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SoftVue™ is indicated for use as a B-mode ultrasonic imaging system for imaging of a patient's breast when used with an automatic scanning curvilinear array transducer. The device is not intended to be used as a replacement for screening mammography.

SoftVue™ 3.0 is indicated for use as a B-mode ultrasonic imaging system for imaging of a patient's breast using a curvilinear array transducer that completely surrounds the breast. The SoftVue™ System is comprised of the following subsystems: Transducer, Table/Housing Assembly, Water Conditioning System, Computer Control System, Image Reconstruction System, Power System, and the Data Acquisition System.

The SoftVue™ System has a built-in curvilinear transducer that is used to acquire ultrasound data. Data are acquired from a patient lying prone on the table with their breast submerged in an imaging chamber filled with warm (body temperature) water. The breast is positioned in the center of the transducer. A Breast Interface Assembly connects to the distal end of the breast using a gentle vacuum, drawing the breast downward to center, shape, and stabilize the breast for optimal imaging. Cameras located at the bottom of the imaging chamber provide a live video feed to the system operator to aid in positioning the patient's breast.

Once the scan is initiated, the transducer collects data that are processed to produce a series of B-mode ultrasound image slices that can be stacked to yield a volumetric ultrasound image of the breast. SoftVue™ also outputs colorized and grayscale relative stiffness image stacks that provide the radiologist with additional reference information.

The Water Control System is used to de-gas and warm the water that is used as the image acquisition medium. The Computer Control System controls the functionality of the other subsystems. The reconstruction engine processes the image data acquired by the transducer ring into B-mode ultrasound images.

The system includes a touchscreen display (user interface). The touchscreen display allows the user to control the system and perform an imaging procedure. Patient information is entered into the system using the QWERTY keyboard on the touchscreen display, or the user can import the patient information from a DICOM modality worklist on an externally networked RIS server. Device errors and warnings are displayed on the touchscreen display.

SoftVue™ outputs the images to an externally networked PACS server which allows the images to be stored until they are reviewed on a workstation.

The provided text describes the Delphinus Medical Technologies SoftVue device (K172256).

Here's an analysis of the acceptance criteria and the study conducted:

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

The document does not explicitly state "acceptance criteria" for clinical performance in a table format. Instead, it refers to "non-clinical design verification and validation testing" and "phantom imaging analysis" to demonstrate that the device "successfully meets the requirements of its intended use."

The intended use is: "SoftVue™ is indicated for use as a B-mode ultrasonic imaging system for imaging of a patient's breast when used with an automatic scanning curvilinear array transducer. The device is not intended to be used as a replacement for screening mammography."

Therefore, the implicit acceptance criteria from the non-clinical testing are that the device effectively performs B-mode ultrasonic imaging of the breast and adheres to safety standards.

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

• Sample Size: The document mentions a "phantom imaging analysis." Phantoms are模拟 objects used for testing and calibration, not human subjects. Therefore, there is no sample size of patients/images in the traditional sense for a clinical test set.
• Data Provenance: Not applicable as the testing was performed on a phantom, not human data.

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

Not applicable. Phantom studies do not typically involve human experts establishing ground truth in the same way clinical studies do. The "ground truth" for a phantom would be its known physical properties and the expected image output from an ideal system.

4. Adjudication method for the test set

Not applicable. Adjudication methods like 2+1 or 3+1 are used for human expert consensus on clinical cases. This study involved a phantom imaging analysis.

5. If a multi reader multi case (MRMC) comparative effectiveness study was done, If so, what was the effect size of how much human readers improve with AI vs without AI assistance

No. The document explicitly states: "No clinical testing is required to be performed to assess the changes to the SoftVue System." This indicates that no MRMC study, or any clinical study involving human readers, was conducted. The device is referred to as an "imaging system," and there is no mention of AI assistance for human readers.

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

Yes, in a way. The "phantom imaging analysis" assesses the performance of the device itself (the "algorithm only" or system performance) against established requirements. The B-mode imaging and other outputs (colorized and grayscale relative stiffness image stacks) are generated by the system without human intervention in the generation process.

