Lung Car 1.1
by Medicsight
510(k) K041807
Active
Valid until
No expiry
FDA recalls
None foundDocuments
Trust
Unverified
Regulatory status
| Region | Authorization | Number | Date | Status |
|---|---|---|---|---|
| United States | FDA 510(k)SESE | NumberK041807 | Date22 Jul 2004 | StatusActive |
Recalls
No FDA recalls found in the data synced 22 Oct 2026.
Coverage
- Active
- Expiring
- Withdrawn
- Listed
- Not covered
United States — FDA 510(k) Active
Specification
- 510(k) Number
- FDA Product Code
- OEB
- Device Class
- Class II
- Regulation
- 21 CFR Part 892.2050
- Advisory Committee
- Radiology
- FDA Decision
- SESE Substantially Equivalent
Overview
AI-generated summary, 1 Oct 2026
The Lung Car 1.1 is a Computer-Aided Detection System for Lung Nodules (OpenFDA classification: Computer-Aided Detection, Lung Computed Tomography System), designed to assist radiologists in identifying and evaluating pulmonary nodules on computed tomography (CT) scans. It operates as a secondary analysis tool, enhancing the detection sensitivity of lung nodules while minimizing false positives through automated image processing and algorithmic analysis. The system is classified as a Class II medical device under FDA regulations, with a product code of OEB, and falls under the Radiology specialty category.
The Lung Car 1.1 is supplied as a software-based solution integrated with CT imaging systems, intended for use in radiology departments within hospital or clinical settings. It is not a standalone imaging device but requires compatibility with existing CT scanners for nodule analysis. The system underwent a FDA 510(k) clearance (K041807) via the Special 510(k) pathway, with a decision date of July 22, 2004, and is classified under the Radiology advisory committee. No sterility or single-use/reusable specifications are noted; its regulatory status is explicitly tied to the submitted 510(k) clearance documentation.
Other devices from Medicsight
Questions
AI-generated answers, 1 Oct 2026
What is the Lung Car 1.1 used for?
The Lung Car 1.1 is a Computer-Aided Detection (CAD) system specifically designed to assist radiologists in identifying and analyzing pulmonary nodules on computed tomography (CT) scans. It acts as a secondary analysis tool, improving detection sensitivity while minimizing false positives through automated image processing and algorithmic analysis. This helps radiologists focus on suspicious findings, enhancing diagnostic accuracy in lung nodule evaluation.
How is the Lung Car 1.1 supplied and where can it be used?
The Lung Car 1.1 is supplied as a software-based solution that integrates with existing CT imaging systems. It is intended for use exclusively in radiology departments within hospital or clinical settings. Since it is not a standalone device, compatibility with the specific CT scanner in use is required for proper nodule analysis.
Is the Lung Car 1.1 sterile or single-use?
The Lung Car 1.1 is a software-based system and does not require sterility or single-use considerations. It is designed for repeated use within compatible CT imaging environments, as long as the software remains properly maintained and updated. There are no restrictions on reuse, as the system operates digitally without physical consumables.
What regulatory pathway was followed for the Lung Car 1.1?
The Lung Car 1.1 received FDA clearance through the Special 510(k) pathway, with a clearance number of K041807. The decision was made by the Radiology (RA) advisory committee on July 22, 2004, and the device was deemed substantially equivalent to a predicate under FDA regulations. This classification falls under product code OEB and is categorized under the Radiology specialty.
Does the Lung Car 1.1 work with all CT scanners?
The Lung Car 1.1 is designed to integrate with CT imaging systems, but compatibility depends on the specific manufacturer and model of the scanner. Medicsight’s documentation and technical support should be consulted to confirm whether a particular CT system is supported. The system is not a standalone device, so proper integration with the existing imaging infrastructure is essential for accurate nodule detection and analysis.