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syngo.CT Lung CAD device is a computer-aided detection (CAD) tool designed to assist radiologists in the detection of solid and subsolid pulmonary nodules during review of multi-detector computed tomography (MDCT) from multivendor examinations of the chest. The software is an adjunctive tool to alert the radiologist to regions of interest (ROI) that may be otherwise overlooked.

The syngo.CT Lung CAD device may be used as a concurrent first reader followed by a full review of the case by the radiologist or as second reader after the radiologist has completed his/her initial read.

The syngo.CT Lung CAD device may also be used in "solid-only" mode, where potential (or suspected) sub-solid and/or fully calcified CAD findings are filtered out.

The software device is an algorithm which does not have its own user interface component for displaying of CAD marks. The Hosting Application incorporating syngo. CT Lung CAD is responsible for implementing a user interface.

Siemens Healthcare GmbH intends to market the syngo.CT Lung CAD which is a medical device that is designed to perform CAD processing in thoracic CT examinations for the detection of solid pulmonary nodules (between 3.0 mm and 30.0mm) and subsolid nodules (between 5.0 mm and 30.0mm) in average diameter. The device processes images acquired with multi-detector CT scanners with 16 or more detector rows recommended.

The syngo.CT Lung CAD device supports the full range of nodule locations (central, peripheral) and contours (round, irregular).

The syngo.CT Lung CAD sends a list of nodule candidate locations to a visualization application, such as syngo MM Oncology, or a visualization rendering component, which generates output images series with the CAD marks superimposed on the input thoracic CT images to enable the radiologist's review. syngo MM Oncology (FDA clearanceK211459 and subsequent versions ) is deployed on the syngo.via platform (FDA clearance K191040 and subsequent versions), which provides a common framework for various other applications implementing specific clinical workflows (but are not part of this clearance) to display the CAD marks. The syngo.CT Lung CAD device may be used either as a concurrent first reader, followed by a review of the case, or as a second reader only after the initial read is completed

The provided text describes the Siemens syngo.CT Lung CAD (Version VD30) and its substantial equivalence to its predicate device (syngo.CT Lung CAD Version VD20). The primary change in VD30 is the introduction of a "solid-only" mode. The acceptance criteria and study details are primarily focused on demonstrating that the VD30 in "solid-only" mode is not inferior to VD20 in standard mode, and that VD30 in standard mode is not inferior to VD20 in standard mode. Since the document primarily focuses on demonstrating non-inferiority to a predicate device, explicit acceptance criteria values (e.g., minimum sensitivity thresholds) are not explicitly stated as numerical targets. Instead, the acceptance is based on statistical non-inferiority.

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

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

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

• Sample Size: 712 CT thoracic cases.
• Data Provenance: Retrospectively collected data from 3 sources:
  • The UCLA study (232 cases)
  • The original PMA study (145 cases)
  • Additional cases (335 cases)

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

The document differentiates ground truth establishment based on the data source:

• UCLA data: Reference standard (ground truth) was determined as part of the reader study for the predicate device (K203258). The number and qualifications of experts are not explicitly stated for this subset in the provided text for VD30, but it refers to the predicate clearance.
• PMA study cases: 18 readers were used. Qualifications are not explicitly stated, but 9 of the 18 readers were needed for declaring a true nodule.
• Additional cases: 7 readers were used. Qualifications are not explicitly stated, but 4 of the 7 readers were needed for declaring a true nodule.

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

The adjudication method varied based on the data source:

• PMA study cases: 9 out of 18 readers were needed for declaring a true nodule. This suggests a majority consensus from a large panel.
• Additional cases: 4 out of 7 readers were needed for declaring a true nodule. This also suggests a majority consensus.
• UCLA data: "Reference standard for the UCLA data was determined as part of the reader study (K203258)." Specific adjudication details for this subset are not provided in this document but are referenced to the predicate device's clearance.

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

A MRMC comparative effectiveness study involving human readers with and without AI assistance is not explicitly described in this document. The statistical analysis performed was a standalone performance analysis to demonstrate substantial equivalence between two CAD versions (VD30 vs VD20), focusing on the algorithm's performance metrics (sensitivity, FPs).

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

Yes, a standalone performance analysis was done. The document states: "The standalone performance analysis was designed to demonstrate the substantial equivalence between syngo.CT Lung CAD VD30A (VD30) and the predicate device syngo.CT Lung CAD VD20."

