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ScreenDx is a software-only device that receives and analyzes lung computed tomography (CT) imaging data in order to assess for interstitial lung findings compatible with interstitial lung disease. The device supplements the standard-of-care workflow by providing a qualitative output of imaging findings based on pattern recognition, in order to provide adjunctive information as part of a referral pathway to an appropriately qualified clinician. Patients with positively identified patterns may undergo assessment for lung fibrosis, but ScreenDx does not replace the current standard of care methods for diagnosis of lung fibrosis and the results of the device are not intended to rule-out or rule-in lung fibrosis. The results of ScreenDx are intended to be used only by clinicians qualified in the care of lung disease, in conjunction with the patient's clinical history, symptoms, and other diagnostic tests, as well as the clinician's professional judgment.

The input to ScreenDx is a DICOM-compliant lung CT scan. Clinical case eligibility includes the following criteria:
Age > 22 years old.

ScreenDx is a computer-assisted analysis software device. The software analyzes lung computed tomography (CT) imaging data to provide a qualitative output assessing for interstitial lung findings compatible with interstitial lung disease. The software system is based on a software algorithm component and connection Application Programing Interface (API) to enable image transfer and notifications. The device consists of the following 3 components:

• (1) Image Receiver API for image acquisition;
• (2) Ingestion Pipeline and Analysis System for image processing; and
• (3) Output API for notification transmission.
• (1) The Image Receiver API is accessed via any technologically compliant system (e.g., DICOM, PACS). The case is submitted to the device through the API directly. The API passes the data to the Ingestion Pipeline and Analysis System.
• (2) The Ingestion Pipeline and Analysis System accepts the images, selects cases appropriate for processing, processes the images for analyses, analyzes the images, and stores the images. This system includes the analysis algorithm that identifies lung abnormalities in the case. No diagnostic information is generated from the software.
• (3) The Output API transmits the result of interstitial lung findings compatible with interstitial lung disease to an assigned set of users in the hospital or clinic, specialists who will then review the case. The Output API is integrated into the hospital or clinic notification software (e.g., EHR, messaging system).

The Analysis System is composed of a 3-D deep learning algorithm trained to identify interstitial lung findings compatible with interstitial lung disease. The training dataset included >3,000 lung CT cases from five different data sources from numerous clinical facilities. The algorithm takes in the ingested CT scan, runs it through the locked model, and classifies whether interstitial lung findings compatible with interstitial lung disease appear to be present. The average patient age was 63 years with male and females representing 51.5% and 48.5% of the patient population respectively. All major CT manufacturers were included, and prevalence of positive cases was 24%.

The software output is a binary Positive (Suggestive of ILD)/Negative result for interstitial lung findings compatible with interstitial lung disease. The output is stored for all cases run. Workflow for managing the output is customizable and under the control of the hospital or clinic making use of the device. For example, one workflow can include configuring the output to list Positive cases in a worklist for clinician review (e.g. a dedicated clinician within the pulmonary clinic environment). Another workflow may include integration with dedicated 3rd party software for workflow management of Positive cases. Regardless of method for case list management, cases with a Positive result will be reviewed for consideration of whether additional work-up is clinically indicated.

No analyzed images or other visually assessed features are output by the device. No regions of interest are either input or provided as an output. Additionally, the software does not provide localization information and there is no filtering, post processing, or annotations. The device is designed to not interrupt standard workflows and operates only in parallel, identifying patients who may benefit from additional follow-up for possible lung fibrosis, based on interstitial lung findings compatible with interstitial lung disease.

The Imvaria ScreenDx device is a software-only device designed to analyze lung CT imaging data for interstitial lung findings compatible with interstitial lung disease (ILD). It provides a qualitative output (Positive/Negative) to supplement standard-of-care workflow as part of a referral pathway to a qualified clinician.

1. Table of Acceptance Criteria and Reported Device Performance

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

• Pivotal Study Test Set: 3,018 unique patients.
  • Data Provenance: Multiple datasets from multiple clinical sites. The report does not specify the country of origin but states it was a retrospective, multicenter study.
• Additional Independent Validation Study Test Set: 2,482 cases.
  • Data Provenance: Collected prospectively by an independent organization. The report does not specify the country of origin.

