Search Results

Neurovascular Insight V1.0 is an optional user interface for use on a compatible technical integration environment and designed to be used by trained professionals with medical imaging education including, but not limited to, physicians. Neurovascular Insight V1.0 is intended to:

• Display and, if necessary, export neurological DICOM series and outputs provided by compatible processing docker applications, through the technical integration environment.
• Allow the user to edit and modify parameters that are optional inputs of aforementioned applications. These modified parameters are provided by the technical integration environment as inputs to the docker application to reprocess the outputs. When available, Neurovascular Insight V1.0 display can be updated with the reprocessed outputs.
• If requested by an application, allow the user to confirm information before displaying associated outputs and export them.

The device does not alter the original image information and is not intended to be used as a diagnostic device. The outputs of each compatible application must be interpreted by the predefined intended users, as specified in the application's own labeling. Moreover, the information displayed is intended to be used in conjunction with other patient information and based on professional judgment, to assist the clinician in the medical imaging assessment. It is not intended to be used in lieu of the standard care imaging.

Trained professionals are responsible for viewing the full set of native images per the standard of care.

Neurovascular Insight V1.0 is an optional user interface for use on a compatible technical integration environment and designed to be used by trained professionals with medical imaging education including, but not limited to, physicians and medical technicians.

It is worth noting that Neurovascular Insight V1.0 is an evolution of the FDA cleared medical device Olea S.I.A. Neurovascular V1.0 (K223532).

Neurovascular Insight V1.0 does not contain any calculation feature or any algorithm (deterministic or AI).

The provided FDA 510(k) clearance letter for Neurovascular Insight V1.0 states that the device "does not contain any calculation feature or any algorithm (deterministic or AI)." Furthermore, it explicitly mentions, "Neurovascular Insight V1.0 provides no output. Therefore, the comparison to predicate was based on the comparison of features available within both devices. No performance feature requires a qualitative or quantitative comparison and validation."

Based on this, it's clear that the device is a user interface and does not include AI algorithms or generate outputs that would require a study involving acceptance criteria for AI performance (e.g., sensitivity, specificity, accuracy). Therefore, the questions related to AI-specific performance criteria, ground truth establishment, training sets, and MRMC studies are not applicable to this particular device.

The "study" conducted for this device was a series of software verification and validation tests to ensure its functionality as a user interface and its substantial equivalence to its predicate.

Here's a breakdown of the requested information based on the provided document, highlighting where the requested information is not applicable due to the device's nature:

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

Note: As the device is a user interface without AI or output generation, there are no quantitative performance metrics like sensitivity, specificity, or accuracy that would typically be associated with AI algorithms. The acceptance criteria relate to the successful execution of software functionalities.

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

• Sample Size for Test Set: Not explicitly stated as a number of patient cases or images, as the testing was focused on software functionality rather than AI performance on a dataset. The testing refers to "software modules verification tests" and "software validation test."
• Data Provenance: Not applicable in the context of clinical data for AI development/validation, as the device doesn't use or produce clinical outputs requiring such data. The testing was internal software validation.

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

• Not Applicable: Given that the device is a user interface and does not utilize AI or produce diagnostic outputs, there was no need to establish clinical ground truth for a test set by medical experts in the traditional sense. The "ground truth" for its functionality would be the design specifications and successful execution of intended features. The document mentions "operators" who "reported no issue" during usability testing, but these are likely system testers/engineers, not clinical experts establishing diagnostic ground truth.

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

• Not Applicable: No clinical ground truth was established, so no adjudication method was required.

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

• No: The document explicitly states, "Neurovascular Insight V1.0 does not contain any calculation feature or any algorithm (deterministic or AI)." Therefore, an MRMC study comparing human readers with and without AI assistance was not performed.

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

• No: The device does not contain an algorithm, only a user interface. Standalone algorithm performance testing is not applicable.

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

• Not Applicable: No clinical ground truth was established, as the device is a user interface without AI or diagnostic output generation. The "ground truth" for its validation was adherence to software specifications and intended functionalities.

8. The sample size for the training set

• Not Applicable: The device does not contain any AI algorithms, therefore, no training set was used.

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

• Not Applicable: No training set was used.

Ask a specific question about this device

Neuro Insight V1.0 is an image processing solution. It is intended to assist appropriately trained medical professionals in their analysis workflow on neurological MRI images.

Neuro Insight V1.0 is composed of two subsets, including an image processing application package (NeuroPro) and an optional user interface (Neuro Synchronizer).

NeuroPro is an image processing application package that computes maps, extracts and communicates metrics which are to be used in the analysis of multiphase or monophase neurological MR images.

NeuroPro can be integrated and deployed through technical integration environment, responsible for transferring, storing, converting formats and displaying of DICOM imaging data.

Neuro Synchronizer is an optional dedicated interface allowing the viewing, manipulation, and comparison of neurological medical imaging and/or multiple time-points, including post-processing results provided by NeuroPro or any other results from compatible processing applications.

Neuro Synchronizer is a medical image management application intended to enable the user to edit and modify parameters that are optional inputs of aforementioned applications. These modified parameters are provided through the technical integration environment as inputs to the application to reprocess outputs. If necessary, Neuro Synchronizer provides the user with the option to validate the information.

Neuro Synchronizer can be integrated in compatible technical integration environments.

The device does not alter the original medical image. Neuro Insight V1.0 is not intended to be used as a standalone diagnostic device and should not be used as the sole basis for patient management decisions. The results of Neuro Insight V1.0 are intended to be used in conjunction with other patient information and based on professional judgment to assist with reading and interpretation of medical images. Users are responsible for viewing full images per the standard of care.

Neuro Insight (NEU_INS_MM) V1.0 product is a neurological image analysis solution, composed of several image processing applications and optional visualization and manipulation features.

Neuro Insight V1.0 is composed of two subsets:

• NeuroPro (NEU_PRO_MR) as an image application package, responsible for the processing of specific neurological MR Images.
• Neuro Synchronizer (NEU_HMI_MM) as an optional image analysis environment, that provides the user interface which has visualization and manipulation tools and allows the user to edit the parameters of compatible applications.

Neuro Insight does not alter the original medical image and is not intended to be used as a diagnostic device.

Here's a breakdown of the acceptance criteria and the study details for the Neuro Insight V1.0 device, based on the provided FDA 510(k) clearance letter:

Acceptance Criteria and Device Performance

1. Table of Acceptance Criteria and Reported Device Performance

The provided document describes a validation study comparing Neuro Insight V1.0 to the predicate device, Olea Sphere® V3.0, focusing on parametric maps computation and co-registration.

