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AVIEW provides CT values for pulmonary tissue from CT thoracic and cardiac datasets. This software could be used to support the physician quantitatively in the diagnosis, follow up evaluation of CT lung tissue images by providing image segmentation of sub-structures in lung, lobe, airways and cardiac, registration and expiration which could analyze quantitative information such as air trapped index, and inspiration/ expiration ratio. And also, volumetric and structure analysis, density evaluation and reporting tools. AVIEW is also used to store, transfer, inquire and display CT data set on premise and as cloud environment as well to allow users to connect by various environment such as mobile devices and chrome browser. Characterizing nodules in the lung in a single study, or over the time course of several thoracic studies. Characterizations include nodule type, location of the nodule and measurements such as size (major axis), estimated effective diameter from the volume of the nodule, volume of the nodule, Mean HU(the average value of the CT pixel inside the nodule in HU), Minimum HU, Max HU, mass(mass calculated from the CT pixel value), and volumetric measures(Solid major; length of the longest diameter measured in 3D for solid portion of the nodule, Solid 2nd Major: The longest diameter of the solid part, measured in sections perpendicular to the Major axis of the solid portion of the nodule), VDT (Volume doubling time), and Lung-RADS (classification proposed to aid with findings). The system automatically performs the measurement, allowing lung nodules and measurements to be displayed and, integrate with FDA certified Mevis CAD (Computer aided detection) (K043617). It also provides CAC analysis by segmentation of four main artery (right coronary artery, left main coronary, left anterior descending and left circumflex artery then extracts calcium on coronary artery to provide Agatston score, volume score and mass score by whole and each segmented artery type. Based on the score, provides CAC risk based on age and gender.

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. And is intended for use as diagnostic patient imaging which is intended for the review and analysis of CT scanning. Provides following features as semi-automatic nodule management, maximal plane measure, 3D measures and columetric measures, automatic nodule detection by integration with 3rd party CAD. Also provides Brocks model which calculated the malignancy score based on numerical or Boolean inputs. Follow up support with automated nodule matching and automatically categorize Lung-RADS score which is a quality assurance tool designed to standardize lung cancer screening CT reporting and management recommendations that is based on type, size, size change and other findings that is reported. It also automatically analyzes coronary artery calcification which support user to detect cardiovascular disease in early stage and reduce the burden of medical.

The provided FDA 510(k) summary for the AVIEW 2.0 device (K200714) primarily focuses on establishing substantial equivalence to a predicate device (AVIEW K171199, among others) rather than presenting a detailed clinical study demonstrating its performance against specific acceptance criteria.

However, based on the nonclinical performance testing section and the overall description, we can infer some aspects and present the available information regarding the device's capabilities and how it was tested. It is important to note that explicit acceptance criteria and detailed clinical study results are not fully elaborated in the provided text. The document states: "This Medical device is not new; therefore, a clinical study was not considered necessary prior to release. Additionally, there was no clinical testing required to support the medical device as the indications for use is equivalent to the predicate device."

Here's a breakdown of the requested information based on the provided text:

1. Table of Acceptance Criteria and Reported Device Performance

Note: The document does not explicitly state "acceptance criteria" with numerical or performance targets. Instead, it describes general validation methods and "performance tests" that were conducted to ensure functionality and reliability. The "Reported Device Performance" here refers to the successful completion or validation of these functions.

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

The 510(k) summary does not specify the sample size used for the test set(s) used in the performance evaluation, nor does it detail the data provenance (e.g., country of origin, retrospective or prospective). It simply mentions "software verification and validation" and "nonclinical performance testing."

3. Number of Experts Used to Establish Ground Truth and Qualifications

The document does not provide information on the number of experts used to establish ground truth or their specific qualifications for any of the nonclinical or performance tests mentioned. Given that no clinical study was performed, it is unlikely that medical experts were involved in establishing ground truth for a test set in the conventional sense for clinical performance.

4. Adjudication Method

No information is provided regarding an adjudication method. Since the document states no clinical study was conducted, adjudication by multiple experts would not have been applicable for a clinical performance evaluation.

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

No, a Multi-Reader Multi-Case (MRMC) comparative effectiveness study was not reported. The document explicitly states: "This Medical device is not new; therefore, a clinical study was not considered necessary prior to release. Additionally, there was no clinical testing required to support the medical device as the indications for use is equivalent to the predicate device." Therefore, there is no mention of an effect size for human readers with or without AI assistance.

6. Standalone Performance Study

Yes, a standalone (algorithm only without human-in-the-loop) performance evaluation was conducted, implied by the "Nonclinical Performance Testing" and "Software Verification and Validation" sections. The "Performance Test" section specifically lists several automatic and semi-automatic functions (e.g., "Auto Lung & Lobe Segmentation," "Airway Segmentation," "CAC Performance") that were tested for the device's inherent capability.

7. Type of Ground Truth Used

The document does not explicitly state the type of ground truth used for each specific performance test. For software components involving segmentation, it is common to use expert-annotated images (manual segmentation by experts) as ground truth for a quantitative comparison. For calculations like Agatston score, or VDT, the ground truth would likely be mathematical computations based on established formulas or reference standards applied to the segmented regions. However, this is inferred, not explicitly stated.

8. Sample Size for the Training Set

The document does not specify the sample size for any training set. It mentions the use of "deep-learning algorithms" for segmentation, which implies a training phase, but details about the training data are absent.

9. How Ground Truth for the Training Set Was Established

The document does not specify how the ground truth for any training set was established. While deep learning is mentioned for certain segmentation tasks, the methodology for creating the labeled training data is not detailed.

