(25 days)
KONICAMINOLTA DI-X1 is a software device that receives digital x-ray images and data from various sources (i.e. RF Units, digital radiographic devices or other imaging sources). Images and data can be stored, communicated, processed and displayed within the system and/or across computer networks at distributed locations. It is not intended for use in diagnostic review for mammography.
KONICAMINOLTA DI-X1 is a software device that performs image processing and display using Xray digital images (single-frame images, multi-frame images) generated by various diagnostic imaging modality consoles. It is a standalone software device intended to install onto off-the shelf Servers and PCs.
KONICAMINOLTA DI-X1 receives X-ray digital images, including serial images, processes the received images, as well as displays and sends the resulting images to PACS and other devices. In addition, KONICAMINOLTA DI-X1 can display images through the browser connection with the client that displays and process images, and instruct transmission of images.
The personal computer used in KONICAMINOLTA DI-X1 stores the same data in two hard disks in real time using RAID1 mirroring function. Thus, even if one hard disk is defective, operations can be continued with the other hard disk which has the same data.
Modifications are made to add the TD-MODE, Position Tracking and Signal Value Change. The TD-MODE is designed to extract the initial contour of the tracheal wall. The TD-MODE also displays the minimum and maximum tracheal diameter in the frame. The subject device also incorporates Position Tracking and Signal Value Change. Position Tracking is used to track the reference point specified in a frame and display a graph of the position change data. Signal Value Change graphically displays the signal value change within the ROI specified in a frame.
The provided document is a 510(k) summary for the KONICAMINOLTA DI-X1 device. It describes the device, its intended use, and compares it to a predicate device. However, it explicitly states that "No clinical studies were required to support the substantial equivalence" and that "Performance tests demonstrate that the KONICAMINOLTA DI-X1 performs according to specifications and functions as intended."
This indicates that the submission relies on non-clinical performance data and comparison to a predicate device rather than a comprehensive clinical study involving human readers and AI assistance. Therefore, many of the requested details, such as specific acceptance criteria for AI performance, sample sizes for test sets in a clinical AI study, expert qualifications, adjudication methods, MRMC studies, standalone AI performance, and ground truth establishment for a training set in an AI context, are not available in this document.
The document discusses modifications to the device (TD-MODE, Position Tracking, and Signal Value Change) which are new features. While these new features clearly have specific functions (e.g., "TD-MODE is designed to extract the initial contour of the tracheal wall" and "displays the minimum and maximum tracheal diameter"), the document does not provide quantitative acceptance criteria or detailed study results for the performance of these specific new features. Instead, it broadly states that "All the verification activities required by the specification and the risk analysis for the KONICAMINOLTA DI-X1 were performed and the results showed that the predetermined acceptance criteria were met."
Given this, I can only provide information based on what is stated in the document.
Based on the provided FDA 510(k) summary for KONICAMINOLTA DI-X1:
1. A table of acceptance criteria and the reported device performance:
The document states that "All the verification activities required by the specification and the risk analysis for the KONICAMINOLTA DI-X1 were performed and the results showed that the predetermined acceptance criteria were met." However, the document does not detail these specific acceptance criteria in a table or quantify the reported device performance against them. It only provides a general statement of compliance.
The new features added are:
- TD-MODE: Designed to extract the initial contour of the tracheal wall and display minimum and maximum tracheal diameter in the frame.
- Position Tracking: Used to track a specified reference point and display a graph of position change data.
- Signal Value Change: Graphically displays signal value change within a specified ROI.
No quantitative performance metrics or specific acceptance criteria for these features are provided in the summarized text.
2. Sample size used for the test set and the data provenance:
The document explicitly states: "No clinical studies were required to support the substantial equivalence." This means there was no clinical test set of patient data used in the typical sense for evaluating AI performance on diagnostic tasks. The evaluation was based on non-clinical performance tests and comparison to a predicate device. Therefore, no information on data provenance (country of origin, retrospective/prospective) for a clinical test set is available.
3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts:
Since no clinical study with a test set for diagnostic performance was conducted or reported in this summary, there is no information provided on the number or qualifications of experts used to establish ground truth.
4. Adjudication method (e.g. 2+1, 3+1, none) for the test set:
As no clinical test set for diagnostic performance was utilized, 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 states: "No clinical studies were required to support the substantial equivalence." Thus, an MRMC comparative effectiveness study was not performed or, if it was, the results are not included in this 510(k) summary. Therefore, no effect size for human reader improvement with AI assistance is available.
6. If a standalone (i.e. algorithm only without human-in-the-loop performance) was done:
The document describes the device as a "software device that performs image processing and display" and includes features like TD-MODE, Position Tracking, and Signal Value Change. While these are algorithmic functions, the document only states that "Performance tests demonstrate that the KONICAMINOLTA DI-X1 performs according to specifications and functions as intended." It does not provide specific standalone quantitative performance metrics or a detailed study of the algorithm's performance independent of human interaction for diagnostic purposes.
7. The type of ground truth used (expert consensus, pathology, outcomes data, etc.):
Given the lack of a clinical study assessing diagnostic performance, no type of ground truth (expert consensus, pathology, outcomes data) for such a study is mentioned. The "ground truth" for the device's functionality would likely be derived from the software's specified outputs for given inputs based on engineering verification tests, rather than clinical diagnostic ground truth.
8. The sample size for the training set:
The document does not describe the use of machine learning or AI models that would require a "training set" in the context of diagnostic image analysis. Instead, it describes a software device with image processing and display functions. Therefore, no information on the sample size of a training set is provided.
9. How the ground truth for the training set was established:
As no training set for machine learning or AI diagnostic models is mentioned, this information is not applicable and not provided.
In summary, the provided 510(k) summary primarily focuses on demonstrating substantial equivalence to a predicate device based on common intended use, technological characteristics, and principle operations, supported by non-clinical performance verification. It does not contain the details of a clinical study for AI performance as typically seen for devices that provide diagnostic interpretations or assistance.
§ 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).