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

• Parametric Maps & Co-registration Study:

  • Sample Size:
    • Parametric maps: 30 anonymized brain MRI cases
    • Co-registration: 60 anonymized brain MRI cases
  • Data Provenance: Not explicitly stated as retrospective or prospective, or country of origin for these specific comparison studies. However, the BET deep learning algorithm training and testing data mentions sourcing from multiple MRI system manufacturers (GE Healthcare, Siemens, Philips, Canon/Toshiba) implying a diverse, likely multi-center, dataset. Given the anonymization and comparison with a predicate, it's highly likely this was a retrospective study.

• Brain Extraction Tool (BET) Validation (Deep Learning):

  • Test Set Sample Size: 100 cases
  • Data Provenance: Sourced to ensure broad representativeness depending on manufacturer, magnetic field, acquisition parameters, origin, patient age and sex. Cases collected from multiple MRI system manufacturers (GE Healthcare, Siemens, Philips, Canon/Toshiba) and varying magnetic fields (1.5T, 3T). Patients included 51% male, 43% female, with varied age (mean age 60 years, range 14 to 100 years for available data). This suggests diverse origin, likely global or at least multi-site.

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

• Parametric Maps & Co-registration Study:

  • Number of Experts: Three (3)
  • Qualifications: US board-certified neuroradiologists.

• Brain Extraction Tool (BET) Validation (Deep Learning):

  • Number of Experts: Expert clinicians performed manual segmentation, following criteria defined by a US board-certified neuroradiologist. Each segmentation was reviewed by a neuroradiologist and a research engineer.

4. Adjudication Method for the Test Set

• Parametric Maps & Co-registration Study: The document states that the comparison was done "by three US board-certified neuroradiologists." For the parametric maps, it involved "statistical and/or visual analysis of the results." For co-registration, it was "visual analysis." This implies a consensus or agreement among the three experts, but a specific adjudication method (e.g., majority vote, independent review with arbitration) is not explicitly detailed.

• Brain Extraction Tool (BET) Validation (Deep Learning): Manual segmentation was "reviewed by a neuroradiologist and a research engineer to ensure consistency and accuracy across the dataset." This suggests a review process, but not a specific adjudication method like 2+1.

5. If a Multi Reader Multi Case (MRMC) Comparative Effectiveness Study was done

• No, a traditional MRMC comparative effectiveness study aiming to quantify the improvement of human readers with AI vs. without AI assistance was not explicitly described.
  • The studies focused on the substantial equivalence of the device's output to a predicate (for parametric maps) and the acceptability of the device's output (for co-registration), as evaluated by human readers. It also validated the performance of the deep learning algorithm (BET) against expert-annotated ground truth. These are standalone evaluations of the device's output, not human-in-the-loop performance studies.

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

• Yes, a standalone evaluation of the deep learning Brain Extraction Tool (BET) algorithm was performed.
  • The algorithm's performance was assessed by comparing its automated segmentations to expert-annotated ground truth masks. The metric used was the DICE coefficient, which is a common measure of spatial overlap for segmentation tasks. This is a direct measure of the algorithm's performance without a human in the loop for the actual output generation being assessed.

7. The Type of Ground Truth Used

• Parametric Maps & Co-registration Study:

  • Predicate Device Output: For parametric maps, the ground truth was effectively the results generated by the predicate device (Olea Sphere® V3.0), against which Neuro Insight V1.0's outputs were compared.
  • Expert Visual Assessment: For co-registration, the ground truth was based on the "acceptable for reading and interpretation" visual assessment by three US board-certified neuroradiologists.

• Brain Extraction Tool (BET) Validation (Deep Learning):

  • Expert Consensus/Manual Annotation: Ground truth brain masks were created by "experienced clinicians following a standardized annotation protocol defined by a U.S. board-certified neuroradiologist." Each segmentation was "reviewed by a neuroradiologist and a research engineer to ensure consistency and accuracy." This strongly indicates expert consensus / manual annotation.

8. The Sample Size for the Training Set

• Brain Extraction Tool (BET) Validation (Deep Learning):
  • Training Set Sample Size: 199 cases
  • Validation Set Sample Size: 63 cases (used for model tuning during development)

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

• Brain Extraction Tool (BET) Validation (Deep Learning):
  • Ground truth brain masks were created specifically by "experienced clinicians following a standardized annotation protocol defined by a U.S. board-certified neuroradiologist."
  • The protocol included all brain structures (hemispheres and lesions) while explicitly excluding non-brain anatomical elements (skull, eyeballs, optic nerves).
  • Each segmentation was "reviewed by a neuroradiologist and a research engineer to ensure consistency and accuracy across the dataset." This method of establishing ground truth for the training set is consistent with the test set and involves expert consensus/manual annotation and review.

Ask a specific question about this device

CT Perfusion V1.0 is an automatic calculation tool indicated for use in radiology. The device is an image processing software allowing computation of parametric maps from CT Perfusion data and extraction of volumes of interest based on numerical thresholds applied to the aforementioned maps. Computation of mismatch between extracted volumes is automatically provided.

The device is intended to be used by trained professionals with medical imaging education including but not limited to, physicians and medical technicians in the imaging assessment workflow by extraction and communication of metrics from CT Perfusion dataset.

The results of CT Perfusion V1.0 are intended to be used in conjunction with other patient information and, based on professional judgment, to assist the clinician in the medical imaging assessment. Trained professionals are responsible for viewing the full set of native images per the standard of care.

The device does not alter the original image. CT Perfusion V1.0 is not intended to be used as a standalone diagnostic device and shall not be used to take decisions with diagnosis or therapeutic purposes. Patient management decisions should not solely be based on CT Perfusion V1.0 results.

CT Perfusion V1.0 can be integrated and deployed through technical platforms, responsible for transferring, storing, converting formats, notifying of detected image variations and display DICOM of imaqing data.

The CT perfusion V1.0 application can be used to automatically compute qualitative as well as quantitative perfusion maps based on the dynamic (first-pass) effect of a contrast agent (CA). The perfusion application assumes that the input data describes a well-defined and transient signal response following rapid administration of a contrast agent.

Olea Medical proposes CT Perfusion V1.0 as an image processing application, Picture Archiving Communications System (PACS) software module that is intended for use in a technical environment, which incorporates a Medical Image Communications Device (MICD) (21 CFR 892.2020) as its technical platform.

CT Perfusion V1.0 image processing application is designed as a docker installed on a technical platform, a Medical Image Communications Device.

The CT Perfusion V1.0 application takes as input a full CT perfusion (CTP) sequence acquired following the injection of an iodine contrast agent.

