Search Results

DeltaView is intended to generate a secondary residual image based on a current and prior chest x-ray image of the same patient, resulting in improved detection of lung nodules that have changed between the two examinations. The DeltaView image provides adjunctive information and is not a substitute for the original PA/AP image. This device is intended to be used by trained professionals, such as physicians and radiologists, on patients with risk of having lung nodules, and is not intended to be used on pediatric patients.

DeltaView is a dedicated post-processing application which registers current and prior chest exams to provide an image that shows areas of change.

Here's a breakdown of the acceptance criteria and study details for the DeltaView™ device based on the provided text:

Acceptance Criteria and Device Performance

Study Details

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

   • The document does not explicitly state the sample size (number of cases or images) used for the test set.
   • The document does not specify the data provenance (e.g., country of origin, retrospective or prospective).

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

   • The document does not specify the number of experts or their qualifications for establishing ground truth.

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

   • The document does not specify an adjudication method for the test set.

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:

   • Yes, an MRMC reader study was done.
   • Effect size: The area under the LROC curve (AUCLROC) "increased from 0.477 to 0.536 (12.4%)" when radiologists were aided by DeltaView compared to unaided interpretation. This indicates a 12.4% improvement in the AUCLROC.

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

   • The document does not state that a standalone (algorithm-only) performance study was conducted. The study described focuses on human-in-the-loop performance with the device as an adjunct.

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

   • The type of ground truth used is not explicitly stated. However, the study evaluated "the detection of actionable lung nodules," implying that a determination of the presence or change of such nodules constituted the ground truth.

7. The sample size for the training set:

   • The document does not provide information on the sample size used for the training set.

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

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

Ask a specific question about this device

SoftView is intended to generate an enhanced, secondary digital radiographic image of the chest. The enhanced AP or PA image of the chest provides improved visibility of the lung parenchyma through bone suppression and tissue equalization, and may facilitate discerning the presence or absence of nodules. The SoftView image provides adjunctive information and is not a substitute for the original PA/AP image. This device is intended to be used by trained professionals, such as physicians, radiologists, and technicians on patients with risk of having lung nodules and is not intended to be used on pediatric patients.

SoftView is a dedicated post-processing application which suppresses bone structures from digital radiographic image of the chest.

Here's a breakdown of the acceptance criteria and study information for the SoftView™ device, extracted from the provided text:

SoftView™ Acceptance Criteria and Study Information

The acceptance criteria for SoftView™ were demonstrated through a reader study and a comparative analysis of the contrast-to-noise ratio (CNR) against a predicate device.

1. Table of Acceptance Criteria and Reported Device Performance

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

The document does not explicitly state the exact sample size for the test set used in the reader study or the comparative analysis. It mentions "an independent dataset" for the comparative analysis of CNR.

The data provenance is not specified (e.g., country of origin, retrospective or prospective).

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

This information is not provided in the given text. The document refers to "detecting actionable lung nodules" in the reader study, implying a clinical assessment, but does not detail how the ground truth for these nodules was established or by whom.

4. Adjudication Method for the Test Set

The adjudication method for establishing ground truth for the test set is not provided in the document.

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

Yes, a multi-reader study was conducted. The study assessed the benefit of SoftView™ to radiologists for detecting actionable lung nodules.

Effect Size:
The mean difference in the area under the LROC curve was -0.098 (95% CI: -0.116 to -0.080), which is described as a statistically significant improvement for human readers with the aid of SoftView™. The sensitivity for detecting nodules increased from 49.5% without SoftView to 66.3% with SoftView.

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

A standalone performance study of the algorithm without human intervention was not explicitly described in terms of diagnostic effectiveness for nodules. The "Comparative Analysis" focused on the algorithm's output (soft tissue image) against a predicate device's output based on CNR, which is a technical image quality metric rather than direct standalone diagnostic performance. The primary effectiveness study was a reader study with human readers.

7. The Type of Ground Truth Used

The type of ground truth used for the reader study is not explicitly stated. It refers to "actionable lung nodules," implying clinical diagnosis or follow-up, but the method (e.g., expert consensus, pathology, outcome data) is not detailed.
For the comparative analysis, the ground truth was the soft tissue images produced by the predicate DES devices (GE and Fuji), used to compare the CNR of residual bone.

8. The Sample Size for the Training Set

The document does not provide information regarding the sample size used for the training set. It mentions that the model is "built from DES data by using simple image features extracted from the standard PA, along with target values derived from a DES soft tissue image," but the size of this training data is not specified.

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

The ground truth for the training set (from which the SoftView™ model was built) was established by using target values derived from a DES soft tissue image. This means that commercially available Dual Energy Subtraction (DES) systems were used as the reference standard to create the "ideal" bone-suppressed images that the SoftView™ algorithm was trained to replicate. The exact process of "deriving target values" is not detailed further.

Ask a specific question about this device

The FirstView™ is intended to display and manipulate x-ray images.
The FirstView™ is indicated for displaying and manipulating chest x-ray images. It acts as an interface with the CAD component, if installed or available at the clinical site, and provides display of information contained within the DICOM header of the images. This device is intended to be used by trained professionals, such as physicians, radiologists, and technicians.

The FirstView™ consists of a dedicated server that has been programmed with a database and server software, as well as client software that is loaded onto an existing workstation. New images, which may be a chest x-ray or CAD result, are sent to the FirstView™ server from a Digital or from the PACS network, prompting FirstView to query the PACS for additional images related to that study. The FirstView™ server manages the images within its Riverain proprietary database.

The provided text concerns the 510(k) summary for the FirstView™ device, a system for displaying and manipulating chest x-ray images. However, the document does not contain information related to specific acceptance criteria, formal study design, sample sizes for test or training sets, ground truth establishment, expert qualifications, adjudication methods, or MRMC studies that would typically be described as "proving the device meets the acceptance criteria."

Instead, the document focuses on the device's substantial equivalence to predicate devices for its intended use as a "Picture archiving and communications system" (PACS). The 510(k) process primarily evaluates whether a new device is as safe and effective as a legally marketed predicate device, rather than requiring extensive clinical trials to meet novel performance criteria in the way a PMA (Premarket Approval) might.

Therefore, many of the requested sections regarding acceptance criteria and study details cannot be filled from the provided text. The information available is presented below within the constraints of the provided document.

Acceptance Criteria and Device Performance

The document does not explicitly state quantitative acceptance criteria or report specific device performance metrics in the format typically used for demonstrating performance against such criteria (e.g., sensitivity, specificity, accuracy).

Instead, the "acceptance criteria" in this context appear to be met by demonstrating substantial equivalence to legally marketed predicate devices.

Study Details (Based on available information)

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

   • The document does not report any specific test set sample size or data provenance (e.g., country of origin, retrospective/prospective). The 510(k) submission primarily relies on comparison to predicate devices, not independent performance testing against a specific dataset.

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

   • The document does not mention a test set with ground truth established by experts.

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

   • The document does not describe any adjudication method for a test set.

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:

   • The document does not describe an MRMC comparative effectiveness study. The FirstView™ is described as an image display and manipulation system, potentially interfacing with CAD, but not inherently as an AI-powered diagnostic aid that would typically undergo such a study to evaluate human reader improvement.

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

   • The document does not describe any standalone algorithm performance study.

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

   • The concept of "ground truth" for a specific performance evaluation test set is not mentioned in the document.

7. The sample size for the training set:

   • The document does not report any training set sample size.

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

   • The document does not report how ground truth for a training set was established, as it does not mention a training set.

Ask a specific question about this device