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Digidelca provides the radiologists or pneumologists the ability to acquire chest xray images by filmless radiography (digital radiography), based on CCD technology. The x-ray transmission profile of the chest is converted into an electronic, digital image in real-time. Chest images become available for preview by the x-ray technician on the operators workstation only seconds after the x-ray exposure. After acceptance by the tech, digital (DICOM) images can be stored on electronic media, as e.g. CD-ROM, magnetic disk, or be exported to a (DICOM/PACS) network, c.q. clinical review station or to a film printer.

Digidelca is a product which was developed around hardware and software components which are already market within the United States and therefore substantial equivalence is claimed to Electrodelca for the electrical, mechanical and optical system layout and to AMBER-DU and HyperPACS for the software configuration:

• The Oldelft Electrodelca (tm) mass chest x-ray system (K892659) for the camera . housing basic design, the image intensifier tube, the elevator stand basic design and the compatible x-ray tube and high voltage generator selection. The image capturing device in Digidelca is a TDI CCD sensor, instead of radiographic film.
• The Rogan HyperPACS (tm) software package (K950343) for the principal software ● tasks for the Operators Workstation: image acquisition and storage, image export, network interfacing.
• The Oldelft Amber-DU (K973219) software package (reconfigured) for user interfacing . at the Operators Workstation.

The Operators Workstation displays screens to the operator for data input and for data and image display. The Digidelca software is configured as a dedicated shell around the Rogan software which operates in the background to perform essential functions for data acquisition, image storage and image export. The OWS allows for importing patient demographic information and exportation of digital images into which patient demographics have been incorporated (DICOM compatible data structures).

The Date Entry System is an additional pc platform which is used to copy patient demographics to a card formation carrier by a thermal printing process. The card is handed out to the examinee who will submit it to the Digidelca(-M) operator prior to making his/het x-rays. Patient demographics are scanned from the card by a two dimensional barcode reader and thereby entered into the Digidelca(-M) operators workstation.

Here's an analysis of the provided text regarding the acceptance criteria and study for the Digidelca device:

1. Table of Acceptance Criteria and Reported Device Performance

The provided text does not explicitly state specific numerical acceptance criteria for the Digidelca device in terms of performance metrics (e.g., sensitivity, specificity, resolution targets with numerical thresholds). Instead, the performance is described qualitatively and comparatively to the predicate device, Electrodelca.

Therefore, the table below reflects the implied acceptance criteria based on the claims of substantial equivalence and the qualitative performance descriptions.

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

• Sample Size: The document does not specify a numerical sample size for the clinical test set. It only mentions that the device is "clinically used for both in-patients and out-patients" at a beta test site.
• Data Provenance: The clinical testing was conducted at a "beta test site in The Netherlands (Twenteborg Hospital, Almelo)." The data is prospective as it describes ongoing clinical use since mid-1997.

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

• Number of Experts: The document does not explicitly state the number of experts. It refers to "radiologists" (plural), implying more than one.
• Qualifications of Experts: The experts are described as "radiologists." No further details on their experience level (e.g., years of experience) are provided.

4. Adjudication Method for the Test Set

The document does not specify an adjudication method (e.g., 2+1, 3+1). It states that "images are generally appreciated by radiologists as 'better' then those from Electrodelca," which suggests a qualitative assessment by multiple radiologists, but not a formal adjudication process to establish a definitive ground truth.

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

• Was it done? The document does not describe a formal MRMC comparative effectiveness study in the sense of a controlled study comparing human readers with and without AI assistance.
• Effect Size: Therefore, no effect size of human readers improving with AI vs. without AI assistance is reported.

6. Standalone (Algorithm Only Without Human-in-the-Loop Performance) Study

The Digidelca device, as described, is an x-ray system for acquiring digital images. While it mentions "Digital augmentations," these are enhancements to the imaging process, not a standalone AI algorithm for interpretation or diagnosis. Therefore, a standalone (algorithm only) performance study as typically understood for AI diagnostic tools was not performed or described in this document. The focus is on the imaging system itself, with human radiologists performing the interpretation.