By processing these input image series, the application provides the following outputs:

• . Parametric maps.
• Volume 1 and volume 2 segmentation in DICOM format. Fusion of segmented Volume 1 and 2 and CTP map could be provided in PNG and DICOM secondary captures.
• . Mismatch computation:
  • Mismatch volume = Volume 2-Volume 1
  • Mismatch ratio = Volume 2/Volume 1 O
  • Relative Mismatch = (Volume 2-Volume 1)/Volume 2*100. O

The CT Perfusion V1.0 offers automatic volume seqmentations based on a set of maps and thresholds. The user is able to tune/adjust these thresholds and the maps associated to thresholds in the configuration files.

Here's an analysis of the acceptance criteria and study for the CT Perfusion V1.0 device, based on the provided FDA 510(k) summary:

Acceptance Criteria and Device Performance

Study Details

1. Sample size used for the test set and the data provenance: Not explicitly stated. The document mentions "all cases" for volume comparison, implying a dataset was used, but the specific number of cases is not provided. The provenance (country of origin, retrospective/prospective) is also not stated.

2. Number of experts used to establish the ground truth for the test set and the qualifications of those experts: Not applicable. The study compares the performance of the new device to a predicate device, Olea Sphere V3.0, not to expert-derived ground truth.

3. Adjudication method for the test set: Not applicable, as the comparison is against a predicate device's output rather than an expert-adjudicated ground truth.

4. 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: No. This was not a MRMC comparative effectiveness study involving human readers with and without AI assistance. The study focuses on comparing the new device's output to a predicate device's output.

5. If a standalone (i.e., algorithm only without human-in-the-loop performance) was done: Yes, this was a standalone comparison of the CT Perfusion V1.0 algorithm's outputs against the Olea Sphere V3.0 algorithm's outputs.

6. The type of ground truth used: The "ground truth" for this study was the output of the predicate device, Olea Sphere V3.0.

7. The sample size for the training set: Not applicable. The document states that "CT Perfusion V1.0 does not contain any AI-based algorithms. All calculations are based on deterministic algorithms." Therefore, there is no training set in the machine learning sense.

8. How the ground truth for the training set was established: Not applicable, as there is no training set for a deterministic algorithm.

Ask a specific question about this device

Olea S.I.A. Neurovascular V1.0 is an optional user interface for use on the Olea Medical technical integration platform Olea S.I.A. V1.0 and is designed to be used by trained radiologists and surgeons.

Olea S.I.A. Neurovascular V1.0 is intended to:

• display MR and CT series and outputs provided by compatible docker applications processing, through the technical integration platform,
• . allow the user to edit and modify parameters that are optional inputs of aforementioned applications. These modified parameters are provided through the technical integration platform as inputs to the docker application to reprocess outputs. When available, Olea S.I.A. Neurovascular V1.0 display can be updated with the reprocessed outputs.

The device does not alter the original image information and is not intended to be used as a diagnostic device and shall not be used to take decisions with diagnosis or therapeutic purposes. The information displayed is intended to be used in conjunction with other patient information and based on professional judgment, to assist the clinician in the medical imaging assessment.

Trained radiologists and surgeons are responsible for viewing the full set of native images per the standard of care.

Olea S.I.A. Neurovascular V1.0 is a visualization of docker applications results. It is an interface embedded on Olea S.I.A. V1.0 platform and uses the components of Olea S.I.A. V1.0 platform to display the outputs of docker applications to which it is entirely dedicated.

Olea S.I.A. Neurovascular V1.0 allows the user to visualize native DICOM series and docker applications outputs and to modify parameters that are optional inputs of these applications.

Olea S.I.A. Neurovascular V1.0 interaction with the Olea S.I.A. technical platform and docker applications

To be used, Olea S.I.A. Neurovascular V1.0 needs:

• o a technical base, which is provided by Olea S.I.A. V1.0 platform; and
• 0 one or more applications installed on the Olea S.I.A. V1.0 platform that provide outputs that can be managed by Olea S.I.A. Neurovascular V1.0.

The technical platform:

• receives outputs from docker applications; ●
• o makes these outputs available to related DICOM viewers. If the docker applications are compatible with Olea S.I.A. Neurovascular V1.0, they are proposed as the default software to visualize the outputs;
• 0 retrieves the modified parameters in Olea S.I.A. Neurovascular V1.0 to be able to relaunch the concerned docker applications with these new input parameters.

Olea S.I.A. Neurovascular V1.0 does not contain any calculation feature or any algorithm (deterministic or Al).

Olea S.I.A. Neurovascular V1.0 operating principles and technological characteristics

Olea S.I.A. Neurovascular V1.0 is an interface embedded on Olea S.I.A. V1.0 platform. Olea S.I.A. Neurovascular V1.0 communicates with API that exists on Olea S.I.A. V1.0 platform only. Olea S.I.A. Neurovascular V1.0 is launched via a link to a web browser and a secure connection.

Olea S.I.A. Neurovascular V1.0 receives data coming from docker applications. These data can either be maps, VOIs, or metrics. Olea S.I.A. Neurovascular V1.0 is able to display these results on a dedicated user interface designed in accordance with the clinical need that provides tools to visualize and manipulate images. There is no Olea S.I.A. Neurovascular V1.0 functionality on top of the applications cleared functionalities: the subject device only serves for display of cleared outputs and/or information.

Olea S.I.A. Neurovascular V1.0 also gives the possibility to edit and modify parameters that are optional inputs of aforementioned applications. These modified parameters are provided through the technical integration platform as inputs to the docker application to reprocess outputs. When available, Olea S.I.A. Neurovascular V1.0 display is updated with the reprocessed outputs.

The provided text describes Olea S.I.A. Neurovascular V1.0, a device that primarily serves as a user interface for displaying outputs from compatible docker applications and allowing users to modify parameters for reprocessing. It does not perform any calculations or algorithms itself.

Here's an analysis of the acceptance criteria and study information provided (or lacking thereof):

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

The document does not explicitly state quantitative acceptance criteria for Olea S.I.A. Neurovascular V1.0 beyond demonstrating substantial equivalence to the predicate device, Olea Sphere® V3.0. Since the device's function is primarily visualization and parameter modification, and it contains no algorithms, the performance criteria are focused on its functionality and safety rather than diagnostic accuracy.

2. Sample size used for the test set and the data provenance (e.g., country of origin of the data, retrospective or prospective)

The document does not provide information on the sample size used for the test set, nor the data provenance (country of origin, retrospective or prospective). This is because the device is a visualization and interface tool, not a diagnostic algorithm that processes patient data for clinical outcomes. The validation focuses on software functionality and equivalence rather than clinical performance on a dataset of patient images.

