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A VIEW provides CT values for pulmonary tissue from CT thoracic datasets. This software can be used to support the physician quantitatively in the diagnosis, followup evaluation of CT lung tissue images by providing image segmentation of sub-structures in the left and right lung (e.g., the five lobes and airway), volumetric and structural analysis, density evaluations and reporting tools. AVIEW is also used to store, transfer, inquire and display CT data sets. A VIEW is not meant for primary image Interpretation in mammography.

The AVIEW is a software product which can be installed on a PC. It shows images taken with the interface from various storage devices using DICOM 3.0 which is the digital image and communication standard in medicine. It also offers functions such as reading, manipulation, analyzing, post-processing, saving and sending images by using the software tools.

The provided text describes the AVIEW software, a medical device for processing CT thoracic datasets, and its substantial equivalence to predicate devices. However, the document does not contain the specific details required to fully address your request regarding acceptance criteria and the study that proves the device meets them.

Here's a breakdown of what information is available and what is missing:

Information Available:

• Indications for Use: AVIEW "provides CT values for pulmonary tissue from CT thoracic datasets. This software can be used to support the physician quantitatively in the diagnosis, followup evaluation of CT lung tissue images by providing image segmentation of sub-structures in the left and right lung (e.g., the five lobes and airway), volumetric and structural analysis, density evaluations and reporting tools. AVIEW is also used to store, transfer, inquire and display CT data sets. AVIEW is not meant for primary image Interpretation in mammography."
• Performance Data: "Verification, validation and testing activities were conducted to establish the performance, functionality and reliability characteristics of the modified devices. The device passed all of the tests based on pre-determined Pass/Fail criteria."
• Tests Conducted:
  • Unit test
  • System test
  • DICOM test
  • LAA analysis test
  • LAA size analysis test
  • Airway wall measurement test
  • Reliability test
  • CT image compatibility test
• Conclusion: The device is deemed "substantially equivalent to the predicate device" based on "technical characteristics, general functions, application, and intended use," and "nonclinical tests demonstrate that the device is safe and effective."

Information Missing (and why based on the document):

1. A table of acceptance criteria and the reported device performance: While various tests are listed (e.g., LAA analysis test, Airway wall measurement test), the document explicitly states these are "nonclinical tests." It does not provide specific quantitative acceptance criteria or corresponding reported device performance values for these tests. The nature of these tests appears to be functional and reliability-focused rather than clinical performance metrics. For example, it doesn't state "AVIEW achieved X% accuracy for LAA analysis against ground truth Y" or "Airway wall measurement deviation was within Z mm."

2. Sample size used for the test set and the data provenance: The document mentions "CT thoracic datasets" but does not specify the sample size for any test set or the provenance (e.g., country of origin, retrospective/prospective nature) of the data used for testing.

3. Number of experts used to establish the ground truth for the test set and their qualifications: The document states, "Results produced by the software tools are dependent on the interpretation of trained and licensed radiologists, clinicians and referring physicians as an adjunctive to standard radiology practices for diagnosis." However, it does not specify how many, if any, experts were used to establish ground truth for the test set, nor their specific qualifications, for the performance testing cited.

4. Adjudication method for the test set: No information is provided regarding any adjudication methods (e.g., 2+1, 3+1) used for establishing ground truth for the test set.

5. If a multi-reader multi-case (MRMC) comparative effectiveness study was done, and the effect size of how much human readers improve with AI vs without AI assistance: The document explicitly states that AVIEW is "not meant for primary image Interpretation in mammography" and that its results "are dependent on the interpretation of trained and licensed radiologists, clinicians and referring physicians as an adjunctive to standard radiology practices for diagnosis." This suggests it's an assistive tool, but no MRMC study comparing human readers with and without AI assistance, or any effect size, is mentioned. The "Performance Data" section focuses on "nonclinical tests" for software functionality.

6. If a standalone (i.e. algorithm only without human-in-the-loop performance) was done: The provided detail about "LAA analysis test," "LAA size analysis test," and "Airway wall measurement test" implies standalone algorithmic performance was assessed in these nonclinical tests. However, specific performance metrics (e.g., accuracy, precision, recall) from a standalone evaluation are not provided.

7. The type of ground truth used: For the mentioned performance tests (e.g., LAA, airway wall measurement), the type of ground truth used is not explicitly specified. It's implied that these are technical validations against known values or established methods, but whether this involved expert consensus on clinical cases, pathology, or outcomes data is not detailed.

8. The sample size for the training set: No information is provided about a training set or its size, as the document refers to "Verification, validation and testing activities" as "nonclinical tests" demonstrating substantial equivalence, not a machine learning model's development.

9. How the ground truth for the training set was established: Since no training set information is provided, this cannot be answered.

In summary, the document serves as an FDA 510(k) clearance letter and summary, which primarily focuses on demonstrating "substantial equivalence" to predicate devices through technical characteristics and "nonclinical tests" for functionality and reliability. It does not provide the detailed clinical performance study data that would include specific acceptance criteria, sample sizes (for test or training sets), expert qualifications, or ground truth establishment methods typical for AI-based diagnostic/assistive tools evaluated for quantitative clinical outcomes.

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The CT Dual Energy Image View application accepts CT images acquired using different tube voltages and/or tube currents of the same anatomical location. The material composition of body regions may be determined using the energy dependence of the attenuation coefficients of different materials. This approach enables images to be generated at multiple energies within the available spectrum to visualize and analyze information about anatomical and pathological structures.

