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510(k) Data Aggregation
(166 days)
Synapse 3D Base Tools (V7.0)
Synapse 3D Base Tools is medical imaging software that is intended to provide trained medical professionals with tools to aid them in reading, interpreting, reporting, and treatment planning. Synapse 3D Base Tools accepts DICOM compliant medical images acquired from a variety of imaging devices including, CT, MR, CR, US, NM, PT, and XA, etc.
This product is not intended for use with or for the primary diagnostic interpretation of Mammography images. Synapse 3D Base Tools provides several levels of tools to the user: Basic imaging tools for general images, including 2D viewing, volume rendering and 3D volume viewing, orthogonal / oblique / curved Multi-Planar Reconstructions (MPR), Maximum (MIP), Average (RaySum) and Minimum (MinIP) Intensity Projection, 4D volume viewing, image fusion, image subtraction, surface rendering, sector and rectangular shape MPR image viewing, MPR for dental images, creating and displaying multiple MPR images along an object, time-density distribution, basic image processing, noise reduction, CINE, measurements, annotations, reporting, printing, storing, distribution, and general image management and administration tools, etc.
• Tools for regional segmentation of anatomical structures within the image data, path definition through vascular and other tubular structures, and boundary detection.
• Image viewing tools for modality specific images, including CT PET fusion and ADC image viewing for MR studies.
• Imaging tools for CT images including virtual endoscopic viewing, dual energy image viewing.
• Imaging tools for MR images including delayed enhancement image viewing, diffusion-weighted MRI image viewing.
The intended patient population for all applications implemented as base tools is limited to adult population (over 22 years old).
The 3D image analysis software Synapse 3D Base Tools (V7.0) is medical application software running on Windows server/client configuration installed on commercial general-purpose Windows-compatible computers. It offers software tools which can be used by trained professionals to interpret medical images obtained from various medical devices, to create reports, or to develop treatment plans.
The provided text details the FDA 510(k) clearance for Synapse 3D Base Tools (V7.0). It primarily focuses on demonstrating substantial equivalence to a predicate device and includes information on nonclinical and certain clinical performance testing for newly added deep-learning-based organ segmentation features.
Here's an analysis of the acceptance criteria and study that proves the device meets them, based on the provided text:
Acceptance Criteria and Reported Device Performance
The core of acceptance criteria for this 510(k) submission appears to be demonstrating substantial equivalence to a predicate device (Synapse 3D Base Tools V6.6 K221677) and proving the safety and effectiveness of new features, particularly those utilizing deep learning for automatic or semi-automatic organ extraction.
While no explicit "acceptance criteria" table is provided in the document in terms of specific thresholds for the overall device functionality, the performance section for the deep learning models serves as such for those specific features. The acceptance criterion for these features is implicitly showing a high Dice Similarity Coefficient (DICE) score, indicating strong agreement between the automated segmentation and the ground truth.
