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Voxel Dosimetry is a software application for nuclear medicine. Based on user input, Voxel Dosimetry calculates a volumetric map of the distribution of absorbed radiation dose on the voxel level for patients who have been administered with radioisotopes. Voxel Dosimetry presents the results to the user and the result can be stored for future analysis.

Voxel Dosimetry is intended for patients of any age and gender undergoing radionuclide therapy.

Voxel Dosimetry is only intended for calculating dose for FDA approved radiopharmaceuticals. Voxel Dosimetry should not be used to deviate from approved product dosing and administration instructions. Refer to the product's prescribing information for instructions.

Voxel Dosimetry is a standalone software application designed to assist the user in absorbed dose calculations at voxel level using a single volumetric image or a time series of images taken after the treatment dose is given to the patient.

Voxel Dosimetry can perform absorbed dose calculations at an organ level (VOI) for right and left kidneys, right and left lungs, liver and spleen, utilizing deep learning based semi-automatic segmentation. The results of the organ segmentation are always displayed overlaid on the CT and functional images for the user to review, and changes can be made manually to all or part of an organ region. The intended workflow is that the user shall review and correct the segmentation before approving the final result.

The provided FDA 510(k) clearance letter and summary for Voxel Dosimetry (K243919) describe performance data to support substantial equivalence. While it states that algorithms perform as expected and meet acceptance criteria, it lacks specific details on the acceptance criteria themselves and the full experimental setup. The information is high-level and does not provide the granular data typically found in a full study report.

Based on the provided text, here's an attempt to describe the acceptance criteria and study proving the device meets them, with explicit notes on what information is not present in the document.

Acceptance Criteria and Device Performance Study for Voxel Dosimetry

The Voxel Dosimetry software (K243919) underwent non-clinical performance evaluation of its algorithms to demonstrate that added features perform as expected and meet pre-set acceptance criteria, thereby supporting the safety and substantial equivalence of the device.

1. Table of Acceptance Criteria and Reported Device Performance

The FDA 510(k) summary lists several new features and states that testing showed results met acceptance criteria. However, the specific quantitative acceptance criteria (e.g., "DICE coefficient > 0.9," "mean error 0.95") are not explicitly stated in the provided document. Similarly, the numerical results for the reported device performance are also not provided. The document only indicates that the results "meet the acceptance criteria."

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

The document does not specify the sample sizes used for the test sets for any of the performance evaluations. It also does not provide information on the data provenance (e.g., country of origin of the data, whether it was retrospective or prospective).

3. Number of Experts and Qualifications for Ground Truth Establishment

For "manual segmentation" and "manual method" comparisons, human experts were presumably involved in establishing the ground truth. However, the document does not specify the number of experts used or their qualifications (e.g., radiologist with X years of experience).

4. Adjudication Method for the Test Set

The document does not provide any information regarding the adjudication method used (e.g., 2+1, 3+1, none) for the test set ground truth establishment.

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

The document does not mention if a multi-reader multi-case (MRMC) comparative effectiveness study was conducted to evaluate how much human readers improve with AI vs. without AI assistance. The performance evaluations described are primarily focused on the algorithmic performance and comparisons to existing methods or internal consistency.

6. Standalone (Algorithm Only) Performance

Yes, standalone performance was evaluated for the algorithms. The text explicitly states, "Non-clinical performance testing for added features shows that the algorithms perform as expected and results were within pre-set acceptance criteria." The comparisons mentioned (e.g., against manual segmentation, scientific computing language, state-of-the-art devices) indicate an assessment of the algorithm's output directly.

7. Type of Ground Truth Used

The types of ground truth used, as inferred from the text, include:

• Manual method/manual segmentation: For non-rigid alignment and semi-automatic organ segmentation, the device's output was compared against manual methods, implying human-derived ground truth.
• Scientific computing language: For single time point studies, integration results were compared to those from "a scientific computing language widely referenced in medical publications," which serves as a highly robust computational ground truth.
• State-of-the-art devices: For organ-based dose calculation, the device's results were compared to those from a "state-of-the-art device," essentially using an established, clinically validated device as ground truth.
• Internal consistency/reproducibility: For lesion segmentation and GPU vs. CPU comparison, reproducibility and consistency were a key aspect of the ground truth assessment.

8. Sample Size for the Training Set

The document does not provide any information about the sample size used for the training set for the deep learning-based semi-automatic organ segmentation.

9. How Ground Truth for the Training Set Was Established

The document does not specify how the ground truth for the training set was established for the deep learning model. It only mentions that the semi-automatic organ segmentation was "using deep learning" and was "tested against manual segmentation" for the test set.

