 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.
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8. Substantial Equivalence:
The RUS is substantially equivalent to the following predicate devices:
- Primary Predicate Device: Visible Patient Suite (K212896) by Visible Patient, SAS
- Reference Device: Synapse 3D Base Tools v6.6 (K221677) by FUJIFILM Corporation
8.1.Comparison Chart
| Elements of
| Comparison | Subject Device | Primary Predicate | Additional Predicate | Comparison |
|---|---|---|---|---|
| Device Name | RUS | Visible patient Suite | Synapse 3D Base Tools v6.6 | |
| 510# | K233457 | K212896 | K221677 | |
| Manufacturer | Hutom | Visible Patient, SAS | FUJIFILM Corporation | |
| Classification | ||||
| Name | System, Image Processing, | |||
| Radiological | System, Image Processing, | |||
| Radiological | System, Image Processing, | |||
| Radiological | Same | |||
| Regulation | ||||
| Number No. | 21 CFR 892.2050 | 21 CFR 892.2050 | 21 CFR 892.2050 | Same |
| Product Code | QIH | LLZ | LLZ | Same |
| Classification | Class II | Class II | Class II | Same |
| Indications for use | RUS 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. RUS | ||||
| accepts DICOM compliant medical | ||||
| images acquired from iodine contrast- | ||||
| enhanced abdomen CT. | ||||
| This product is not intended for use | ||||
| with or for the primary diagnostic | ||||
| interpretation of Mammography | 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 | Synapse 3D Base Tools is medical | |||
| imaging software that is intended to | ||||
| provide trained medical professionals | ||||
| with tools to aid them in reading, | ||||
| interpreting, reporting, and treatment | ||||
| planning. Synapse 3D Base Tools | ||||
| accepts DICOM compliant medical | ||||
| images acquired from a variety of | ||||
| imaging devices including, CT, MR, | ||||
| CR, US, NM, PT, and XA, etc. This | ||||
| product is not intended for use with | ||||
| or for the primary diagnostic | Same | |||
| Elements of | ||||
| Comparison | Subject Device | Primary Predicate | Additional Predicate | Comparison |
| images. | interpretation of Mammography | interpretation of Mammography | ||
| images. | images. Synapse 3D Base Tools | |||
| The software provides several | The software provides several | provides several levels of tools to the | ||
| categories of tools. It includes basic | categories of tools. It includes basic | user: Basic imaging tools for general | ||
| imaging tools for general images, | imaging tools for general images, | images, including 2D viewing, | ||
| including 2D viewing, volume | including 2D viewing, volume | volume rendering and 3D volume | ||
| rendering and 3D volume viewing, | rendering and 3D volume viewing, | viewing, orthogonal/ oblique/ curved | ||
| image fusion, surface rendering, | orthogonal Multi-Planar | Multi-Planar Reconstructions (MPR), | ||
| measurements, reporting, storing, | Reconstructions (MPR), image fusion, | Maximum (MIP), Average (RaySum) | ||
| general image management and | surface rendering, measurements, | and Minimum (MinIP) Intensity | ||
| administration tools, etc. | reporting, storing, general image | Projection, 4D volume viewing, | ||
| management and administration tools, | image fusion, image subtraction, | |||
| It includes a basic image processing | etc. | surface rendering, sector and | ||
| workflow and a custom UI to | It includes a basic image processing | rectangular shape MPR image | ||
| segment anatomical structures, which | workflow and a custom UI to segment | viewing, MPR for dental images, | ||
| are visible in the image data (bones, | anatomical structures, which are | creating and displaying multiple | ||
| organs, vascular structures, etc.), | visible in the image data (bones, | MPR images along an object, time- | ||
| including interactive segmentation | organs, vascular/airway structures, | density distribution, basic image | ||
| tools, basic image filters, etc. | etc.), including interactive | processing, noise reduction, CINE, | ||
| segmentation tools, basic | measurements, annotations, | |||
| It also includes detection and labeling | image filters, etc. | reporting, printing, storing, | ||
| tools of organ segments, including | It also includes detection and labeling | distribution, and general image | ||
| path definition through vascular and | tools of organ segments (liver, lungs | management and administration | ||
| interactive labeling. | and kidneys), including path | tools, etc. | ||
| definition through vascular/airway, | -Tools for regional segmentation of | |||
| The software is designed to be used | approximation of vascular/airway | anatomical structures within the | ||
| by trained professionals (including | territories from tubular structures and | image data, path definition through | ||