Table of Acceptance Criteria and Reported Device Performance (for Deep Learning Segmentation)
| Segmented Structure (Modality) | Number of Cases | Acceptance Criteria (Implicit) - High DICE Score | Reported Device Performance (Average DICE) |
|---|---|---|---|
| Duodenum (CT) | 30 | High DICE | 0.85 |
| Stomach (CT) | 30 | High DICE | 0.96 |
| Lung section (Left S1S2) (CT) | 30 | High DICE | 0.92 |
| Lung section (Left S3) (CT) | 30 | High DICE | 0.88 |
| Lung section (Left S4) (CT) | 30 | High DICE | 0.75 |
| Lung section (Left S5) (CT) | 30 | High DICE | 0.81 |
| Lung section (Left S6) (CT) | 30 | High DICE | 0.9 |
| Lung section (Left S8) (CT) | 30 | High DICE | 0.85 |
| Lung section (Left S9) (CT) | 30 | High DICE | 0.73 |
| Lung section (Left S10) (CT) | 30 | High DICE | 0.87 |
| Lung section (Right S1) (CT) | 30 | High DICE | 0.89 |
| Lung section (Right S2) (CT) | 30 | High DICE | 0.89 |
| Lung section (Right S3) (CT) | 30 | High DICE | 0.91 |
| Lung section (Right S4) (CT) | 30 | High DICE | 0.88 |
| Lung section (Right S5) (CT) | 30 | High DICE | 0.85 |
| Lung section (Right S6) (CT) | 30 | High DICE | 0.9 |
| Lung section (Right S7) (CT) | 30 | High DICE | 0.8 |
| Lung section (Right S8) (CT) | 30 | High DICE | 0.84 |
| Lung section (Right S9) (CT) | 30 | High DICE | 0.71 |
| Lung section (Right S10) (CT) | 30 | High DICE | 0.83 |
| Pancreas section (Body) (CT) | 29 | High DICE | 0.91 |
| Pancreas section (Head) (CT) | 29 | High DICE | 0.95 |
| Pancreas section (Tail) (CT) | 29 | High DICE | 0.99 |
| Spleen (CT) | 35 | High DICE | 0.95 |
| Pancreas duct (CT) | 29 | High DICE | 0.74 |
| Pancreas (CT) | 30 | High DICE | 0.86 |
| ROI (CT)* | 29 | High DICE | 0.85 |
| Liver section (S1) (CT) | 31 | High DICE | 0.99 |
| Liver section (S2) (CT) | 31 | High DICE | 0.99 |
| Liver section (S3) (CT) | 31 | High DICE | 0.97 |
| Liver section (S4) (CT) | 31 | High DICE | 0.97 |
| Liver section (S5) (CT) | 31 | High DICE | 0.92 |
| Liver section (S6) (CT) | 31 | High DICE | 0.94 |
| Liver section (S7) (CT) | 31 | High DICE | 0.98 |
| Liver section (S8) (CT) | 31 | High DICE | 0.97 |
| Gall bladder (CT) | 37 | High DICE | 0.92 |
| Bronchus (CT) | 30 | High DICE | 0.87 |
| Lung lobe (Left Lower) (CT) | 30 | High DICE | 0.99 |
| Lung lobe (Left Upper) (CT) | 30 | High DICE | 0.99 |
| Lung lobe (Right Lower) (CT) | 30 | High DICE | 0.99 |
| Lung lobe (Right Middle) (CT) | 30 | High DICE | 0.97 |
| Lung lobe (Right Upper) (CT) | 30 | High DICE | 0.99 |
| Pulmonary Arteries (CT) | 30 | High DICE | 0.83 |
| Pulmonary Veins (CT) | 30 | High DICE | 0.85 |
| Pancreas vessel (CT) | 30 | High DICE | 0.9 |
| Prostate (MRI) | 30 | High DICE | 0.9 |
| Rectal ROI (tumor) (MRI)* | 27 | High DICE | 0.75 |
| Ureter (T2) (MRI) | 33 | High DICE | 0.63 |
| Bladder (MRI) | 35 | High DICE | 0.93 |
| Pelvis (MRI) | 34 | High DICE | 0.94 |
| Seminal vesicle (MRI) | 32 | High DICE | 0.7 |
| Ureter (T1Dynamic) (MRI) | 33 | High DICE | 0.76 |
| Prostate tumor (DWI) (MRI)* | 36 | High DICE | 0.65 |
| Prostate tumor (T2) (MRI)* | 39 | High DICE | 0.6 |
| Kidney tumor (MRI)* | 31 | High DICE | 0.88 |
| Left Kidney (MRI) | 31 | High DICE | 0.97 |
| Right Kidney (MRI) | 31 | High DICE | 0.98 |
| ROI (MRI)* | 133 | High DICE | 0.72 |
| Rectal muscularis propria (MRI) | 32 | High DICE | 0.91 |
| Mesorectum (MRI) | 32 | High DICE | 0.9 |
| Pelvic vessel (Artery) (MRI) | 30 | High DICE | 0.81 |
| Pelvic vessel (Vein) (MRI) | 30 | High DICE | 0.8 |
| Kidney vessel (Artery) (MRI) | 32 | High DICE | 0.92 |
| Kidney vessel (Vein) (MRI) | 32 | High DICE | 0.86 |
| Pelvic nerve (MRI) | 30 | High DICE | 0.7 |
| Levator ani muscle (MRI) | 30 | High DICE | 0.77 |
| Overall (Total cases) | 1086 | Consistent and Acceptable Performance | Range of 0.60 to 0.99 (Average DICE) |
Note: For items marked with an asterisk (*), the extraction is performed semi-automatically. All others are executed automatically. The acceptance criterion is "High DICE," as no specific quantitative threshold is given, but the reported values generally indicate good agreement. "Additional distance based metrics 95% Hausdorff Distance and Mean Surface Distance were also reported along with the subgroup analysis. Detailed results are reported in the labeling."
