(16 days)
Lung CAR I.1 is a PC-based, stand-alone, non-invasive, image analysis software application for the display and visualization of 2D and 3D medical image data of the lung derived from CT scans, for the purpose of assisting radiologists and other clinicians in the evaluation of lung lesions (e.g. nodules). The software provides functionality for the user to extract the region of interest (ROI) either manually using a drawing tool, or "semi-automatically" through the user selecting either a single or double seed point followed by interactive fine-tuning the boundaries of the ROI. Lung CAR 1.1 provides quantative information for measurement of lesion volume and other measured characteristics over time allowing the user to review and track any changes in the physician-indicated nodules or lesions.
Lung CAR 1.1 contains additional imaging tools which allow enhancement of specified features, and which the clinician can view simultaneously with the nonenhanced view.
Lung CAR™ (Computer Assisted Reader) 1.1 is a software tool designed to assist radiologists and other clinicians in the evaluation of nodules and other lesions in the lug. The software allows the user to select Regions of Interest either manually or by selecting a single or double seed point, followed by semi-automatic detection of the ROI boundary. It provides 2D and 3D visualisation of nodules and other lesions, and measurement of nodule characteristics such as size and volume. The further features of Lung CAR™ 1.1 as compared to the cleared device are a series of filters, the results of which are presented in a Joint Reader filter view (enhanced and non-enhanced data viewed simultaneously). These filters are an edge enhancement filter, noise removal filters and a sphericity filter. The sphericity filter enhances structures of images with spherical elements within certain Hounsfield Unit (HU) ranges that are defined by the user. This enhancement can aid the user when looking at a highlighted area (sphere) as a potential spherical nodule.
Given the provided documentation, I can extract information related to the device description, intended use, and comparison to predicate devices, but there is no information about acceptance criteria or a specific study that proves the device meets acceptance criteria. The text focuses on establishing substantial equivalence to existing predicate devices for regulatory approval, rather than detailing a performance study with acceptance criteria.
The document is a 510(k) summary of safety and effectiveness for Medicsight Lung CAR™ Release 1.1. It states that "Test data are provided to validate the performance of the system and its substantial equivalence to the predicate devices." However, these "test data" are not described in this summary.
Therefore, I cannot fulfill all parts of your request. I will report on what is available and indicate where information is missing.
Here's a breakdown of the available information and what is missing:
1. A table of acceptance criteria and the reported device performance:
- Missing. The document does not provide a table of acceptance criteria or reported device performance metrics against specific criteria. It asserts substantial equivalence based on functional features and intended use.
2. Sample size used for the test set and the data provenance:
- Missing. The document mentions "Test data are provided to validate the performance," but does not specify the sample size of the test set, the country of origin of the data, or whether it was retrospective or prospective.
3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts:
- Missing. This information is not present in the provided text.
4. Adjudication method (e.g. 2+1, 3+1, none) for the test set:
- Missing. This information is not present.
5. 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:
- Missing. The document does not describe an MRMC study or any quantitative improvement in human reader performance with the device.
6. If a standalone (i.e. algorithm only without human-in-the-loop performance) was done:
- The device is described as "designed to assist radiologists and other clinicians," indicating it's a human-in-the-loop system, not a standalone algorithm for diagnosis. No standalone performance study details are provided.
7. The type of ground truth used (expert consensus, pathology, outcomes data, etc):
- Missing. The document does not specify how ground truth was established for any test data.
8. The sample size for the training set:
- Missing. No information on a training set is provided.
9. How the ground truth for the training set was established:
- Missing. No information on a training set or its ground truth establishment is provided.
Summary of available information:
- Device Name: Lung CAR™ Release 1.1
- Manufacturer: Medicsight PLC.
- Intended Use: PC-based, stand-alone, non-invasive, image analysis software for display and visualization of 2D and 3D medical image data of the lung from CT scans, to assist clinicians in evaluating lung lesions (e.g., nodules). It allows manual or semi-automatic Region of Interest (ROI) extraction and provides quantitative information for measurement of lesion volume and other characteristics for tracking changes over time. It also contains additional imaging tools for enhancement.
- Predicate Devices:
- Conclusion of document: "Lung CAR 1.1 does not raise any new potential safety risks and is equivalent in performance to existing legally marketed devices. Lung CAR 1.1 is therefore substantially equivalent with respect to safety and effectiveness to the predicate devices."
The document functions as a regulatory submission (510(k) summary) aiming to establish substantial equivalence for market clearance, rather than a detailed scientific study report. Therefore, detailed performance metrics, study designs, sample sizes, and ground truth methodologies are not typically included in this type of summary.
§ 892.2050 Medical image management and processing system.
(a)
Identification. A medical image management and processing system is a device that provides one or more capabilities relating to the review and digital processing of medical images for the purposes of interpretation by a trained practitioner of disease detection, diagnosis, or patient management. The software components may provide advanced or complex image processing functions for image manipulation, enhancement, or quantification that are intended for use in the interpretation and analysis of medical images. Advanced image manipulation functions may include image segmentation, multimodality image registration, or 3D visualization. Complex quantitative functions may include semi-automated measurements or time-series measurements.(b)
Classification. Class II (special controls; voluntary standards—Digital Imaging and Communications in Medicine (DICOM) Std., Joint Photographic Experts Group (JPEG) Std., Society of Motion Picture and Television Engineers (SMPTE) Test Pattern).