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Lung-CAD is a computer-assisted detection (CADe) software device that analyzes chest radiograph studies for lung hyperinflation. The device uses a deep learning algorithm to identify regions of interest (ROIs) with lung hyperinflation and produces boxes around the ROIs.

Lung-CAD is intended for use as a concurrent reading aid for physicians interpreting chest X-rays. The device is not intended for clinical diagnosis of any disease. It does not replace the role of other diagnostic testing in the standard of care for lung parenchymal findings. Lung-CAD is indicated for adults only.

Lung-CAD is computer-assisted detection (CADe) software designed to increase the accurate detection of lung hyperinflation. Lung-CAD's output is available for physicians interpreting chest radiographs as a concurrent reading aid. The device helps physicians more effectively identify lung hyperinflation. Lung-CAD does not replace the role of the physician or of other diagnostic testing in the standard of care and does not provide a diagnosis for any disease. Lung-CAD uses modern deep learning and computer vision techniques to analyze chest radiographs.

For each image within a study, Lung-CAD generates a DICOM Presentation State file (output overlay). If any region of interest (ROI) is detected by Lung-CAD in the study, the output overlay for each image includes "Lung hyperinflation". In addition, if ROI(s) are detected in an image, bounding boxes surrounding each detected ROI are included in the output overlay for that image and are labeled with the radiographic findings: "Lung hyperinflation". If no ROI is detected by Lung-CAD in the study, the output overlay for each image will include the text "No Lung-CAD ROI(s)" and no bounding boxes will be included. Regardless of whether an ROI is detected, the overlay includes text identifying the X-ray study as analyzed by Lung-CAD and a customer configurable message containing a link or instructions, for users, to access labeling documents. The Lung-CAD overlay can be toggled on or off by the physician within their Picture Archiving and Communication System (PACS) viewer, allowing for concurrent review of the X-ray study.

Here's a breakdown of the acceptance criteria and the study proving the device meets them, based on the provided FDA 510(k) summary for Lung-CAD:

1. Table of Acceptance Criteria and Reported Device Performance

The acceptance criteria are implicitly defined by the reported performance metrics that demonstrate substantial equivalence and effectiveness. While explicit "acceptance criteria" are not listed as pass/fail thresholds in this summary, the strong statistical significance and high performance metrics indicate successful validation.

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Lung-CAD is a computer-assisted detection (CADe) software device that analyzes chest radiograph studies for interstitial thickening. The device uses a deep learning algorithm to identify regions of interstital thickening and produces boxes around the ROIs.

Lung-CAD is intended for use as a concurrent reading aid for physicians interpreting chest X-rays. The device is not intended for clinical diagnosis of any disease. It does not replace the role of other diagnostic testing in the standard of care for lung parenchymal findings. Lung-CAD is indicated for adults only.

Lung-CAD is computer-assisted detection (CADe) software designed to increase the accurate detection of interstitial thickening. Lung-CAD's output is available for physicians interpreting chest radiographs as a concurrent reading aid. The device helps physicians more effectively identify interstitial thickening. Lung-CAD does not replace the role of the physician or of other diagnostic testing in the standard of care and does not provide a diagnosis for any disease. Lung-CAD uses modern deep learning and computer vision techniques to analyze chest radiographs.

For each image within a study, Lung-CAD generates a DICOM Presentation State file (output overlay). If any ROI is detected by Lung-CAD in the study, the output overlay for each image includes "Interstitial thickening". In addition, if ROI(s) are detected in an image, bounding boxes surrounding each detected ROI are included in the output overlay for that image and are labeled with the radiographic finding: "Interstitial thickening". If no ROI is detected by Lung-CAD in the study, the output overlay for each image will include the text "No Lung-CAD ROI(s)" and no bounding boxes will be included. Regardless of whether an ROI is detected, the overlay includes text identifying the X-ray study as analyzed by Lung-CAD and a customer configurable message containing a link or instructions, for users, to access labeling documents. The Lung-CAD overlay can be toggled on or off by the physician within their Picture Archiving and Communication System (PACS) viewer, allowing for concurrent review of the X-ray study.

Here's a breakdown of the acceptance criteria and the study proving the device meets them, based on the provided text:

Acceptance Criteria and Reported Device Performance

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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 syngo.CT Lung CAD device is a Computer-Aided Detection (CAD) tool designed to assist radiologists in the detection of solid and subsolid (part-solid and ground glass) 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 otherwise be 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 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 (partsolid and ground glass) 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.

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 clearance K191309) is deployed on the syngo.via platform (FDA clearance K191040), 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 subject device and predicate device have the same basic technical characteristics. This does not introduce new types of safety or effectiveness concerns as demonstrated by the statistical analyses and results of the reader study and additional evaluations results documented in the Statistical Analysis.