Study that Proves the Device Meets Acceptance Criteria
The study described is a performance testing for the new deep-learning-based automatic or semi-automatic organ extraction functions.
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Sample size used for the test set and the data provenance:
- Sample Size: 1086 cases were collected for performance testing.
- Data Provenance: The data was collected newly from US patient populations across various regions: US_East (295 cases), US_Midwest (175 cases), US_Southeast (185 cases), US_Southwest (73 cases), and US_Northwest (4 cases). This indicates a prospective data collection specifically for this testing, originating from the US. The text also mentions the test data is "independence from training data."
- Demographics: The test set included 672 men, 414 women, and a range of ages from 22 to 120+ years old. Modalities covered CT and MRI from various major manufacturers (SIEMENS, GE, PHILIPS, CANON, FUJIFILM).
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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 the ground truth. It only states that the performance testing used an "average DICE" score, implying a comparison against some form of expertly derived ground truth.
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Adjudication method (e.g. 2+1, 3+1, none) for the test set:
- The document does not specify any adjudication method for establishing the ground truth.
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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 was not explicitly mentioned or performed as part of this submission for demonstrating substantial equivalence. The clinical testing mentioned focused on the standalone performance of the new deep learning features (i.e., automatic or semi-automatic segmentation accuracy) rather than human reader performance with or without AI assistance. The submission states, "The subject of this 510(k) notification, Synapse 3D Base Tools does not require clinical studies to support safety and effectiveness of the software."
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If a standalone (i.e. algorithm only without human-in-the-loop performance) was done:
- Yes, a standalone performance evaluation was done for the automatic (and semi-automatic) deep learning segmentation functions. The Dice Similarity Coefficient (DICE) scores provided are a measure of the algorithm's performance in segmenting anatomical structures compared to a ground truth, without human intervention in the segmentation process itself, although some extractions are noted as "semi-automatic" where human interaction would refine the output.
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The type of ground truth used (expert consensus, pathology, outcomes data, etc.):
- The text implies the ground truth for the segmentation tasks was established by expert consensus/manual annotation (as DICE is a metric comparing algorithmic output to a reference segmentation, typically derived from expert outlines). However, the specific method (e.g., single expert, multi-expert consensus) is not detailed. It mentions "Additional distance based metrics 95% Hausdorff Distance and Mean Surface Distance were also reported," which are also used for comparing segmentation masks to a ground truth.
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The sample size for the training set:
- The document does not explicitly provide the sample size for the training set. It only states that the test data was "independence from training data," implying a separate training dataset was used.
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How the ground truth for the training set was established:
- The document does not provide details on how the ground truth for the training set was established. However, for deep learning segmentation, it is typically established through manual annotation by qualified experts.
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(154 days)
Synapse 3D Base Tools v6.6
Synapse 3D Base Tools is medical imaging software that is intended to provide trained medical professionals with tools to aid them in reading, interpreting, and treatment planning. Synapse 3D Base Tools accepts DICOM compliant medical images acquired from a variety of imaging devices including, CT, MR, CR, US, NM, PT, and XA, etc. This product is not intended for use with or for the primary diagnostic interpretation of Mammography images.
