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UniSyn is a software application for image registration and fusion display of scanned image data from CT, PET, SPECT, MR and other medical scanners. It is to be used by qualified radiology and nuclear medicine professionals. UniSyn creates multi-planar reformat and maximum intensity projection displays of the data and provides measurements such as area, volume and Standard Uptake Values for user defined regions on the image.

For use with internally administered radioactive products. UniSyn can estimate radiation dose from internalized radioactivity in the human body as a result of a diagnostic or therapeutic medical procedure involving radioactive materials. UniSyn should not be used to deviate from approved product dosing and administration instructions. Refer to the product's prescribing information for instructions.

UniSyn Molecular Imaging (MI) is a Software as a Medical Device (SaMD) that supports the visualization, manipulation and analysis of medical image data acquired or used in radiology and nuclear medicine centers. UniSyn Ml is only intended to be used by qualified radiology and nuclear medicine professionals. Univer-interface components: a patient study browser and the UniSyn MI viewer. The software is available in both thick and can be integrated to launch from PACS software.

Using UniSyn MI users can coregister anatomical and visualize them in fused and/or standalone display, e.g. single or multi-modal combinations of PET, SPECT, CT, and MR images. Users can also visualize and process planar nuclear medicine (NM) images acquired as single of multi-frame images. The layout of the UniSyn MI viewer is highly customisable, a typical layout for a PET/CT study would include of the PET and CT series in multiplanar reformatted (MPR) views as well as a 3D maximum-intensity (MIP) projection rendering of the PET series.

UniSyn MI provides tools to zoom, pan, stack, and window-level the displayed series. Our triangulation tool can be used to localize a single anatomical point of interest among all MPR and MP views of the rest (RO) tools are available to delineate 2D and 3D regions and then compute image statistics within those regions, e.g. ROI area/volumes, minimum, mean and standard deviation of image pixel values. Various image segmentation tools are included with UniSyn MI to facilitate ROI delineation based on image pixel data.

UniSyn MI includes a tool for absorbed dose estimation associated with internally deposited with diagnostic and therapeutic medical procedures. Absorbed dose estimates are based on single- or multi-time point activity measurements of molecular images and absorbed dose coefficients (S-Values) that are based on computational human models.

Once a user has completed their review or analysis of a given study, UniSyn MI provides tools to generate reports and export exemplary image data to share with referring physicians to substantiate their findings.

The medical device described in the document is "UniSyn Molecular Imaging (6-3-1)". It is a software application for image registration, fusion display, and analysis of medical image data (CT, PET, SPECT, MR) used by radiology and nuclear medicine professionals. It also estimates radiation dose from internalized radioactivity.

Here's an analysis of the provided information regarding acceptance criteria and the study that proves the device meets them:

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

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

• Normal Organ Dosimetry: "Published data" and "published literature" were used for comparison. The specific number of patients or organs within these published data sets is not specified. Data provenance is implied to be from existing medical literature.
• Tumor Dosimetry: Comparisons were made using "tumor sizes ranging from 3.9 to 600 cc," implying a range of scenarios were tested against the OLINDA/EXM v1.0 sphere model. The specific number of tumor cases or datasets used is not specified. Data provenance is implied to be through comparison with an existing, cleared device (OLINDA/EXM v1.0).
• Radionuclides Evaluated: Fluorine-18, Gallium-177, Technetium-99m, and Yttrium-90 were evaluated. The number of samples for each radionuclide is not specified.
• The document does not specify countries of origin, nor whether the data was retrospective or prospective. It mainly focuses on comparison with existing validated models and literature.

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

The document does not specify the number of experts or their qualifications used to establish ground truth for the test set. Ground truth was established by:

• Comparison to "values from published data" for normal organ dosimetry.
• Comparison to the "sphere model of OLINDA/EXM v1.0 (K033960)" for tumor dosimetry.

4. Adjudication method for the test set

The document does not specify an adjudication method for the test set. The testing involved direct comparison to reference values from published literature or a validated software model, rather than expert adjudication of device outputs.

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 explicitly mentioned for the UniSyn Molecular Imaging device in this document. The study focused on the validation of the dosimetry model's accuracy against established references, not human-in-the-loop performance or improvement with AI assistance.

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

Yes, a standalone performance evaluation was done for the dosimetry model. The testing involved comparing the device's dose estimates directly against reference values from published data and another FDA-cleared device (OLINDA/EXM v1.0). This evaluates the algorithm's performance in isolation from human interpretation.

For functionalities shared with the predicate (image registration, visualization, measurements, etc.), "existing verification and validation testing protocols" were used, which would also likely be standalone performance evaluations of the software's capabilities.

7. The type of ground truth used

The ground truth used was:

• Published Literature/Data: For normal organ dosimetry, comparison was made to established values in published literature.
• Validated Software Model: For tumor dosimetry, comparison was made to the sphere model of an FDA-cleared device, OLINDA/EXM v1.0 (K033960), which itself serves as a recognized ground truth for dose estimation in that context.

