(36 days)
LMS-Liver is an image analysis software application for evaluating CT images covering the liver area. It is designed to assist radiologists in the evaluation and documentation of lesions. It also provides tools for assessment of lesion evolution over time. LMS-Liver offers measurement tools and 3D registration techniques for characterization and follow-up of the lesions. It also offers reporting capabilities making it possible to generate standardized reports.
LMS-Liver is intended to be used by radiologists and other clinicians qualified to interpret CT images.
LMS-Liver device is designed to be used with CT images covering the liver area in adult patients.
LMS-Liver is an image analysis software application for evaluating CT images covering the liver area. It is designed to assist radiologists in the evaluation and documentation of lesions. It also provides tools for assessment of lesion evolution over time. LMS-Liver offers measurement tools and 3D registration techniques for characterization and follow-up of the lesions. It also offers reporting capabilities making it possible to generate standardized reports. LMS-Liver can segment hepatic lesions identified by the user with a double click (seed point). Once a lesion is segmented, the software computes its characteristics such as size, volume and intensity.
LMS-Liver can match and compare lesions present in two different datasets of the same patient acquired at different dates and compute their difference of size and volume.
The provided 510(k) summary for LMS-Liver does not contain detailed information about specific acceptance criteria or an explicit study proving the device meets them in the way typically expected for a performance study. Instead, the submission focuses on demonstrating substantial equivalence to predicate devices based on functional characteristics and intended use.
Here's an analysis of the provided information relative to your requested categories:
1. Table of Acceptance Criteria and Reported Device Performance
No specific quantitative acceptance criteria (e.g., sensitivity, specificity, accuracy thresholds) or corresponding reported performance metrics (e.g., 90% sensitivity achieved) are provided in the document. The submission relies on a qualitative comparison to predicate devices, stating that LMS-Liver is "equivalent in function to existing legally marketed devices."
| Acceptance Criteria Category | Acceptance Criteria (Not Explicitly Stated) | Reported Device Performance (Not Explicitly Stated) |
|---|---|---|
| Functionality | Expected to perform image visualization, lesion analysis, 3D registration, follow-up comparison, and reporting. | Stated to perform these functions, similar to predicate devices. |
| Safety | Residual risks acceptable. | Concluded that residual risks are acceptable. |
| Effectiveness (Implied by equivalence) | Equivalent in function and intended use to predicate devices. | Stated to be equivalent in function to existing legally marketed devices. |
2. Sample size used for the test set and the data provenance (e.g., country of origin of the data, retrospective or prospective)
The document does not describe a formal performance study with a test set of images. There is no mention of a specific sample size, data provenance, or whether the data was retrospective or prospective.
3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts (e.g., radiologist with 10 years of experience)
Since no formal test set or performance evaluation is described, there is no information about experts used to establish ground truth.
4. Adjudication method (e.g., 2+1, 3+1, none) for the test set
Without a described test set or performance study, no adjudication method is mentioned.
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
The document does not describe an MRMC comparative effectiveness study. The focus is on the device's standalone functionality and its equivalence to other software, not on how it improves human reader performance.
6. If a standalone (i.e. algorithm only without human-in-the-loop performance) was done
While the device's functionality is described for assisting radiologists, the submission primarily focuses on the device's capabilities in isolation ("LMS-Liver can segment hepatic lesions... computes its characteristics... match and compare lesions..."). This implies a standalone evaluation of its features and functions, but without specific performance metrics. The comparison chart with predicate devices (Siemens Syngo TrueD software and Cedara I-Response/PET/CT) focuses on feature parity.
7. The type of ground truth used (expert consensus, pathology, outcomes data, etc)
As no explicit performance study or test set is described, there is no mention of the type of ground truth used.
8. The sample size for the training set
The document does not provide any information about a training set or its sample size. This is common for submissions focused on feature equivalence rather than AI/ML model performance, especially in 2007.
9. How the ground truth for the training set was established
Since no training set is mentioned, there is no information on how its ground truth would have been established.
Summary of the Study (as presented in the 510(k)):
The provided 510(k) summary does not describe a conventional clinical or performance study with acceptance criteria and measured device performance in the modern sense of AI/ML device evaluations. Instead, the "study" proving the device meets acceptance criteria** is implicitly the comparison to predicate devices and a hazard analysis.**
- Acceptance Criteria (Implicit): That the device performs functions similar to the legally marketed predicate devices (Siemens Syngo TrueD software and Cedara I-Response; Cedara PET/CT) and does not introduce new safety risks.
- Study: The submission relies on a substantial equivalence comparison chart (section titled "Substantial Equivalence Comparison Chart") that lists functional similarities between LMS-Liver and the predicate devices. It also states that a "comprehensive hazard analysis" was conducted, concluding that "residual risks are acceptable." This hazard analysis serves as the safety "study."
In conclusion, the document demonstrates substantial equivalence by outlining the device's features and comparing them to those of established predicate devices, and by performing a safety assessment, rather than by presenting a detailed performance study with quantitative acceptance criteria and measured results on a specific dataset. This approach was more common in 510(k) submissions of that era for image analysis software.
§ 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).