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Lung CARE CT is a self-contained image analysis software package for evaluating CT volume data sets. Combining enhanced commercially available digital image processing tools with optimized workflow and reporting tools, the software is designed to support the physician in confirming the presence of absence of physician-identified lung lesions (eg. nodules) in addition to evaluation, documentation and follow-up of any such lesions using standard or low-dose spiral CT scanning. This evaluation tool allows for volumetric analysis of pulmonary nodule or lesion size over time, helping the Physician to assess the changes in their growth. It is also designed to help the physician classify conspicuous regions of tissue unambiguously, with respect to their size, dimensions, shape and position.

This premarket notification covers Siemens LungCARE CT software package. It is based on Siemens syngo software platform. Lung CARE CT is a self-contained image analysis software package for cvaluating CT volume data sets. Combining enhanced commercially available digital image processing tools (MIP, MPR, SSD, VRT), evaluation tools (volumetric estimation using consistent standardized measurement protocol, comparator tool for nodule matching by synchronization of two datasets, classification of nodules using configurable descriptors) and reporting tools (targeted presets, saved lesion) with optimized workflow palette, the software package is designed to support the physician in confirming the presence of physician identified lung lesions (eg. nodulcs) in addition to evaluation, documentation and follow-up of any such lesions using standard or low-dose spiral CT scanning. This visualization tool allows for volumetric analysis of pulmonary nodule or lesion size over time, helping the Physician to assess the changes in their growth. It is also designed to help the physician classify conspicuous regions of tissue unambiguously, with respect to their size, dimensions, shape and and position.

This document describes the LungCARE CT software package, a 3D CT reconstruction software designed to assist physicians in evaluating lung lesions. The submission includes a summary of pre-clinical and clinical information, and a declaration of substantial equivalence to previously cleared devices.

Here's an analysis of the provided information concerning acceptance criteria and the supporting studies:

1. Table of Acceptance Criteria and Reported Device Performance

The submission does not explicitly state specific quantitative acceptance criteria for performance metrics (e.g., minimum accuracy, sensitivity, or reproducibility thresholds). Instead, the studies focus on demonstrating the capabilities and reproducibility of the device's volumetric measurements under various conditions.

• Volumetric estimation using standardized measurement protocol.
• Nodule matching by synchronization of two datasets (comparator tool).
• Classification of nodules using configurable descriptors.
• Targeted presets and saved lesion reporting tools.
• Visualization for volumetric analysis of pulmonary nodule or lesion size over time to assess growth.
• Classification of conspicuous tissue regions by size, dimensions, shape, and position. (Implied, as these are features of the device, not an evaluated performance metric in the studies described). |

2. Sample Sizes and Data Provenance

A. Lung Phantom Bench Testing Study (Kohl et al.):

• Sample Size: Not explicitly stated, but it's a bench testing study using a lung phantom, implying manufactured phantoms rather than patient data.
• Data Provenance: Not applicable as it's a phantom study.

B. Clinical Evaluation (Wormanns et al.):

• Sample Size (Test Set): 10 patients with pulmonary metastatic disease. A total of 150 pulmonary nodules were manually marked and evaluated across these patients.
• Data Provenance: Not explicitly stated (e.g., country of origin), but it is a clinical evaluation, therefore retrospective patient data since the study was conducted to evaluate the software.

3. Number of Experts and their Qualifications for Ground Truth

• A. Lung Phantom Bench Testing Study (Kohl et al.): Not applicable, as this was a phantom study and did not involve human interpretation or ground truth establishment in the traditional sense for medical imaging. The "ground truth" would be the known physical dimensions or changes in the phantom.
• B. Clinical Evaluation (Wormanns et al.): Not explicitly stated how many experts were involved in manually marking the 150 pulmonary nodules. The qualifications of these individuals are also not specified (e.g., "radiologist with 10 years of experience").

4. Adjudication Method

• The information provided does not specify an adjudication method for either study (e.g., 2+1, 3+1, none).
• For the clinical study, it states that "150 pulmonary nodules were manually marked and then evaluated," which implies a single expert or a non-adjudicated process unless specified otherwise.

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

• No MRMC comparative effectiveness study is described where human readers' performance with and without AI assistance is compared. The clinical study focused on the reproducibility of the software's volumetric measurements, not on the human reader's improvement with the software.

6. Standalone (Algorithm Only) Performance

• Yes, a standalone study was performed. The "Wormanns et al." clinical evaluation assessed the reproducibility of volumetric measurements using LungCARE CT where "The proposed segmentation results, provided by the software package, were not modified by the user." This indicates the study evaluated the algorithm's performance in segmenting and measuring nodules without human intervention to adjust its output.

7. Type of Ground Truth Used

• A. Lung Phantom Bench Testing Study (Kohl et al.): The ground truth would be based on the known physical properties and dimensions of the phantom and the controlled changes applied to it.
• B. Clinical Evaluation (Wormanns et al.): The ground truth for the 150 pulmonary nodules was established by manual marking. This implies expert human identification and marking of the nodules, which then serves as a reference for the software's measurements. This is a form of expert consensus or expert-derived ground truth, though the number and qualifications of experts are not specified, nor is an explicit consensus process mentioned.

8. Sample Size for the Training Set

• The document does not provide any information about the training set used for the LungCARE CT software. The studies described are evaluation studies of an already developed product.

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

• Since there is no information about the training set, there is also no information on how its ground truth was established.

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