8. The sample size for the training set

The document does not specify the sample size for any training set. Given that the testing methods involve comparisons to established models and published data, it is likely that the dosimetry model relies on pre-existing scientific understanding and computational models rather than a machine learning approach that requires a distinct "training set."

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

As no specific training set is mentioned (implying a non-machine learning approach for the core dosimetry calculations), the establishment of ground truth for a training set is not applicable in this document. The underlying principles and S-values used in the dosimetry model would be based on established scientific principles and data.

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The system is intended for use by Nuclear Medicine (NM) or Radiology practitioners and referring physicians for display, processing, archiving, printing, reporting and networking of NMI data, including planar scans (Static, Whole Body, Dynamic, Multi-Gated) and tomographic scans (SPECT, dedicated PET or Camera-Based-PET) acquired by gamma cameras or PET scanners. The system can run on dedicated workstation or in a server-client configuration.

The NM or PET data can be coupled with registered and or fused CT or MR scans, and with physiological sigmals in order to depict, localize, and/or quantify the distribution of radionuclide tracers and anatomical structures in scamed body tissue for clinical diagnostic purposes.

The DaTQUANT optional application enables visual evaluation and quantification of 1231-ioflupane (DaTscanTM) images. DaTQUANT Normal Database option enables quantification relative to normal population databases of 1231-ioflupane (DaTscanTM) images. These applications may assist in detection of loss of functional dopaminergic neuron terminals in the striatum, which is correlated with Parkinson disease.

The Q.Lung AI application may aid physicians in:

-Diagnosis of Pulmonary Embolism (PE), Chronic Obstructive Pulmonary Disease (COPD), Emphysema and other lung deficiencies.

-Assess the fraction of total lung function provided by a lobe or whole lung for Lung cancer resection requiring removal of an entire lobe, bilobectomy, or pneumonectomy.

The Q.Brain application allows the user to visualize and quantify relative changes in the brain's metabolic function or blood flow activity between a subject's images and controls, which may be resulting from brain functions in: -Epileptic seizures

-Dementia. Such as Alzheimer's disease, Lewy body dementia, Parkinson's disease with dementia, vascular dementia, and frontotemporal dementia.

-Inflammation

-Brain death

-Cerebrovascular disease such as Acute stroke, Chronic and acute ischemia

-Traumatic Brain Injury (TBI)

When integrated with the patient's clinical and diagnostic information may aid the physician in the interpretation of cognitive complaints, neuro-degenerative disease processes and brain injuries.

The Alcyone CFR application allows for the quantification of coronary vascular function by deriving Myocardial Blood Flow (MBF) and then calculating Coronary Flow Reserve (CFR) indices on data acquired on PET scamers and on stationary SPECT scanners with the capacity for dynamic SPECT imaging. These indices may add information to physicians using Myocardial Perfusion Imaging for the diagnosis of Coronary Artery Disease (CAD).

The Exini Bone application is intended to be used with NM bone scans for the evaluation of adult male patients with bone metastases from prostate cancer. Exini Bone quantifies the selected lesions and provides a Bone Scan Index value as adjunct information related to the progression of disease.

The Q.Liver application provides processing, quantification, and multidimensional review of Liver SPECT/PET and CT images for display, segmentation, and a calculation of the SPECT 'liver to lune' shunt value and the patient's Body Surface Area (BSA) for use in calculating a therapeutic dose for Selective Internal Radiation Therapy (SIRT) treatment using a user defined formula.

The O.Thera AI application allows physicians review and monitor patient radiation doses derived from nuclear medicine imaging data, including SPECT/CT, PET/CT, and Whole-body Planar images, and from biological samples from the patient. The application provides estimates of isotope residence time, absorbed dose, and equivalent dose at the whole organ level, as well as estimates of whole-body effective dose. The output from Q.Thera AI may aid physicians in monitoring patient radiation doses.

For use with internally administered radioactive products. O.Thera AI should not be used to deviate from approved product dosing and administration instructions. Refer to the product's prescribing informations.

Xeleris V Processing and Review System is a Nuclear Medicine Software system that is designed for general nuclear medicine processing and review procedures for detection of radioisotope tracer uptake in the patient's body, using a variety of individual processing applications orientated to specific clinical applications. It includes all of the clinical applications and features in the current production version of the predicate Xeleris V and, introduces two clinical applications

Q.Thera AI: The Q.Thera Al application allows physicians review and monitor patient radiation doses derived from nuclear medicine imaging data, including SPECT/CT, and Whole-body Planar images, and from biological samples from the patient. The application provides estimates of isotope residence time, absorbed dose, and equivalent dose at the whole organ level, as well as estimates of whole-body effective dose. The output from Q.Thera Al may aid physicians in monitoring patient radiation doses.

