(70 days)
The ImMerge Image Correlation Software Version 2.0 is intended to provide precise spatial registration of two image sets for the purpose of enhancing the imaging information presented to a physician. The resulting image sets can then be used for diagnosis and planning treatments such as image guided surgery, stereotactic radiosurgery, or radiotherapy.
image correlation system
This 510(k) summary does not contain the typical level of detail required to thoroughly describe the acceptance criteria and the study proving the device meets them. This document is a very high-level summary that primarily focuses on demonstrating substantial equivalence to a predicate device.
However, based on the provided text, here's what can be extracted and what information is missing:
Acceptance Criteria and Study Details (Based on K991468 510(k) Summary)
The provided 510(k) summary for the ImMerge™ Image Correlation System Version 2.0 is highly condensed and focuses on demonstrating substantial equivalence to a predicate device (ImMerge V1.0, K970623). As such, it does not explicitly state specific quantitative acceptance criteria or detailed study results for device performance. The primary "proof" of meeting criteria in this type of submission is the demonstration that the modified device performs "the same as or similar to" the predicate device for its intended use.
Here's a breakdown of the requested information, indicating what is present and what is absent:
1. Table of Acceptance Criteria and Reported Device Performance
| Acceptance Criteria | Reported Device Performance |
|---|---|
| Not explicitly stated in the provided document. | Not explicitly stated in the provided document. The document implies that the device achieved "precise spatial registration" similar to its predicate. The key assertion is that the "technological characteristics are the same as or similar to those found with the predicate device." |
- Missing Information: Specific quantitative metrics for "precise spatial registration" (e.g., target registration error, spatial accuracy thresholds) are not provided. There are no performance results like sensitivity, specificity, accuracy, or specific error rates.
2. Sample Size Used for the Test Set and Data Providence
- Sample Size (Test Set): Not specified.
- Data Provenance: Not specified (e.g., country of origin, retrospective/prospective).
- Missing Information: The number of cases, images, or subjects used to evaluate the automatic image correlation feature are not mentioned.
3. Number of Experts and Qualifications for Ground Truth
- Number of Experts: Not specified.
- Qualifications of Experts: Not specified.
- Missing Information: There is no mention of experts being involved in establishing a ground truth for any test set or how their qualifications were assessed.
4. Adjudication Method for the Test Set
- Adjudication Method: Not specified.
- Missing Information: Given the lack of a defined test set and experts, no adjudication method is described.
5. Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study
- MRMC Study Done: No, this type of study is not mentioned or described in the summary.
- Effect Size (AI vs. without AI): Not applicable, as no MRMC study is reported.
- Missing Information: MRMC studies are generally more recent requirements or for devices with a higher potential impact on interpretation. This older 510(k) summary is unlikely to include such a study.
6. Standalone Performance Study (Algorithm Only)
- Standalone Study Done: Not explicitly stated as a separate, formal "standalone" study with specific metrics. However, the update is to provide an "automatic first attempt at image correlation," implying the algorithm's standalone function is what was modified and likely tested. The overall 510(k) process for this device type typically involves verifying that the algorithm itself performs as intended without human intervention for the automated task.
- Missing Information: While the function is "automatic," specific quantitative results or a dedicated standalone performance study with detailed metrics are not provided.
7. Type of Ground Truth Used
- Type of Ground Truth: Not explicitly stated. For image correlation systems, ground truth often involves fiducial markers in phantoms, manually registered images by experts, or known anatomical landmarks.
- Missing Information: The method for establishing the true, correct registration is not described.
8. Sample Size for the Training Set
- Sample Size (Training Set): Not specified.
- Missing Information: This 510(k) predates the common emphasis on deep learning and large-scale training sets for medical devices. If machine learning was used at all (unlikely for "V2.0" in 1999 to the extent seen today), the size of any training data would not typically be disclosed in such a summary from that era.
9. How Ground Truth for Training Set Was Established
- Ground Truth for Training Set: Not specified.
- Missing Information: Similar to point 8, this detail would not typically be found in a 510(k) summary from 1999, especially without explicit mention of machine learning.
Summary of the Document's Information:
The 510(k) summary for the ImMerge™ Image Correlation System Version 2.0 serves to demonstrate substantial equivalence to an earlier version (V1.0, K970623). The "study" referenced implicitly is the comparative analysis showing that the updated device, with its new "automatic first attempt at image correlation" feature, maintains the same or similar technological characteristics and performs comparably to the predicate device for its intended use of precise spatial registration for diagnosis and treatment planning. The document lacks specific quantitative performance data, sample sizes, expert involvement details, or ground truth methodologies that are common in more recent and detailed clinical study reports.
§ 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).