3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts (e.g., radiologist with 10 years of experience)

This information is not applicable and therefore not provided in the document. Olea S.I.A. Neurovascular V1.0 does not perform any interpretations or generate diagnostic outputs that would require a ground truth established by experts. Its function is to display results from other (compatible docker) applications, and the validation focuses on the correct display and interaction functionality, not on evaluating diagnostic accuracy.

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

This information is not applicable and therefore not provided. Since the device does not make diagnostic interpretations or classifications that would require expert consensus or adjudication on a test set, no such method was employed.

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

A multi-reader multi-case (MRMC) comparative effectiveness study was not done. This is consistent with the device's intended use as a visualization and parameter modification interface, rather than an AI-assisted diagnostic tool. No AI enhancement for human readers is described or evaluated.

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

A standalone performance evaluation (algorithm only) was not done for Olea S.I.A. Neurovascular V1.0. This device explicitly states it "does not contain any calculation feature or any algorithm (deterministic or AI)." Its primary role is to serve as an interface for outputs from other applications.

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

No "ground truth" in the diagnostic sense (expert consensus, pathology, outcomes data) was used. The validation was based on internal verification and validation testing to confirm that the product specifications are met and that the device operates as intended and in a manner substantially equivalent to the predicate. This would involve checking the correct display of images and outputs, the functionality of interface tools, and the ability to modify parameters as designed.

8. The sample size for the training set

This information is not applicable and therefore not provided. Olea S.I.A. Neurovascular V1.0 does not contain any AI algorithms and thus does not have a "training set."

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

This information is not applicable and therefore not provided, as the device does not have a training set or AI algorithms requiring ground truth establishment.

Ask a specific question about this device

MR DWI/FLAIR Measurement V1.0 is an image processing application indicated for use in the analysis of:

(1) MR Diffusion-weighted imaging (DWI)

(2) MR FLAIR images.

The device is intended to be used by trained professionals with medical imaging education including, but not limited to, physicians and medical technicians in the imaging assessment workflow:

• computation of the map relative to the water diffusion, i.e., ADC map; .
• . extraction and communication of metrics derived from the above map, i.e., hypointense area on ADC, and FLAIR series as well as ratios with contralateral information on FLAIR images.

The results of MR DWI/FLAIR Measurement V1.0 are intended to be used in conjunction with other patient information and, based on professional judgment, to assist the clinician in the medical imaging assessment. Trained professionals are responsible for viewing the full set of native images per the standard of care.

The device does not alter the original medical image. MR DWI/FLAIR Measurement V1.0 is not intended to be used as a standalone diagnostic device and shall not be used to take decisions with diagnosis or therapeutic purposes. Patient management decisions should not solely be based on MR DWI/FLAIR Measurement V1.0 results.

MR DWI/FLAIR Measurement V1.0 can be integrated and deployed through technical platforms responsible for transferring, storing, converting formats, notifying of detected image variations and display of DICOM imaging data.

Olea Medical proposes MR DWI/FLAIR Measurement V1.0 as an image processing application, Picture Archiving Communications System (PACS) software module that is intended for use in a technical environment which incorporates a Medical Image Communications Device as its technical platform.

MR DWI/FLAIR Measurement V1.0 is an executable application which can run on the OLEA Platform. The OLEA Platform is a Medical Image Communications Device and outside the scope of this submission. MR DWI/FLAIR Measurement V1.0 is a docker totally independent from the OLEA platform in which it is integrated and has a dedicated Input/Output channels to be able to be integrated and deployed through any compatible configurable technical platform. Input DICOM images are received via the dedicated file system in which the application is integrated. When launched, the MR DWI/FLAIR Measurement V1.0 will retrieve and automatically analyze the image series. The output images will be sent to the same dedicated file system and can be visualized from any DICOM viewer by loading these results from the allocated file system.

To be used, the MR DWI/FLAIR Measurement V1.0 docker needs an independent technical base, which is provided by a Medical Image Communications Device (MICD). The technical platform allows the docker to:

• receive the inputs
• provide the outputs
• . visualize the outputs through Olea Platform viewer and/or export to other third party DICOM viewers.

The provided text describes the 510(k) clearance for Olea Medical's MR DWI/FLAIR Measurement V1.0. While it details performance testing, it does not explicitly state "acceptance criteria" in the format of a table with specific thresholds. Instead, it presents the results of comparative testing against a predicate device (Olea Sphere V3.0) and indicates that the performance demonstrated substantial equivalence.

Here's a breakdown of the requested information based on the provided text, with an acknowledgment where specific details (like explicit acceptance criteria thresholds) are not explicitly stated:

Device Performance and Acceptance Criteria Study Details

1. Table of Acceptance Criteria and Reported Device Performance

The acceptance criteria are not explicitly stated as numerical thresholds for specific metrics (e.g., "Dice score > 0.90"). Instead, the acceptance is demonstrated through comparative testing showing substantial equivalence to the predicate device, Olea Sphere V3.0. The performance is reported in terms of agreement and similarity with the predicate.

Implicit Acceptance: The performance metrics, particularly the low bias and tight limits of agreement for FLAIR measurements and the excellent DICE indexes, demonstrate that the MR DWI/FLAIR Measurement V1.0 performs comparably to the predicate device, thus meeting the implicit acceptance criterion of substantial equivalence.

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

• Test Set (for comparative clinical image study): Not explicitly stated how many cases were used for the comparative clinical image study that produced the Bland-Altman and DICE index results.
• Test Set (for Diffusion Brain Extraction Tool - BET): 28 cases from multiple institutions.
• Data Provenance: Cases came from multiple institutions (for the BET algorithm testing), different from the training and validation sets. DICOM data were sourced from Siemens, General Electric, Philips, and Canon manufacturers. The text does not specify the country of origin or whether the data was retrospective or prospective.

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

• Number of Experts: Not explicitly stated for the overall "comparative clinical image study."
• Qualifications of Experts: For the Diffusion Brain Extraction Tool (BET) algorithm, the reference standard was established by "expert clinicians." Specific qualifications (e.g., years of experience, subspecialty) are not provided beyond "expert clinicians."

4. Adjudication Method for the Test Set

• Adjudication Method: Not explicitly stated. The text mentions "manual segmentation performed by expert clinicians" for the BET ground truth, implying individual expert assessment, but does not detail a consensus or adjudication process.