Synapse 3D Base Tools provides several levels of tools to the user:
Basic imaging tools for general images, including 2D viewing, volume rendering and 3D volume viewing, orthogonal / oblique / curved Multi-Planar Reconstructions (MPR), Average (RaySum) and Minimum (MinIP) Intensity Projection, 4D volume viewing, image subtraction, surface rendering, sector and rectangular shape MPR image viewing, MPR for dental images, creating and displaying multiple MPR images along an object, time-density distribution, basic image processing, noise reduction, CINE, measurements, annotations, reporting, printing, storing, distribution, and general image management and administration tools, etc.
- Tools for regional segmentation of anatomical structures within the image data, path definition through vascular and other tubular structures, and boundary detection.
-Image viewing tools for modality specific images, including CT PET fusion and ADC image viewing for MR studies. -Imaging tools for CT images including virtual endoscopic viewing and dual energy image viewing.
-Imaging tools for MR images including delayed enhancement image viewing, diffusion-weighted MRI image viewing.
The 3D image analysis software Synapse 3D Base Tools (V6.6) is medical application software running on Windows server/client configuration installed on commercial general-purpose Windows-compatible computers. It offers software tools which can be used by trained professionals to interpret medical images obtained from various medical devices, to create reports, or to develop treatment plans.
Synapse 3D Base Tools is connected through DICOM standard to medical devices such as CT, MR, CR, US, NM, PT, XA, etc. and to a PACS system storing data generated by these medical devices, and it retrieves image data via network communications based on the DICOM standard. The retrieved image data are stored on the local disk managed by Synapse 3D Base Tools (V6.6), and the associated image-related information of the image data is registered in its database and is used for display, image processing, analysis, etc. Images newly created by Synapse 3D Base Tools (V6.6) not only can be display, but also can be printed on a hardcopy using a DICOM printer or a Windows printer.
Synapse 3D Base Tools (V6.6) is a basic software module that works with other cleared clinical applications, including Synapse 3D Cardiac Tools (K200973), Synapse 3D Perfusion Analysis (K162287), Synapse 3D Lung and Abdomen Analysis (K130542), Synapse 3D Liver and Kidney Analysis (K142521), Synapse 3D Nodule Analysis (K120679), Synapse 3D Colon Analysis (K123566), Synapse 3D Tensor Analysis (K141514) and Synapse 3D Blood Flow Analysis (K191544). All these software modules consist of the Synapse 3D product family.
Synapse 3D Base Tools can be integrated with Fujifilm's Synapse PACS, and can be used as a part of a Synapse system. Synapse 3D Base Tools also can be integrated with Fujifilm's Synapse Cardiovascular for cardiology purposes.
The provided text does not contain detailed acceptance criteria and a study proving that the device meets these criteria in the typical format expected for a medical device with an AI/ML component affecting diagnostic accuracy. The device, "Synapse 3D Base Tools v6.6", is described as medical imaging software for viewing, interpreting, and planning, and its predicate device (Synapse 3D Base Tools v6.1) and other reference devices are also imaging software tools.
Instead, the submission focuses on demonstrating substantial equivalence to a predicate device, Synapse 3D Base Tools (V6.1) (K203103), by confirming similar indications for use and technical characteristics. The key performance information provided is about software development processes, verification and validation, and cybersecurity, rather than specific diagnostic performance metrics (e.g., sensitivity, specificity, AUC) for novel AI features impacting clinical decisions.
However, based on the information provided, particularly about the Dual Energy Analysis module and PixelShine, we can infer some aspects related to acceptance and performance.
Here's an analysis of the provided text in relation to your questions:
1. A table of acceptance criteria and the reported device performance
The document does not explicitly state a table of quantitative acceptance criteria for diagnostic performance metrics (e.g., sensitivity, specificity, accuracy) that the device must meet to be considered "accepted." This is likely because the device is primarily a "Medical image management and processing system" (21 CFR 892.2050) and not a primary diagnostic AI intended to make a diagnosis itself, but rather to provide "tools to aid them [trained medical professionals] in reading, interpreting, and treatment planning."