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

The ground truth for the "phantom imaging analysis" would be the known properties of the phantom itself. This could include:

• Known size, shape, and location of simulated lesions or structures within the phantom.
• Known acoustic properties (e.g., speed of sound, attenuation) of the phantom materials.
• Expected image quality metrics (e.g., resolution, contrast, artifact levels) when imaging the known phantom structures.

8. The sample size for the training set

Not applicable. The document describes a "phantom imaging analysis" for verification and validation of a medical imaging device. It does not mention machine learning or AI components that would require a "training set" in the context of algorithm development. The system functions as an imaging device, not a diagnostic AI algorithm.

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

Not applicable, as no training set is mentioned or implied for this device.

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SoftVue™ is indicated for use as a B-mode ultrasonic imaging of a patient's breast when used with an automatic scanning curvilinear array transducer. The device is not intended to be used as a replacement for screening mammography.

SoftVue™ is indicated for use as a B-mode ultrasonic imaging system for imaging of a patient's breast using a curvilinear array transducer that completely surrounds the breast. The modification of SoftVue™ will allow the system to generate colorized and grayscale relative stiffness ultrasound images in addition to the grayscale B-mode images generated by the unmodified SoftVue™ system. This new feature will allow the user to be able to determine whether a region of interest is harder or softer than the surrounding tissue. No clinical diagnostic claims are being made.

SoftVue™ is comprised of the following subsystems: the Transducer, Table/Housing Assembly, Water Conditioning System, Computer Control System, Image Reconstruction System, Power System, and the Data Acquisition System.

Soft Vue™ has a built-in curvilinear transducer that is used to acquire ultrasound data. Data are acquired from a patient lying prone on the table with their breast submerged in an imaging chamber filled with warm (body temperature) water. The breast is positioned in the center of the transducer. A camera, located at the bottom of the imaging chamber provides a live video feed to the system operator to aid in positioning the patient's breast. Once the scan is initiated, the transducer collects data that are processed to produce a series of B-mode ultrasound image slices that can be stacked to yield a volumetric ultrasound image of the breast. Soft Vue™ also outputs colorized and grayscale relative stiffness image stacks that provide the radiologist with additional reference information.

The Water Control System is used to de-gas and warm the water that is used as the image acquisition medium. The Computer Control System controls all of the functionality of the other subsystems. The reconstruction engine processes the image data acquired by the transducer ring into B-mode ultrasound images.

The system includes a barcode reader and a touchscreen display (user interface). The touchscreen display allows the user to perform an imaging procedure. Patient information is entered into the system using either the QWERTY keyboard on the touchscreen display, using the barcode reader, or the user can import the patient information from a DICOM modality worklist on an externally networked RIS server. Device errors and warnings are displayed on the touchscreen display.

SoftVue™ outputs the images to an externally networked PACS server which allows the images to be stored until they are reviewed on a workstation.

The provided text does not contain detailed acceptance criteria for a specific device performance study beyond safety testing and general system requirements. It describes modifications to an existing device ("SoftVue") and a small-scale clinical study for "image characterization" of a new feature (color relative stiffness images). The study is not presented as a formal performance study demonstrating device efficacy against predefined acceptance criteria.

However, I can extract information related to the device's technical specifications and the qualitative observations from the clinical study, which could be interpreted as a form of "reported performance" against an implicit goal of providing "qualitative tissue stiffness information."

Here's an attempt to structure the information based on your request, highlighting what is implicitly or explicitly stated and noting where information is absent:

1. Table of Acceptance Criteria and Reported Device Performance

• All 4 cancers were characterized as "stiff" (red/green, redder than background).
• 2 fibroadenomas were "mixed," 1 was "stiff" (red), and 1 was "soft" (blue).
• 4 cysts were "soft" (blue/green, bluer than background), while 1 was "mixed." This demonstrates SoftVue's ability to qualitatively measure tissue stiffness. |
  | Image Quality: Improve image quality of B-mode images (implicit acceptance of improved image quality) | "Updates to the pre-processing of the attenuation image result in better gain correction and equivalent contrast for the B-mode image."
  "Updates to the sound speed image parameters results in better delay correction and equivalent contrast for the B-mode image." (These are described as improvements, implying they met the goal of improved quality.) |

