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Comprehensive software package for visualization and analysis of thoracic CT datasets, which is intended to help the reading physician to analyze regions of the lung, such as nodules and other lung parameters, and to generate an automatic report.

The Acculmage PrimeLung software module is an additional software option to K990241, AccuView Diagnostics Imaging Workstation with AccuScore, AccuAnalyze, AccuShade, AccuVRT and AccuMIP plug-ins. The AccuShade plug-in is not currently marketed, the AccuMIP plug-in is currently marketed with the name ProjectorPro. The Acculmage PrimeLung plug-in provides visualization and analysis tools for viewing regions of the lung, and generating reports with patient information, images, results and recommendations.

Here's an analysis of the provided 510(k) summary regarding the acceptance criteria and the study that proves the device meets those criteria:

Evaluation of Acceptance Criteria and Device Performance for PrimeLung (K024149)

Based on the provided 510(k) summary for PrimeLung (K024149), the information regarding acceptance criteria and performance studies is limited and primarily focuses on functional verification rather than clinical accuracy for diagnostic tasks.

1. Table of Acceptance Criteria and Reported Device Performance

The provided document does not explicitly state quantitative acceptance criteria for diagnostic performance (e.g., sensitivity, specificity for nodule detection or characterization). Instead, the "test results" section describes functional verification.

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

The document states: "The testing performed showed that auto-matching comparison tool provides very reliable results with 100% of matching accuracy on the specified data sets."

• Sample Size: The exact sample size used for the "specified data sets" is not provided.
• Data Provenance: The country of origin and whether the data was retrospective or prospective is not specified.

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

• The document does not specify the number of experts or their qualifications used to establish ground truth for any of the testing. The "100% matching accuracy" for the comparison tool implies that there was a reference standard, but how that standard was derived is not detailed.

4. Adjudication Method for the Test Set

• The document does not mention any adjudication method (e.g., 2+1, 3+1, none) for the test set.

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

• No, an MRMC comparative effectiveness study that assesses human reader improvement with AI assistance versus without AI assistance was not mentioned or reported in this 510(k) summary. The summary focuses on the device's functional equivalence to predicate devices and its own functional performance.

6. If a Standalone (i.e., Algorithm Only Without Human-in-the-Loop Performance) Was Done

• The reported performance for the "auto-matching comparison tool" achieving "100% matching accuracy on the specified data sets" seems to be a standalone algorithm performance metric. However, this is a very specific function and not a comprehensive diagnostic standalone claim (e.g., for nodule detection or characterization).
• The overall context of the device as "intended to help the user to analyze lung nodules" suggests it's an assistive tool, not a standalone diagnostic. Therefore, a comprehensive standalone performance study for diagnostic tasks was not explicitly stated or reported.

7. The Type of Ground Truth Used

• For the "auto-matching comparison tool," the ground truth was likely established by manual matching performed by a human expert or a pre-defined reference, against which the automated matching was compared. The exact nature of this ground truth (e.g., consensus, pathology, follow-up) for general nodule analysis is not specified.
• For other functionalities like GUI conformity and report generation, the "ground truth" is simply adherence to the functional specification.

8. The Sample Size for the Training Set

• The document does not mention any training set or its sample size. This is consistent with the era of the submission (2002-2003) where deep learning and large-scale training datasets were not standard practice for medical device submissions of this type. The device's functionality appears to be based on more traditional image processing algorithms rather than machine learning requiring a distinct training phase.

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

• As no training set is mentioned (see point 8), the method for establishing its ground truth is also not applicable/provided.

Summary of Study Limitations and Nature:

The "study" described in the 510(k) summary is primarily a functional verification test (also known as verification and validation testing) rather than a clinical performance study. It confirms that the software's user interface works as designed, its features perform their intended actions, and a specific "auto-matching comparison tool" achieves high accuracy for matching tasks on limited, unspecified data. It does not provide clinical performance metrics like sensitivity, specificity, or reader agreement for diagnostic tasks such as nodule detection or characterization, which are common in more recent AI/CADe submissions. The submission relies on substantial equivalence to predicate devices that also provided visualization and analysis tools.

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