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

The ground truth for the test set was established via clinical diagnosis derived directly from the data sources. These methodologies for clinical diagnosis were via combined clinical, radiological, laboratory, and/or pathological assessments. The document does not specify the number of experts or their specific qualifications (e.g., "radiologist with 10 years of experience") but implies that the diagnoses were established by qualified clinicians.

4. Adjudication Method for the Test Set

The document states that diagnostic information had been recorded independently for each case via combined clinical, radiological, laboratory, and/or pathological assessments. It does not mention a specific adjudication method like "2+1" or "3+1" for expert review of the test set by the study authors. The ground truth was based on pre-existing clinical diagnoses from various sources.

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

No MRMC comparative effectiveness study was performed or reported. The study focuses on the standalone performance of the algorithm. Therefore, there is no reported effect size of how much human readers improve with AI vs. without AI assistance.

6. Standalone Performance

Yes, a standalone performance study was done. The pivotal study and the additional independent validation study evaluated the algorithm's performance without a human-in-the-loop, reporting sensitivity and specificity metrics.

7. Type of Ground Truth Used

The ground truth used was expert consensus clinical diagnosis (derived from combined clinical, radiological, laboratory, and/or pathological assessments). The presence or absence of the pattern assessed by ScreenDx was intended to correlate with a clinical diagnosis that included lung fibrosis (e.g., IPF, fibrotic NSIP, and other related diagnoses).

8. Sample Size for the Training Set

The algorithm was trained on a dataset that included >3,000 lung CT cases from five different data sources from numerous clinical facilities. Data checks were completed to ensure no overlap between training and test sets.

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

The document states that the training dataset included cases used to identify interstitial lung findings compatible with interstitial lung disease. While specific details on how the ground truth for the training set was established are not explicitly provided, it is highly likely that similar methodologies as for the test set (clinical diagnosis via combined clinical, radiological, laboratory, and/or pathological assessments) were employed, as this is standard practice for medical imaging AI development based on clinical outcomes. However, the document does not definitively state this for the training set.

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Imbio IQ-UIP is a computer-aided software indicated for use in passively notifying specialists associated with interstitial lung disease (ILD) centers of radiological findings suggestive of radiological usual interstitial pneumonia (UIP) in non-contrast, chest CT scans of adults. Imbio IQ-UIP uses an artificial intelligence algorithm to analyze images and identify positive findings on a worklist application separate from and in parallel to the standard of care radiological image interpretation. Identification of positive findings include summary reports with a clinical guideline reference for the definition of UIP pattern that are meant for informational purposes only. The device does not alter the original medical image and is not intended to be used as a diagnostic device.

The results of Imbio IQ-UIP are used to notify specialists at an ILD center of radiological findings that may be consistent with UIP. These specialists are qualified clinicians experienced in evaluating chest CTs for ILD. Input images originate from within the same hospital network associated with the ILD center. The results of Imbio IQ-UIP are intended to be used in conjunction with additional patient information and based on the user's professional judgment, to assist with the review of medical images. Notified clinicians are responsible for viewing full image series and making final clinical determinations.

Imbio IQ-UIP is a computer-aided software indicated for use in notifying specialists associated with Interstitial Lung Disease (ILD) Centers of radiological findings suggestive of radiological Usual Interstitial Pneumonia (UIP) in non-contrast, chest CT scans of adults.

Imbio IQ-UIP uses an artificial intelligence algorithm to analyze images and identify positive findings on a worklist application separate from and in parallel to the standard of care radiological image interpretation. Identification of positive findings include summary reports with a clinical guideline reference for the definition of UIP pattern that are meant for informational purposes only. The device does not alter the original medical image and is not intended to be used as a diagnostic device.