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

• Test Set Sample Size:
  • Phantom Study: 1 anthropomorphic breast phantom with 7 inclusions.
  • Clinical Study (Image Characterization): 10 patient exams, yielding a total of 13 imaged masses.
    • Breast cup size ranged from B to DDD.
    • Breast densities: 5 scattered, 2 heterogeneous, 3 dense (one extremely dense).
• Data Provenance:
  • Phantom Study: Not explicitly stated, but typically labs or manufacturers produce phantoms.
  • Clinical Study: Prospective. "Patient data were collected as part of the clinical study that Delphinus currently has in process under pre-IED # I120143." The modified SoftVue system was installed at the Alexander J Walt Breast Center at the Karmanos Cancer Institute in Detroit, Michigan, indicating U.S. origin.

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

• Phantom Study: The "ground truth" for the phantom was based on its known, predetermined characteristics (stiff/soft inclusions). No human experts were required to establish this ground truth, but the qualitative assessment against the known properties would have been done by the study team.
• Clinical Study:
  • Number of Experts: One expert.
  • Qualifications: "board certified radiologist, Dr. Peter Littrup."
  • Role: Reviewed all patient images from the SoftVue system and guided the identification of lesions.

4. Adjudication Method for the Test Set

• Phantom Study: Not applicable; the ground truth was inherent to the phantom's design.
• Clinical Study: No formal adjudication method involving multiple experts is described for the clinical image analysis. The "ground truth" (lesion identification and likely pathology) was guided by "Previous biopsies and/or imaging." The evaluation of the SoftVue color stiffness images was performed by a single board-certified radiologist (Dr. Peter Littrup), matching the SoftVue appearance/assessment against the lesion pathology and characteristics derived from prior imaging/biopsy.

5. If a Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study was done, If so, what was the effect size of how much human readers improve with AI vs without AI assistance

• No, a Multi-Reader Multi-Case (MRMC) comparative effectiveness study was not done.
• This study was for image characterization of a new feature, not explicitly a comparative effectiveness study of human readers with/without AI assistance. The device is an imaging system, and the new feature provides "additional reference information for radiologists," but its impact on reader performance or an effect size is not reported.

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

• Yes, in a qualitative sense for the new feature. The device now generates and outputs color and grayscale relative stiffness images. The phantom study, in particular, could be seen as a standalone evaluation of the algorithm's ability to represent stiffness, as it directly correlated the device's output (color) to the known physical properties of the phantom inclusions without human interpretation as a primary outcome. The clinical observations by the radiologist then confirmed that these images provided "qualitative tissue stiffness information."

7. The Type of Ground Truth Used

• Phantom Study: Known, predetermined physical characteristics (stiffness, size) of inclusions within an anthropomorphic breast phantom.
• Clinical Study: A combination of "previous biopsies and/or imaging" to identify and characterize lesions (appearance, size, location) and their pathology (cancer, fibroadenoma, cyst).

8. The Sample Size for the Training Set

• The document describes modifications to an existing device ("SoftVue") and the evaluation of a new feature for that device (color and grayscale relative stiffness images). It does not mention the use of a "training set" in the context of machine learning or AI algorithm development for these new features. The focus is on verifying the system's performance and characterizing the new image type. Therefore, there is no explicit mention of a training set sample size. The "image characterization" study (both phantom and clinical) serves more as a validation/demonstration of the new feature's output.

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

• As no training set is mentioned for the development of the new feature related to stiffness imaging, this question is not applicable based on the provided text. The document refers to updates in pre-processing and sound speed image parameters to improve image quality, which are likely engineering/physics-based improvements rather than AI model training.

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SoftVue™ is indicated for use as a B-mode ultrasonic imaging system for imaging of a patient's breast when used with an automatic scanning curvilinear array transducer. The device is not intended to be used as a replacement for screening mammography.