Q.Thera AI is a modification to the predicate's Dosimetry Toolkit application for enhancing site's dosimetry workflow through the following updates:

• Image Pre-Processing: Q.Thera Al uses the predicate's Q.Volumetrix MI application for image preprocessing, bringing additional automated organ segmentations as well as enabling dosimetry on PET/CT imaging data.
• Dosimetry Calculations: Q.Thera Al adds calculation of radiation doses to Dosimetry Toolkit's previous determination of isotope residence time. Similar to the reference Olinda/EXM (K163687), the added calculations follow the guidelines published by the Medical Internal Radiation Dose (MIRD) committee of the Society of Nuclear Medicine (SNM) and models from publication Nº 89 of the International Commission on Radiological Protection (ICRP).

Generate Planar: The Generate Planar application produces 2D derived planar images from 3D SPECT images that are acquired using GE Healthcare's StarGuide SPECT-CT system (K210173). Generate Planar was first cleared on Xeleris 4.0 (K153355). It was also included in StarGuide's 510(k) clearance for producing derived planar images from hybrid SPECT-CT studies. Xeleris V brings the Generate Planar application from Xeleris 4.0 and expands it to also produce derived planar images from SPECT-only studies.

This document does not contain the specific acceptance criteria or a detailed study proving the device meets those criteria, as typically found in a clinical study report. The document is a 510(k) summary for the Xeleris V Processing and Review System, which focuses on demonstrating substantial equivalence to a predicate device rather than presenting a de novo clinical trial with detailed performance metrics and acceptance thresholds.

However, based on the information provided, we can infer some aspects related to the evaluation of the new applications, Q.Thera AI and Generate Planar, that are part of the Xeleris V system.

Here's a breakdown of the available information:

1. Table of acceptance criteria and reported device performance:

The document does not provide a table with explicit acceptance criteria (e.g., minimum sensitivity, specificity, accuracy) or quantitative reported device performance for the Q.Thera AI and Generate Planar applications against predefined thresholds.

Instead, the non-clinical testing sections describe the scope of testing for these new applications:

• Q.Thera AI: "Bench testing for Q.Thera AI confirmed the correctness of the resulting radiation doses across different possible combinations (e.g. models, organs, isotopes) of calculations."
• Generate Planar: "For Generate Planar, bench testing demonstrated similarity between derived planar images produced from SPECT only studies to derived planar images produced from SPECT-CT studies. Similarity was demonstrated using representative clinical datasets for a variety of factors that impact attenuation levels (e.g. body region, BMI)."

These statements highlight that the "acceptance criteria" were qualitative demonstrations of "correctness" for Q.Thera AI calculations and "similarity" for Generate Planar images. There are no numerical performance metrics or thresholds mentioned.

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

• Q.Thera AI: The document mentions "different possible combinations (e.g. models, organs, isotopes) of calculations" for bench testing, but does not specify a sample size for the test set or the number of cases. The data provenance is also not explicitly stated (e.g., country of origin, retrospective/prospective).
• Generate Planar: "representative clinical datasets for a variety of factors that impact attenuation levels (e.g. body region, BMI)" were used. Again, the specific sample size, number of cases, and data provenance are not provided.

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

This information is not provided. The testing described is bench testing focusing on internal correctness and similarity, not necessarily involving expert-derived ground truth on a test set of patient cases for diagnostic accuracy.

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

Not applicable, as no external expert review or adjudication of performance on a clinical test set is described.

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 comparative effectiveness study is mentioned. The document explicitly states: "The proposed Xeleris V did not require clinical studies to support substantial equivalence." This implies that no studies comparing human reader performance with and without AI assistance were conducted as part of this submission.

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

The descriptions of "bench testing" for both Q.Thera AI and Generate Planar imply standalone evaluations of the algorithms' outputs against expected "correctness" or "similarity" without human intervention for interpretation or diagnosis. However, specific standalone performance metrics (e.g., accuracy against a gold standard) are not provided.

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

• Q.Thera AI: The "correctness of the resulting radiation doses" implies a ground truth based on established dosimetric models and calculations (e.g., "MIRD committee of SNM and ICRP Publication 89"). This would be a ground truth derived from established scientific/medical formulas and guidelines rather than expert consensus on patient data or pathology.
• Generate Planar: "similarity between derived planar images" suggests a ground truth or reference for comparison were other derived planar images (from SPECT-CT studies as cleared on Xeleris 4.0), rather than a clinical ground truth like pathology.

8. The sample size for the training set:

The document does not provide information about the training set size for the AI components of Q.Thera AI or Generate Planar. Given the nature of the description (dosimetry calculations based on models and similarity of image generation), it's possible that these are more rule-based or model-based applications rather than deep learning models requiring large training datasets, but this is not explicitly stated.

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

This information is not provided, as details about a training set are absent.

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