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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.

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KONICAMINOLTA DI-X1 is a software device that receives digital x-ray images and data from various sources (i.e. R/F 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 X-ray 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. The modifications are made on software to the identified predicate device to add the PC client to connect to the server using the browser on a personal computer to display images for a WEB reference. In addition, additional imaging processing MODES are implemented into the subject device. The subject device also modifies the graphical display to compare the past exam. graphs based on the measurement values in a chronological order.

Let's break down the information about the acceptance criteria and performance study for the KONICAMINOLTA DI-X1 device based on the provided FDA 510(k) summary.

It's important to note that the provided document does not contain a detailed performance study with human readers, specific metrics for AI performance (like sensitivity/specificity), or the methodologies for establishing ground truth for a test set. The submission states that "No clinical studies were required to support the substantial equivalence." This implies that the device's modifications are considered minor enough that extensive clinical validation, as would be expected for a novel AI diagnostic device, was not necessary.

The focus of this submission is on demonstrating substantial equivalence to a predicate device (K182431) for a medical image management and processing system, not a new diagnostic AI algorithm. The "modifications are made on software... to add the PC client to connect to the server using the browser... and additional imaging processing MODES are implemented... The subject device also modifies the graphical display to compare the past exam. graphs based on the measurement values in a chronological order." This suggests the "performance data" refers to validation of these functional additions and software changes, rather than a diagnostic accuracy study.

Therefore, many of the requested points regarding AI performance and human reader studies cannot be precisely answered from this document.

Here's the breakdown based on the provided text, with clarifications where information is absent:

1. Table of Acceptance Criteria and Reported Device Performance

2. Sample Size Used for the Test Set and Data Provenance

The document does not specify a distinct "test set" in the context of a clinical performance study for an AI algorithm. The performance data section states: "All the verification activities required by the specification and the risk analysis for the KONICAMINOLTA DI-X1 were performed and the results demonstrated that the predetermined acceptance criteria were met. No clinical studies were required to support the substantial equivalence."

This indicates that the "testing" was likely functional and verification testing of the software's new features and overall operation, rather than a diagnostic accuracy evaluation on a patient image dataset. Therefore, information regarding data provenance (country of origin, retrospective/prospective) and sample size for such a test set is not provided because such a clinical test set was not deemed necessary for this submission.

3. Number of Experts Used to Establish Ground Truth for the Test Set and Qualifications

Not applicable. As noted above, no clinical study requiring expert-established ground truth on a test set for diagnostic accuracy was reported or required for this 510(k) submission.

4. Adjudication Method for the Test Set

Not applicable. No clinical study requiring ground truth adjudication was reported.

5. Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study

No. The document explicitly states: "No clinical studies were required to support the substantial equivalence." Therefore, an MRMC study comparing human readers with and without AI assistance was not performed or reported.

6. Standalone (Algorithm Only) Performance

No. This device is described as a "medical image management and processing system" with enhancements, not a standalone AI diagnostic algorithm performing a specific diagnostic task (like detecting a disease). Its primary function is image handling, processing, and display. Therefore, a standalone performance metric (e.g., sensitivity/specificity for a disease) is not provided or applicable in the context of this submission.

7. Type of Ground Truth Used

Based on the lack of a clinical study, specific "ground truth" for diagnostic accuracy (e.g., pathology, outcomes data, expert consensus) was not established or used for performance evaluation in this 510(k). The "performance tests" focused on verifying the software's functional specifications.

8. Sample Size for the Training Set

Not applicable. This device is described as an image management and processing system, not a device incorporating a machine learning model that requires a "training set" in the typical sense of AI development for diagnostic tasks. The "modifications" were software developments, not AI model training.

9. How the Ground Truth for the Training Set Was Established

Not applicable, as there was no AI model training set as described in typical AI/ML submissions.

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KONICAMINOLTA DI-X1 is a software device that receives digital x-ray images and data from various sources (i.e. R/F 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 receives digital x-ray images and data from various sources (i.e. R/F 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.

The provided text describes a 510(k) premarket notification for the "KONICAMINOLTA DI-X1" device. It clearly states the device's indications for use and regulatory information, but it does not contain any information regarding acceptance criteria, device performance studies, sample sizes, ground truth establishment, or expert qualifications. Therefore, I cannot populate the requested tables or answer the specific questions about the device's performance evaluation.

The document is a clearance letter from the FDA, confirming the device's substantial equivalence to a predicate device, and outlining general regulatory requirements. It does not include the technical study details that would typically be found in a submission's performance section.

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