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.
Enhance your ILD assessment with AI.
Trusted by leading ILD programs and pulmonary
clinics across the USA.
Using fully automated artificial intelligence and non-invasive CT scans of the lungs, lung function testing, and other data, Fibresolve is a pioneering, first-of-its-kind, FDA-authorized technology for the non-invasive assessment of ILD and IPF.
There's More in the Data.
Clinical, PFT, and CT data hold diagnostic signal beyond what manual review alone extracts. Fibresolve surfaces it.
Non-invasive Tech.
Analysis uses only previously collected data rather than requiring new invasive tissue or blood sample collection.
Augments Expert Clinicians.
Adds a distinct, objective input from data already collected, alongside radiology review and the MDD.
When to Order FIBRESOLVE
Cases of:
- New ILD workup; or
- Indeterminate CT patterns; or
- Diagnostic uncertainty.
Simple to get started
No contracting. No installation.
No required EMR/PACS integration.
No practice expense.
We manage the insurance benefit.
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”
The AMA has established CPT code 0880T, which physicians may report for the professional interpretation associated with AI-based CT analysis in interstitial lung disease. Determination of whether a CPT® code applies to a given technology or service is based solely on the CPT® Code description.
Request more info and test requisition forms
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.
Tambe AP, Boente RD, George G, Kheir F, Tahamtani Omran O, Selvan KC. Clinical utility of an FDA-authorized artificial intelligence imaging platform in interstitial lung disease diagnosis. Diagnostics. 2026;16(15):2445. [link]
Tahamtani-Omran O, Kalra A, Muelly M, Reicher J, Ahmed R. Paired validation of a multimodal vision-transformer system for the non-invasive diagnosis of idiopathic pulmonary fibrosis. Diagnostics. 2026;16(15):2398. [link]
Tambe A, Selvan KC. Clinical utility of Fibresolve, an imaging artificial intelligence tool, in the diagnosis of idiopathic pulmonary fibrosis [abstract]. Am J Respir Crit Care Med. 2026;212(suppl 1):aamag162.2459. [link]
Callahan SJ, Scholand MB, Kalra A, Muelly M, Reicher JJ. Multi-modal machine learning classifier for idiopathic pulmonary fibrosis predicts mortality in interstitial lung diseases. Respir Investig. 2025 Aug 6;63(5):1012-1017. doi: 10.1016/j.resinv.2025.07.021. [link]
Uribe J, Kalra J, Muelly M, Reicher J, Kheir F. Clinical experience with the first FDA-authorized artificial intelligence tool in interstitial lung disease and idiopathic pulmonary fibrosis [abstract]. Am J Respir Crit Care Med. 2025;211:A1709. [link]
Callahan SJ, Scholand MB, Kalra A, Muelly M, et al. Expert Center External Validation of a CT-Based Deep Learning Algorithm to Predict Diagnosis of Idiopathic Pulmonary Fibrosis. Chest. 2024;166(4):A3312-A3313. [link]
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]
