(210 days)
Surgical Reality Viewer is a medical imaging visualization software intended to assist trained healthcare professionals with preoperative and intraoperative visualizations, by displaying 2D and 3D renderings of DICOM compliant patient images and normal anatomic segmentations derived from patient images as well as functions for manipulation of segmentations and 3D models.
Surgical Reality Viewer assists the trained healthcare professional who is responsible for making all final patient management decisions.
The machine learning algorithms in use by Surgical Reality Viewer are intended for use on adult patients aged 22 years and over.
Surgical Reality Viewer is medical imaging visualization software that accepts DICOM compliant images (e.g. CT-scans or MR images) and segmentation files in various 3D object file formats (e.g. NifTi, OBJ, MHD, STL, etc.). The device can generate preliminary segmentations of normal anatomy on demand using machine learning and computer vision algorithms. It provides tools for editing and/or creating segmentations using various built-in 2D and 3D image manipulation functions. The software generates a 3D segmented view of the loaded patient data, either on a supported 2D or 3D screen, and offers features such as pre-operative (re)viewing of DICOM data overlaid with segmentation, (intra/post)operative visualization of anatomical structures, 2D-viewing, volume rendering, surface rendering, immersive and interactive 3D-viewing, 2D and 3D measuring of DICOM image data, storing on a local device, anatomic labelling including segmentation tools, and tools for annotations, brushing or carving of anatomical structures. Surgical Reality Viewer runs on a dedicated computer within the customer environment, meeting specific hardware requirements including a Windows operating system (version 10 or higher), GPU (Nvidia GeForce 2070), CPU (Intel i7), 16GB RAM, and at least 100GB free hard drive space.
Here's a breakdown of the acceptance criteria and study details for the Surgical Reality Viewer, based on the provided FDA 510(k) clearance letter and summary:
Acceptance Criteria and Reported Device Performance
The provided document details the performance of the machine learning algorithms for various anatomical segmentations using the Sørensen–Dice coefficient (DSC). Additionally, it describes a qualitative assessment of suitability.
Table of Acceptance Criteria (Implicit) and Reported Device Performance
| Anatomical Structure | Metric (Implicit Acceptance Criteria) | Reported Device Performance |
|---|---|---|
| Lobe segmentation | Average Sørensen–Dice coefficient (DSC) | 0.97 |
| - LUL | DSC | 0.98 |
| - LLL | DSC | 0.98 |
| - RUL | DSC | 0.98 |
| - RLL | DSC | 0.98 |
| - RML | DSC | 0.96 |
| Vessel segmentation | Average Sørensen–Dice coefficient (DSC) | 0.84 |
| - Artery | DSC | 0.84 |
| - Vein | DSC | 0.83 |
| Airway segmentation | Sørensen–Dice coefficient (DSC) | 0.96 |
| Aorta segmentation | Sørensen–Dice coefficient (DSC) | 0.96 |
| Pulmonary segmentation | Average Sørensen–Dice coefficient (DSC) | 0.85 |
| - Left segments | DSC | 0.85 |
| - Right segments | DSC | 0.85 |
| Qualitative Scores (Suitability) | (Score 1-5, higher is better) | Reported Scores: |
| Airways segmentations | Suitability score | 4.8 |
| Artery segmentations | Suitability score | 4.8 |
| Vein segmentations | Suitability score | 4.9 |
| Lobe Segmentations | Suitability score | 5.0 |
| Pulmonary lobe segments | Suitability score | 4.7 |
| Aorta segmentations | Suitability score | 5.0 |
Note on Acceptance Criteria: The document directly presents the performance metrics (DSC and qualitative scores). While explicit numerical acceptance criteria (e.g., "must be >= 0.95 DSC") are not stated, the reported high performance figures implicitly demonstrate the device meets acceptable levels for these metrics.
Study Details
1. Sample Size Used for the Test Set and Data Provenance
- Sample Size: 102 CT images (Each study belonged uniquely to a single patient subject).
- Data Provenance: 60 (n=60) scans were obtained from the United States. The remaining 42 scans' country of origin is not specified, but the document mentions "geographical location" as a subgroup for generalizability.
- Retrospective/Prospective: Not explicitly stated, but the mention of "curated datasets" and "clinical testing dataset" without ongoing patient enrollment suggests a retrospective study.