The development of the deep learning inference model utilized anonymized, multi-center, retrospective, volumetric chest CT scans from several different, private and public data sources including multiple hospitals, clinical imaging centers, and imaging databases. Chest CT datasets were identified where each dataset represented an individual subject and acquisition. Data was subdivided into "bins" between the two stages of model development roughly 80%:20%: 1) model training and validation (i.e., hyper-parameter tuning) and 2) model testing (i.e. performance assessment). Site independence was maintained for several of the databases with clinical location data labels by randomly assigning each clinic location an integer value between 1 and 1000. Then, increasing from the lowest to highest random integer value, all data sets from a specific clinic location were assigned to the training bin until 80% of the total number of datasets from a database had been assigned to the training bin. The remaining were assigned to the testing bin. The testing data set was locked and quarantined from the datasets used in the device's model training and validation.

The results of Imbio IQ-UIP are intended to be used in conjunction with other patient information and based on the user's professional judgment, to assist with the review of medical images. Notified clinicians are responsible for viewing full image series and making final clinical determinations.

This document details the acceptance criteria and the study that proves the device (Imbio IQ-UIP) meets these criteria, based on the provided FDA 510(k) summary.

Device Name: Imbio IQ-UIP
Intended Use: Computer-aided software indicated for passively notifying specialists associated with interstitial lung disease (ILD) centers of radiological findings suggestive of radiological usual interstitial pneumonia (UIP) in non-contrast, chest CT scans of adults. It uses an AI algorithm to analyze images and identify positive findings on a worklist application, separate from and in parallel to standard-of-care radiological image interpretation. The device does not alter the original medical image and is not intended to be used as a diagnostic device.

1. Table of Acceptance Criteria and Reported Device Performance

The acceptance criteria are not explicitly stated as quantitative thresholds in the provided document. However, the study focuses on evaluating the device's performance metrics (AUC ROC, PPV, Specificity, Sensitivity) in identifying radiological UIP patterns. The "acceptance" is implied by the reported performance figures that demonstrate the device's ability to meet its intended purpose of identifying findings "suggestive of radiological usual interstitial pneumonia."

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

• Sample Size for Test Set: 804 individual patient images.
• Data Provenance: Anonymized, multi-center, retrospective, volumetric chest CT scans from several different, private and public data sources including multiple hospitals, clinical imaging centers, and imaging databases. The country of origin is not explicitly stated but can be inferred to be primarily the United States given the use of U.S. board-certified radiologists for ground truthing.

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

• Number of Experts: Five experts (referred to as "truthers").
• Qualifications of Experts:
  • U.S. board-certified radiologists.
  • Practicing within the United States.
  • Minimum of 5+ years experience evaluating chest CTs for ILDs.
  • Clinical familiarity with using the ATS/ERS/JRS/ALAT diagnostic categories for UIP pattern.
  • None involved in the development of the algorithm/device, ensuring independence.

4. Adjudication Method for the Test Set

The document does not explicitly state the adjudication method (e.g., 2+1, 3+1). It only mentions that five experts "performed ground truthing" of the performance datasets. Therefore, the specific method for resolving disagreements or arriving at a consensus ground truth amongst the five experts is not detailed.

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

The provided information does not indicate that an MRMC comparative effectiveness study was done to compare human readers with AI assistance vs. without AI assistance. The study focuses on a standalone performance assessment of the AI algorithm.

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

Yes, a standalone performance assessment was done. The reported performance metrics (AUC ROC, PPV, Specificity, Sensitivity) are from the device's independent analysis of images, without human intervention during the assessment. The document explicitly calls this "standalone performance assessment."

7. The Type of Ground Truth Used

The ground truth used was expert consensus based on the evaluation by five U.S. board-certified radiologists with specific experience in ILD and UIP pattern diagnosis using established clinical guidelines (ATS/ERS/JRS/ALAT diagnostic categories).

8. The Sample Size for the Training Set

The document states that data was subdivided into "bins" for model development, with roughly 80% assigned to model training and validation (i.e., hyper-parameter tuning) and 20% for model testing (performance assessment). Since the test set was 804 images, the total number of unique datasets used for both training/validation and testing would be approximately 804 / 0.20 = 4020.
Therefore, the training set sample size would be approximately 3216 datasets (80% of 4020).