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Hybrid Viewer is a software application for nuclear medicine and radiology. Based on user input, Hybrid Viewer processes, displays and analyzes nuclear medicine and radiology imaging data and presents the result to the user. The result can be stored for future analysis.

Hybrid Viewer is equipped with dedicated workflows which have predefined settings and layouts optimized for specific nuclear medicine investigations,

The software application can be configured based on user needs.

The investigation of physiological or pathological states using measurement and analysis functionality provided by Hybrid Viewer is not intended to replace visual assessment. The information obtained from viewing and/or performing quantitative analysis on the images is used, in conjunction with other patient related data, to inform clinical management,

Hybrid Viewer provides general tools for viewing and processing nuclear medicine and radiology images. It includes software fornuclear medicine (NM) processing studies for specific darts of the body and specific diseases using predefined workflows. Hybrid Viewer 7.0 includes the following additional clinical features compared to Hybrid Viewer 2.8:

• Additional DICOM file support for Segmentation (SEG), RT Dose and CT Fluoroscopy

· Three energy window (TEW) correction for whole body studies

• Automatic motion correction and additional motion correction option for dual isotope

• Display quantitative SPECT studies in SUV units

• Additional NM processing workflows for:

• Assessment of the percentage of activity which is shunted to the lung prior to Y90 microsphere treatment planning.

• Assessment of the ratio of activity in the heart compared to the mediastinum. The workflow contains specific options for the GE Healthcare product AdreView™, a radiopharmaceutical agent used in the detection of primary or secondary pheochromocytoma or neuroblastoma as an adjunct to other diagnostic tests.

• Assessment of the relative uptake in right, left and duplex kidneys in DMSA™ (dimercaptosuccinic acid) SPECT studies. This is an extension of an existing workflow for planar DMSA studies.

This document describes the premarket notification (510(k)) for the Hermes Medical Solutions AB Hybrid Viewer (Premarket Notification Number K241364).

1. Table of Acceptance Criteria and Reported Device Performance

The provided text does not contain a specific table of acceptance criteria or reported device performance in the traditional sense of a clinical study with quantitative metrics. However, it outlines the validation approach for new clinical features and indicates that they met acceptance criteria.

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

The document does not specify a "test set" in terms of patient cases or images for a clinical performance evaluation. The validation described is primarily analytical verification and scripted verification testing of software features. Therefore, information on sample size and data provenance (e.g., country of origin, retrospective/prospective) for a clinical test set is not provided.

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

This information is not provided. The validation relied on "relevant publications" for analytical verification and scripted software testing, rather than expert-established ground truth on a specific clinical test set.

4. Adjudication Method for the Test Set

This information is not provided, as the validation method did not involve an adjudication process on a clinical test set with experts.

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

An MRMC comparative effectiveness study is not mentioned as part of this submission. The device is described as a software application for viewing, processing, displaying, and analyzing imaging data, and its "measurement and analysis functionality...is not intended to replace visual assessment. The information obtained from viewing and/or performing quantitative analysis on the images is used, in conjunction with other patient related data, to inform clinical management." This implies an assistance role rather than a standalone diagnostic or primary reader device that would typically warrant an MRMC study for AI improvement.

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

While the device performs calculations and analyses, the document emphasizes that it is "not intended to replace visual assessment" and its output is used "in conjunction with other patient related data." This suggests that a standalone, algorithm-only performance evaluation, typically associated with AI algorithms making diagnostic decisions without human intervention, was not the focus of this submission given its intended use as an assistive tool for image processing and analysis. The validation focused on the accuracy of the software's processing and computational features.

7. The Type of Ground Truth Used

For the new clinical workflows employing advanced calculations (DMSA SPECT, Lung Liver Shunt, DICOM SEG file support), the ground truth for validation was based on "relevant publications" referenced in the verification documents. This suggests that established scientific and clinical literature provided the reference for the expected outcomes of these calculations.

For new features without advanced calculations, the "ground truth" was essentially the pre-defined acceptance criteria used in the scripted verification testing.

8. The Sample Size for the Training Set

No information regarding a "training set" or machine learning (ML)/AI model development is provided in the document. The description of the device's validation focuses on analytical verification and scripted testing of its processing and display functionalities, which are characteristic of traditional software development and verification rather than ML model training.

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

As no training set is mentioned, this information is not applicable.

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AFFINITY is a software application used to process, display, and manage nuclear medicine and other medical imaging data transferred from other workstations, PACS or acquisition stations. The information acquired from viewing the images is used, in conjunction with other patient related data, for diagnosis and monitoring of disease.

Warning! This application is not intended to replace visual assessment of tumors. The application is to provide pre-selection of lesions for visual confirmation and to provide consistency and reproducibility when assessing tumor response to treatment

Affinity is a software application used to process, display, analyse and manage nuclear medicine and other medical imaging data transferred from other workstations, PACS or acquisition stations. The information acquired from viewing the images is used, in conjunction with other patient related data, for diagnosis and monitoring of disease.