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

• MRMC Study: No, a multi-reader multi-case (MRMC) comparative effectiveness study was not reported. The study focused on comparing the device's measurements directly to a predicate device's measurements (algorithm-to-algorithm comparison for the main performance metrics and algorithm-to-expert segmentation for the BET component), rather than human readers with and without AI assistance.
• Effect Size: Not applicable, as no MRMC study was performed.

6. Standalone (Algorithm Only Without Human-in-the-Loop Performance) Study

• Yes, the performance data presented (Bland-Altman analysis for FLAIR measurements and DICE indexes) represents a standalone comparison between the subject device's algorithmic outputs and the predicate device's outputs. The Diffusion Brain Extraction Tool (BET) component also had standalone performance evaluated against expert manual segmentations.
• The device is explicitly stated as "not intended to be used as a standalone diagnostic device" and results are to be used "in conjunction with other patient information" and to "assist the clinical imaging assessment." The performance study, however, validates the algorithm's output accuracy against a reference.

7. Type of Ground Truth Used

• For the Diffusion Brain Extraction Tool (BET) algorithm, the ground truth was expert manual segmentation.
• For the overall device performance (Relative/Normalized FLAIR measurements, ADC hypointense area segmentation), the "ground truth" was essentially the outputs of the predicate device (Olea Sphere V3.0), as the study aimed to demonstrate substantial equivalence by comparing the new device's outputs to the established predicate's outputs.

8. Sample Size for the Training Set

• Training Set (for Diffusion Brain Extraction Tool - BET): 218 cases.

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

• The text states, "The reference standard was established by manual segmentation performed by expert clinicians" for the Diffusion Brain Extraction Tool (BET) algorithm. This implies that the ground truth for training (and validation/testing) was established through expert manual segmentation.

Ask a specific question about this device

MR Diffusion Perfusion Mismatch V1.0 is an automatic calculation tool indicated for use in radiology. The device is an image processing software allowing computation of parametric maps from (1) MR Diffusion-weighted imaging (DWI) and (2) MR Perfusion-weighted imaging (PWI) and extraction of volumes of interest based on numerical thresholds applied to the aforementioned maps. Computation of mismatch between extracted volumes is automatically provided.

The device is intended to assist trained radiologists and surgeons in the imaging assessment workflow by extraction and communication of metrics from MR Diffusion-weighted imaging (DWI) and MR Perfusion-weighted imaging (PWI).

The results of MR Diffusion Perfusion Mismatch V1.0 are intended to be used in conjunction with other patient information and, based on professional judgment, to assist the clinician in the medical imaging assessment. Trained radiologists and surgeons are responsible for viewing the full set of native images per the standard of care.

The device does not alter the original medical image. MR Diffusion Mismatch V1.0 is not intended to be used as a standalone diagnostic device and shall not be used to make decisions with diagnosis or therapeutic purposes. Patient management decisions should not solely be based on MR Diffusion Perfusion Mismatch V1.0 results.

MR Diffusion Perfusion Mismatch V1.0 can be integrated and deployed through technical platforms, responsible for transferring, storing, converting formats, notifying of detected image variations and display of DICOM imaging data.

The MR Diffusion Perfusion Mismatch V1.0 application can be used to automatically compute gualitative as well as quantitative perfusion maps based on the dynamic (first-pass) effect of a contrast agent (CA). The perfusion application assumes that the input data describes a well-defined and transient signal response following rapid administration of a contrast agent.

Olea Medical proposes MR Diffusion Perfusion Mismatch V1.0 as an image processing application, Picture Archiving Communications System (PACS) software module that is intended for use in a technical environment which incorporates a Medical Image Communications Device as its technical platform.

1. Table of Acceptance Criteria and Reported Device Performance:

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

The sample size used for the comparative clinical image study (test set) was 30 cases. The data provenance (country of origin, retrospective/prospective) is not explicitly stated in the provided text.

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

The evaluation was performed by one US board-certified neuroradiologist. The specific experience in years is not mentioned, but the board certification implies a certain level of expertise.

4. Adjudication Method for the Test Set:

The text describes qualitative assessments ("substantially equivalent," "visually equivalent") and quantitative statistical analyses (Bland-Altman, DICE index, correlations). These were reviewed and deemed acceptable by a single US board-certified neuroradiologist. There is no mention of a multi-reader adjudication method like 2+1 or 3+1.

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:

No, an MRMC comparative effectiveness study involving human readers' improvement with AI assistance versus without AI assistance was not conducted or reported in this document. The study focused on the equivalence between the new device and a predicate device.

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

Yes, the study primarily evaluated the standalone performance of the MR Diffusion Perfusion Mismatch V1.0 algorithm in comparison to the predicate device, Olea Sphere® V3.0. The "human-in-the-loop" aspect was limited to the expert's qualitative assessment of the output generated by the algorithms.

7. The Type of Ground Truth Used:

The ground truth for the comparison was essentially the output of the predicate device, Olea Sphere® V3.0, which served as the reference for evaluating the performance of MR Diffusion Perfusion Mismatch V1.0. This was augmented by expert qualitative assessment from a US board-certified neuroradiologist to confirm "substantial equivalence" and "acceptability" of the differences.

8. The Sample Size for the Training Set:

The sample size for the training set is not mentioned in the provided text. The document focuses on the performance testing against a predicate device.

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

The method for establishing ground truth for any training set is not mentioned as the document does not elaborate on the training process for the device.

Ask a specific question about this device

Functional MR V1.0 is an optional image processing software application that is intended for use on Olea Sphere® V3.0 software package. It is intended to be used by trained professionals including, but not limited to, physicians, MR technicians, radiographers.

Functional MR V1.0 includes a software module that computes the activation map from a BOLD sequence and supports the visualization, analysis of activation maps.

Functional MR V1.0 can also be used to provide reproducible measurements of derived maps. These measurements include thresholds modification and ROI analysis.

Functional MR V1.0 may also be used as an image viewer of multi-modality digital images, including BOLD and DTI images.

When interpreted by a skilled physician, Functional MR V1.0 provides information that may be used in a clinically useful context. Patient management decisions should not be based solely on the results of Functional MR V1.0.

The functional MRI technique consists of analyzing the blood-oxygen-level dependent (BOLD) contrast images. This is a type of specialized brain and body scan is used to map neural activity in the brain or spinal cord of humans by imaging the change in blood flow (hemodynamic response) related to energy use by brain cells.

Olea Medical proposes the Functional MR V1.0 as an optional medical viewing, analysis and processing, Picture Archiving Communications System (PACS) software module that is intended for use with the Olea Sphere® V3.0 software package (K152602). Functional MR V1.0 software application runs on a standard "off-the-shelf" PC workstation.