However, we can infer the acceptance for new features based on comparative testing for substantial equivalence:
| Acceptance Criterion (Inferred from Substantial Equivalence Review) | Reported Device Performance / Method of Proof |
|---|---|
| Overall Functionality and Design Specifications | "Test results showed that all tests passed successfully according to the design specifications." "All of the different components... have been stress tested to ensure that the system as a whole provides all the capabilities necessary to operate according to its intended use and in a manner substantially equivalent to the predicate devices." |
| Dual Energy Image Viewing Feature | The added "Dual Energy image viewing" feature in v6.6 is stated to be "the same as the feature available on the syngo.CT Dual Energy ('Reference Device'), which was cleared by the FDA under K133648." The module was "evaluated via comparative testing on patient data with the reference device syngo.CT Dual Energy (K133648)." |
| PixelShine Feature | The "PixelShine" feature is "the embedded functionality of previously-cleared PixelShine (cleared by CDRH via K161625)." This implies that its integration into Synapse 3D Base Tools v6.6 maintains the already cleared performance of the standalone PixelShine. |
| Software Development Process Adherence | Software development plan, hazard analysis, risk management, requirements analysis, architectural design, detailed design, unit implementation/verification, integration testing, system testing, release, and maintenance processes were followed and described. |
| Cybersecurity | Confidentiality, integrity, and availability are maintained in accordance with FDA guidance (Section 6, October 2, 2014 guidance). DICOM standard communication assures adequate protection. |
| Adherence to Performance Standards | Compliance with DICOM Set (PS 3.1-3.20) (2016), IEC 62304 Ed 1.1 2015-06, and ISO 14971:2019. |
2. Sample size used for the test set and the data provenance
The document mentions "actual clinical images" for benchmark performance testing and "patient data" for comparative testing of the Dual Energy Analysis module. However, it does not specify the sample size for these test sets, nor does it explicitly state the country of origin or whether the data was retrospective or prospective.
3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts
The document does not provide any information regarding the number or qualifications of experts used to establish ground truth for any test set. Given the nature of the device as a processing and viewing tool, the "ground truth" for its functions would typically be the accuracy of the image processing (e.g., correct segmentation boundaries, accurate measurements compared to manual gold standard) rather than a clinical diagnosis.
4. Adjudication method for the test set
The document does not specify any adjudication method (e.g., 2+1, 3+1) for establishing ground truth or evaluating performance.
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
The document does not mention an MRMC comparative effectiveness study directly assessing human reader improvement with or without AI assistance. The comparative testing described is for the Dual Energy Analysis module against a reference device's feature, not a human reader study. The device provides "tools to aid" professionals, implying assistance, but no study is presented to quantify this aid's effect on reader performance.
6. If a standalone (i.e. algorithm only without human-in-the-loop performance) was done
The document states: "benchmark performance testing was conducted using actual clinical images to help demonstrate that the semi-automatic or automatic segmentation, detection, and registration functions implemented in Synapse 3D Base Tools achieved the expected accuracy performance." This indicates that standalone performance testing was conducted for these specific algorithmic functions. However, it does not provide specific metrics or results for these standalone algorithms.
7. The type of ground truth used (expert consensus, pathology, outcomes data, etc.)
The document does not explicitly state the type of ground truth. For "segmentation, detection, and registration functions," the ground truth would typically be manual delineation or measurement by experts (expert consensus or "gold standard" annotations), confirmed by visual inspection or perhaps clinical follow-up for specific detection tasks, but this is not detailed in the provided text.
8. The sample size for the training set
The document does not specify any training set sample size. This device is presented more as an update to an existing image processing software and an integration of already cleared functionalities (like PixelShine and the Dual Energy feature from K133648), rather than a new AI/ML algorithm that requires a detailed description of its training data.
9. How the ground truth for the training set was established
Since no training set information is provided, there is no information on how ground truth for any training set was established.
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(118 days)
Synapse 3D, Synapse 3D Base Tools v6.1
Synapse 3D Base Tools is medical imaging software that is intended to provide trained medical professionals with tools to aid them in reading, interpreting, and treatment planning. Synapse 3D Base Tools accepts DICOM compliant medical images acquired from a variety of imaging devices including, CT, MR, CR, NM, PT, and XA, etc.