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

The ground truth was established through expert consensus/reader review.

• For PMA cases: 9 out of 18 readers' consensus.
• For additional cases: 4 out of 7 readers' consensus.
• For UCLA data: Reference standard from a reader study.

8. The sample size for the training set

The document does not explicitly state the sample size for the training set. It mentions that the algorithm is based on Convolutional Networks (CNN) and that the lung segmentation algorithm for VD30 in particular is "trained on lung CT data including comorbidities for robustness," but the specific number of cases for this training set is not provided.

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

The document does not explicitly describe how the ground truth for the training set was established. It only states that the lung segmentation algorithm was "trained on lung CT data" and that the overall algorithm uses CNNs, implying supervised learning, which would require ground truth annotations. However, the method of obtaining these annotations is not detailed.

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The QT Scanner 2000 Model A is for use as an ultrasonic imaging system to provide reflection-mode and transmissionmode images of a patient's breast. The QT Scanner 2000 Model A software also calculates the breast fibroglandular tissue volume (FGV) value and the ratio of FGV to total breast volume (TBV) value as determined from reflection-mode and transmission-mode ultrasound images of a patient's breast. The device is not intended to be used as a replacement for screening mammography.

The QT Scanner 2000 Model A is indicated for use by trained healthcare professionals in environments where healthcare is provided to enable breast imaging in adult patients.

The QT Scanner 2000 Model A ("QT Scanner") is an automated, software-controlled ultrasound imaging system which performs a standardized scan of the whole breast without the use of ionizing radiation, compression, or contrast injection; and generates both reflection-mode and transmission-mode breast images. The QT Scanner consists of a Patient Scanning System, an Operator Console, an optional offboard image processor, and the OTviewer software.

The Patient Scanning System contains the necessary electronics which perform acquisition and initial processing of the breast images and further provides a support table which allows the patient to rest comfortably while the scanning takes place. The scan tank is centered below a patient's breast and contains the ultrasound transducer arrays. The transducer arrays include a set of three reflection transducers that transmit pulsed ultrasound plane waves into targeted tissues using the water bath in the scan tank as a coupling medium. An additional transmitter and receiver array pair collect the ultrasound energy to provide speed of sound values.

During scanning, a patient lies prone on the examination table with the breast suspended in a warm water bath maintained near skin temperature. Images are automatically acquired on a pendant breast positioned with the nipple as a point of reference. The transducer arrays rotate about a vertical axis to circle the breast in the coronal plane. The array is then translated vertically, and the scanning process is repeated until the entire breast is scanned, allowing B-scan images to be constructively combined into tomographic, speed of sound and reflection ultrasound images.

The QT Scanner outputs the images to a server which allows the images to be stored until they are reviewed on a Viewer Console running the QTviewer™ software. Alternatively, raw data files can be output to a server and remotely constructively combined into tomographic, speed of sound and reflection ultrasound images. Coronal, axial and sagittal images are generated for review by the radiologist. The QTviewer software also provides a number of analytics capabilities, such as biometric measurement, manual segmentation, and Region of Interest calculations. The QTviewer software also provides the "Fibroglandular Volume" (FGV) which is display of calculated fibroglandular tissue volume within a breast, expressed in dimensions of volume, as well as a ratio of the volume of fibroglandular tissue within the breast volume to the total breast volume, from QT Scanner breast images.

The QTviewer software also provides the "Fibroglandular Volume" (FGV) which is display of calculated fibroglandular tissue volume within a breast, expressed in dimensions of volume, as well as a ratio of the volume of fibroglandular tissue within the breast volume to the total breast volume (TBV), provided as FGV/TBV. The process for calculating FGV and FGV/TBV is based on image segmentation methods. The first step is segmentation of the whole breast from the surrounding water. Attenuation images are used to identify the boundary of the breast assuming that attenuation anywhere outside the breast (within water) is essentially zero. From skin inward, every pixel is labelled as breast tissue. The next step identifies the pixels in the vicinity of the boundary as border pixels and which constitute the skin of the breast. The pixels labelled as surrounding water and skin are removed from the breast and the remaining breast volume is deemed as TBV. In the next step, pixel values from the segmented speed of sound image are provided to a one-dimensional fuzzy c-means (FCM) algorithm to partition of data set into two clusters: fibroglandular tissue and fat. Once FCM is trained, a membership map of fibroglandular tissue is generated and an empirically chosen threshold is applied to binarize the fibroglandular tissue membership map which constitutes fibroglandular tissue volume (FGV). The ratio of FGV to TBV (FGV/TBV) is then calculated by dividing the volume of the fibroglandular tissue by the volume of the whole breast.