This application is not intended to replace visual assessment of tumors. The primary purpose of the application is to provide pre-selection of lesions for visual confirmation and to provide consistency and reproducibility when assessing tumor response to treatment.

Affinity can process data as whole body or constrained field of view (e.g. abdomen, brain) where scanning was performed with any of the following modalities PET/CT, MR or tomographic reconstructed SPECT/CT. The studies are read in DICOM format and if there are studies with different modalities, on the same patient, a co-registration and alignment is performed.

Software output - After the studies have been processed, these are presented visually in 2D or 3D for the user who can use tools to quantify the significant parameters SUV, SUVR, SUVbsa, SUVIbm, SUVbw, SUV Peak, SUV Mean, TLG and MTV.

Here's a breakdown of the acceptance criteria and study information for the Affinity device, based on the provided document:

Acceptance Criteria and Device Performance

Study Details

1. Sample Sizes and Data Provenance

• Test Set (for Split tool intra/inter-variability): 13 subjects (7 males, 6 females) who received F18FDG-PET/CT studies.
• Data Provenance: Not explicitly stated (e.g., country of origin). The study states "13 subjects... that received a F18FDG-PET/CT study," implying prospective data collection for this specific validation, but this is not explicitly confirmed.
• Test Set (for Hottest Connected SUV peak value accuracy): A single synthetic software-generated study containing 8 voxels.
• Data Provenance: Synthetic (software-generated).

2. Number of Experts and their Qualifications for Ground Truth

• For Split tool intra/inter-variability: No experts were used to establish ground truth in the traditional sense. The validation focused on the reproducibility of the tool itself when used by validators.
• For Hottest Connected SUV peak value accuracy: The "ground truth" was established by manual calculation. No specific number or qualifications of human experts were mentioned for this manual calculation in the provided text.

3. Adjudication Method for the Test Set

• For Split tool intra/inter-variability: Two validators repeated the process 5 times each. The variability between these repeat measurements and between the two validators was assessed. This is not a classic adjudication method for ground truth, but rather a reproducibility assessment.
• For Hottest Connected SUV peak value accuracy: Direct comparison to manual calculation. No adjudication method involving multiple human readers was described.

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

• No, an MRMC comparative effectiveness study involving human readers improving with AI vs. without AI assistance was not conducted or reported in the provided text. The studies focused on the performance characteristics of specific software features.

5. Standalone Performance Study

• Split tool intra/inter-variability: Yes, the study assesses the standalone reproducibility of the "threshold/split tool" by having two validators use the tool on patient data.
• Hottest Connected SUV peak value accuracy: Yes, the study assesses the standalone accuracy of the "Hottest Connected SUV peak value" by comparing its output to manual calculations.

6. Type of Ground Truth Used

• For Split tool intra/inter-variability: The "ground truth" for this test was the assumption that the tool should produce consistent quantitative values. The study measured the reproducibility of these measurements rather than comparing them to a separate, external ground truth like pathology. The process involved defining regions based on a predefined threshold, splitting them, and then deleting specific anatomical regions (brain, heart, urinary bladder) and ensuring pathological areas connected to non-pathological ones were deleted based on clinical importance. This implies an implicit clinical understanding, but not a formally established "ground truth" for each specific lesion.
• For Hottest Connected SUV peak value accuracy: Manual calculation on a synthetic dataset. This serves as the "truth" against which the algorithm's output is compared.

7. Sample Size for the Training Set

• The document does not specify a sample size for the training set. The descriptions provided relate to verification and validation testing, not model training.

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

• Since no information on a training set was provided, the method for establishing its ground truth is also not available in the document.

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AFFINITY is a software application used to process, display, and manage nuclear medicine and other medical imaging data transferred from other workstations, PACS or acquisition stations. The information acquired from viewing the images is used, in conjunction with other patient related data, for diagnosis and monitoring of disease.

The Affinity v1.0 is a Viewer that will be the first Hermes application released on the new development platform Affinity.

The application provides 2D and 3D visualization and processing of medical images in Digital Imaging and Communications in Medicine (DICOM) format from different modalities, such as PET/CT, MR and tomographic reconstructed SPECT from SPECT/CT. Affinity supports coregistration, with the exception of 2D images, and fusion of multiple time points with studies in the same frame of reference, different tracers, and modalities.