Here's a breakdown of the acceptance criteria and the study proving the device's performance, based on the provided text:

1. Table of Acceptance Criteria and Reported Device Performance:

The document primarily focuses on demonstrating the improvement of the new VD20 version over the predicate VC30, rather than explicitly listing fixed "acceptance criteria" with numerical targets in a single table. However, based on the statistical analysis summary and the comparison tables, we can infer the performance goals and the reported outcomes:

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The syngo.CT Lung CAD VC30 device is a Computer-Aided Detection (CAD) tool designed to assist radiologists in the detection of solid pulmonary nodules during review of multi-detector computed tomography (MDCT) examinations of the chest. The software is an adjunctive tool to alert the radiologist to regions of interest that may have been initially overlooked. The syngo.CT Lung CAD device is intended to be used as a second reader after the radiologist has completed his/her initial read.

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 ≥ 3.0 mm in size. The device processes images acquired with Siemens multi-detector CT scanners with 4 or more detector rows.

The syngo.CT Lung CAD device supports the full range of nodule locations (central, peripheral) and contours (round, irregular). The detection performance of the syngo.CT Lung CAD device is optimized for nodules between 3.0 mm and 20.0 mm in size.

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 for use in a second reader mode. syngo MM Oncology (FDA clearance K191309) is implemented on the syngo.via platform (FDA clearance K191040), 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 is intended to be used as a second reader only after the initial read is completed.

The subject device and the predicate device has the same basic technical characteristics as the predicate; however, the fundamental technology has been replaced by deep learning technology. Specifically, the predicate VC20 uses feature-based and Machine Learning whereas the current VC30 uses algorithms based on Convolutional Neural Networks. This does not introduce new types of safety or effectiveness concerns. In particular, as demonstrated by the statistical analysis and results of the standalone benchmark evaluations:

i. The standalone accuracy has been shown not only to be non-inferior but actually superior to that of the device and
ii. The marks generated by the two devices have been shown to be reasonably consistent.

This device description holds true for the subject device, syngo.CT Lung CAD, software version VC30, as well as the predicate device, syngo.CT Lung CAD, software version VC20.

Here's a breakdown of the acceptance criteria and the study proving the device meets them, based on the provided FDA 510(k) summary for syngo.CT Lung CAD (VC30):

Device Name: syngo.CT Lung CAD (VC30)
Intended Use: A Computer-Aided Detection (CAD) tool to assist radiologists in the detection of solid pulmonary nodules (≥ 3.0 mm) during review of multi-detector computed tomography (MDCT) examinations of the chest. It's an adjunctive tool to alert radiologists to initially overlooked regions of interest, used as a second reader after the radiologist's initial read.

1. Table of Acceptance Criteria and Reported Device Performance

The document primarily focuses on demonstrating non-inferiority and superiority to the predicate device rather than explicitly stating acceptance criteria with numerical targets for metrics like sensitivity or specificity. However, based on the conclusions regarding "standalone accuracy" and "false positive rate," we can infer the implicit criteria and the reported performance as comparative to the predicate.

Note: The document describes the study as a "standalone benchmark evaluation" focused on comparing VC30's performance to VC20's. Specific numerical metrics for sensitivity, specificity, or FPs are not provided in this summary, but the conclusions about superiority and reduction in FPs serve as the performance statement.

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

• Sample Size for Test Set: The document states that "The endpoints to establish meaningful and statistically relevant performance and equivalence of the device and sample size were considered and defined as part of the test protocols." However, the specific number of cases or nodules in the test set is not provided in this summary.
• Data Provenance: Not explicitly stated regarding country of origin. The document mentions "Non-clinical performance testing was performed at various levels for verification and validation of the device intended use and to ensure safety and effectiveness." It does not specify if the data was retrospective or prospective.

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

• Number of Experts: Not specified in the provided text.
• Qualifications of Experts: Not specified in the provided text.

4. Adjudication Method for the Test Set

• Adjudication Method: Not specified in the provided text. The document refers to "ground truth" but does not detail the method by which it was established.

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

• MRMC Study: The document does not mention a multi-reader multi-case (MRMC) comparative effectiveness study comparing human readers with AI vs. without AI assistance. The study described is a "standalone benchmark evaluation" comparing the performance of the new AI algorithm (VC30) to the previous algorithm (VC20).
• Effect Size of Human Improvement: Not applicable, as no MRMC study is detailed here.

6. Standalone (Algorithm Only) Performance Study

• Standalone Study: Yes, a standalone study was done. The document explicitly states: "The standalone performance test proved that the standalone sensitivity of syngo.CT Lung CAD VC30 is superior to that of syngo.CT Lung CAD VC20 (predicate) and the false positive rate improved (reduced)."