The Vitrea® CT Dual Energy Image View is a post-processing software application which functions on the Vitrea® Platform, cleared by K150258.This application allows you to load and combine two CT images, acquired from a Toshiba CT scanner with a Dual Energy protocol, of the same anatomical location; one obtained with low kV tube voltage and one obtained with high kV tube voltage. Vitrea® computes a Blended and an Enhanced image with adjustable virtual energy levels, a Virtual Non-Contrast (VNC) image, an lodine Map overlaid on one of the original or derived images, a CT graph, and a best Contrast-to-Noise Ratio (CNR) image. For the Raw Data Analysis workflow, Vitreally calculates a Monochromatic image.

The software supports Toshiba dual-energy studies containing image-based and projection-based reconstruction techniques.

• lmage-based reconstructed data consists of a standard image reconstruction of the two ● scans. Use the Dual Energy Image Domain (Two Volumes) workflow for these datasets.
• Projection-based reconstructed data consists of special material image reconstruction where . the two scans are combined to create two sets of images where a predefined material is defined, typically lodine/Water and/or Calcium/Water. Use the Dual Energy Raw Data Analysis (Four or Six Volumes) workflow for these datasets.
• NOTE: It is important that a description of the material is contained in the DICOM Image Comments tag (0020, 4000) for each dataset at the time of the scan. For lodine/Water projection-based reconstruction, the lodine material image should be labeled "I/H20," and the corresponding Water material image should be labeled "H20/1." For the Calcium/Water projection-based reconstruction, the Calcium material image should be labeled "Ca/H20," and the corresponding Water material image should be labeled "H20/Ca."

NOTE: The dual-energy datasets must be coincident with the same frame of reference UID.

NOTE: Available with Toshiba datasets only.

Here's a summary of the acceptance criteria and the study conducted for the Vital Images, Inc. Vitrea® CT Dual Energy Image View device, based on the provided text:

Important Note: The provided document is a 510(k) summary, which focuses on demonstrating substantial equivalence to a predicate device rather than presenting a detailed independent study with specific numerical acceptance criteria and performance metrics for the new device. Therefore, the "acceptance criteria" discussed are largely qualitative and relate to the device functioning as intended and being comparable to the predicate, rather than precise quantitative thresholds. Similarly, "reported device performance" is described qualitatively as meeting those intentions.

Acceptance Criteria and Reported Device Performance

Study Details

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

• Test Set (External Validation):

  • Clinical Radiologist: Evaluated "multiple test cases." The exact number of cases is not specified.
  • Radiological Technologists (2): Provided feedback on "a series of questions."
  • Data Provenance: The document does not explicitly state the country of origin or whether the data was retrospective or prospective. Given the context of comparing to a predicate device, it's likely existing data or a simulated environment was used, but this is not confirmed.

• Test Set (Internal Validation / Phantom Testing):

  • Sample Size: "Various phantoms." The exact number or types are not specified.
  • Data Provenance: Not specified, but likely generated internally for phantom testing.

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

• Number of Experts: Three experienced clinical experts were involved in the external validation of the software's features. Specifically, one clinical radiologist and two radiological technologists.
• Qualifications: "Experienced clinical experts," "clinical radiologist," and "Radiological Technologists." Specific years of experience or board certifications are not provided.

4. Adjudication method for the test set:

• The document does not describe a formal adjudication method (like 2+1 or 3+1 consensus).
• Instead, it states that the clinical radiologist evaluated test cases comparing the subject device to the predicate device and rated "quality and performance." The two radiological technologists provided feedback on performance and usability.
• The conclusion states that "All external validation testing passed with each expert confirming the Vitrea® CT Dual Energy Image View software fulfills the intended use and meets the needs of the user," implying individual expert agreement rather than a formal consensus process between them for ground truth.

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

• No MRMC comparative effectiveness study was done in the sense of evaluating human reader performance with and without AI assistance to quantify improvement.
• The study involved comparing the new device's output and usability to a predicate device by expert users. It was a comparison of tool equivalence rather than an assessment of human performance augmentation.

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

• The device is a "post-processing software application."
• "Internal Validation (Phantom Testing)" included "side-by-side visual comparisons of dual energy algorithm outputs with Toshiba Display Console as the predicate device using phantom data." This indicates some form of standalone evaluation against a reference/predicate system, but it's not explicitly framed as an "algorithm-only" performance study in isolation from any human interpretation. However, the direct comparison of "algorithm outputs" suggests an assessment of the algorithm's results themselves.

7. The type of ground truth used:

• For the external validation, the ground truth was effectively the expert opinion/comparison of the clinical radiologist against the output of the predicate device (Toshiba Dual Energy System Package K132813). The experts assessed whether the new device's features produced images of comparable "quality and performance" and fulfilled the "intended use."
• For internal validation (phantom testing), the "Toshiba Display Console" served as the reference for "side-by-side visual comparisons of dual energy algorithm outputs," implying its output was considered the reference or "ground truth" for comparison.

8. The sample size for the training set:

• The document does not mention a training set or any machine learning/AI model training. The Vitrea® CT Dual Energy Image View is described as a "post-processing software application" that "computes" various images based on acquired CT data. It does not appear to be an AI/ML device that requires a training set in the conventional sense. The "algorithm" mentioned refers to the computational methods for generating the dual-energy derived images, not a learned model.

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

• As no training set is mentioned (see point 8), this information is not applicable.

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