Affinity is developed with Microsoft Visual Studio on the .NET framework environment and designed for high throughput clinical scenarios with fast image loading and configurable workflows and layouts. In the design, emphasis has been placed on ease of use, where the user can easily access tools for 3D ROI and uptake analysis. The application supports pre-selection and automatic detection of all uptake areas within the body above a certain threshold level, by using threshold region tool. Where the user can define a threshold for any modality and unit within that modality to include all pixels of the study in a region or islands of regions.

Based on selected regions, quantification of the following parameters can be done SUV, SUVR, SUVbsa, SUVlbm, SUVbw, SUV Peak, SUV Mean, TLG and MTV.

Here's a summary of the acceptance criteria and the study conducted for the Affinity device based on the provided document:

1. Table of Acceptance Criteria and Reported Device Performance

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The intended use of Voxel Dosimetry™ is to provide estimates (deterministic) of absorbed radiation dose at voxel as a result of administering one of the supported radionuclides and to provide a dose map. This is dependent on input data regarding bio distribution being supplied to the application.

Voxel Dosimetry™ only allows voxel-based dose calculations for patients who have been administered with radioisotopes.

Warning! The Voxel Dosimetry™ is only intended for calculating dose for FDA approved radiopharmaceuticals for any clinical purpose, and calculation of unapproved drugs can only be used for research purpose.

Voxel Dosimetry™ is a tool for voxel level absorbed dose calculation resulting from radiotracer injection. Voxel Dosimetry™ workflow consists of the following steps:

• SPECT/CT or PET/CT DICOM data loading from the data manager GOLD or PACS
• Registration of different time-points to a common reference study
• Generation and integration of voxel-level time-activity curves
• Voxel-level absorbed dose calculation using a Monte Carlo-method
• Saving of the absorbed dose-map back to GOLD or PACS in DICOM format

Here's a breakdown of the acceptance criteria and the study proving the device meets them, based on the provided text:

Acceptance Criteria and Device Performance

• For Ga68 (Kidney, Tumor, Spleen)
• For I123 (Kidney, Tumor, Spleen)
• For I131 (Kidney, Tumor, Spleen)
• For In111 (Kidney, Tumor, Spleen)
• For Lu177 (Kidney, Tumor, Spleen)
• For Tc99m (Kidney, Tumor, Spleen)
• For Y90 (Kidney, Tumor, Spleen) | - Ga68: 6%, 6%, 7%
• I123: 3%, 1%, 2%
• I131: 7%, 2%, 3%
• In111: 11%, 7%, 7%
• Lu177: 7%, 3%, 3%
• Tc99m: 8%, 7%, 6%
• Y90: 12%, 8%, 8% |
  | Dose Calculation Accuracy | |
  | Difference in Voxel Dosimetry™ dose compared to PENELOPE dose.
• For I123 (Kidney, Tumor, Spleen)
• For I131 (Kidney, Tumor, Spleen)
• For Ga68 (Kidney, Tumor, Spleen)
• For In111 (Kidney, Tumor, Spleen)
• For Lu177 (Kidney, Tumor, Spleen)
• For Tc99m (Kidney, Tumor, Spleen)
• For Y90 (Kidney, Tumor, Spleen) | - I123: 2%, 3%, 3%
• I131: 3%, 3%, 3%
• Ga68: 12%, 12%, 12%
• In111: 2%, 2%, 3%
• Lu177: 1%, 1%, 1%
• Tc99m: 2%, 3%, 3%
• Y90: 5%, 6%, 4% |
  | Correlation with Predicate Device (OLINDA/EXM® v2.0)
• Pearson's r for left kidney doses
• Pearson's r for right kidney doses | - r_left = 0.97
• r_right = 0.98 |
  | Relative Difference from Predicate Device (OLINDA/EXM® v2.0)
• Average relative difference in kidney doses | - -2% |
  | Safety and Effectiveness | The stated differences in cumulated activities and doses in phantom studies are considered small, with the exception of Ga68, which has high positron energy. The differences between SMC and OLINDA/EXM® v2.0 in kidney dosimetry (2%) are less than the known uncertainty in Lu177 kidney dosimetry, indicating no impact on safety or effectiveness. |
  | Compliance with Software Specifications | "The testing results support that all the software specifications have met the acceptance criteria." |

Study Details

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

   • Phantom Testing:
     • The exact "sample size" in terms of number of different XCAT phantoms generated is not explicitly stated. However, it involved generating an XCAT phantom for each isotope tested (Ga68, I123, I131, In111, Lu177, Tc99m, Y90), with four time points for each isotope.
     • Provenance: Synthetic/simulated data (XCAT phantom).
   • Clinical Data Comparison:
     • Patient Sample Size: Six patients, twelve treatment cycles.
     • Provenance: This appears to be retrospective clinical data, as patients underwent Lu177-DOTATE treatments and were scanned at specific time points. The publication (Hippeläinen et al., 2017) suggests it was a real-world study. The specific country of origin is not mentioned.

