Quicktome is intended for display of medical images and other healthcare data. It includes functions for image review, image manipulation, basic measurements, planning, and 3D visualization (MPR reconstructions and 3D volume rendering). Modules are available for image processing and atlas-assisted visualization and segmentation, where an output can be generated for use by a system capable of reading DICOM image sets.

Quicktome is indicated for use in the processing of diffusion-weighted MRI sequences into 3D maps that represent white-matter tracts based on constrained spherical deconvolution methods.

Typical users of Quicktome are medical professionals, including but not limited to surgeons and radiologists.

Device Description

Quicktome is a software-only, cloud-deployed, image processing package which can be used to perform DICOM image viewing, image processing, and analysis.

Quicktome can retrieve DICOM images from picture archiving and communication systems (PACS), acquired with MRI, including Diffusion Weighted Imaging (DWI) sequences, T1, T2, and FLAIR images. Once retrieved, Quicktome removes protected health information (PHI) and links the dataset to an encryption key, which is then used to relink the data back to the patient when the data is exported to the hospital PACS. Processing is performed on the anonymized dataset in the cloud. Clinicians are served the processed output for planning and visualization on their local machine.

The software provides a workflow for a clinician to:

Select a patient case for planning and visualization,
Confirm image quality,
Explore anatomical regions, network templates, tractography bundles, and parcellations,
Create and edit regions of interest, and
Export objects of interest in DICOM format for use in systems that can view DICOM images.

AI/ML Overview

The provided document is a 510(k) summary for the Quicktome device. It outlines the regulatory clearance process and describes the device's intended use and performance validation. However, it does not contain specific acceptance criteria tables nor detailed results of a study proving the device meets those criteria.

The document broadly mentions performance and comparison validations were performed. It states:

"Performance and comparison validations were performed to show equivalence of generated tractography and atlas method."
"Evaluations included protocols to demonstrate performance and equivalence of tractography bundle and anatomical region generation (including acceptable co-registration of bundles and regions with underlying anatomical scans), and evaluation of the algorithm's performance in slice motion filtration and skull stripping."

Without specific acceptance criteria and detailed study results from the provided text, I cannot fill out the requested table or fully describe the study in the detail you've asked for points 1-9.

If the information were available, here's how I would structure the answer based on the typical requirements for such a study:

Based on the provided 510(k) summary for Quicktome (K203518), the document states that performance and comparison validations were conducted. However, it does not explicitly detail the specific acceptance criteria, nor does it provide a table of reported device performance against those criteria. Therefore, the following sections will indicate where information is not provided in the given text.

1. A table of acceptance criteria and the reported device performance

Acceptance Criteria Category	Specific Acceptance Criterion (Not Provided in document)	Reported Device Performance (Not Provided in document)
Tractography Bundle Generation	(e.g., Accuracy of tract reconstruction)	(e.g., Quantitative metrics like Dice similarity, mean distance)
Anatomical Region Generation	(e.g., Accuracy of segmentation)	(e.g., Quantitative metrics like Dice similarity, boundary distance)
Co-registration with Anatomical Scans	(e.g., Alignment accuracy)	(e.g., Quantitative metrics like registration error)
Slice Motion Filtration Performance	(e.g., Effectiveness in reducing motion artifacts)	(No specific metrics provided)
Skull Stripping Performance	(e.g., Accuracy of skull removal)	(No specific metrics provided)
Equivalence to Predicate Device	(Specific metrics for equivalence)	(General statement of equivalence)
Usability	(e.g., User satisfaction, task completion rate)	(Summative usability evaluation performed)

2. Sample sized used for the test set and the data provenance

Test Set Sample Size: Not provided. The document states, "Performance and comparison evaluations were performed by representative users on a dataset not used for development composed of normal and abnormal brains." The specific number of cases or subjects in this dataset is not mentioned.
Data Provenance: The document does not explicitly state the country of origin. It indicates the dataset included "normal and abnormal brains" and was "not used for development." It does not specify if the data was retrospective or prospective.

