Search Results

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.

Ask a specific question about this device

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.

Ask a specific question about this device

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.

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.

The provided text is a 510(k) summary for the Hybrid3D device, specifically focusing on its Lung Lobe Quantification module. However, it does not contain the detailed study information required to answer your specific questions about acceptance criteria and how they were met.

The document broadly states:

• "The testing results supports that all the software specifications have met the acceptance criteria." (Page 4, Section H)
• "Comparisons were made between the module Lung Lobe Quantification in Hybrid3D v1.0 and Hermes Medical Imaging Suite v5.6 (K153056), Hybrid3D v1.0 and PMOD. The results showed a good compliance." (Page 4, Section I)

This general statement indicates that testing was performed and acceptance criteria were met, but it does not provide the specifics of those criteria, the reported device performance, sample sizes, expert qualifications, or adjudication methods.

Therefore, I cannot extract the information required for your request from the provided text. To answer your questions, I would need a more detailed scientific or clinical study report, which is typically found in the full 510(k) submission, but not in this summary document.

Ask a specific question about this device