The provided text describes a 510(k) premarket notification for the Olea Medical Functional MR V1.0 software. The submission aims to demonstrate substantial equivalence to a predicate device, nordicBrainEx v2.3.7. However, the document does not contain specific acceptance criteria, a detailed study proving the device meets those criteria, or the specific information requested in your numbered list regarding performance metrics, sample sizes, expert involvement, and ground truth establishment.

The document primarily focuses on:

• The device's intended use and functionality.
• A comparison of technological characteristics with the predicate device.
• A general statement about internal verification and validation testing, and a comparison study with the predicate.

Here's a breakdown of what is available and what is missing from the provided text, in response to your request:

1. Table of Acceptance Criteria and Reported Device Performance

The document provides a "Predicate Device Comparison Table" which lists functionalities and indicates whether both devices possess them. This table does not represent acceptance criteria as performance thresholds (e.g., sensitivity, specificity, accuracy). It merely confirms feature commonality.

Note: The "Reported Acceptance Criteria Met" column is inferred from the statement: "The result of this comparison demonstrates that Functional MR V1.0 has a safety and effectiveness profile similar to the predicate device." This implies that for all features shared with the predicate, the performance was deemed comparable. However, no quantitative performance metrics or specific acceptance criteria are provided.

2. Sample size used for the test set and the data provenance (e.g. country of origin of the data, retrospective or prospective)

• Missing Information: The document does not specify the sample size of the test set, the country of origin of the data, or whether the study was retrospective or prospective. It only states that "additional validation testing to compare the results of Functional MR V1.0 with the predicate."

3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts (e.g. radiologist with 10 years of experience)

• Missing Information: This information is not provided. The term "ground truth" is not explicitly mentioned in the context of the comparison study.

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

• Missing Information: This information is not provided.

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

• No MRMC Study Described: The document describes a comparison of the results of Functional MR V1.0 with the predicate nordicBrainEx v2.3.7. It does not mention a multi-reader multi-case (MRMC) comparative effectiveness study evaluating human reader performance with and without AI assistance. Functional MR V1.0 is image processing software, not explicitly an AI-assisted diagnostic tool for human readers in the traditional sense discussed by MRMC studies. The device provides "information that may be used in a clinically useful context," but "Patient management decisions should not be based solely on the results of Functional MR V1.0."

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

• Yes (Implicit): The comparison study appears to be a standalone comparison of the software's output ("compare the results of Functional MR V1.0 with the predicate"). The document states it "evaluate performance of BOLD sequence analysis and visualization, viewing and measurement tools..." This implies evaluating the algorithm's output directly against the predicate's output, without human interpretation as part of the primary performance metric.

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

• Implicit Ground Truth: Predicate Device Output: The comparison used the "results of Functional MR V1.0 with the predicate. nordicBrainEx v2.3.7 (NordicNeurolab AS®) was used as a comparison for Functional MR V1.0". This suggests that the output/results of the legally marketed predicate device served as the reference or de facto "ground truth" for the comparison for the functionalities being evaluated. There is no mention of an independent, expert-established ground truth, pathology, or outcomes data.

8. The sample size for the training set

• Not Applicable / Not Provided: As this device is primarily described as image processing software performing calculations and visualizations rather than a machine learning model requiring a distinct training set (though it could conceptually use ML for some features), a "training set" is not explicitly mentioned or quantified. If an ML component exists, its training set details are not provided.

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

• Not Applicable / Not Provided: Similar to point 8, this information is not available given the context of the document.

Ask a specific question about this device

breastscape V1.0 is an optional image processing software application that is intended for use on Olea Sphere 3.0 software package. It is intended to be used by trained breast imaging physicians and trained MRI technologists.

breastscape V1.0 includes a software module (BreastApp) that supports the visualization, analysis, and reporting of lesions measurements and analysis. breastscape V1.0 supports the evaluation of dynamic MR data acquired during contrast administration and the calculation of parameters related to the uptake characteristics.

breastscape V1.0 performs other user selected processing functions (such as image subtraction, multiplanar and oblique reformats, 3D renderings).

The resulting information can be displayed in a variety of formats, including a parametric image overlaid onto the source image.

breastscape V1.0 can also be used to provide measurements of the segmented tissue volumes (volumes of interest) based on uptake characteristics. These measurements include volume measurement, distances of volumes of interest to anatomical landmarks, 3D longest diameter and 2D long and short axis.

breastscape V1.0 includes the option to add annotations based on the fifth edition of the American College of Radiology's Breast Imaging Reporting and Data System (BI-RADS®) Breast Imaging Atlas.

breastscape V1.0 may be used as an image viewer of multi-modality digital images, including ultrasound and mammography. breastscape V1.0 is not intended for primary interpretation of digital mammography images.

breastscape V1.0 includes a software module (BreastLoc) to assists users in planning MR guided breast interventional procedures. Using information from MR images, regarding userspecified target lesion and fiducial location coordinates, the software gives calculation of the targeted region of interest (such as suspected lesion) depth.

When interpreted by a skilled physician, breastscape V1.0 provides information that may be used for screening, diagnosis, and interventional planning.

Patient management decisions should not be based solely on the results of breastscape V1.0.

breastscape V1.0 is an optional PACS software tool that is intended for use with the Olea Sphere V3.0 software package, cleared under K152602. The software accesses image series in DICOM format through Olea Sphere V3.0, which is a software package used to perform image viewing, processing and analysis of medical images.

breastscape V1.0 is made of two software modules: BreastApp and BreastLoc.

1. BreastApp
   BreastApp is designed to assist in the visualization, analysis and reporting of Magnetic Resonance Imaging (MRI) breast studies. This module supports the evaluation of dynamic MR breast data acquired during contrast administration (DCE-MRI), and the calculation of parameters related to the lesion uptake characteristics. This module provides semi-automatic segmentation of volumes of interest, distance measurements and lesion volume measurements.