| physicians, surgeons and technicians) | interactive labeling. | vascular and other tubular structures, | ||
| and is intended to assist the clinician | The software is designed to be used | and boundary detection. | ||
| who is solely responsible for making | by trained professionals (including | -Image viewing tools for modality | ||
| all final patient management | physicians, surgeons and technicians) | specific images, including CT PET | ||
| decisions. | and is intended to assist the clinician | fusion and ADC image viewing for | ||
| who is solely responsible for making | MR studies. | |||
| Elements of | ||||
| Comparison | Subject Device | Primary Predicate | Additional Predicate | Comparison |
| all final patient management | ||||
| decisions. | -Imaging tools for CT images | |||
| including virtual endoscopic viewing | ||||
| and dual energy image viewing. | ||||
| -Imaging tools for MR images | ||||
| including delayed enhancement | ||||
| image viewing, diffusion-weighted | ||||
| MRI image viewing. | ||||
| Intended user | Trained professionals (including | |||
| physicians, surgeons and technicians) | Trained professionals (including | |||
| physicians, surgeons and technicians) | Trained medical professionals | Same | ||
| Where used | Hospital | Hospital | Hospital | Same |
| Type of input data | CT | CT, MR | CT, MR, CR, US, NM, PT, and XA, | |
| etc | Same | |||
| Data information | ||||
| processing | Anonymization (Some Dicom Tag) | |||
| Pseudonymization (Patient name, ID) | Anonymization (DICOM data, patient | |||
| information) | Anonymization (Patient name, ID, | |||
| study list) | Similar | |||
| 2D viewing | YES | YES | YES | Same |
| Image Storing | ||||
| (DICOM SCP) | YES | YES | YES | Same |
| Image | ||||
| Communication | ||||
| (DICOM SCU) | YES | YES | YES | Same |
| Printing (DICOM | ||||
| SCU) | YES | YES | YES | Same |
| Measurements (2D | ||||
| and 3D) | YES | YES | YES | Same |
| Reporting | YES | YES | YES | Same |
| Volume Rendering | ||||
| and 3D viewing | YES | YES | YES | Same |
| Image fusion | YES | YES | YES | Same |
| Surface fusion | YES | YES | YES | Same |
| Image subtraction | ||||
| (3D) | YES | YES | YES | Same |
| Elements of | ||||
| Comparison | Subject Device | Primary Predicate | Additional Predicate | Comparison |
| General image | ||||
| data management | ||||
| and | ||||
| administration | ||||
| tools | YES | YES | YES | Same |
| Segmentation | YES | YES | YES | Same |
| Virtual | ||||
| Endoscopic | ||||
| Simulator | YES | No | YES | Same |
| Product | ||||
| Availability | Software product | Software product | Software product | Same |
| Hardware platform | Windows PC | Windows PC | Windows PC | Same |
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8.2. Substantial Equivalence Discussion
The Substantial Equivalence (SE) Comparison Table (Table 8.1) in which we compare the differences and similarities of the proposed device to the predicate device follows in this Section and the reference device is only similar with function (tool) compared with the subject device.
The subject device is substantially equivalent to the predicate device in the following ways:
-
. Indications for Use
The subject device has the same indications for use as the predicate devices. -
. Where used
The subject device shares the same usage environment as the predicate device. -
. Type of input data
The input data for the subject device consists of CT images, which the input data type of the predicate device. -
Basic imaging tools .
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The fundamental imaging tools, such as 2D/3D viewing, image storage, communication, printing, reporting, and rendering, are identical to those of the predicate device. These tools are part of the predicate device.
- Segmentation ●
The application segments and reconstructs various anatomical structures including organs (such as the liver, stomach, spleen, gallbladder, and pancreas), vessels, and skin. The predicate devices' segmentation areas encompass those of the subject device. The subject device has similar or different technical characteristics to the predicate devices in the following ways:
Technological characteristics ●
The RUS has the same principles of operation as its predicate device, but there are some differences in technical characteristics. There is a difference between anonymization in data information processing. While the predicate devices anonymize DICOM data and patient information (such as ID and name), the subject device pseudonymizes patient names and IDs while also anonymizing certain DICOM tags. The validation test has confirmed the effectiveness of this data information processing. This difference does not raise a new concern in safety or effectiveness.