The provided text describes the 510(k) premarket notification for the QT Scanner 2000 Model A. The key difference between the subject device and its predicate is the addition of an automated calculation of breast fibroglandular tissue volume (FGV) and the ratio of FGV to total breast volume (TBV). The information for describing acceptance criteria and the study that proves the device meets them primarily relates to this new FGV/TBV feature.

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

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

The document does not explicitly state numerical acceptance criteria for the FGV/TBV calculation. Instead, it describes demonstrating "strong correlation" with breast MRI as the performance measure.

2. Sample size used for the test set and the data provenance (e.g., country of origin of the data, retrospective or prospective)

• Sample Size: 53 breasts from 29 patients.
• Data Provenance: Retrospective study. The country of origin is not specified, but given the FDA submission, it is likely U.S. data or data compliant with U.S. regulatory standards.

3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts (e.g., radiologist with 10 years of experience)

The document states that the comparison was made to values "as determined via breast MRI," implying that the MRI values served as the ground truth. However, it does not specify the number of experts or their qualifications used to establish this ground truth from the MRI data.

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

The document does not describe any adjudication method. It simply states that the FGV and FGV/TBV values determined by the QT Scanner were compared to those determined by breast MRI.

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. The study described is a direct comparison of the device's numerical output (FGV/TBV) against breast MRI, not a human-in-the-loop study assessing reader performance improvement with AI assistance.

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

Yes, a standalone performance evaluation was done for the FGV/TBV calculation capability. The study directly compared the algorithm's output (FGV and FGV/TBV) with values derived from breast MRI, without human intervention in the calculation process.

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

The ground truth used was values determined via breast MRI. This is a clinical imaging modality, not explicitly expert consensus from multiple readers, pathology, or outcomes data.

8. The sample size for the training set

The document does not specify the sample size for the training set used for the FGV/TBV calculation algorithm. The study described is a retrospective clinical validation study for the algorithm's performance, not a description of its development or training.

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

The document does not describe how the ground truth for the training set was established, as it focuses on the clinical validation study.

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MI View&GO is a medical diagnostic application for viewing, manipulation, quantification, analysis and comparison of medical images with one or more time-points. MI View&GO supports functional data, such as position emission tomography (PET) or nuclear medicine (NM), as well as anatomical datasets, such as computed tomography (CT) or magnetic resonance (MR).

MI View&GO is intended to be utilized by appropriately trained health care professionals to aid in the management of diseases associated with oncology, neurology, and organ function. The images and results produced by MI View&GO can also be used by the physician to aid in radiotherapy treatment planning.

MI View&GO is a software-only medical device which will be delivered in conjunction with Siemens SPECT/CT and PET/CT scanners. MI View&GO software provides additional specific capabilities for handling of PET and SPECT as well as CT and MR data directly at the acquisition console.

The MI View&GO software integrates molecular imaging more efficiently in the clinical environment by providing an interface for its users to review, post-process and read medical images immediately after acquisition. The purpose of the MI View&GO is to allow the technologist and reading physician to:

• Review acquired and reconstructed images at the scanner console
• Determine that the acquired data is of sufficient quality for reading, so the patient can be released.
• Prepare images for reading
• Perform a basic read

This FDA 510(k) summary for Siemens' MI View&GO VA20A software describes modifications to an existing medical image management and processing system. The summary does not include a detailed study proving the device meets specific acceptance criteria in the format requested.

The document states that "Verification and Validation activities have been successfully performed on the software package, including assurance that functions work as designed, performance requirements and specifications have been met, and that all hazard mitigations have been fully implemented. All testing has met the predetermined acceptance values." However, it does not provide a table of acceptance criteria and reported device performance, nor does it specify the methodologies, sample sizes, or ground truth establishment for such testing.

Therefore, I cannot provide the requested information from the provided text. The document focuses on demonstrating substantial equivalence to a predicate device rather than presenting a detailed performance study with quantitative acceptance criteria.

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