2. Number of Experts Used to Establish the Ground Truth for the Test Set and Qualifications of Those Experts
- Number of Experts: Not explicitly stated as a specific number. The document mentions "trained professionals" who generated the initial segmentations and "thoracic surgeons with a minimum of 2 years professional working experience" who verified these segmentations. This implies at least two distinct groups of experts were involved, potentially multiple individuals within each group.
- Qualifications of Experts:
- Initial Segmentation Generation: "Trained professionals." (Specific professional background and experience level not detailed).
- Segmentation Verification: "Thoracic surgeons with a minimum of 2 years professional working experience."
3. Adjudication Method (for the Test Set)
- Adjudication Method: Not explicitly stated. The process described is "segmented by trained professionals and the segmentations were verified by thoracic surgeons." This suggests a single ground truth was established after the verification step, but the specific process for resolving discrepancies (e.g., consensus, tie-breaking by a third expert) is not detailed. It does not mention a 2+1 or 3+1 method.
4. If a Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study Was Done
- MRMC Study: No, an MRMC comparative effectiveness study was not explicitly described. The study focuses on the standalone performance of the algorithm against ground truth, and separate qualitative scoring of the suitability of segmentations. There is no mention of comparing human readers with and without AI assistance to determine an "effect size" of improvement.
5. If a Standalone (i.e., algorithm only without human-in-the-loop performance) Was Done
- Standalone Study: Yes, a standalone performance study was done. The "Performance was verified by comparing segmentations generated by the machine learning models against ground truth segmentations generated by trained professionals." This directly assesses the algorithm's performance without a human in the loop for generating the primary segmentation output being evaluated for accuracy.
6. The Type of Ground Truth Used
- Type of Ground Truth: The ground truth for the quantitative analysis (DSC) was established by "expert consensus" (or at least expert-verified segmentations). Specifically, "segmentations generated by trained professionals and the segmentations were verified by thoracic surgeons." For the qualitative assessment, "medical professionals were tasked to qualitatively score the suitability of the segmentations provided through the Viewer," which is also an expert-based evaluation of the AI output.
7. The Sample Size for the Training Set
- Training Set Sample Size: Not explicitly stated. The document mentions "Each of the algorithms has been trained and tuned on curated datasets representative of the intended patient population," but does not provide a specific number for the training set. It only states that a "CT image was either part of the tuning or testing dataset and not in both," indicating that the 102 CT images used for testing were separate from the training/tuning data.
8. How the Ground Truth for the Training Set Was Established
- Training Set Ground Truth: Not explicitly stated. The document mentions "trained and tuned on curated datasets representative of the intended patient population." While not explicitly detailed, it's reasonable to infer that a similar expert-driven process (like the ground truth establishment for the test set) would have been used for creating the ground truth in the training dataset to ensure high-quality training data.
FDA 510(k) Clearance Letter - Surgical Reality Viewer
Page 1
U.S. Food & Drug Administration
10903 New Hampshire Avenue
Silver Spring, MD 20993
www.fda.gov
Doc ID # 04017.08.02
January 29, 2026
MedicalVR B.V.
℅ Leon Doorn
Regulatory Affairs Consultant
MedQAIR Services B.V.
Rooseveltlaan 56-2
Amsterdam, 1078NL
Netherlands
Re: K252091
Trade/Device Name: Surgical Reality Viewer
Regulation Number: 21 CFR 892.2050
Regulation Name: Medical Image Management And Processing System
Regulatory Class: Class II
Product Code: QIH
Dated: December 24, 2025
Received: December 29, 2025
Dear Leon Doorn:
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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K252091 - Leon Doorn Page 2
Additional information about changes that may require a new premarket notification are provided in the FDA guidance documents entitled "Deciding When to Submit a 510(k) for a Change to an Existing Device" (https://www.fda.gov/media/99812/download) and "Deciding When to Submit a 510(k) for a Software Change to an Existing Device" (https://www.fda.gov/media/99785/download).
Your device is also subject to, among other requirements, the Quality System (QS) regulation (21 CFR Part 820), which includes, but is not limited to, 21 CFR 820.30, Design controls; 21 CFR 820.90, Nonconforming product; and 21 CFR 820.100, Corrective and preventive action. Please note that regardless of whether a change requires premarket review, the QS regulation requires device manufacturers to review and approve changes to device design and production (21 CFR 820.30 and 21 CFR 820.70) and document changes and approvals in the device master record (21 CFR 820.181).
