FIBRESOLVE for ILD and IPF Analysis — FDA authorized, evidence-based AI and machine learning-based algorithm assessment of ILD and IPF. Read the full release.

Lung Fibrosis. Resolved.

Advanced pulmonary care at the convergence of lung science and machine learning.

LEARN ABOUT THE CLARITY OF FIBRESOLVE

Using fully automated artificial intelligence and non-invasive CT scans of the lungs, Fibresolve is a pioneering, first-of-its-kind, FDA-authorized technology for the non-invasive assessment of ILD and IPF.

Better Diagnosis.

Demonstrated to increase IPF diagnoses by >3x in challenging ILD and IPF cases in the pre-invasive setting.

Non-invasive Tech.

Analysis uses only previously collected data rather than requiring new invasive tissue or blood sample collection.

Adjunct for Experts.

Provides an AI trained in thousands of complex ILD and IPF cases to augment clinical experts in the pre-invasive setting.

Optimizing Flow
with FIBRESOLVE

No need for complex changes to workflow – Fibresolve works integration-free, set up in clinical workflows for effective assessment of ILD and IPF without disrupting routine practices. We run the AI analysis for you, analyzing standard chest CT scans acceptable in a range of formats and generating a streamlined Fibresolve report to serve as adjunct to clinical diagnosis.

Fibresolve is trained in thousands of cases with tissue pathology and lung fibrosis follow-up, in order to maximize non-invasive performance in differentiating IPF from other forms of ILD, thereby assisting with assessment consistent with ATS Guidelines.

Fibresolve is FDA authorized to serve as an adjunct in the diagnosis of idiopathic pulmonary fibrosis (IPF) prior to invasive testing. Rx only.

WITH CATEGORY III CODE 0880T FOR TEST RESULT INTERPRETATION

Category III CPT® Code 0880T: “Physician or other qualified healthcare professional interpretation and report”

Determination of whether a CPT® code applies to a given technology or service is based solely on the CPT® Code description.

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Clinical Validation

Warning: The following list of scientific publications may contain information on the use of technology that is not part of FDA-authorized applications. It represents peer-reviewed research, including potential off-label descriptions.

Moran-Mendoza O, Singla A, Kalra A, Muelly M, Reicher JJ. Computed tomography machine learning classifier correlates with mortality in interstitial lung disease. Respir Investig. 2024 May 20;62(4):670–676. [link]

Bradley J, Huang J, Kalra A, Reicher J. External validation of Fibresolve, a machine-learning algorithm, to non-invasively diagnose idiopathic pulmonary fibrosis. Am J Med Sci. 2023 Dec 24:S0002-9629(23)01475-1. doi: 10.1016/j.amjms.2023.12.009. [link]

Ahmad Y, Mooney J, Allen IE, et al. A machine learning system to indicate diagnosis of idiopathic pulmonary fibrosis non-invasively in challenging cases. Diagnostics (2024). [link]

Maddali M, Kalra A, Muelly M, Reicher J. Development and validation of a CT-based deep learning algorithm to augment non-invasive diagnosis of idiopathic pulmonary fibrosis. Poster presented at: 2023 American Thoracic Society Conference; May, 2023; Washington DC. [link]

Chang M, Reicher JJ, Kalra A, et al. Analysis of validation performance of a machine learning classifier in interstitial lung disease cases without definite or probable usual interstitial pneumonia pattern on CT using clinical and pathology-supported diagnostic labels. J Digit Imaging. Inform. med. (2024). [link]

Moran Mendoza O, Reicher J, Singla A. Chest computed tomography machine learning classifier for idiopathic pulmonary fibrosis predicts mortality in interstitial lung diseases. Oral presentation at: 2023 American Thoracic Society Conference; May, 2023; Washington DC. [link]

Ahmad Y, Li J, Mooney J, Allen I, Seaman J, Kalra A, Muelly M, Reicher J. Predicting interstitial pulmonary fibrosis using a machine learning classifier in cases without definite or probable usual interstitial pneumonia pattern on computed tomography. Poster presented at: 2023 American Thoracic Society Conference; May, 2023; Washington DC. [link]

Maddali MV, Kalra A, Muelly M, Reicher JJ. Development and validation of a CT-based deep learning algorithm to augment non- invasive diagnosis of idiopathic pulmonary fibrosis. Respir Med. 2023 Oct 13:219:107428. [link]

Ahmad Y, Mooney J, Allen I, Seaman J, Kalra A, Muelly M, Reicher J. A machine learning system to predict diagnosis of idiopathic pulmonary fibrosis non-invasively in challenging cases. Poster presented at: 2023 American Thoracic Society Conference; May, 2023; Washington DC. [link]

Selvan KC, Reicher J, Muelly M, Kalra A, Adegunsoye A. Machine learning classifier is associated with mortality in interstitial lung disease: a retrospective validation study leveraging registry data. BMC Pulm Med. 2024 May 23;24(1):254. [link]

Selvan KC, Reicher J, Muelly M, Kalra A, Adegunsoye O. Machine learning classifier predicts mortality in interstitial lung disease: a validation study. Poster presented at: 2024 American Thoracic Society Conference; May, 2024; San Diego, CA. [link]

Callahan SJ, Scholand MB, Kalra A, Muelly M, Reicher J. Multi-modal machine learning classifier for idiopathic pulmonary fibrosis predicts mortality in interstitial lung diseases. Poster presented at: 2024 American Thoracic Society Conference; May, 2024; San Diego, CA. [link]