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CT:VQ software is a non-invasive image post-processing technology, using CT lung images to provide clinical decision support for thoracic disease diagnosis and management in adult patients. It utilizes two non-contrast chest CT studies to quantify and visualize ventilation and perfusion.

Quantification and visualizations are provided as DICOM images. CT:VQ may be used when Radiologists, Pulmonologists, and/or Nuclear Medicine Physicians need a better understanding of a patient's lung function and/or respiratory condition.

CT:VQ is a Software as a Medical Device (SaMD) technology, which can be used in the analysis of a paired (inspiratory/expiratory) non-contrast Chest CT. It is designed to measure regional ventilation (V) and regional perfusion (Q) in the lungs.

The Device provides visualization and quantification to aid in the assessment of thoracic diseases. These regional measures are derived from the lung tissue displacement, the lung volume change, and the Hounsfield Units of the paired (inspiratory/expiratory) chest CT.

The Device outputs DICOM images containing the ventilation output and perfusion output, consisting of a series of image slices generated with the same slice spacing as the expiration CT. In each slice the intensity value for each voxel represents either the value of ventilation or the value for perfusion, respectively, at that spatial location. Additional Information sheet is also generated containing quantitative data, such as lung volume.

Here's a breakdown of the acceptance criteria and the study details for the CT:VQ device, based on the provided FDA 510(k) summary:

Acceptance Criteria and Device Performance

The acceptance criteria for CT:VQ are implicitly demonstrated through its strong performance in clinical studies, showing agreement with established gold standards. While explicit numerical acceptance criteria are not provided in a table format within the summary, the narrative describes the goals of the study:

• Consistency/Agreement with Nuclear Medicine Imaging (SPECT/CT): The device's regional ventilation and perfusion measurements should align well with SPECT/CT findings.
• Correlation with Pulmonary Function Tests (PFTs): CT:VQ metrics should statistically correlate with standard PFTs like DLCO and FEV1/FVC ratio.
• Interpretability and Clinical Actionability: The outputs should be clear, understandable, and useful for clinicians.
• Safety and Effectiveness Profile: The device should have a safety and effectiveness profile similar to the primary predicate device.

Table of Acceptance Criteria and Reported Device Performance (as inferred from the text):

Study Details

Here's a breakdown of the specific information requested about the studies:

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

• Test Set Sample Sizes:
  • Reader Performance Study: n=77
  • Standalone Performance Assessment: n=58 (a subset of the overall clinical studies data)
• Data Provenance:
  • Country of Origin: Not explicitly stated, but the submission is from 4DMedical Limited in Australia, and the FDA clearance is in the US. The description mentions "clinically-acquired data included paired chest CTs acquired on CT scanners across a range of manufacturers and models and at different institutions, across a diverse range of patients." This suggests multi-institutional data, potentially from various geographic locations, but this is not confirmed.
  • Retrospective or Prospective: Not explicitly stated whether the studies were retrospective or prospective. The description "clinical studies were also conducted to demonstrate the safety and efficacy... in the context of clinical care" and comparing with "gold-standard and best practice measures for respiratory diagnosis" often implies retrospective analysis of existing data combined with prospective data collection, but this is not definitive in the text.

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

• Number of Experts: Not explicitly stated for establishing ground truth, although for the Reader Performance Study, "clinicians with expertise in thoracic imaging and pulmonary care" were involved in rating the outputs. The implication is that these experts, along with SPECT/CT and PFT results, contributed to the ground truth.
• Qualifications of Experts: "Clinicians with expertise in thoracic imaging and pulmonary care." No specific number of years of experience or board certifications (e.g., radiologist with 10 years of experience) is provided.

3. Adjudication method (e.g. 2+1, 3+1, none) for the test set:

• Adjudication Method: Not explicitly stated. The summary mentions "Inter-reader variability was not significantly different for CT:VQ than for SPECT," which implies multiple readers, but the method for resolving discrepancies or establishing a final ground truth from multiple readers is not detailed.

