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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 standard-of-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 must include 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 system developed for qualitative disease assessment of DICOM-compliant chest computed tomography (CT) imaging. The software system is based on a machine learning model component and a Docker based HTTP/1.1 Representational State Transfer (REST) software application programming interfaces (APIs) to enable image transfer, analysis, and output of results.
The device consists of the following 3 components: (1) Image Receiver 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). The hospital or clinic either accesses the API directly via secure software integration and submits the images electronically; or the images are transmitted manually (e.g. by mail) to the device manufacturer and the case is submitted to the device through the API directly by the manufacturer. The API passes the images to the Ingestion Pipeline and Analysis System (2).
(2) (a) The Ingestion Pipeline and (b) Analysis System accept the images, select cases appropriate for processing, process the images for analysis, and analyze the images. This Analysis System includes the Fibresolve Model Inference Graph that generates the assessment for the case. The final device output report data including 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 Fibresolve Model Inference Graph, the core component of the Analysis System, is an ensemble 3D deep learning model developed and trained using images from multiple facilities. Analysis System algorithm development phases included model pre-training, model training to the disease target, architecture optimization, threshold determination, and validation. No segmentation is performed as part of the Analysis System.
  (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 visual output for diagnostic use. The source images are reviewed for subjective assessment prior to submission to the device using the facility's standard diagnostic viewer as part of routine standard-of-care and the source images can be reassessed by the clinical team at any time before or after submission of the case to the device. The system only assesses for the target disease described in the Intended Use and does not replace imaging interpretation generally.

The provided FDA 510(k) clearance letter and summary for Fibresolve (with PCCP) do not include the specific details of the performance study that proves the device meets acceptance criteria. The document states that "Software Verification and Validation (per IEC 62304) were performed to demonstrate safety based on current industry standards. The results of these tests indicate that the subject device is equivalent to the predicate device." However, it does not provide specific performance metrics, acceptance criteria, or details about the study design (e.g., sample size, expert review, ground truth establishment) for the original Fibresolve device.

The section on the Predetermined Change Control Plan (PCCP) mentions that "changes are evaluated via pre-specified statistical analyses in-line with those as part of the original device testing, to ensure, at minimum, non-inferior absolute performance, and potential improvements in performance, training data, or generalizability." This implies that such studies were conducted for the original device, but the details are not part of this 510(k) summary.

Therefore, while the document confirms a study was done for safety and equivalence, it does not contain the specific information requested about acceptance criteria and the detailed performance study. To answer your questions comprehensively, you would need to refer to the original 510(k) submission for the predicate device, Fibresolve (DEN220040).

Based on the provided text, I can only state that the specific details requested are NOT present in this document.

If such information were present, it would typically be found in a "Performance Data" or "Clinical Performance Testing" section, which is absent here. The document focuses on the new submission for the PCCP and compares it to the predicate device, assuming the predicate's performance data is already established.

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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

Study Information

1. Sample Size used for the Test Set and Data Provenance:
* Total Test Set: 300 ILD cases.
* Primary Analysis Subset (relevant for indications, slice thickness <3mm): 137 cases.
* Data Provenance: Sourced from the Lung Tissue Research Consortium (LTRC), collected from sites independent of device development. The test dataset acquisition period ranges from 2005 until 2018. The country of origin for the data is not explicitly stated but implies US-based given the FDA review. The data is retrospective.

2. Number of Experts used to establish the ground truth for the test set and the qualifications of those experts:
* Expert Panel (Primary Clinical Comparator): 5 experts.
* Qualifications: Expert pulmonologists and thoracic radiologists.
* Panel for Retrospective UIP Pattern Assignment: One expert pulmonologist and one expert radiologist pair.

3. Adjudication Method for the test set:
* Expert Panel (EP): Performed a chart review and returned a binary result ("IPF" or "not IPF") based on the 2018 ATS IPF Guidelines. It is not explicitly stated if there was an adjudication beyond the panel's consensus decision, but the description implies a single agreed-upon decision from the 5 experts.
* Reference Standard Diagnosis (Ground Truth): Assigned to each case by a Multidisciplinary Discussion (MDD) from the LTRC. This implies a consensus process among the MDD members.
* Radiological UIP Pattern Category: Retrospectively assigned by panel consensus of an expert pulmonologist and radiologist pair.

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:
* A comparative effectiveness study was done comparing the device's standalone performance to an Expert Panel (EP) of human readers. However, this was a standalone comparison and not a multi-reader multi-case (MRMC) study examining human readers with AI assistance versus without AI assistance. Therefore, no effect size for human reader improvement with AI assistance is provided or applicable from this study design.

5. If a standalone (i.e., algorithm only without human-in-the-loop performance) was done:
* Yes, the device's standalone performance (algorithm only) was assessed. The reported sensitivity and specificity values (e.g., 55% sensitivity and 82% specificity for <= 3mm slice thickness) are for the Fibresolve device operating in standalone mode.

6. The type of ground truth used (expert consensus, pathology, outcomes data, etc.):
* The primary reference standard diagnoses for the entire dataset were established by an MDD (Multidisciplinary Discussion) from the LTRC, assessing all available clinical, imaging, laboratory, demographic, and pathologic data, with approximately 1 year of clinical information. This represents a comprehensive, expert consensus ground truth that includes pathology and outcomes data.
* For the subset of cases with slice thickness ≤3 mm, the radiological UIP pattern category was further retrospectively assigned by panel consensus of an expert pulmonologist and radiologist pair.

7. The sample size for the training set:
* The document states, "The analysis algorithm is a 3D deep learning model developed and trained using images from multiple facilities." However, the specific sample size for the training set is not provided in the given text.

8. How the ground truth for the training set was established:
* The document states the model was "developed and trained using images from multiple facilities." However, the method of establishing ground truth for the training set is not explicitly detailed in the provided text. It can be inferred that it likely followed a similar rigorous process to the test set, given the context of a medical device, but no specifics are given.

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