(562 days)
Not Found
Not Found
Yes
The document explicitly states that the device uses "machine learning pattern recognition" and a "deep learning algorithm" which are forms of AI/ML.
No
This device provides a diagnostic classification and adjunctive information for diagnosis, which is not considered a therapeutic function.
Yes
The "Intended Use / Indications for Use" section explicitly states that "Fibresolve... provide a diagnostic subtype classification in suspected cases of interstitial lung disease (ILD)" and "the tool is used to serve as an adjunct in the diagnosis of idiopathic pulmonary fibrosis (IPF)...".
Yes
The device is explicitly described as a "software-only device" and "Software as a Medical Device (SaMD)". Its components are all software-based APIs and systems for image processing and analysis. There is no mention of any accompanying hardware that is part of the medical device itself.
Based on the provided information, Fibresolve is not an In Vitro Diagnostic (IVD) device.
Here's why:
- IVD Definition: In Vitro Diagnostic devices are defined as those intended for use in the collection, preparation, and examination of specimens taken from the human body (such as blood, urine, tissue) to provide information for diagnostic purposes.
- Fibresolve's Function: Fibresolve analyzes imaging data (lung CT scans), which are not specimens taken from the human body in the sense of biological samples. It processes and interprets medical images.
- Intended Use: The intended use clearly states that Fibresolve receives and analyzes lung CT imaging data. It provides a diagnostic classification based on imaging findings.
- Device Description: The device description details the processing of DICOM-compliant chest CT imaging.
While Fibresolve is a medical device used for diagnostic purposes, its input is imaging data, not biological specimens. Therefore, it falls under the category of medical imaging software or a Software as a Medical Device (SaMD) that aids in diagnosis, but it is not an IVD.
No
The letter states "Control Plan Authorized: Yes" but does not explicitly state that the FDA has reviewed and approved or cleared a Predetermined Change Control Plan (PCCP) for this specific device.
Intended Use / Indications for Use
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.
Product codes
QWO
Device Description
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.
Mentions image processing
Yes
Mentions AI, DNN, or ML
Yes
Input Imaging Modality
Computed Tomography (CT)
Anatomical Site
Lung
Indicated Patient Age Range
Age > 22 years old.
Intended User / Care Setting
Clinicians qualified in the care of lung disease, specifically in caring for patients with ILD.
Description of the training set, sample size, data source, and annotation protocol
The analysis algorithm is a 3D deep learning model developed and trained using images from multiple facilities. No further details provided.
Description of the test set, sample size, data source, and annotation protocol
The data used in all performance testing was sourced from the Lung Tissue Research Consortium (LTRC), with the dataset collected from sites independent from those used in device development. In total, 300 ILD cases were included, with primary analysis centered on a subset of 137 cases with CT reconstruction slice thickness 80%, device sensitivity non-inferior to EP). Device sensitivity 41% (95% CI: 30-52%) and specificity 87% (95% CI: 81-91%). EP sensitivity 13% (95% CI 6.8-23) and specificity 96% (95% CI: 93-98%).
- For on-label
N/A
0
DE NOVO CLASSIFICATION REQUEST FOR FIBRESOLVE
REGULATORY INFORMATION
FDA identifies this generic type of device as:
Radiology software for referral of findings related to fibrotic lung disease. Radiology software for referral of findings related to fibrotic lung disease is a prescription image processing device that analyzes computed tomography images to suggest the presence of disease or of an imaging finding suggestive of disease. The output of this device is intended to be used as adjunctive information as part of a referral pathway in the overall diagnostic assessment process.
NEW REGULATION NUMBER: 21 CFR 892.2085
CLASSIFICATION: Class II
PRODUCT CODE: QWO
BACKGROUND
DEVICE NAME: Fibresolve
SUBMISSION NUMBER: DEN220040
DATE DE NOVO RECEIVED: June 29, 2022
SPONSOR INFORMATION:
Imvaria, Inc. 2930 Domingo Ave. #1496 Berkeley, CA 94705
INDICATIONS FOR USE
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.
1
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.
LIMITATIONS
- . The sale, distribution, and use of the Fibresolve are restricted to prescription use in accordance with 21 CFR 801.109.
- . Fibresolve cannot be used to rule out IPF.
- Always ensure results should be used in conjunction with other clinical and diagnostic . findings, consistent with professional standards of practice, including information obtained by alternative methods, and clinical evaluation, as appropriate. The output should not be solely relied upon as the sole determinant for referral and adequate patient follow-up or management.