Please be advised that FDA's issuance of a substantial equivalence determination does not mean that FDA has made a determination that your device complies with other requirements of the Act or any Federal statutes and regulations administered by other Federal agencies. You must comply with all the Act's requirements, including, but not limited to: registration and listing (21 CFR Part 807); labeling (21 CFR Part 801); medical device reporting (reporting of medical device-related adverse events) (21 CFR Part 803) for devices or postmarketing safety reporting (21 CFR Part 4, Subpart B) for combination products (see https://www.fda.gov/combination-products/guidance-regulatory-information/postmarketing-safety-reporting-combination-products); good manufacturing practice requirements as set forth in the quality systems (QS) regulation (21 CFR Part 820) for devices or current good manufacturing practices (21 CFR Part 4, Subpart A) for combination products; and, if applicable, the electronic product radiation control provisions (Sections 531-542 of the Act); 21 CFR Parts 1000-1050.
All medical devices, including Class I and unclassified devices and combination product device constituent parts are required to be in compliance with the final Unique Device Identification System rule ("UDI Rule"). The UDI Rule requires, among other things, that a device bear a unique device identifier (UDI) on its label and package (21 CFR 801.20(a)) unless an exception or alternative applies (21 CFR 801.20(b)) and that the dates on the device label be formatted in accordance with 21 CFR 801.18. The UDI Rule (21 CFR 830.300(a) and 830.320(b)) also requires that certain information be submitted to the Global Unique Device Identification Database (GUDID) (21 CFR Part 830 Subpart E). For additional information on these requirements, please see the UDI System webpage at https://www.fda.gov/medical-devices/device-advice-comprehensive-regulatory-assistance/unique-device-identification-system-udi-system.
Also, please note the regulation entitled, "Misbranding by reference to premarket notification" (21 CFR 807.97). For questions regarding the reporting of adverse events under the MDR regulation (21 CFR Part 803), please go to https://www.fda.gov/medical-devices/medical-device-safety/medical-device-reporting-mdr-how-report-medical-device-problems.
For comprehensive regulatory information about medical devices and radiation-emitting products, including information about labeling regulations, please see Device Advice (https://www.fda.gov/medical-devices/device-advice-comprehensive-regulatory-assistance) and CDRH Learn (https://www.fda.gov/training-and-continuing-education/cdrh-learn). Additionally, you may contact the Division of Industry and Consumer Education (DICE) to ask a question about a specific regulatory topic. See the DICE website (https://www.fda.gov/medical-devices/device-advice-comprehensive-regulatory-
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K252091 - Leon Doorn Page 3
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, PhD
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
Please type in the marketing application/submission number, if it is known. This textbox will be left blank for original applications/submissions. K252091
Please provide the device trade name(s). Surgical Reality Viewer
Please provide your Indications for Use below.
Surgical Reality Viewer is a medical imaging visualization software intended to assist trained healthcare professionals with preoperative and intraoperative visualizations, by displaying 2D and 3D renderings of DICOM compliant patient images and normal anatomic segmentations derived from patient images as well as functions for manipulation of segmentations and 3D models.
Surgical Reality Viewer assists the trained healthcare professional who is responsible for making all final patient management decisions.
The machine learning algorithms in use by Surgical Reality Viewer are intended for use on adult patients aged 22 years and over.
Please select the types of uses (select one or both, as applicable).