The subject device. RUS, is equivalent to the predicate device in terms of the same indications for use. intended user, target population, use environment, type of input data, and basic imaging tools. Performance tests were carried out to assess the functionality of RUS, the subject device. The test results of all conducted tests support that the substantially equivalent to the predicate device.
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9. Performance Data:
Safety and performance of RUS 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 62304:2006/Amd 1: 2015- Medical device software – Software life cycle processes, in addition to the FDA Guidance documents. "Guidance for the Content of Premarket Submissions for Software Contained in Medical Devices" and "Content of Premarket Submission for Management of Cybersecurity in Medical Devices."
- · Software Verification/Validation Tests
- · Performance Tests (Segmentation Accuracy, pneumoperitoneum, Length Measurement)
Three machine learning models are included in RUS. (Organ: CADD U-NET, Vessel: 3D U-NET, Pneumoperitoneum: Linear regression). These models were verified with datasets of actual CT imaging studies of patients. A total of 60 imaging studies were used to evaluated the device. No dataset contained more than one imaging study from any particular patient. No imaging study used to verify performance was used for training; independence of training and testing data were enforced at the level of the scanning institution, namely, studies sourced from a specific institution were used for either training or testing but could not be used for both. The data used in the device validation ensured diversity in patient population and CT system manufacturer. The data acquired from different CT systems and acquisition condition to reflect the intended use environment and the recommended CT settings. The data includes patients with and without disease.
Performance was verified by comparing segmentations and pneumoperitoneum generated by the machine learning models against segmentations generated by medical professionals and 3D scan data form the same imaging study.
h-Space is divided into Organ. Vessel, and Pneumoperitoneum, and the target performance is described. For Organ Segmentation, the target performance is set DSC 0.920 by referring and literature on Multi-Organ Segmentation.1 According to the literature, using various methods to calculate the DSC results for multiple organ segmentations yielded a maximum average value of 0.918. Therefore, to set a higher target, we referred to relevant papers and established criteria of organ segmentation at 0.920.
For Vessel Segmentation, we set a target performance of DSC 0.890 by referring to the literature.2 This literature describes a framework for vessel segmentation. It reports cases of achieving high DSC on vessel data, making it a valuable resource for establishing benchmarks for vessel segmentation. Among the various structures' vessel segmentation scores, we set the highest value of 0.890 as our criteria.
For Pneumoperitoneum, the target performance was set to MAE ± 1.083mm based on the
- 2 Giles Tetteh, Velizar Efremov, Nis D. Forker, Matthia, Bruno Weber, Claus Zimmer, Marie Piraud and Bjön H. Menze (2020, Dec). DeepVesselNet: Vessel Segmentation, Centerline Detection in 3D Angiographic Volumes. from https://doi.org/10.339/frins.2020.592352
1 Yucheng Tang, Dong Yang, Wenqi Li, Holger Roth, Bennett Landman, Daguang Xu, Vishwesh Nath, Ai Hatamizadel (2022, March). Self-Supervised Pre-Training of Swin Transformers for 3D Medical Image Analysis. arXiv:2111.14791v2 from https://doi.org/10.48550/arXiv.2111.14791
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validation data during the model development process.
The performance of the machine learning models, characterized by the Dice coefficient Scores (DSC) and Mean Absolute Error (MEA), was as follows: Organ 0.927 DSC; Vessel 0.920 DSC; Pneumoperitoneum +/- 0.972 mm;
The accuracy of length measurement features has been validated on phantom data and hu3D data. The type of measurements verified were distances between two points (Ruler function). The measurements produced by RUS were verified to be accurate within a mean difference of +/- 10%.
10. Conclusion:
The subject device is substantially equivalent in the areas of technical characteristics, general function, application, and indications for use. The test results also support the substantial equivalence to the predicate devices. Therefore, we conclude that the subject device described in this submission is substantially equivalent to the predicate device.