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

Number of Experts: Not provided. The document states, "Performance and comparison evaluations were performed by representative users." It does not specify how many, if any, specific experts established ground truth, or if ground truth was established by automated means (e.g., via algorithm from the predicate).
Qualifications of Experts: Not provided. The document refers to "representative users" but does not detail their professional qualifications (e.g., radiologist, surgeon, years of experience, subspecialty).

4. Adjudication method (e.g. 2+1, 3+1, none) for the test set

Adjudication Method: Not provided. The document does not describe any specific adjudication process for establishing ground truth or resolving discrepancies in the test set evaluations.

5. If a multi reader multi case (MRMC) comparative effectiveness study was done, If so, what was the effect size of how much human readers improve with AI vs without AI assistance

MRMC Study Conducted: The document mentions "Adjudication of Results for studies conducted per representative users" but does not explicitly state that it was a multi-reader, multi-case (MRMC) comparative effectiveness study designed to show human reader improvement with AI assistance.
Effect Size of Human Reader Improvement: Not provided. The document focuses on the device's standalone performance and comparison/equivalence to a predicate device, rather than the performance of human readers assisted by Quicktome versus unassisted.

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

Standalone Performance: Yes, implicitly. The performance and comparison validations, including evaluation of "tractography bundle and anatomical region generation," "co-registration," "slice motion filtration," and "skull stripping," indicate an assessment of the algorithm's output independently, even if "representative users" were involved in judging that output. The device itself is software for processing images.

7. The type of ground truth used (expert consensus, pathology, outcomes data, etc)

Type of Ground Truth: Not explicitly detailed. The document states "performance and equivalence of tractography bundle and anatomical region generation" were evaluated. This implies a reference or ground truth was used for comparison. Given the context, it's highly likely that ground truth for tractography and anatomical regions would be derived either from:
- Expert Consensus/Manual Delineation: Experts manually segmenting or defining tracts/regions.
- Validated Reference Software/Algorithm: Using output from an established, highly accurate (perhaps manually curated) system or the predicate device as a "ground truth" for comparison.
- The document implies equivalence to the predicate device was a key benchmark, suggesting its outputs played a role in the "ground truth" for comparison.

8. The sample size for the training set

Training Set Sample Size: Not provided. The document mentions the test set was "not used for development," implying a separate training/development set existed, but its size is not specified.

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

Training Set Ground Truth Establishment: Not provided. The document does not detail how ground truth was established for any data used during the development or training phase of the algorithm.

Summary

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Omniscient Neurotechnology Pty Ltd (08t) % Arie Henkin Head, QA/RA Level 10, 580 George Street Sydney, NSW 2000 AUSTRALIA

Re: K203518

Trade/Device Name: Quicktome Regulation Number: 21 CFR 892.2050 Regulation Name: Picture archiving and ommunications system Regulatory Class: Class II Product Code: LLZ Dated: February 11, 2021 Received: February 16, 2021

Dear Arie Henkin:

We have reviewed your Section 510(k) premarket notification of intent to market the device referenced above and have determined the device is substantially equivalent (for the indications for use stated in the enclosure) to legally marketed predicate devices marketed in interstate commerce prior to May 28, 1976, the enactment date of the Medical Device Amendments, or to devices that have been reclassified in accordance with the provisions of the Federal Food, Drug, and Cosmetic Act (Act) that do not require approval of a premarket approval application (PMA). You may, therefore, market the device, subject to the general controls provisions of the Act. Although this letter refers to your product as a device, please be aware that some cleared products may instead be combination products. The 510(k) Premarket Notification Database located at https://www.accessdata.fda.gov/scripts/cdrh/cfdocs/cfpmn/pmn.cfm identifies combination product submissions. The general controls provisions of the Act include requirements for annual registration, listing of devices, good manufacturing practice, labeling, and prohibitions against misbranding and adulteration. Please note: CDRH does not evaluate information related to contract liability warranties. We remind you, however, that device labeling must be truthful and not misleading.