BreastApp provides the features below:

• Visualization of registered MR image series. It includes well-established to standard image viewing, MIPs, reformats and 3D volume rendering.
• Visualization of Mammography and Ultrasound image series for display purpose only.
• Evaluation of dynamic MR breast data acquired during contrast administration. It includes:
  • The computation of image subtractions (subtractions of each time point/phase image of the dynamic series with the 1st time point (baseline) image to highlight tissue with contrast enhancement).
  • The display of time intensity signal curves (kinetics curves) showing tissue contrast enhancement evolution over time.
  • The detection and display of the kinetics curve showing the worst kinetics behavior (most important washout among pixels having peak enhancement superior at 50% enhancing threshold).
  • The computation of semi-quantitative kinetics maps that are derived from the time intensity signal curves and showing uptake characteristics (e.g., Time to Maximum contrast Enhancement, Wash in, Washout, etc.).
• Automatic detection of breast morphological structures. It includes the automatic detection of nipple position, chest and skin border. The user can further adjust them if needed.
• Semi-automatic lesion segmentation. It includes:
  • Highlighting tissues showing significant contrast agent uptake based on an uptake threshold.
  • Semi-automatic segmentation of the suspected lesion identified by the user. The user can further adjust the segmentation if needed or even manually segment the suspected lesion.
  • Automatic computation of the suspected lesion 2D/3D diameter and lesion volume.
• Automatic computation of distances between the suspected lesion and the morphological structures (distances to nipple, chest and skin). The user can further adjust the distances if needed.
• Reporting of user-selected findings and assessment through a dedicated breast report. It includes the option to add annotations based on the Fifth Edition of American College of Radiology's Breast Imaging Reporting and Data System (BI-RADS®) Breast Imaging Atlas. The software automatically reports the localization of the suspected lesion on a dedicated breast sector map. The position can be further adjusted by the user if needed.
• Follow-up for multiple (more than two) studies from same patient. It includes tools to enhance the visualization and analysis of patient follow-up studies through the same layout.

2. BreastLoc

The BreastLoc module is designed to assist users in planning MR-guided breast interventional procedures. Based on user-specified target lesion and fiducial location coordinates, BreastLoc is used to compute and display the following features:

• Needle insertion block position within the grid diagram;
• Needle insertion point activation in the block;
• Depth of introducer, representing the graduation value where to put the depth stop on the introducer sheath;
• Needle insertion path display on native images.

The provided text describes Olea Medical's breastscape V1.0 and its substantial equivalence to predicate and reference devices. However, it does not explicitly state "acceptance criteria" or provide a detailed "study that proves the device meets the acceptance criteria" in the format requested.

The document focuses on demonstrating substantial equivalence through various validation and verification tests, software functionality comparisons, and a general statement about clinical performance. It mentions "performance evaluation support that the minor difference in the technological characteristics do not raise different questions of safety and effectiveness" and "software validation testing demonstrates that the device operates as safely and effectively as its predicate device and does not raise different questions of safety and effectiveness."

While the document indicates that the device's performance was evaluated, it does not provide the specific quantitative acceptance criteria or the detailed results of a study designed to prove the device met those criteria in a structured manner.

Therefore, the following information is extracted and inferred from the text provided. Many points cannot be fully answered due to the absence of specific details in the input text.

1. Table of Acceptance Criteria and Reported Device Performance

Not explicitly stated in the document. The document asserts that validation testing confirms product specifications are met and that the device operates as safely and effectively as its predicate.

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

• Sample Size for Test Set: Not explicitly stated. The document mentions "anonymized images from a cohort of patients" were used for additional validation testing to compare breastscape V1.0 with the predicate and reference devices. The size of this cohort is not specified.
• Data Provenance: Not specified. The document does not mention the country of origin of the data or whether it was retrospective or prospective.

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

Not explicitly stated. The document mentions "direct manual measurements (ground truth)" were used for BreastApp™, but it does not specify the number or qualifications of the experts who performed these measurements. The device is intended for use by "trained breast imaging physicians and trained MRI technologists."

4. Adjudication method for the test set

Not explicitly stated.

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

Not explicitly stated. The document mentions "additional validation testing to compare the results of breastscape V1.0 with the predicate and reference devices," but this test appears to be focused on comparing the device's output to the predicate/reference rather than assessing human reader performance with and without AI assistance.

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

Yes, in part. The document states that "direct manual measurements (ground truth)" were used to evaluate "automatically calculated metrics and parametric maps" for BreastApp™. This suggests an evaluation of the algorithm's output against a ground truth without human interpretation in the loop for that specific aspect. Further, the primary validation and verification tests for the software modules themselves would be standalone algorithm performance tests.

7. The type of ground truth used

• For BreastApp™: "Direct manual measurements (ground truth)" and "Kinetics plugin, viewing tools, follow-up feature, breast dedicated report, mammography loading and visualization, measurements modifications within Olea Sphere V3.0" were used. This indicates a combination of expert-derived manual measurements and reference to the established functionality of other cleared systems or modules.
• For BreastLoc™: It was compared against MultiView™ MR Breast V4.0.3.2 (Hologic®) to evaluate "performance of the MR guided breast intervention procedural planning." This implies the predicate device's accepted output served as a reference for ground truth or comparison.

8. The sample size for the training set

Not explicitly stated. The document focuses on validation and verification rather than detailing the training of potential AI/ML components (though "parametric image maps" and "semi-automatic segmentation" may involve such components).

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

Not explicitly stated.

Ask a specific question about this device

Olea Sphere V3.0 is an image processing software package to be used by trained professionals including but not limited to physicians and medical technicians. The software runs on a standard "off-the-shelf" workstation and can be used to perform image viewing, processing, image collage and analysis of medical images are acquired through DICOM compliant imaging devices and modalities.

Olea Sphere V3.0 provides both viewing and analysis capabilities of functional and dynamic imaging datasets acquired with MRI or other relevant modalities, including a Diffusion Weighted MRI (DWI) / Fiber Tracking Module and a Dynamic Analysis Module (e.g. dynamic exogenous or endogenous contrast enhanced imaging data for MRI and CT). The DWI Module is used to visualize local water diffusion properties from the analysis of diffusion-weighted MRI data. The Fiber Tracking feature utilizes the directional dependency of the white matter structure in the brain or more generally the central nervous system.

The Dynamic Analysis Module is used for visualization and analysis of dynamic imaging data, showing properties of changes in contrast while repeating acquisitions (e.g. over time without variable acquisition parameters) where such techniques are useful or necessary. This functionality is referred to as:

Perfusion Module - the calculation of parameters related to tissue flow (perfusion) and tissue blood volume.

Permeability Module - the calculation of parameters related to leakage of injected contrass material from intravascular to extracellular space.

Arterial Spin Labeling (ASL) Module - the calculation of parameters related to tissue flow based on a MR technique using the water in arterial blood as endogenous tracer to evaluate the perfusion.

Relaxometry Module - the calculation of parameters related to the MR longitudinal and transversal relaxation time and rate.

Metabolic Module - the calculation of parameters related to the fat fraction based on a MR technique using opposedphase imaging.

Olea Sphere V3.0 is a medical viewing, analysis and processing, Picture Archiving Communications System (PACS) software, compliant with the DICOM standard and running on Windows or Linux operating systems.