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

The document states that for model development, data was comprised of "anonymized, multi-center, retrospective, volumetric chest CT scans from several different, private and public data sources including multiple hospitals, clinical imaging centers, and imaging databases." It does not explicitly detail the method for establishing ground truth for the training set. However, given the nature of AI/ML model development for medical imaging, it is highly probable that the training data was also annotated or labeled by experts, or derived from clinical records/diagnoses that implicitly represent ground truth. The emphasis on independent "truthers" for the test set suggests a rigorous approach to testing, but the specifics of training data labeling are not provided in this summary.

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Fibresolve is a software-only device that receives and analyzes lung computed tomography (CT) imaging data in order to provide a diagnostic subtype classification in suspected cases of interstitial lung disease (ILD). The device supplements the standardof-care workflow by providing a qualitative, diagnostic classification output of imaging findings based on machine learning pattern recognition, in order to provide adjunctive information as part of a referral pathway to an appropriate Multidisciplinary Discussion (MDD) or as part of an MDD. Specifically, the tool is used to serve as an adjunct in the diagnosis of idiopathic pulmonary fibrosis (IPF) prior to invasive testing. The results of Fibresolve are intended to be used only by clinicians qualified in the care of lung disease, specifically in caring for patients with ILD, in conjunction with the patient's clinical history, symptoms, and other diagnostic tests, as well as the clinician's professional judgment.

The input to Fibresolve is a DICOM-compliant lung CT scan. Clinical case eligibility includes the following criteria:

Age > 22 years old.

Pulmonary symptoms suggestive of possible ILD including IPF.

Fibresolve is a Software as a Medical Device (SaMD) for the qualitative disease assessment of DICOM-compliant chest computed tomography (CT) imaging for the detection of image content consistent with patterns found in patients with idiopathic pulmonary fibrosis (IPF). Fibresolve uses a deep learning algorithm that assesses CT images for patterns consistent with specific disease diagnosis, identifying patterns consistent with IPF among cases of Interstitial Lung Disease (ILD). Fibresolve produces a binary output ("Suggestive of IPF" or "Inconclusive"). Fibresolve does not provide any visual aid to the clinician to assist in interpreting the image.

The device consists of the following 3 components: (1) Image Receiver application programming interface (API) for image acquisition in the cloud; (2) Ingestion Pipeline and Analysis System for image processing and analysis; and (3) Output API for device output transmission.

(1) The Image Receiver API is accessed via any DICOM-compliant system (e.g., PACS). Images are submitted through the API by the hospital or clinic, or by the manufacturer. The API passes the images to the Ingestion Pipeline and Analysis System (2). The input to the device is a single stack of axial slice, DICOM-compliant, 3 mm thickness or less lung CT images from a list of validated device manufacturers.

(2) (a) The Ingestion Pipeline and (b) Analysis System accepts the images, selects cases appropriate for processing, processes the images for analyzes the images, and stores the images. This Analysis System includes the analysis algorithm that generates the assessment for the case. The device outputs a report which includes identifying information and technical details about the case data and a binary result stating whether the data are determined to be suggestive for the target disease state.

• . The Ingestion Pipeline identifies applicable CT imaging series from the case and verifies that the series is valid, completes quality checks, and confirms adequacy for analysis.
• The analysis algorithm is a 3D deep learning model developed and trained using images . from multiple facilities. No segmentation is performed as part of the Analysis Algorithm.

(3) The Output API transmits the report data for the clinician to review. The Output API is either integrated into the hospital or clinic notification software (e.g., electronic health record) for electronic transmission or the device manufacturer transmits the Report in human-readable format directly (e.g., via fax). The clinician then incorporates the device Report as part of diagnostic decision-making.

The system does not include an image viewer or produce visual output for diagnostic use.

Design Limitation: The device does not interpret images according to established clinical radiological features: the device cannot be used to infer the presence or absence of radiological features associated with the disease or condition named in the indications for use.

Here's a breakdown of the acceptance criteria and the study details for Fibresolve, as extracted from the provided text:

Acceptance Criteria and Device Performance

| Acceptance Criteria (Pre-specified in Study) | Reported Device Performance (for relevant

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