If your device is classified (see above) into either class II (Special Controls) or class III (PMA), it may be subject to additional controls. Existing major regulations affecting your device can be found in the Code of Federal Regulations, Title 21, Parts 800 to 898. In addition, FDA may publish further announcements concerning your device in the Federal Register.

Please be advised that FDA's issuance of a substantial equivalence determination does not mean that FDA has made a determination that your device complies with other requirements of the Act or any Federal statutes and regulations administered by other Federal agencies. You must comply with all the Act's requirements, including, but not limited to: registration and listing (21 CFR Part 807); labeling (21 CFR Part 801); medical device reporting of medical device-related adverse events) (21 CFR 803) for

March 9, 2021

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devices or postmarketing safety reporting (21 CFR 4, Subpart B) for combination products (see https://www.fda.gov/combination-products/guidance-regulatory-information/postmarketing-safety-reportingcombination-products); good manufacturing practice requirements as set forth in the quality systems (QS) regulation (21 CFR Part 820) for devices or current good manufacturing practices (21 CFR 4, Subpart A) for combination products; and, if applicable, the electronic product radiation control provisions (Sections 531-542 of the Act); 21 CFR 1000-1050.

Also, please note the regulation entitled, "Misbranding by reference to premarket notification" (21 CFR Part 807.97). For questions regarding the reporting of adverse events under the MDR regulation (21 CFR Part 803), please go to https://www.fda.gov/medical-device-safety/medical-device-reportingmdr-how-report-medical-device-problems.

For comprehensive regulatory information about mediation-emitting products, including information about labeling regulations, please see Device Advice (https://www.fda.gov/medicaldevices/device-advice-comprehensive-regulatory-assistance) and CDRH Learn (https://www.fda.gov/training-and-continuing-education/cdrh-learn). Additionally, you may contact the Division of Industry and Consumer Education (DICE) to ask a question about a specific regulatory topic. See the DICE website (https://www.fda.gov/medical-device-advice-comprehensive-regulatoryassistance/contact-us-division-industry-and-consumer-education-dice) for more information or contact DICE by email (DICE@fda.hhs.gov) or phone (1-800-638-2041 or 301-796-7100).

Sincerely.

For

Thalia T. Mills, Ph.D. Director Division of Radiological Health OHT7: Office of In Vitro Diagnostics and 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) K203518

Device Name Quicktome

Indications for Use (Describe)

Quicktome is indicated for use in the processing of diffusion-weighted MRI sequences into 3D maps that represent whitematter tracts based on constrained spherical deconvolution methods.

Typical users of Quicktome are medical professionals, including but not limited to surgeons and radiologists.

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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Image /page/3/Picture/0 description: The image shows the logo for Omniscient Neurotechnology. The logo features the letters "ost" in a stylized font, with the "o" and "s" overlapping and colored in shades of blue and orange. To the right of the letters, the words "OMNISCIENT" and "NEUROTECHNOLOGY" are written in a smaller, sans-serif font. The word "OMNISCIENT" is above the word "NEUROTECHNOLOGY".

5.0 510(k) Summary

5.1 Submitter

Omniscient Neurotechnology Pty Ltd (08t) Level 10, 580 George Street Sydney, NSW 2000 Australia

Contact Person: Arie Henkin

Date Prepared: February 11, 2021

5.2 Device

Name of Device:	Quicktome
Common or Usual Name: Neurosurgical Planning and Visualization Software
Classification:	Picture archiving and communications system (21 CFR 892.2050)
Regulatory Class:	Class II
Product Code:	LLZ

5.3 Predicate Device

StealthViz Advanced Planning Application with StealthDTI Package, K081512

This Predicate has not been subject to a design-related recall.

Reference Device: iPlan Cranial, K113732

5.4 Device Description

Quicktome is a software-only, cloud-deployed, imaqe processing package which can be used to perform DICOM image viewing, image processing, and analysis.