☑ Prescription Use (Part 21 CFR 801 Subpart D)
☐ Over-The-Counter Use (21 CFR 801 Subpart C)
SurgicalReality Viewer Page 9 of 31
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510(k) Summary
Title: 510(k) Summary
Revision: 2.0
1 of 6
1 Company details
510(k) submission - Surgical Reality Viewer
| Field | Value |
|---|---|
| Submission date | 30.06.2025 |
| Type of 510(k) Submission | Traditional |
| 510(k) Number | K252091 |
Submitter details
| Field | Value |
|---|---|
| Company Name | MedicalVR B.V. |
| Company Address | Markkaweg 22153 NB Nieuw VennepThe Netherlands |
Contact person
| Field | Value |
|---|---|
| Company contact | Chris Hordijk |
| Title | CEO |
| chris@surgicalreality.com | |
| Cell | +31 6 52 29 51 00 |
Submission correspondent
| Field | Value |
|---|---|
| Submission correspondent | L.J. Doorn |
| leondoorn@medqair.com | |
| Cell | +31 61 10 66 42 1 |
2 Device details
2.1 Device identification
| Field | Value |
|---|---|
| Common name | Surgical Reality Viewer |
| Proprietary / Trade name | Surgical Reality Viewer |
| Classification name | Automated Radiological Image Processing Software |
| Regulation number | 892.2050 |
| Product code | QIH |
| Device class | II |
| Review panel | Radiology |
| Special controls | None |
| 510(k) number | K252091 |
2.2 Predicate identification
| Field | Value |
|---|---|
| Trade name | Ceevra Reveal 3 |
| Classification name | Automated Radiological Image Processing Software |
| Regulation number | 892.2050 |
| Product code | QIH |
| Device class | II |
| Review panel | Radiology |
| Submitter / 510(k) holder | Ceevra, Inc |
| 510(k) number | K222676 |
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2.3 Indications for use
Surgical Reality Viewer is a medical imaging visualization software intended to assist trained healthcare professionals with preoperative and intraoperative visualizations, by displaying 2D and 3D renderings of DICOM compliant patient images and normal anatomic segmentations derived from patient images as well as functions for manipulation of segmentations and 3D models.
Surgical Reality Viewer assists the trained healthcare professional who is responsible for making all final patient management decisions.
The machine learning algorithms in use by Surgical Reality Viewer are intended for use on adult patients aged 22 years and over.
2.4 Intended use
Surgical Reality Viewer accepts DICOM compliant images from CT or MRI scanners and segmentation files in various 3D object file formats (NifTi, OBJ, MHD, etc.).
Surgical Reality Viewer can generate preliminary segmentations of normal anatomy on demand using machine learning and computer vision algorithms. Segmentations can be edited and / or created using the various built-in 2D and 3D image manipulation functions.
The software is designed to be used by trained healthcare professionals (e.g. physicians, nurses or technicians) and is intended to assist the clinician who is ultimately responsible for making all final patient management decisions.
2.5 Equivalence
Equivalence is claimed against Ceevra Reveal 3 (K222676) which also comprises a medical image viewer device, including segmentation features driven by Artificial Intelligence algorithms.
The device indications for use, intended use and technological characteristics are considered equivalent, and therefore the performance of Ceevra Reveal 3 demonstrates safety and performance for the Surgical Reality Viewer.
Ceevra Reveal 3 is, as Surgical Reality Viewer regulated under the 'QIH' Product Code and regulated per CFR 892.2050.
3 Device description
3.1 Input
Surgical Reality Viewer accepts DICOM compliant images (e.g. CT-scans or MR images) and segmentation files in various 3D object file formats (e.g. Nifti, OBJ, MHD, STL, etc.).
The DICOM files need to be manually acquired by the user from the PACS to a local storage location from where they can be loaded by Surgical Reality Viewer.
Optionally, the DICOM files can be acquired from a service node – identified by a unique network port - that runs on the local area network and can be connected with using a secure connection.
3.2 Output
The software generates a 3D segmented view of the loaded patient data, either on a supported 2D or 3D screen. The user interface of Surgical Reality Viewer will provide tools to the user such as: pre-
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operative (re)viewing of DICOM data, overlaid with segmentation, (intra/post)operative visualization of anatomical structures, 2D-viewing, volume rendering, surface rendering, immersive and interactive 3D-viewing, 2D and 3D measuring of DICOM image data, storing on a local device and anatomic labelling including segmentation tools, tools for annotations, brushing or carving (removing) of anatomical structures
3.3 Operating principle
Surgical Reality Viewer runs entirely within the customer environment, on a dedicated computer assigned by the customer. Surgical Reality Viewer allows the user to manually obtain DICOM files from within the customer system or from a specific location on the local area network. Within Surgical Reality Viewer, the user selects the relevant scan series from the Surgical Reality Viewer workspace, the software opens the scan series in the connected viewing screen to enable the healthcare professional to use and interact with the scan data.