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

   • Phantom Testing: Ground truth was established by analytical or established Monte Carlo methods (PENELOPE, mono-exponential model). No human experts were directly involved in establishing this ground truth.
   • Clinical Data Comparison: The comparison was against the predicate device OLINDA/EXM® v2.0, which itself is a calculation tool. While presumably experts would have performed the initial OLINDA/EXM® calculations, the text doesn't specify experts for this comparison's ground truth beyond the output of the predicate.

3. Adjudication method for the test set:

   • No adjudication method (like 2+1 or 3+1) is mentioned, as the ground truth for both test sets (phantom and clinical comparison) was established via computational models or comparison with another software, not human consensus.

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 MRMC or human-in-the-loop study with human readers/AI assistance was conducted or reported. This device is a dose calculation software, not an AI-assisted diagnostic tool for human interpretation.

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

   • Yes, the performance evaluation was entirely a standalone assessment of the algorithm. Its calculations were compared against analytical results (phantom studies) or another standalone algorithm (OLINDA/EXM® v2.0).

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

   • Phantom Testing:
     • For cumulated activity: Analytical integration of a mono-exponential model.
     • For dose calculations: Monte Carlo simulation results from PENELOPE code.
   • Clinical Data Comparison: Reference standard was the output of the legally marketed predicate device, OLINDA/EXM® v2.0.

7. The sample size for the training set:

   • The document does not explicitly mention a "training set" in the context of machine learning model development. This device appears to be based on a Semi-Monte Carlo (SMC) method for dose calculation, which is a physics-based model rather than a data-driven machine learning model requiring a specific training set. Therefore, this question is not directly applicable in the typical sense. The underlying physics models and S-values might be "trained" or derived from theoretical physics and extensive pre-computed data, but not in the sense of a deep learning model.

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

   • As explained above, there's no mention of a traditional machine learning "training set" or its ground truth establishment in the provided text. The SMC method is a computational technique based on physical principles, not a model learned from labeled data in the usual machine learning context.

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Hybrid3D that provides software applications used to process, display, and manage nuclear medicine and other medical imaging data transferred from other workstation or acquisition stations.

HERMES Hybrid3D is a reading and processing module for the advanced needs in medical imaging. It offers multi-modal (PET/CT/MR/SPECT) coregistration and interactive fusion of multiple datasets. HybridViewer 3D handles viewing and fusion of multi-sequence MRI studies with oblique orientation and allows switching between original and standard TCS view orientation as well as defining own slice directions. 3D seqmentation, cropping and interpolation techniques allow complex tasks in VOI definition and can cover cases like cavities, splitting structures into subsections or logic operations (compute intersections, merge, grow). Results can be imported and exported as DICOM and are therefore available for research in 3rd party tools. Additionally, it provides tools for advanced 3D fusion rendering of studies and VOIs.

Lung Lobe Quantification: The Lung Lobe Quantification module in Hybrid3D, introduces an efficient and automated workflow solution to accurately compute 3D lobar anatomy from CT (with or without contrast). The workflow supports the addition of functional images (SPECT V/Q, SUV SPECT, CT iodine maps, hyperpolarized xenon MRI, etc.) to accurately relate lobar anatomy to function. No changes have been made to Lung Lobar Quantification since the previous release.

TumorFinder: The Tumor Finder wizard provides automatic segmentation of lesions in a PET study or a combined PET/CT study pair, based on criteria relative to a background volume placed in the liver or mediastinum. This reduces the time required for tumor delineation. It also provides both visual and statistical evaluation of tumor burden, which helps with comparing follow up studies.

SIRT: Selective Internal Radionuclide Therapy (SIRT), is currently used in the treatment of liver tumors either from primary liver cancer or metastatic disease (e.g. colorectal primary cancer). The SIRT wizard provides processing for SIRT planning and verification.

The provided text is a 510(k) summary for the medical device Hybrid3D v3.0. While it discusses software features, regulatory details, and some testing, it does not contain a detailed study proving the device meets specific acceptance criteria in the manner typically expected for AI/Machine Learning-based medical devices.

Instead, the performance evaluation in this document focuses on:

• Comparison to a predicate device (Hybrid3D v2.0): Stating "The proposed device will use similar technology and fundamental concepts and operation are also the same," and "The comparisons between Hybrid 3D v3.0 and Hybrid 3D v2.0 (K171719) were part of the test procedure for V3.0 and showed good results." This implies a functional equivalence rather than a new clinical performance study.
• Validation of specific calculations for the SIRT module: This is a validation of the accuracy of mathematical computations within a specific module, not a broad clinical performance assessment of features like image processing or tumor finding from AI.