This product is not intended for use with or for the primary diagnostic interpretation of Mammography images. Synapse 3D Base Tools provides several levels of tools to the user:
Basic imaging tools for general images, including 2D viewing, volume rendering and 3D volume viewing, orthogonal / oblique / curved Multi-Planar Reconstructions (MPR), Average (RaySum) and Minimum (MinIP) Intensity Projection. 4D volume viewing, image subtraction, surface rendering, sector and rectangular shape MPR image viewing, MPR for dental images, creating and displaying multiple MPR images along an object, time-density distribution, basic image processing, CINE, measurements, annotations, reporting, storing, distribution, and general image management and administration tools, etc.
-Tools for regional segmentation of anatomical structures within the image data, path definition through vascular and other tubular structures, and boundary detection.
-Image viewing tools for modality specific images, including CT PET fusion and ADC image viewing for MR studies. -Imaging tools for CT images including virtual endoscopic viewing.
-Imaging tools for MR images including delayed enhancement image viewing, diffusion-weighted MRI data analysis.
The 3D image analysis software Synapse 3D Base Tools (V6.1) is medical application software running on Windows server/client configuration installed on commercial general-purpose Windows-compatible computers. It offers software tools which can be used by trained professionals to interpret medical images obtained from various medical devices, to create reports, or to develop treatment plans.
Synapse 3D Base Tools is connected through DICOM standard to medical devices such as CT, MR, CR, US, NM, PT, XA, etc. and to a PACS system storing data generated by these medical devices, and it retrieves image data via network communications based on the DICOM standard. The retrieved image data are stored on the local disk managed by Synapse 3D Base Tools (V6.1), and the associated image-related information of the image data is registered in its database and is used for display, image processing, analysis, etc. Images newly created by Synapse 3D Base Tools (V6.1) not only can be displayed on a display, but also can be printed on a hardcopy using a DICOM printer or a Windows printer.
Synapse 3D Base Tools (V6.1) is a basic software module that works with other cleared clinical applications, including Synapse 3D Cardiac Tools (K200973), Synapse 3D Perfusion Analysis (K162287), Synapse 3D Lung and Abdomen Analysis (K130542), Synapse 3D Liver and Kidney Analysis (K142521), Synapse 3D Nodule Analysis (K120679), Synapse 3D Colon Analysis (K123566), Synapse 3D Tensor Analysis (K141514) and Synapse 3D Blood Flow Analysis (K191544). All these software modules consist of the Synapse 3D product family.
Synapse 3D Base Tools can be integrated with Fujifilm's Synapse PACS, and can be used as a part of a Synapse system. Synapse 3D Base Tools also can be integrated with Fujifilm's Synapse Cardiovascular for cardiology purposes.
The provided text describes Synapse 3D Base Tools v6.1, a medical imaging software. However, it does not include specific acceptance criteria or a detailed study proving the device meets particular performance metrics. Instead, the document focuses on regulatory compliance, substantial equivalence to a predicate device, and general software development and testing procedures.
Here's an analysis of what can be extracted and what information is missing:
1. Table of Acceptance Criteria and Reported Device Performance
No specific acceptance criteria or quantitative performance metrics are provided in the document. The text generally states that "Test results showed that all tests passed successfully according to the design specifications," but it does not detail what those design specifications or acceptance criteria were.
2. Sample Size Used for the Test Set and Data Provenance
- Sample Size for Test Set: Not specified. The document mentions "benchmark performance testing was conducted using actual clinical images," but the number of images or cases used is not provided.
- Data Provenance: Not specified. It only mentions "actual clinical images" without details on country of origin, whether the data was retrospective or prospective, or other demographic information.
3. Number of Experts Used to Establish Ground Truth for the Test Set and Their Qualifications
Not specified. The document does not describe the establishment of a ground truth for a test set, nor does it mention the involvement or qualifications of experts for this purpose.
4. Adjudication Method for the Test Set
Not applicable/Not specified. Since the document doesn't detail a test set with ground truth established by experts, an adjudication method is not mentioned.
5. If a Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study Was Done
No MRMC comparative effectiveness study is mentioned. The submission focuses on device functionality and equivalence, not human reader performance with or without AI assistance.
