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The Digora for Windows 2.0 is a software device intended to using and managing dental x-ray images sent by Digora imaging plate scanner, storing the images and allowing the user to process and examine the images in order to achieve improved diagnoses. The software can also innager other imaging devices such as larger size imaging plate scanners and intraoral video cameras.

Digora for Windows 2.0 is a dental imaging, image processing and archiving software, mainly to be used with Soredex Digora scanner (K934949). With this software is is possible to scan intra oral images using the Digora scanner, perform functions like edge enhancement, 3D emboss, density and contrast manipulation, length and angulation measurement with the image. The software also archives the images in the database, from where images can be stored in image media like CD-rom's.

The provided text describes a 510(k) submission for "Digora for Windows 2.0" dental imaging software. However, it does not contain detailed information regarding specific acceptance criteria, a comprehensive study design with sample sizes, expert qualifications, or ground truth establishment for performance evaluation.

The document focuses on establishing substantial equivalence to a predicate device ("RadWorks Medical Imaging Software") rather than presenting a detailed de novo performance study.

Based on the information provided, here's what can be extracted and what is missing:

Acceptance Criteria and Reported Device Performance

The document does not explicitly state quantitative acceptance criteria or detailed reported device performance metrics (e.g., accuracy, sensitivity, specificity, F1-score) in a tabular format related to diagnostic image quality or clinical outcomes.

Instead, the acceptance criteria are implicitly tied to demonstrating safety and effectiveness and substantial equivalence to the predicate device. The performance is described in terms of its features and capabilities being "substantially equivalent" to the predicate, with the implication that these features contribute to improved diagnoses.

Detailed Study Information (Missing or Not Applicable)

The document alludes to "laboratory and clinical tests" but provides no details on the methodology, sample sizes, or specific results of these tests. This 510(k) summary is primarily focused on demonstrating substantial equivalence, which often relies on a comparison of features and intended use rather than a full-scale, de novo clinical performance study with detailed metrics.

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

   • N/A (Not provided). The document mentions "clinical tests" and "laboratory tests" but does not specify the sample size of images or patients used for performance evaluation, nor does it state the country of origin or whether the data was retrospective or prospective.

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

   • N/A (Not provided). Ground truth establishment details are absent.

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

   • N/A (Not provided). Adjudication methods are not mentioned.

4. 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:

   • N/A (Not provided). There is no mention of an MRMC study or any assessment of human reader improvement with or without AI assistance. This device is described as imaging software with processing capabilities, not explicitly as an AI diagnostic aid in the modern sense.

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

   • N/A (Not provided). No standalone performance metrics are discussed. The software is intended to allow a user to "process and examine the images in order to achieve improved diagnoses," implying human interpretation.

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

   • N/A (Not provided). The method for establishing ground truth is not specified.

7. The sample size for the training set:

   • N/A (Not provided). Training set information is not included, as the document doesn't detail the development or validation of machine learning algorithms requiring a distinct training phase.

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

   • N/A (Not provided). As no training set is discussed, its ground truth establishment is also not detailed.

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Tomographic X-ray System, which is intended for diagnostic dental radiography at the dento-maxillofacial region and additionally cephalometry. The imaging methods are narrow beam radiography and spiral tomography and central projection cephalometry.

Cranex Tome Ceph is a high frequency Tomographic, Radiographic system capable of producing dental panoramic and tomographic images, TMJ (TemporoMandibular Joint) tomographic images and cephalometric images. It is available in two models with (Cranex Tome Ceph) and without (Cranex Tome) the cephalometric option. The Cranex TOME uses the principle of autotomography with a spiral blurring movement. This movement, which is similar to the movement used by the Soredex SCANORA®, is achieved by tilting and rotating the C-arm at the same time.

The Cranex Tome comprises of

• moving column

• C-arm

• An adjustable two-position chin support is used to carry out all exposure procedures.

• Four-point head support and bite-block system

• A large adjustable mirror

• The patient positioning lights

• control panel

• graphical display

• wide range of x-ray programs for mandible, maxilla, temporomandibular joint,

sinus, and cephalometric exposures.

• high-frequency generator

-15x30 cm panoramic cassette for paroramic and tomographic imaging

And the following with the cephalometric unit

• Soft tissue filter adjustment with LED positioning indicators

• selectable magnification ratio and a rotatable head support with positioning stops every 45 degrees.

-The system software makes it impossible to take an exposure if the cephalometric cassette is the wrong size or incorrectly positioned.

-Accepts 18x24, 8"x10" or 24x30 cm cephalometric cassettes

• The cephalometric arm can be mounted on either the left- or the right-hand side of the unit

-An cephalometric kit is available so that Cranex TOME can be upgraded to a Cranex TOME Ceph .

The submitted text does not contain detailed information about specific acceptance criteria and a study proving the device meets them in the format typically required for modern medical device submissions. This document is a 510(k) summary from 1998, which typically focuses on demonstrating substantial equivalence to predicate devices rather than providing detailed performance metrics from a dedicated study.

However, based on the provided text, I can infer and extract some information:

1. A table of acceptance criteria and the reported device performance

The document broadly states "Safety and effectiveness is demonstrated by: - clinical tests - software verification, validation and certification procedures - risk analysis - risk management file". It concludes that the device is "safe and effective when the unit is used as labeled." However, no specific performance metrics like sensitivity, specificity, accuracy, or quantitative image quality assessments are provided, nor are numerical acceptance criteria against which they would be measured.

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 mentions "clinical tests" as part of the safety and effectiveness demonstration. However, it does not provide any details about:

• The sample size of patients or images used in these clinical tests.
• The data provenance (e.g., country of origin, retrospective or prospective nature).

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)

The document does not provide any information about experts involved in establishing ground truth for any tests, nor their qualifications.

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

The document does not provide any information about an adjudication method.

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

This document is from 1998, well before AI assistance in radiology was prevalent. It does not mention any MRMC study or AI assistance. The focus is on the device itself as an imaging system, not on software-assisted interpretation.

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

Given the device is an X-ray imaging system, and the submission date, the concept of "standalone algorithm performance" as understood today (e.g., for AI/CAD systems) is not relevant. The device's performance is inherently tied to producing images for human interpretation. Therefore, no standalone algorithm-only performance assessment would have been conducted or reported in this context.

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

The document does not explicitly state the type of ground truth used for its "clinical tests." However, for an imaging device, ground truth often involves clinical diagnosis, expert radiological interpretation, or in some cases, pathology, depending on the specific application. Without further detail, it's impossible to specify.

8. The sample size for the training set

The device is a hardware imaging system, not an AI algorithm that requires a training set in the modern sense. Therefore, the concept of a "training set sample size" as applies to machine learning models does not apply to this submission.

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

As explained above, the concept of a "training set" in the context of an AI algorithm does not apply to this device. Therefore, this question is not applicable.

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