Therefore, for many of your specific questions, the information is not present in the provided text. I will address what is available and clearly state what is missing.

Here's a breakdown based on the provided text:

1. Acceptance Criteria and Reported Device Performance

The document does not present a formal table of general acceptance criteria for the entire Hybrid3D device, nor does it provide a comprehensive "reported device performance" in terms of clinical metrics (e.g., sensitivity, specificity, accuracy).

However, it does provide implicit acceptance criteria and reported "performance" for the SIRT (Selective Internal Radionuclide Therapy) module's calculations:

2. Sample Size and Data Provenance

The document does not specify sample sizes (e.g., number of patients, number of images) used for any test set or the provenance (country of origin, retrospective/prospective) of any data used for testing. The validation described for the SIRT module appears to be a comparison of calculation results against a spreadsheet, not a clinical data set.

3. Number and Qualifications of Experts for Ground Truth

The document does not mention the use of experts or their qualifications for establishing ground truth for any test set, as the described validation is for calculation accuracy against spreadsheet formulae, not expert interpretation of images.

4. Adjudication Method

The document does not mention any adjudication method, as it does not describe a process involving multiple readers or complex ground truth establishment.

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

The document does not indicate that an MRMC comparative effectiveness study was performed, or any effect size of AI assistance on human readers. The device is primarily described as a software for processing, display, and management of imaging data, and specific quantification modules, not an AI-assisted diagnostic aid that directly impacts human reader performance in a comparative study.

6. Standalone (Algorithm Only) Performance

The document describes the device's functional capabilities (image processing, display, quantification for SIRT, TumorFinder, Lung Lobe Quantification), but it does not present a standalone performance study (e.g., sensitivity, specificity, accuracy) for these algorithmic features, especially for TumorFinder or Lung Lobe Quantification, against a clinical ground truth. The "testing results supports that all the software specifications have met the acceptance criteria" is a very general statement. The specific validation described is for the calculation accuracy of the SIRT module.

7. Type of Ground Truth Used

For the specific validation described for the SIRT module, the "ground truth" used was:

• Spreadsheet calculations based on formulae published by SIRTEX for Resin Microspheres and BTG for Glass Theraspheres. This is a form of scientific/mathematical ground truth for the accuracy of internal calculations, not a clinical ground truth like expert consensus, pathology, or outcomes data.

For other modules like TumorFinder or Lung Lobe Quantification, the document does not describe how their performance was validated or what type of ground truth was used.

8. Sample Size for Training Set

The document does not mention any training set sample size, which suggests that the development did not involve a machine learning model that required a distinct training phase in the context of this 510(k) submission. Given the description focusing on image processing, co-registration, 3D segmentation, and rule-based quantification (TumorFinder "based on criteria relative to a background volume"), it's plausible the "AI" aspects are more algorithmic and rule-based rather than deep learning requiring large training sets.

9. How Ground Truth for Training Set Was Established

Since no training set is mentioned, this information is not applicable/provided.

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HERMES Medical Imaging suite that provides software applications used to process, display, analyze and manage nuclear medicine and other medical imaging data transferred from other workstation or acquisition stations.

The base product design of Hermes Medical Imaging Suite v5.7 is the same as for the Hermes Medical Imaging Suite v5.6 (K153056). The following modules have been added in this applicatio to Hybrid Viewer NM Processing: Colonic Transit, Remnant Liver, Parathyroid, Dosimetry, Classic DMSA, Oesophageal Transit/Reflux, HIDA, Salivary Gland, Bone3Phase Analysis and Uniformity.

Here's an analysis of the provided text regarding the acceptance criteria and study details for the Hermes Medical Imaging Suite v5.7:

Understanding the Device:

The device, HERMES Medical Imaging Suite v5.7, is a software application suite for processing, displaying, analyzing, and managing nuclear medicine and other medical imaging data. It has added several new modules compared to its predecessor (v5.6).

Challenges in Extracting Information:

The provided text is a 510(k) summary, which is a regulatory document focused on demonstrating substantial equivalence to a predicate device. It doesn't present a traditional "acceptance criteria" table or a single, comprehensive study report with detailed methodologies. Instead, it describes a series of comparative tests against predicate devices and manual calculations. Therefore, the "acceptance criteria" are implied by the comparisons and the statement that "the testing results supports that all the software specifications have met the acceptance criteria." The reported performance is the outcome of these comparisons.

1. Table of Acceptance Criteria and Reported Device Performance

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Hybrid3D that provides software applications used to process, display, and manage nuclear medicine and other medical imaging data transferred from other workstation or acquisition stations.