Surgical Reality Viewer must be installed on a system that meets specific requirements.
3.4 Hardware requirements
Surgical Reality Viewer can be installed on any computer system that runs the Windows operating system, version 10 or higher. Additionally, the following minimum hardware requirements are specified:
- GPU: Nvidia GeForce 2070
- CPU: Intel i7
- Memory (RAM): 16GB
- Hard drive: at least 100GB free space.
3.5 Software
Surgical Reality Viewer is able to read and write image files in the following file formats:
- DICOM files containing pixel data in JPEG format;
- NifTi files, containing segmentation voxel data;
- MHD files, also containing segmentation voxel data;
- OBJ files, which contain surface meshes;
4 Predicate comparison
4.1 Indications for use
In the following overview the comparison between the subject device's indications for use and the predicate's indications for use is provided. Any differences between the subject device and the predicate device are highlighted.
Table 1: Comparison of the Indications for Use between the subject device (Surgical Reality Viewer) and Ceevra Reveal 3
| Subject DeviceSurgical Reality Viewer(K252091) | PredicateCeevra Reveal 3(K222676) |
|---|---|
| Indications for use / Intended UseSurgical Reality Viewer is a medical imaging visualization software intended to assist trained | Indications for use / Intended UseCeevra Reveal 3 is intended as a medical imaging system that allows the processing, |
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| Subject DeviceSurgical Reality Viewer(K252091) | PredicateCeevra Reveal 3(K222676) |
|---|---|
| healthcare professionals with preoperative and intraoperative visualizations, by displaying 2D and 3D renderings of DICOM compliant patient images and normal anatomic segmentations derived from patient images as well as functions for manipulation of segmentations and 3D models.Surgical Reality Viewer assists the trained healthcare professional who is responsible for making all final patient management decisions.The machine learning algorithms in use by Surgical Reality Viewer are intended for use on adult patients aged 22 years and over. | review, analysis, communication and media interchange of multi-dimensional digital images acquired from CT or MR imaging devices and that such processing may include the generation of preliminary segmentations of normal anatomy using software that employs machine learning and other computer vision algorithms.It is also intended as software for preoperative surgical planning, and as software for the intraoperative display of the aforementioned multi-dimensional digital images. Ceevra Reveal 3 is designed for use by healthcare professionals and is intended to assist the clinician who is responsible for making all final patient management decisions.The machine learning algorithms in use by Ceevra Reveal 3 are for use only for adult patients (22 and over). Three-dimensional images for patients under the age of 22 or of unknown age will be generated without the use of any machine learning algorithms. |
The subject device and the predicate device are indicated to view medical images pre- and intraoperatively acquired by CT or MRI, generate segmentations of supported normal anatomical structures, and indicated for use by the same user, and on the same patient group, with a view to assist the clinician.
4.2 Technological characteristics
In the following Table 2, the technological characteristics of the subject device (Surgical Reality Viewer) are compared against the technological characteristics of the predicate device (Ceevra Reveal 3). Any differences between the two devices are evaluated for their potential implications with regards to the demonstration of Safety and or Performance.
Table 2: Comparison of Technological Characteristics between the Subject device (Surgical Reality Viewer) and the Predicate Device (Ceevra Reveal 3)
| Technological characteristic | Subject DeviceSurgical Reality Viewer(K252091) | PredicateCeevra Reveal 3(K222676) |
|---|---|---|
| Product Code | QIH | QIH |
| Product Regulation | 892.2050 | 892.2050 |
| Supported Imaging Modalities | CT & MRI | CT & MRI |
| Intended Users | Healthcare Professionals | Healthcare Professionals |
| Image Analysis Features | Interactive manipulation and 3D visualization | Interactive manipulation and 3D visualization |
| Pre-operative viewing of 3D images | Yes | Yes |
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| Technological characteristic | Subject DeviceSurgical Reality Viewer(K252091) | PredicateCeevra Reveal 3(K222676) |
|---|---|---|
| Intra-operative viewing of 3D images | Yes | Yes |
| Real-time inter-operative guidance, navigation or otherwise integrated with surgical instruments | No | No |
| Segmentation work performed by | Medical Professional | Internal Operator |
| Built-in features for end-user to compare CT/MRI to device output | No | No |
| Quantitative outputs calculated by device | No | No |
| Semi-automatic segmentation of abnormal anatomy | No | No |
| Semi-automatic segmentation of supported normal anatomy | Yes | Yes |
The subject device includes the exact same technological characteristics as the predicate device. There are no concerns identified by MedicalVR with regards to the features and functionalities of the device which require additional Safety and or Performance data.