The software provides a workflow for a clinician to:

. Select a patient case for planning and visualization,
Confirm image quality, ●
. Explore anatomical regions, network templates, tractography bundles, and parcellations,
Create and edit regions of interest, and
Export objects of interest in DICOM format for use in systems that can view DICOM ● images.

K203518

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Image /page/4/Picture/0 description: The image contains the logo for Omniscient Neurotechnology. The logo consists of the letters "o&t" in a stylized font, with the "o" in blue and the "&" and "t" in red. To the right of the letters is the company name, "OMNISCIENT" in all caps, with "NEUROTECHNOLOGY" in a smaller font below it. The logo is simple and modern, and the colors are eye-catching.

5.5 Indications for Use

Quicktome is indicated for use in the processing of diffusion-weighted MRI sequences into 3D maps that represent white-matter tracts based on constrained spherical deconvolution methods.

Typical users of Quicktome are medical professionals, including but not limited to surgeons and radiologists.

5.6 Comparison of Technological Characteristics with the Predicate Device

In terms of core functionality, technology, and performance, both Subject and Predicate:

Allow import and export of DICOM images to a hospital PACS. ●
Contain a graphical user interface to conduct planning and visualization. ●
. Display MRI anatomical images, as well as tractography constructed from Diffusion Weighted Images, in 2D and 3D views.
. Register tractography and an atlas to the underlying anatomical images.
. Allow adding, removing, and editing of objects (including automatically segmented and manually defined regions of interest).
Are delivered as software on an off-the-shelf hardware platform.

The differences between the Predicate and the Subject device are:

. The delivery platform: StealthViz is a Software-as-Medical-Device loaded onto off-theshelf hardware, such that all processing occurs on the local machine, while the subject device performs processing in the cloud.
. The algorithm for modeling diffusion data and generating tractography: StealthViz generates ROI-based DTI tractography while the subject device generates whole-brain CSD tractography.
. The atlas model: StealthViz uses the Schaltenbrand-Wahren Atlas while the subject device uses the Glasser Atlas.

All verification and validation activities required per the verification and validation plan were performed using cloud-based deployment of the software in production-equivalent state.

Performance and comparison validations were performed to show equivalence of generated tractography and atlas method.

Comparisons to the Predicate device in conjunction with design verification and validation activities described in the 510(k) submission support substantial equivalence of Quicktome.

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Image /page/5/Picture/0 description: The image shows the logo for Omniscient Neurotechnology. The logo features the letters "o&t" in a stylized font, with the "o" in blue and the "t" in red. To the right of the letters is the company name, "OMNISCIENT NEUROTECHNOLOGY", in a smaller, sans-serif font. The word "OMNISCIENT" is in a larger font than "NEUROTECHNOLOGY".

5.7 Performance Data

Software verification and validation testing were conducted and documentation was provided as recommended by the Guidance for Industry and FDA Staff Guidance for the Content of Premarket Submissions for Software Contained in Medical Devices (May 11, 2005).

The software was developed in compliance with the requirements of IEC 62304, IEC 62366, ISO 14971, and NEMA PS 3.1-3.20.

Software Verification

Testing was conducted on software units and modules. System verification was performed to confirm implementation of functional requirements. Cloud infrastructure verification was performed to ensure suitability of cloud components and services.

Software Validation

Summative usability evaluation and design validation were performed by representative users. Performance and comparison evaluations were performed by representative users on a dataset not used for development composed of normal and abnormal brains. Evaluations included protocols to demonstrate performance and equivalence of tractography bundle and anatomical region generation (including acceptable co-registration of bundles and regions with underlying anatomical scans), and evaluation of the algorithm's performance in slice motion filtration and skull stripping. A literature review was conducted for the clinical suitability and appropriate display of network templates.

5.8 Conclusion

The design verifications conducted support the conclusion that Quicktome performs as intended in the specified use conditions. The design validation, along with comparison and performance validation studies, support the conclusion that Quicktome performs comparably to the predicate device that is currently marketed for the same intended use.

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).