HERMES Hybrid3D is a reading and processing module for the advanced needs in medical imaging. It offers multi-modal (PET/CT/MR/SPECT) coregistration and interactive fusion of multiple datasets. HybridViewer 3D handles viewing and fusion of multi-sequence MRI studies with oblique orientation and allows switching between original and standard TCS view orientation as well as defining own slice directions. 3D segmentation, cropping and interpolation techniques allow complex tasks in VOI definition and can cover cases like cavities, splitting structures into subsections or logic operations (compute intersections, merge, grow). Results can be imported and exported as DICOM and are therefore available for research in 3rd party tools. Additionally, it provides tools for advanced 3D fusion rendering of studies and VOIs.

The Lung Lobe Quantification module in Hybrid 3D, introduces an efficient and automated workflow solution to accurately compute 3D lobar anatomy from CT (with or without contrast). The workflow supports the addition of functional images (SPECT V/Q, SUV SPECT, CT iodine maps, hyperpolarized xenon MRI, etc.) to accurately relate lobar anatomy to function.

Here's a breakdown of the acceptance criteria and study information for the Hybrid3D device, extracted from the provided text:

Acceptance Criteria and Device Performance

The provided document describes comparisons between the new device (Hybrid3D v2.0) and its predicate devices (HERMES Medical Imaging Suite v5.6 and Hybrid3D v1.0). The acceptance criteria are implicitly defined by the "good results" or numerical thresholds reported in the comparative testing.

• SUV peak: Up to 10% difference in some cases (r = 0.993) |
  | Quantitative Parameters (SUV max, SUV mean, SUV peak - based on SUV Surface Area, Lean Body Mass, BMI): Generally within 5% of predicate, with SUV peak potentially differing up to 10%. | - SUV max, SUV mean: Generally within 5% (r = 0.999)
• SUV peak: Up to 10% difference in some cases (r = 0.988) |
  | SUV max and SUV mean for Quick VOIs: Good agreement with predicate. | - SUV max: Within 6% (r = 0.991)
• SUV mean: Within 10% (r = 0.955) |
  | Image Labeling: Identical to predicate. | Shown to be the same. |
  | Automatic Registration: Equivalent results to predicate for various patient studies (PT/CT, PT/MR, SPECT/CT). | Results in all were equivalent in the two applications. |
  | RT Structure Set Compatibility: Saved RT structure sets from one application load and give same results in the other, and vice-versa. | Showed good agreement. |

Study Information

The document describes verification and validation testing, focusing on comparisons with predicate devices.

1. Sample sizes used for the test set and the data provenance:

   • Phantom studies: Used for linear measurements and Hounsfield unit estimations. The number of phantom studies is not specified, but it states "phantom studies acquired with cameras from two different vendors."
   • Patient studies: Used for quantitative parameters (SUV max, mean, peak) and automatic registration. The number of patient studies is not specified, but it mentions "patient studies acquired with cameras from two different vendors" for SUV calculations and "serial PT/CT patient studies, a PT study and external MR study, and a SPECT study and external CT study" for automatic registration testing.
   • Data Provenance: Not explicitly stated, but the company is based in Stockholm, Sweden, and the description of "cameras from two different vendors" and various types of patient studies suggests a diverse dataset. It is implied to be retrospective as it's for verification against existing data.

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

   • This information is not provided in the document. The comparisons are made against results generated by the predicate devices, not interpreted by independent human experts. The document does mention "operator variation" as a possible reason for differences in SUV peak and mean values, implying human involvement in drawing VOIs on both the new and predicate applications. However, these operators are not explicitly designated as "experts" for ground truth establishment.

3. Adjudication method for the test set:

   • This information is not provided. The testing appears to be quantitative comparison against predicate device outputs rather than an adjudication process involving human reviewers for discrepancies.

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:

   • A multi-reader multi-case (MRMC) comparative effectiveness study was not done or reported in this document. The device, Hybrid3D, is a software application for processing, displaying, and managing medical imaging data, including automated workflows like lung lobe quantification, but the testing focuses on its performance relative to predicate devices, not on human reader improvement with or without AI assistance.

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

   • The testing described is essentially standalone in the sense that the device's calculations and outputs (linear measurements, Hounsfield units, SUV values, registration, image labeling, RT structure sets) are compared directly with those of the predicate device. While human operators are involved in setting up the comparisons (e.g., drawing VOIs), the evaluation is of the software's output itself. The "Lung Lobe Quantification module" mentioned in the description implies an automated (standalone) workflow component, but dedicated standalone performance of this specific module isn't detailed in the testing summary beyond its general functionality.

6. The type of ground truth used:

   • The ground truth is based on comparison with predicate devices' performance and outputs. For quantitative measures (linear, HU, SUV), the "ground truth" is implied to be the values generated by the predicate device (HERMES Medical Imaging Suite v5.6 and Hybrid3D v1.0). For automatic registration and image labeling, the "ground truth" is equivalence to the predicate's behavior.