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:

• MRMC Study: A "Reader Performance Study" was conducted with n=77 cases, involving "clinicians with expertise in thoracic imaging and pulmonary care." This aligns with the characteristics of an MRMC study.
• Effect Size of Human Reader Improvement with AI vs. without AI assistance: The summary does not provide an effect size or direct comparison of human reader performance with CT:VQ assistance versus without it. The study focused on assessing:
  • Agreement between CT:VQ outputs and SPECT.
  • Interpretability and clinical actionability of CT:VQ outputs.
  • Inter-reader variability of CT:VQ vs. SPECT.
    It does not quantify an improvement in reader accuracy or efficiency due to AI assistance.

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

• Standalone Performance: Yes, a "Standalone Performance Assessment" was performed with a subset of 58 cases. The findings indicated strong regional agreement between CT:VQ and SPECT VQ measurements and stronger associations of CT:VQ perfusion metrics with DLCO compared to SPECT.

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

• Type of Ground Truth: A combination of established clinical diagnostics was used:
  • Nuclear Medicine Imaging (Single photon emission computed tomography, SPECT/CT): Used as a "gold-standard and best practice measure" for regional ventilation and perfusion.
  • Pulmonary Function Tests (PFTs): Specifically Diffusing capacity of the lung for carbon monoxide (DLCO) and FEV1/FVC ratio, used to correlate with CT:VQ outputs.
  • Clinical Diagnosis/Findings: Implied through "Case Studies further illustrated key advantages of CT:VQ... successfully replicated the diagnostic findings of SPECT."

7. The sample size for the training set:

• Training Set Sample Size: Not explicitly stated in the provided text. The summary only mentions the sample sizes for the clinical validation studies (test sets).

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

• Training Set Ground Truth Establishment: Not explicitly stated how the ground truth for the training set was established, as the training set size and characteristics are not detailed. Typically, it would involve similar rigorous processes (e.g., expert annotation, gold-standard imaging modalities, clinical outcomes) as the test set, but this information is absent in this document.

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CT:V software is a non-invasive image processing technology that measures volume changes from paired inspiration-expiration CTs to quantify and visualize regional and global ventilation. These regional measures are derived entirely from the lung tissue displacement and lung volume change between the paired inspiration-expiration chest CTs.

CT:V is for use in adult patients. Quantification and visualizations are provided in the form of a report.

CT:V may be used when physicians need a better understanding of a patient's lung function and/or respiratory condition.

CT:V Software (i.e. the "Device"), also known as "CT:V", is a software-based image processing technology that analyzes two non-contrast Computed Tomography (CT) images of the lungs to quantify the reqional ventilation of pulmonary tissue to support clinicians in their assessment of patient lung conditions and diseases.

CT:V is provided on a 'Software as a Service' basis. The paired CT series, in DICOM format, are transferred electronically to 4DMedical. The images are input into the Software Device that operates in a cloud environment. The Device is fully automated and assesses the CT series and identifies and segments the lungs. CT:V measures ventilation captured in the CTs by directly measuring the motion field of the lung tissue at thousands of points using a form of three-dimensional motion tracking. This motion tracking quantifies the expansion of lung tissue, and hence airflow (i.e. ventilation) is determined. These regional lung motion and ventilation measurements are used to provide quantitative outputs and color maps showing ventilation within a segmented region. An Analysis Report is generated for each completed workflow which is returned to the requesting physician. The ventilation data presented in the CT:V Report provides the user with additional information that may assist in characterizing the patient.

The provided document, an FDA 510(k) summary, describes the CT Lung Ventilation Analysis Software (CT:V) developed by 4DMedical Limited. It details the device's indications for use, its comparison to a predicate device, and the performance testing conducted to demonstrate its safety and effectiveness.

Here's an analysis of the acceptance criteria and the study that proves the device meets them, based on the provided text:

1. Table of Acceptance Criteria and Reported Device Performance

The document does not explicitly present a table of "acceptance criteria" with specific numerical thresholds for accuracy, sensitivity, specificity, etc., as one might typically see for a diagnostic device. Instead, the performance testing focuses on demonstrating the device's ability to consistently measure and visualize regional and global ventilation, and its correlation and consistency with established "gold standard" or "best practice" measures (PFT, SPECT, PET).

The implicit acceptance criteria are framed around the demonstration of correlation, consistency, and substantial equivalence to existing methods for assessing lung function and ventilation.