- . Series slice thickness must be 70 | 15.7 | 16.8 |
| Sex | Female | 49.7 | 46.7 |
| | Male | 50.3 | 53.2 |
| Manufacturer | GE Medical Systems | 27.0 | 10.9 |
| | Philips | 7.7 | 5.1 |
| | Siemens | 63.0 | 81.8 |
| | Toshiba | 2.3 | 2.1 |
Study Limitation: The test dataset acquisition period ranges from 2005 until 2018. The study results may not generalize for cases acquired with more modern equipment and imaging protocols. Special controls (including postmarket data collection) are employed to mitigate the risk associated with uncertain device performance for modern images.
The primary clinical comparator was an Expert Panel (EP. n=5). composed of expert pulmonologists and thoracic radiologists, who performed a chart review blinded to the diagnostic outcome and to all invasive testing, but given access to chart information such as demographics; relevant histories such as medical, smoking, and environmental exposures; medications, pulmonary function tests; rheumatic serologies; and CT imaging. The panel was instructed to return a binary result based on the 2018 ATS IPF Guidelines for the non-invasive diagnosis of IPF, which require a definite usual interstitial pneumonia (UIP) pattern by high resolution CT in conjunction with certain other chart information (Raghu et al. Am J Respir Crit Care Med. 2018: 198(5):e44-e68). Experts were instructed to not identify probable UIP radiographic criteria as IPF.
Study Limitation: Since completion of the study, Guidelines for the non-invasive diagnosis of IPF have been revised (Raghu et al. Am J Respir Crit Care Med. 2022; 205(9):e18-e47). These revisions broaden the CT requirements needed for the non-invasive diagnosis to also include the Probable UIP pattern category, when conside other chart information. Therefore, the Expert Panel in the study operated according to more stringent decision criteria than is currently recommended, and may have exhibited a higher specificity and lower sensitivity than would be expected in current clinical practice as a result.
The LTRC data included reference standard diagnoses, used to establish device and EP performance. The reference standard diagnosis was assigned to each case by an MDD from the LTRC, assessing all clinical, imaging, laboratory, demographic, and pathologic data with an average of approximately 1-year of clinical information for patients in the registry. Final clinical diagnosis was provided. From the total 300-case dataset, 83 IPF cases were identified, with 217 non-IPF, consisting of a variety of other diagnoses including unclassifiable interstitial lung disease (18%), chronic hypersensitivity pneumonitis (17%), and nonspecific interstitial pneumonia (11%). In the subset of cases with slice thickness ≤3 mm, the radiological UIP pattern category was further retrospectively assigned by panel consensus, which was separately composed of an expert pulmonologist and radiologist pair.
4
Study Limitation: The reference standard diagnoses in the test dataset were established from 2005 until 2018. Imaging practices and interpretation guidelines for patients suspected of having an ILD have changed significantly in the past two decades, raising the uncertainty that the reference standard diagnoses used in performance testing reflect current clinical diagnosis practices. Special controls (including postmarket data collection) are employed to mitigate the risk associated with uncertain device performance arising from uncertain reference standard determination.
The primary endpoints of the study were that the device specificity surpass a pre-specified value of 80%, and that the device sensitivity be non-inferior to the EP.
Study Limitation: The Agency disagrees with these endpoints. Splitting sensitivity and specificity primary endpoints to different comparators is not statistically sound as it favors the device in both comparisons, by comparing the sensitivity to a control with a high decision threshold and the specificity to a control with a lower decision threshold. Nevertheless, the totality of evidence regarding device accuracy suggests that it offers a small probable benefit when considered as part of a referral pathway to an MDD or as part of an MDD. The current challenges of diagnosis of IPF were given weight in this assessment.
In the total 300-case dataset, both endpoints were met, with device sensitivity 41% (95% C1: 30-52%) and specificity 87% (95% CI: 81-91%); and EP sensitivity 13% (95% CI 6.8-23) and specificity 96% (95% CI: 93-98%). For the relevant = 1.5 mm, = 1.5 mm, 22 years old. Pulmonary symptoms suggestive of possible ILD including IPF.
The probable benefits outweigh the probable risks for the Fibresolve. The device provides benefits and the risks can be mitigated by the use of general controls and the identified special controls.
CONCLUSION
The De Novo request for the Fibresolve is granted and the device is classified under the following:
Product Code: OWO Device Type: Radiology software for referral of findings related to fibrotic lung disease Class: II Regulation: 21 CFR 892.2085