Soft Vue is an automated tomographic B-mode diagnostic ultrasound system to be used as an adjunct to mammography for imaging a patient's breast. SoftVue is comprised of the following subsystems: the Transducer. Table/Housing. Water Control System, Computer Control System, Image Reconstruction System, Power System, and the Data Acquisition System.

SoftVue has a built-in curvilinear transducer that is used to acquire ultrasound images. Images are acquired from a patient lying prone on the table with their breast submerged in an imaging chamber filled with warm (body temperature) water. The breast is positioned in the center of the transducer. A camera, located at the bottom of the imaging chamber provides a live video feed to the system operator to aid in positioning the patient's breast. Once the scan is initiated. the transducer collects data that are processed to produce a series of ultrasound image slices that can be stacked to yield a volumetric ultrasound image of the breast,

The Water Control System is used to de-gas and warm the water that is used as the image acquisition medium. The Computer Control System controls all of the functionality of the other subsystems. The reconstruction engine processes the image data acquired by the transducer ring into B-mode ultrasound images.

The system includes a barcode reader, touchscreen console (user interface), and display monitor. The touchscreen console and monitor allow the clinical user to perform an imaging procedure on a patient. Patient information is entered into the system using either the touchscreen console or the barcode reader. Device errors and warnings are displayed on the console and/or display.

Soft Vue outputs the image Storage Medium that allows the images to be stored until they are reviewed on a workstation or transferred via DICOM to a PACS environment.

The provided document describes the SoftVue device, its intended use, and its substantial equivalence to a predicate device (Sofia™ (ATUS) Imaging Device). However, it does not explicitly detail acceptance criteria or the full results of a study demonstrating the device meets specific performance criteria in a format that lends itself to a direct fill-in-the-blanks response for all requested points.

Based on the available text, here's what can be extracted and inferred regarding the device's acceptance criteria and the study:

1. Table of Acceptance Criteria and Reported Device Performance:

The document mentions "predetermined specifications and product performance requirements" and "system and performance requirements necessary for its intended use" but does not provide a specific table of these criteria with numerical targets and the SoftVue's reported performance against them. It only gives general statements about meeting requirements.

The only specific performance criteria mentioned are:

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

The document states, "System Verification and Validation Testing will be performed using an anthropomorphic phantom breast designed for measuring resolution and contrast. The breast phantom was seeded with 18 inclusions located in specific locations within the phantom."

• Sample Size for Test Set: 1 anthropomorphic phantom breast with 18 inclusions.
• Data Provenance: This indicates a prospective experimental design using a phantom, not human subject data. The country of origin for the data is not specified, but the device is submitted in the US for FDA clearance.

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

For the phantom study, the "ground truth" is inherent in the design of the phantom itself (known locations and characteristics of the 18 inclusions). Therefore, human experts were not used to establish ground truth for this specific test.

4. Adjudication Method for the Test Set:

Not applicable, as ground truth was established by the phantom's design.

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

No MRMC comparative effectiveness study is mentioned in the provided text. The document focuses on performance testing against specifications and substantial equivalence to a predicate device based on technical characteristics and intended use, not on comparing human reader performance with and without AI assistance.

6. Standalone (Algorithm Only) Performance:

The document describes the SoftVue as a "B-mode ultrasonic imaging system" that processes data to produce "a series of ultrasound image slices that can be stacked to yield a volumetric ultrasound image of the breast." This suggests the algorithm's role is in image acquisition and reconstruction. The performance observed in the phantom study is an indicator of the device's standalone imaging capabilities. However, a specific "standalone performance study" in the context of an AI algorithm making diagnostic decisions (without human-in-the-loop) is not detailed. The device is presented as an imaging system, not an AI diagnostic tool.

7. Type of Ground Truth Used:

For the performance evaluation mentioned, the ground truth was phantom-based, meaning the known characteristics and locations of inclusions within the anthropomorphic phantom.

8. Sample Size for the Training Set:

No information is provided about a training set. This document describes a traditional 510(k) submission for an imaging device, not a machine learning or AI-driven device that typically requires a distinctive training set for model development. The "reconstruction engine processes the image data" (Page 1) but the methodology for its development and any associated training data are not discussed.

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

Not applicable, as no training set is mentioned or described.

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