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

• Clinical Study 1 ( Observational Comparison Study):

  • Sample Size: 32 participants (19 male, 13 female).
  • Data Provenance: Data acquired in the USA.
  • Nature of Data: Clinical data, including healthy participants and those with previously diagnosed lung diseases (15 patients with COPD, remainder healthy). Participants presented with symptoms like shortness of breath, coughing, phlegm, chest tightness. Diverse racial demographic groups.
  • Retrospective or Prospective: Not explicitly stated as prospective, but the description "observational comparison study using data acquired in the USA" suggests it could be either pre-existing data or newly collected for the study. However, the use of "participants were included" leans towards a prospective or newly assembled cohort for the study.

• Clinical Study 2:

  • Sample Size: 17 lung cancer patients.
  • Data Provenance: Publicly available dataset, collected from a single institution in Australia.
  • Nature of Data: Clinical data of lung cancer patients undergoing radiotherapy, with varying lung function.
  • Retrospective or Prospective: Described as "publicly available dataset," implying it was retrospective analysis of pre-existing data.

• Non-Clinical Tests (Benchtop Verification and Validation):

  • Sample Size: Not specified in terms of "cases," but refers to "a range of input parameters covering a spectrum of patient anatomies and breathing physiologies" via synthetically generated phantom image data.
  • Data Provenance: Synthetically generated data.
  • Nature of Data: Synthetic CT image pairs simulating human breath-holds, with known simulated lung physiologies and ventilation volumes ("ground truth").

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

The document does not specify the number or qualifications of experts used to establish the ground truth for the test set (clinical studies).

• For Clinical Study 1, the comparison was made against Pulmonary Function Testing (PFT) and Nuclear Medicine Imaging (SPECT) outcomes. PFTs are objective measurements, and SPECT interpretation would typically be done by nuclear medicine physicians or radiologists, but the document doesn't detail the process or number of readers.
• For Clinical Study 2, the comparison was made against PET (positron emission tomography) outputs. PET interpretations would similarly involve trained specialists, but no specifics are given.
• For the non-clinical tests, the "ground truth" was derived from known simulated lung physiologies and ventilation volumes in synthetically generated phantom images. This ground truth was inherent to the synthetic data generation, not established by human experts.

4. Adjudication Method for the Test Set

The document does not describe any specific adjudication method (e.g., 2+1, 3+1 consensus) for establishing ground truth or for interpreting the comparison modalities (PFT, SPECT, PET) within the clinical studies. The results are presented as quantitative correlations and qualitative consistencies with these established methods.

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

No, the document does not describe a Multi-Reader Multi-Case (MRMC) comparative effectiveness study where human readers' performance with and without AI assistance was evaluated. The studies focused on comparing the AI device's outputs directly with established gold standards (PFT, SPECT, PET) and demonstrating its consistency and correlation.

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

Yes, the performance testing described for CT:V appears to be primarily standalone (algorithm only). The device is described as "fully automated" and assesses the CT series to generate quantitative outputs and color maps, with an "Analysis Report" returned to the requesting physician. The clinical studies directly compare these automated outputs to PFT, SPECT, and PET, demonstrating the algorithm's performance independent of human interpretation improvements due to AI assistance.

7. The Type of Ground Truth Used

• Clinical Study 1:
  • Quantitative aspects: Pulmonary Function Testing (PFT) metrics (TLC, FRC, VC, FEV1, FEV1/FVC) were used as ground truth for correlation. PFTs are objective physiological measurements.
  • Qualitative aspects: SPECT ventilation images were used as a comparative "gold standard" for assessing regional distribution of ventilation. SPECT is a nuclear medicine imaging modality.
• Clinical Study 2:
  • Quantitative aspects: PET (positron emission tomography) ventilation fields were used as a comparative "gold standard" for assessing spatial ventilation distribution at lobar and voxel levels. PET is a nuclear medicine imaging modality.
• Non-Clinical Tests:
  • Simulated ground truth: "Known simulated lung physiologies and ventilation volumes" from synthetically generated phantom images. This is a controlled, artificial ground truth.

8. The Sample Size for the Training Set

The document does not specify the sample size for the training set. It only describes the test sets used for performance validation. This is common in 510(k) summaries, which focus on the validation data rather than the development details.

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

Since the document does not describe the training set size or specific details, it also does not explain how the ground truth for the training set was established.

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