7. The sample size for the training set:

   • This information is not provided. The document describes the device and its validation but does not mention any machine learning components that would require a distinct training set. The "Lung Lobe Quantification module" uses an "efficient and automated workflow solution to accurately compute 3D lobar anatomy from CT," which might involve machine learning, but there is no specific mention of a training set or its size.

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

   • This information is not provided, as no training set is explicitly mentioned or detailed in the document.

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The intended use of OLINDA/EXM is to provide estimates (deterministic) of absorbed radiation dose at the whole organ level as a result of administering any radionuclide and to calculate effective whole-body dose. This is dependent on input data regarding bio distribution being supplied to the application.

The OLINDA/EXM® v2.0 is a modification of OLINDA/EXM® v1.1 (K033960) and includes new human models and nuclides. OLINDA/EXM® 2.0 employs a new set of decay data recommended by the International Commission on Radiological Protection (ICRP). OLINDA/EXM® 2.0 introduces a new series of anthropomorphic human body models (phantoms), so new values of Specific Absorbed Fractions (SAF), di (T←S) were generated. These phantoms were based on updated values of the mass of the target region (mr) recommended by the ICRP. The base product design of OLINDA/EXM® V2.0 is the same as for the OLINDA/EXM® V1.1 (K033960).

The provided document is a 510(k) summary for a medical device called OLINDA/EXM v2.0. This document primarily focuses on demonstrating substantial equivalence to a predicate device (OLINDA/EXM v1.1) rather than presenting a detailed clinical study with acceptance criteria and device performance in the way one might expect for a diagnostic or therapeutic AI device.

However, based on the information provided, here's a breakdown of what can be extracted and what is not explicitly stated in the document regarding acceptance criteria and a study:

1. Table of Acceptance Criteria and Reported Device Performance

The document does not provide a formal table of acceptance criteria with corresponding performance metrics like sensitivity, specificity, accuracy, or effect sizes, as would be common for diagnostic algorithms. Instead, the "acceptance criteria" appear to be related to the verification and validation of the software itself and its consistency with the previous version. The performance is described in terms of "good compliance" with the predicate device.

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

This information is not explicitly provided in the document. The "tests for verification and validation" are mentioned, but the specific details of a "test set" (e.g., number of cases, type of data) are not described. Given that the device calculates radiation dose based on input data regarding biodistribution and relies on established models (ICRP decay data, anthropomorphic phantoms), the testing likely involved comparing output values for a range of inputs rather than a clinical dataset of patient images.

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

This information is not explicitly provided. Since the device calculates deterministic radiation doses based on models, the "ground truth" would likely be derived from established physical and biological models, rather than expert interpretation of medical images or clinical outcomes.

4. Adjudication Method for the Test Set

This information is not explicitly provided. Adjudication methods like 2+1 or 3+1 are typically used when human experts are disagreeing on interpretations for a ground truth. This is not applicable to a dose calculation software validating against established models and data.

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

This information is not explicitly provided, and it is unlikely such a study was performed or needed given the nature of the device. MRMC studies are typically for diagnostic AI systems where human readers interpret medical images. This device is a software tool for calculating radiation dose.

6. If a Standalone Study (Algorithm Only Without Human-in-the-Loop Performance) Was Done

The document implies that the "testing" described for verification and validation was a standalone evaluation of the algorithm's performance against its specifications and the predicate device. The comparison showing "good compliance" between OLINDA/EXM v2.0 and OLINDA/EXM v1.1 suggests an algorithm-only evaluation. However, the exact methodology is not detailed.

7. The Type of Ground Truth Used

The "ground truth" for this device likely refers to:

• Established physical and biological models: The document mentions "new human models and nuclides," "new set of decay data recommended by the International Commission on Radiological Protection (ICRP)," and "updated values of the mass of the target region (mr) recommended by the ICRP." These are the underlying scientific references against which the calculations would be validated.
• Outputs of the predicate device (OLINDA/EXM v1.1): The comparison showing "good compliance" with the predicate device implies that the predicate's outputs served as a reference for validating the new version.

8. The Sample Size for the Training Set

This information is not applicable/provided. OLINDA/EXM v2.0 is a deterministic calculation software based on established physical and biological models, not a machine learning or AI model that requires a "training set" in the conventional sense. It's a software tool that implements mathematical models and data.

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

This information is not applicable/provided for the same reasons as #8. The "ground truth" here is derived from scientific consensus and established data (e.g., ICRP recommendations) that are used as inputs or validation references for the software's calculations, not a "training set" for a learning algorithm.

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