Olea Sphere V3.0 allows the display, analysis and post-processing of medical images. These images, when interpreted by a trained physician, may yield clinically useful information.

The software provides a wide range of basic image processing and manipulation functions, in addition to comprehensive dynamic image processing and display. The main features of the software are:

• Image Loading & Saving
• Image Viewing
• Image Manipulation
• Image Analysis
• Imaging Processing
• Perfusion post-processing
• Permeability post-processing
• Arterial Spin Labeling (ASL)
• Diffusion-Weighted Imaging (DWI) / Tensor Imaging post-processing (DTI) / Intra-Voxel Incoherent Motion (IVIM)
• Fiber Tracking post-processing
• Collage
• Relaxometry post-processing
• Metabolic post-processing

Depending on the purpose of the imaging, the following optional plug-in are used by the software:

• DWI (for MR imaging)
• DTI (for MR imaging)
• Perfusion (for MR and CT imaging)
• Permeability (for MR and CT imaging)
• Kinetics (for MR imaging)
• ASL (for MR imaging)
• Analysis (for MR and CT imaging)
• Olea Vision (for MR imaging)
• IVIM (for MR imaging)
• Collage (for MR imaging)
• Metabolic (for MR imaging)
• Relaxometry (for MR imaging)

The main users of the program are medical imaging professionals who need to visualize and analyze images acquired primarily with MRI or CT systems. Lossy compressed mammographic images and digitized film screen images must not be reviewed for primary image interpretations

The provided text describes Olea Sphere V3.0, an image processing software package, and its substantial equivalence to predicate devices, but it does not contain the detailed acceptance criteria or a specific study proving the device meets acceptance criteria in the format requested.

The document is a 510(k) summary for FDA clearance, which focuses on demonstrating substantial equivalence to previously cleared devices rather than a standalone clinical study with detailed performance metrics against specific acceptance criteria.

Therefore, the following information is not available in the provided text:

• A table of acceptance criteria and reported device performance.
• Sample sizes used for the test set and data provenance (country of origin, retrospective/prospective).
• Number of experts used to establish ground truth and their qualifications.
• Adjudication method for the test set.
• Multi-reader multi-case (MRMC) comparative effectiveness study results or effect size of AI assistance.
• The type of ground truth used (e.g., pathology, outcomes data).
• Sample size for the training set.
• How ground truth for the training set was established.

The document states that "Internal verification and validation testing confirms that the product specifications are met, in support of the substantial equivalence of the intended use and technological characteristic as the predicate devices." It also lists the main groups of tests performed as: Product Risk Assessment, Software modules verification tests, and Software validation test. However, it does not provide the specifics of these tests or their results in terms of acceptance criteria.

Ask a specific question about this device

Olea Sphere is an image processing software package to be used by trained professionals including but not limited to physicians and medical technicians. The software runs on a standard "off-the-shelf" workstation and can be used to perform image viewing, processing and analysis of medical images. Data and images are acquired through DICOM compliant imaging devices and modalities.

Olea Sphere provides both viewing and analysis capabilities of functional and dynamic imaging datasets acquired with MRI or other relevant modalities. including a Diffusion Weighted MRI (DWI) / Fiber Tracking Module and a Dynamic Analysis Module (dynamic contrast enhanced imaging data for MRI and CT).

The DWI Module is used to visualize local water diffusion properties from the analysis of diffusion-weighted MRI data. The Fiber Tracking feature utilizes the directional dependency of the diffusion to display the white matter structure in the brain or more generally the central nervous system.

The Dynamic Analysis Module is used for visualization and analysis of dynamic imaging data, showing properties of changes in contrast over time where such techniques are useful or necessary. This functionality is referred to as:

Perfusion Module - the calculation of parameters related to tissue flow (perfusion) and tissue blood volume.

Permeability Module - the calculation of parameters related to leakage of injected contrast material from intravascular to extracellular space.

Olea Sphere is a medical viewing, analysis and processing software package (PACS) compliant with the DICOM standard and running on Windows, Macintosh or Linux operating systems.

Olea Sphere allows the display, analysis and post-processing of medical images.

These images, when interpreted by a trained physician, may vield clinically useful information.

The provided text describes a submission for 510(k) clearance for the Olea Sphere v2.3, a Picture Archiving Communications System (PACS) with advanced imaging analysis capabilities. However, it explicitly states that no clinical testing was conducted in support of this version. Instead, the substantial equivalence to its predicate device (Olea Sphere) is based on non-clinical performance data and the proven safety and efficacy of similar devices already on the market.

Therefore, many of the requested details about acceptance criteria, study design, ground truth, experts, and sample sizes for clinical validation are not applicable to this submission as no such studies were performed for the Olea Sphere v2.3 itself.

Here's a breakdown of the available information based on your request:

1. Table of Acceptance Criteria and Reported Device Performance:

Since no clinical studies were performed for Olea Sphere v2.3 to establish new clinical acceptance criteria, the "performance" described is largely a comparison to its predicate device and adherence to general PACS standards. The submission highlights technological updates rather than new clinical outcome performance metrics.

2. Sample size used for the test set and the data provenance (e.g. country of origin of the data, retrospective or prospective):

• Not Applicable (N/A). No clinical testing was conducted for Olea Sphere v2.3. The submission focuses on non-clinical verification and validation.

3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts (e.g. radiologist with 10 years of experience):

• N/A. No clinical testing was conducted.

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

• N/A. No clinical testing was conducted.

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:

• N/A. No clinical testing was conducted. The device is a PACS with analysis tools, not specifically an AI-assisted diagnostic tool in the sense of an MRMC study with human readers improving with AI.

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

• While the document mentions "stress testing" and "verification and validation testing" of software components (non-clinical performance data), it does not detail any standalone performance studies in a clinical context. The device is explicitly stated to be "used by trained professionals" for image viewing, processing, and analysis, implying a human-in-the-loop use case.

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

• N/A. For the non-clinical testing, the "ground truth" would likely refer to expected computational outputs or adherence to DICOM standards, rather than clinical ground truth derived from expert consensus or pathology. The document does not specify the ground truth methods for its non-clinical verification.

8. The sample size for the training set:

• N/A. No clinical testing or machine learning model training (in the modern AI sense requiring large labeled datasets) is described in the context of this 510(k) submission. The new features (like Bayesian deconvolution) are described as standard methodologies rather than novel machine learning models requiring extensive training data specific to this device.

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

• N/A. As no dedicated training set for a clinical machine learning model is mentioned, this is not applicable.

Ask a specific question about this device