6. If a Standalone (Algorithm Only Without Human-in-the-Loop) Performance Study Was Done
The document states that "benchmark performance testing was conducted using actual clinical images to help demonstrate that the semi-automatic or automatic segmentation, detection, and registration functions implemented in Synapse 3D Base Tools achieved the expected accuracy performance." This implies some form of standalone evaluation of the algorithm's performance for these specific functions. However, no quantitative results or specific metrics for this standalone performance are provided.
7. The Type of Ground Truth Used (Expert Consensus, Pathology, Outcomes Data, etc.)
Not specified. While "benchmark performance testing" is mentioned for segmentation, detection, and registration, the method for establishing the "ground truth" against which these algorithms were benchmarked is not detailed.
8. The Sample Size for the Training Set
Not specified. The document mentions that some segmentation applications use a "Fully Convolutional Network" (a deep learning method), which implies a training set. However, the size of this training set is not provided.
9. How the Ground Truth for the Training Set Was Established
Not specified. For the deep learning segmentation features, the method of establishing ground truth for the training data is not described.
Summary of what is present in the document:
- Synapse 3D Base Tools v6.1 is an updated version of previously cleared software.
- It provides various image viewing, processing, and analysis tools for trained medical professionals.
- It accepts DICOM images from multiple modalities (CT, MR, CR, US, NM, PT, XA).
- It is not for primary diagnostic interpretation of mammography images.
- Some segmentation features are implemented using deep learning (Fully Convolutional Network).
- Nonclinical testing included standard software development processes (hazard analysis, risk management, requirements analysis, design, integration testing, system testing, etc.).
- "Benchmark performance testing was conducted using actual clinical images to help demonstrate that the semi-automatic or automatic segmentation, detection, and registration functions implemented in Synapse 3D Base Tools achieved the expected accuracy performance."
- All tests passed according to design specifications.
- Cybersecurity measures are in place.
Summary of what is missing/not specified in the document regarding acceptance criteria and performance study details:
- Quantitative acceptance criteria for any specific function.
- Detailed quantitative performance results (e.g., accuracy, precision, recall, Dice score for segmentation).
- Specific sample sizes for test sets or training sets.
- Details on data provenance (e.g., demographics, disease prevalence, acquisition parameters).
- Information on expert involvement in ground truth establishment (number or qualifications).
- Details on ground truth methodology (e.g., expert consensus, pathology reports).
- Results from any MRMC comparative effectiveness studies.
- Specific metrics or results for standalone algorithm performance.
The document appears to be a 510(k) summary focused on demonstrating substantial equivalence primarily through technical comparison and general software validation, rather than a detailed performance study with explicit acceptance criteria and corresponding results for specific AI/ML components.
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(60 days)
SYNAPSE 3D BASE TOOLS
Synapse 3D Base Tools is medical imaging software that is intended to provide trained medical professionals with tools to aid them in reading, interpreting, and treatment planning. Synapse 3D Base Tools accepts DICOM compliant medical images acquired from a variety of imaging devices including, CT, MR, CR, US, NM, PT, and XA, etc.
This product is not intended for use with or for the primary diagnostic interpretation of Mammography images.
Synapse 3D Base Tools provides several levels of tools to the user:
- Basic imaging tools for general images, including 2D viewing, volume rendering and 3D volume viewing, orthogonal / oblique / curved Multi-Pianar Reconstructions (MPR), Maximum (MIP), Average (RaySum) and Minimum (MinIP) Intensity Projection, 4D volume viewing, image fusion, image subtraction, surface rendering, sector and rectangular shape MPR image viewing, MPR for dental images, creating and displaying multiple MPR images along an object, time-density distribution, basic image processing, CINE, measurements, annotations, reporting, printing, storing, distribution, and general image management and administration tools, etc.
- Tools for regional segmentation of anatomical structures within the image data, path definition through vascular and other tubular structures, and boundary detection.
- Image viewing tools for modality specific images, including CT PET fusion and ADC image viewing for MR studies.
