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510(k) Data Aggregation
(299 days)
Omniscient Neurotechnology Pty Ltd (O8t)
The Quicktome Software Suite is composed of a set of modules intended for display of medical images and other healthcare data. It includes functions for image review, image manipulation, basic measurements, planning, 3D visualization (MPR reconstructions and 3D volume rendering) and display of BOLD (blood oxygen level dependent) resting-state MRI scan studies.
Modules are available for image processing, atlas-assisted visualization, resting state analysis and visualization, and target export creation, 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 whitematter tracts based on constrained spherical deconvolution methods and for the use of said maps to select and create exports. Quicktome can generate motor, language, and vision resting state fMRI correlation maps using task-analogous seeds.
Typical users of Quicktome are medical professionals, including but not limited to surgeons, clinicians, and radiologists.
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 receive ("import") DICOM images from picture archiving and communication systems (PACS), acquired with MRI, including Diffusion Weighted Imaging (DWI) sequences, T1, T2, BOLD, and FLAIR images. Quicktome can also receive Resting State functional MRI (rs-fMRI) blood-oxygen-level-dependent (BOLD) datasets. Once received, 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 hospital PACS or other DICOM device.
The software provides a workflow for a clinician to:
- . Select an image for planning and visualization,
- Validate image quality,
- Explore the available anatomical regions, network templates, tractography bundles, and ● parcellations,
- . Select regions of interest,
- . Display resting state fMRI (BOLD) correlation maps using task-analogous seeds for Motor, Vision and Language networks, and
- . Export black and white and color DICOMs for use in systems that can view DICOM images.
The provided text describes the Quicktome Software Suite (K222359), a medical image management and processing system, and its performance evaluation for FDA 510(k) clearance.
Here's a breakdown of the acceptance criteria and study proving the device meets them:
1. Table of Acceptance Criteria and Reported Device Performance
The document doesn't provide a precise, quantified table of acceptance criteria with corresponding performance metrics in a single, clear format. However, it implicitly states the key performance evaluation for the BOLD processing pipeline, which is a significant new feature of this version of the Quicktome Software Suite.
The primary acceptance criteria for the BOLD processing pipeline appears to be the comparability of resting-state fMRI correlation maps generated by Quicktome to task-based fMRI activation maps for a range of pre-specified seeds.
| Acceptance Criteria (Implied) | Reported Device Performance (as stated in the document) |
|---|---|
| Resting-state fMRI correlation maps generated by Quicktome are analytically comparable to task-based fMRI activation maps. | "Analytical evaluation demonstrated that activation in a task-based activation map is represented within the bounds of a correlation map generated with resting-state data when using a range of pre-specified seeds and thresholds, supporting substantial equivalence of the two maps." |
| Clinicians rate the Quicktome-generated resting-state networks as comparable to task-based fMRI maps for clinical intended uses. | "Clinicians rated the networks as comparable per the pre-specified acceptance criteria to task-based fMRI maps for the clinical intended uses of presurgical planning and post-surgical assessment." |
| Software units and modules function as required. | "Testing was conducted on software units and modules. System verification was performed to confirm implementation of functional requirements." |
| Cloud infrastructure is suitable. | "Cloud infrastructure verification was performed to ensure suitability of cloud components and services." |
| Algorithm computations are sound. | "Algorithm performance verification was conducted to ensure computations were sound." |
| Usability and design are validated by representative users. | "Summative usability evaluation and design validation were performed by representative users." |
| BOLD processing pipeline protocols (motion/noise correction, skull stripping, coregistration, noise correction, correlation computation) perform correctly. | "Performance evaluations were conducted for the BOLD processing pipeline. Evaluations included protocols for motion and noise correction, skull stripping, co-registration of anatomical scans and BOLD series, physiological noise correction, and correlation matrix computation." (No explicit pass/fail |
| rates or metrics are provided here beyond the statement that evaluations were conducted for the specified protocols.) |
2. Sample Size Used for the Test Set and Data Provenance
The document does not explicitly state the sample size (number of cases/patients) used for the test set. It mentions "a range of pre-specified seeds and thresholds" for the analytical evaluation and "the networks" for the clinician evaluation, implying multiple cases, but no specific count.
The data provenance (country of origin, retrospective/prospective) for the test set is not specified in the provided text.
3. Number of Experts and Qualifications for Ground Truth
The document states "expert clinician evaluation" and "Clinicians rated the networks," but it does not specify the number of experts used or their specific qualifications (e.g., "radiologist with 10 years of experience").
4. Adjudication Method for the Test Set
The document does not specify an adjudication method (e.g., 2+1, 3+1, none) for the test set's ground truth or clinician evaluation.
5. Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study
The text mentions "expert clinician evaluation" where "Clinicians rated the networks as comparable...to task-based fMRI maps for the clinical intended uses of presurgical planning and post-surgical assessment." This suggests a human-in-the-loop component. However, it does not explicitly describe a traditional MRMC comparative effectiveness study designed to quantify how much human readers improve with AI vs. without AI assistance, nor does it provide an effect size for such improvement. The evaluation focuses on the comparability of the Quicktome-generated maps to established task-based fMRI maps, rather than improvement in human reader performance.
6. Standalone (Algorithm Only) Performance
Yes, a standalone performance evaluation was implicitly done. The "Analytical evaluation" compared the AI-generated resting-state correlation maps to task-based activation maps. This part of the evaluation assesses the algorithm's output directly without human intervention to rate "substantial equivalence."
7. Type of Ground Truth Used
The ground truth used for evaluating the BOLD processing pipeline was task-based fMRI activation maps. These are generally considered a well-established method for localizing brain function.
- Analytical Ground Truth: Task-based fMRI activation maps for direct comparison of spatial patterns and activation.
- Expert Consensus Ground Truth (for clinical relevance): The clinical intended uses for presurgical planning and post-surgical assessment, based on expert opinions validating the comparability of the Quicktome output to established methods.
8. Sample Size for the Training Set
The document does not specify the sample size used for the training set.
9. How Ground Truth for the Training Set Was Established
The document does not describe how the ground truth for the training set was established. It focuses on the validation of the device's performance post-development.
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(98 days)
Omniscient Neurotechnology Pty Ltd (o8t)
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.
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.
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.
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