As the device is supported by Artificial Intelligence to prepopulate semi-automatic segmentations of supported anatomy structures, specific bench tests are executed to demonstrate in verification testing:
- Segmentation accuracy
- Generalizability (across different sub-groups)
In addition thereto, through user validation testing, the ease of use of the software and the segmentations generated by the use of the subject device (Surgical Reality Viewer) will be demonstrated.
4.3 Performance data
4.3.1 Software verification and validation
Surgical Reality Viewer is developed in line with the IEC 62304/2006/Amd 1: 2015 standard on 'Medical device software - Software life cycle processes' in addition to application of the supporting FDA guidance documents on premarket submissions for software, the IEC 81001-5-1 on 'Health software and health IT systems safety, effectiveness and security - Part 5-1: Security — Activities in the product life cycle' and the FDA guidance regarding cybersecurity on quality system considerations and content of premarket submissions.
4.3.2 Machine learning segmentation
Surgical Reality Viewer includes multiple machine learning algorithms which support the segmentation of anatomical structures within CT chest images for 3D visualization. Each of the algorithms has been trained and tuned on curated datasets representative of the intended patient population.
Surgical Reality Viewer's performance has been tested on a clinical testing dataset with a total of 102 CT images. A CT image was either part of the tuning or testing dataset and not in both. Each study belonged uniquely to a single patient subject. Each study has been segmented by trained professionals and the segmentations were verified by thoracic surgeons with a minimum of 2 years professional working experience. In total 60 (n=60) scans were obtained from the United States. Representative numbers in subgroups were included to ensure proper assessment of performance
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amongst patient characteristic subgroups (e.g. geographical location, age, sex, ethnicity,) and imaging characteristic subgroups (e.g. CT scanner manufacturer, contrast vs non-contract enhanced, CT slice-thickness) and acquisition timeframes.
In total 53% of the participants where the age was reported were aged under 60 years of age, 25% between 60 and 70 years of age and 22% over the age of 70. Of 38% of the participants, age was unknown. From the participants for which gender was reported (n=63) 51% were male and 49% were female. Scanner manufacturers included Siemens, GE Healthcare, Philips, Canon and Toshiba. Ethnicity of patients in the datasets was reasonably correlated to the overall US population.
Performance was verified by comparing segmentations generated by the machine learning models against ground truth segmentations generated by trained professionals.
The performance of the machine learning models, characterized by the Sørensen–Dice coefficient (DSC) Conclusion. Lobe segmentation accuracy resulted in an average DICE of 0.97 (LUL: 0.98, LLL: 0.98, RUL: 0.98, RLL: 0.98, RML: 0.96), Vessel segmentation accuracy resulted in an average DICE of 0.84 (Artery: 0.84, Vein: 0.83), Airway segmentation accuracy resulted in an average DICE of 0.96, Aorta segmentation resulted in an accuracy of 0.96, Pulmonary segmentation accuracy resulted in an average DICE 0.85 (left segments: 0.85, right segments: 0.85).
In addition, to verify the usability and clinical usefulness of the segmentations generated by Surgical Reality Viewer medical professionals were tasked to qualitatively score the suitability of the segmentations provided through the Viewer. The score ranged from 1 (minimum) to 5 (maximum) Airways segmentations scored 4.8, artery segmentations scored 4.8, vein segmentations scored 4.9, lobe Segmentations scored 5.0, pulmonary lobe segments scored with 4.7 and aorta segmentations scored 5.0.
5 Conclusion
Surgical Reality Viewer is deemed to be substantially equivalent to its predicate device based on indications for use, technological characteristics and performance testing.
The Surgical Reality Viewer does not raise new questions related to safety or effectiveness.
6 Version History
Version control is handled electronically. In this chapter, only a summary between the different versions is provided to help the reader easily identify the differences between versions.
| Version | Summary of change |
|---|---|
| 2.0 | Updated to reflect the required changes for the 510(k) summary |
| 1.0 | Initial version. |
§ 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).