7. Type of Ground Truth Used

The ground truth used for assessing image quality was expert consensus/opinion from radiologists. They appreciated the images as "better" than those from the predicate device. This is a subjective assessment of image quality and clinical utility, rather than an objective "ground truth" derived from pathology, outcomes data, or a strict consensus protocol.

8. Sample Size for the Training Set

The document does not mention a training set in the context of an AI algorithm learning from data. The device's "software configuration" is based on existing market software packages (Rogan HyperPACS and Oldelft Amber-DU), which would have had their own development and testing, but not a specific training set for an AI component of Digidelca itself.

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

Since no training set for an AI algorithm is mentioned, this information is not applicable to the provided text. The device primarily concerns hardware and software integration for image acquisition and display, not a learned diagnostic model.

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Amber DU provides the Radiologist the ability to acquire chest images on film, to digitize these images real time for analysis on the Operator Workstation and retain the ability to analyze the films on a conventional lightbox, and to export the digital images Dicom 3 conformant to the hospital's network server for storage and eventual retrieval if desired.

The Oldelft Amber DU is comprised of the currently marketed Oldelft Amber AU, the Lumisys Lumiscan 75 LASER Film Digitizer which is commercially available, and a digital workstation based on a commercially available workstation from Rogan Imaging Corporation using software written by Rogan to Oldelft's specifications. The workstation displays screens to the operator for data input and for data and image display. The Oldelft Amber DU display is configured as a shell about the Rogan software which operates in the background to perform essential functions and to provide miniPACS features to Amber DU and allow the importation of patient demographic information and to allow the exportation of digital image data into which patient demographics have been incorporated.

The provided document is a 510(k) submission for the Oldelft Amber DU, primarily focusing on its regulatory approval as a Class II medical device. While it describes the device's components and intended use, it does not contain detailed information about acceptance criteria and a study proving the device meets those criteria, as typically found in clinical performance studies.

Therefore, many of the requested fields cannot be filled from the provided text.

Here's a breakdown of what can and cannot be extracted based on your request:

1. Table of Acceptance Criteria and Reported Device Performance:

The document does not provide a table of acceptance criteria or reported device performance metrics (e.g., sensitivity, specificity, accuracy, F1 score). Its focus is on demonstrating substantial equivalence to a predicate device and adherence to general controls and performance standards for stationary X-ray systems (21 CFR 1020.30 and 21 CFR 1020.31). The "Indications for Use" section describes the functionality of the device rather than quantitative performance.

2. Sample size used for the test set and the data provenance:

Not provided in the document.

3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts:

Not provided in the document. The document mentions "Radiologist" in the indications for use, implying the device is for use by such professionals, but it doesn't describe expert involvement in a test set ground truth establishment.

4. Adjudication method (e.g., 2+1, 3+1, none) for the test set:

Not provided in the document.

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:

Not provided in the document. The Amber DU combines existing technologies like an X-ray system, a film digitizer, and a workstation with software. The submission does not describe it as an "AI" device as we understand it today, nor does it detail MRMC studies or human reader improvement data.

6. If a standalone (i.e. algorithm only without human-in-the-loop performance) was done:

Not provided in the document. The device is described as an "Operator Workstation" that displays screens for "data input and for data and image display," implying a human-in-the-loop operation, not a standalone algorithmic performance.

7. The type of ground truth used (expert consensus, pathology, outcomes data, etc.):

Not provided in the document.

8. The sample size for the training set:

Not applicable or not provided. Given the device's description as a combination of existing hardware and software for image acquisition, digitization, and display, it's unlikely to have a "training set" in the context of a machine learning algorithm.

9. How the ground truth for the training set was established:

Not applicable or not provided.

In summary: The provided document is a regulatory submission for premarket notification (510(k)) that focuses on demonstrating "substantial equivalence" of the Amber DU to a legally marketed predicate device. It addresses regulatory classification, standards, and intended use, but it does not include the detailed performance study results, acceptance criteria, or ground truth establishment methodologies that would be expected for a device making claims about performance benchmarks or AI capabilities. This type of information is typically found in clinical study reports, which are not part of this 510(k) summary.

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