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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.

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.

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.