The SYNAPSE 3D Base Tools software is medical application software running on Windows Server 2008 installed on commercial general-purpose Windows-compatible computers. SYNAPSE 3D Basic Tools software is connected through DICOM standard to other medical devices such as CT, MR, CR, US, NM, PT, XA, etc. and to PACS systems storing data generated by these medical devices. Image data obtained from these devices are used for display, image processing, analysis, etc. SYNAPSE 3D Base Tools cannot be used to interpret Mammography images.
SYNAPSE 3D Base Tools can be integrated with Synapse Workstation (cleared by CDRH via K051553 on 07/07/2005) and can be used as a part of a SYNAPSE system.
SYNAPSE 3 D Base Tools Version 3.0 expands upon the applications listed in Synapse 3D Basic Tools, #K101662 with the addition of the below new applications. In addition, new and improved algorithms are listed in Section 11.1.4.
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Slicer
Slicer creates multiple cross-sectional images along an object such as spine with MPR processing using CT or MR data. The user can adjust the location of reference lines manually to obtain desired slices. -
Combination
Combination can concatenate separated series data acquired for a single body into one single series. -
Dental MPR
Dental MPR creates a cross-sectional image along the specified line on teeth with MPR, including CPR, processing using head CT data. -
ADC Viewer
ADC Viewer accepts MR diffused weighted images, calculates ADC (Apparent Diffusion Coefficients) and EADC (Exponential ADC) values for each pixel using the known equations, and displays color mapped ADC and EADC images.
The provided text describes the Synapse 3D Base Tools device and its regulatory clearance but does not contain the specific details required to fully address all parts of your request regarding acceptance criteria and a definitive study proving the device meets them.
Here's an analysis based on the available information:
1. Table of Acceptance Criteria and Reported Device Performance
The document explicitly states: "Pass/Fail criteria were based on the requirements and intended use of the product. Test results showed that all tests successfully passed." However, it does not provide a detailed table of these criteria nor specific quantitative performance metrics like sensitivity, specificity, or accuracy. It only generically mentions "system level functionality test, segmentation accuracy test, measurement accuracy test, interfacing test, usability test, serviceability test, as well as the test for risk mitigation method."
2. Sample Size Used for the Test Set and Data Provenance
This information is not provided in the document.
3. Number of Experts Used to Establish the Ground Truth for the Test Set and Qualifications
This information is not provided in the document. The document refers to "trained medical professionals" as the intended users of the tools, but not as part of a formal ground truth establishment process for testing.
4. Adjudication Method for the Test Set
This information is not provided in the document.
5. Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study
A MRMC study is not explicitly mentioned or described. The document focuses on the device's functional and performance testing in isolation, rather than its comparative effectiveness with or without human readers.
6. Standalone Performance Study
The document describes "system level functionality test, segmentation accuracy test, measurement accuracy test, interfacing test, usability test, serviceability test," which implies standalone testing of the algorithm's performance on various tasks (segmentation, measurement). However, specific metrics (e.g., accuracy percentages, dice scores) are not reported.
7. Type of Ground Truth Used
The type of ground truth used is not explicitly stated. It can be inferred that for "segmentation accuracy" and "measurement accuracy," some form of reference standard (e.g., manual segmentation by experts, known physical measurements) would have been used.
8. Sample Size for the Training Set
This information is not provided in the document. The document mentions "new and improved algorithms" but does not detail any machine learning model training or the dataset used.
9. How the Ground Truth for the Training Set Was Established
This information is not provided in the document.
Summary based on available information:
The provided 510(k) summary focuses on demonstrating substantial equivalence to predicate devices and confirming that internal testing (verification, validation) against predefined requirements was successful. It states that "Pass/Fail criteria were based on the requirements and intended use of the product. Test results showed that all tests successfully passed." However, it lacks the granular detail about the specific acceptance criteria, test methodologies, sample sizes, expert involvement, and quantitative results that your request outlines. This level of detail is typically found in the full submission or predicate device documentation, not usually in the brief 510(k) summary filed with the FDA.
Therefore, while the device "meets the acceptance criteria" as stated by the submitter, the document does not provide the specific study details nor the acceptance criteria themselves that would allow for a comprehensive answer to your questions.
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