7. Type of Ground Truth Used

• Type of Ground Truth: The document refers to "ground truth" for the test set, stating that it was established to define "meaningful and statistically relevant performance." However, the specific method (e.g., expert consensus, pathology, follow-up outcomes) for establishing this ground truth is not detailed in the provided summary.

8. Sample Size for the Training Set

• Sample Size for Training Set: The document does not provide the sample size used for the training set. It only mentions that the "fundamental technology has been replaced by deep learning technology," indicating a training process was involved.

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

• How Ground Truth for Training Set Was Established: The document does not provide details on how the ground truth for the training set was established. It only describes the functional components of the new syngo.CT Lung CAD VC30 as using Convolutional Neural Networks (CNN) for lung segmentation, candidate generation, feature calculation, and candidate classification, which inherently require labeled training data.

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The syngo.CT Lung CAD device is a computer-aided detection (CAD) tool designed to assist radiologists in the detection of solid pulmonary nodules during review of multi-detector computed tomography (MDCT) examinations of the chest. The software is an adjunctive tool to alert the radiologist to regions of interest (ROI) that may have been initially overlooked. The syngo. CT Lung CAD device is intended to be used as a second reader after the radiologist has completed his/her initial read.

syngo.CT Lung CAD is a medical device that is designed to perform CAD processing in thoracic CT examinations for the detection of solid pulmonary nodules ≥ 3 mm in size. The device processes images acquired with Siemens multi-detector CT scanners with 4 or more detector rows.

The syngo.CT Lung CAD device supports the full range of nodule locations (central, peripheral) and contours (round, irregular). The detection performance of the syngo.CT Lung CAD device is optimized for nodules between 3 mm and 10 mm in size. Additionally, the syngo.CT Lung CAD device can be used in scans with or without contrast enhancement.

The device receives images via an input data interface, performs CAD processing and provides locations of suspected nodules as an output. Specific visualizations, such as the syngo PET&CT Oncology application (K093621) or equivalent Siemens products, should be used (but are not part of this clearance) to display the CAD marks. The syngo.CT Lung CAD device is intended to be used as a second reader only after the initial read is completed.

The provided document, K143196 for syngo.CT Lung CAD, largely focuses on demonstrating substantial equivalence to a predicate device rather than presenting a detailed study proving performance against explicit acceptance criteria with specific metrics. The document states that "Non-clinical tests were conducted... The modifications described in this Premarket Notification were supported with verification and validation testing." However, it does not explicitly outline a table of acceptance criteria nor the corresponding reported device performance.

Nonetheless, based on the information provided, we can infer some aspects of the performance and the nature of the testing:

1. Table of Acceptance Criteria and the Reported Device Performance

The document does not provide a quantitative table of acceptance criteria or reported device performance metrics like sensitivity, specificity, or false positive rates. It generally states that "The results of these tests support the substantial equivalence of this device" and that "Testing, including standalone performance testing, were conducted to assess the new syngo.CT Lung CAD device and compare it to the predicate device with respect to false positives, sensitivity, and the dismissibility of false positives." This implies that these metrics were assessed and found acceptable for substantial equivalence, but the actual numbers and predefined thresholds are not disclosed.

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

The document does not specify the sample size used for the test set or the data provenance (e.g., country of origin, retrospective or prospective). It simply refers to "non-clinical tests" and "testing."

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

This information is not provided in the document.

4. Adjudication Method for the Test Set

This information is not provided in the document.

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

The document mentions that the device is intended to be used as a "second reader after the radiologist has completed his/her initial read." It also states, "Testing, including standalone performance testing, were conducted to assess the new syngo.CT Lung CAD device and compare it to the predicate device with respect to false positives, sensitivity, and the dismissibility of false positives." However, it does not explicitly describe an MRMC comparative effectiveness study that quantitatively assesses how much human readers improve with AI assistance versus without. The focus seems to be on the performance of the CAD system itself and its equivalence to a prior version.

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

Yes, a standalone performance test was done. The document explicitly states: "Testing, including standalone performance testing, were conducted to assess the new syngo.CT Lung CAD device and compare it to the predicate device with respect to false positives, sensitivity, and the dismissibility of false positives." This indicates that the algorithm's performance without direct human intervention was evaluated.

7. The Type of Ground Truth Used

The document does not explicitly state the type of ground truth used (e.g., expert consensus, pathology, outcomes data). Given the context of detecting "solid pulmonary nodules," it is highly likely that the ground truth would have been established by a consensus of expert radiologists or possibly through follow-up imaging or pathology reports where available, but this is not explicitly detailed.

8. The Sample Size for the Training Set

The document does not provide any information regarding the sample size used for the training set.

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

The document does not provide any information on how the ground truth for the training set was established.

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