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510(k) Data Aggregation
(90 days)
The KODAK DIGITAL SCIENCE Dental Scanning System is designed to interface with practice management software. The system allows for much quicker consultations with experts in distant facilities. Also, archiving electronically for faster image recall and assembly of historical studies can be accomplished with ease. The KODAK DIGITAL SCIENCE Dental Scanning System is designed to interface with practice management software.
Intended uses in the dental industry include the following:
- . Scanning of film
When used for diagnostic purposes, the patient population will be the general public, and the diseases/conditions that the device will be used to diagnose are; dental caries, periodontal disease and bone loss, tooth fractures, jaw misalignment, and other diseases and conditions that are encountered by general practitioners and specialists in the dental care field.
Scanning of dental radiographic film
The KODAK DIGITAL SCIENCE™ Dental Scanning System consists of the KODAK Image Magic TM Print Scanner 2000, a transparency adapter, and TWAIN Data Source software. The KODAK Image Magic™ Print Scanner 2000 is a 36 bit 600 dpi flatbed scanner connected to the user's computer through a SCSI interface. The transparency adapter provides an overhead light source for transparent media. The TWAIN Data Source software is used to control the scanner, optimize the scanned image using tone scaling algorithms, and communicate with application software through the TWAIN interface.
Kodak's Dental X-ray Scanner (KODAK DIGITAL SCIENCE™ Dental Scanning System or KDS DSS) is designed to support; dental radiography scanning to convert film to digital form. The KDS DSS is designed to operate on a standard PC-compatible computer and accomplish scanning with a commercially available quality flatbed scanner interfaced to the PC.
The KDS Dental Scanning System is a fully functional digital radiograph scanning system. Features include; advanced graphical user interface, intelligent scanning and image enhancement, scanning of full-mouth sets and other mounts, and interfaces to practice management systems.
The provided text does not contain detailed acceptance criteria and a study demonstrating that the device meets these criteria in the format typically expected. This document is a 510(k) summary for the Kodak Digital Science Dental Scanning System, focusing on establishing substantial equivalence to predicate devices rather than reporting on specific performance studies with acceptance criteria.
However, based on the information provided, we can infer some aspects and highlight what is missing.
Here's an attempt to structure the information, with explicit notes on what is not available in the given text:
1. Table of Acceptance Criteria and Reported Device Performance
Acceptance Criteria (Inferred from Substantial Equivalence) | Reported Device Performance |
---|---|
Functionality: Convert film to digital image | Commercial Flatbed Scanner with TPU |
Functionality: Control of scanner, storage, retrieval, transmission, and receipt of digital images | Personal Computer |
Functionality: User interface for device control | Graphical User Interface (GUI) |
Functionality: Image display and optimization | Image Processing (with proprietary enhancement algorithms) |
Functionality: Integrate with practice management systems | TWAIN Interface |
Image Quality: High definition scanning of images from dental x-ray film for display on monitors (Similar to predicate devices based on input quality) | Similar clarity and definition to predicate devices; ability to enhance poor quality images with proprietary algorithms (implies potential improvement over predicate, but no specific performance metric given). |
Note: The document does not explicitly state numerical acceptance criteria (e.g., minimum resolution, specific image quality metrics, speed, accuracy for diagnostic tasks) or quantitative performance results against such criteria. The "performance" is largely described in terms of functional equivalence and the ability to enhance images.
2. Sample Size Used for the Test Set and Data Provenance
- Sample Size: Not specified.
- Data Provenance: Not specified. The document describes the system's intended use for scanning dental radiographic film, but it does not detail a specific test set of images used for a performance study.
3. Number of Experts Used to Establish Ground Truth and Qualifications
- Number of Experts: Not specified.
- Qualifications of Experts: Not specified.
4. Adjudication Method for the Test Set
- Adjudication Method: Not specified.
5. Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study
- MRMC Study Done? No, an MRMC comparative effectiveness study is not mentioned or described in the provided text. The document focuses on the technical specifications and substantial equivalence of the scanning system itself, not on its impact on human reader performance for diagnostic tasks.
- Effect Size of Human Readers Improvement: Not applicable, as no MRMC study was conducted.
6. Standalone (Algorithm Only) Performance Study
- Standalone Study Done? No, a standalone performance study with specific metrics for the algorithm's diagnostic capabilities (e.g., sensitivity, specificity for detecting caries) is not described. The document discusses "image enhancement" algorithms but does not provide a formal study of their standalone diagnostic performance. The device is a scanner, and its primary function is digitizing film.
7. Type of Ground Truth Used
- Type of Ground Truth: Not specified. If an image quality or diagnostic performance study were conducted and reported, the method of establishing ground truth (e.g., expert consensus, pathology, clinical outcomes) would be crucial, but this information is absent.
8. Sample Size for the Training Set
- Sample Size: Not applicable. The document describes a scanning system with TWAIN software and proprietary image enhancement algorithms. There is no mention of machine learning models requiring "training sets" in the modern sense. The "enhancement algorithms" are likely rule-based or conventional image processing techniques rather than data-driven AI models trained on large datasets.
9. How the Ground Truth for the Training Set Was Established
- Ground Truth Establishment: Not applicable, as there is no mention of a training set for machine learning.
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