Search Results
Found 1 results
510(k) Data Aggregation
(151 days)
The MasterX 800 Series is intended for use by a qualified/trained doctor or technologist. As part of a radiographic system, the MasterX 800 Series is intended to acquire digital radiographic images on adult and pediatric patients. It is suitable for all routine radiography exams, including specialist areas like intensive care or trauma work, excluding fluoroscopy, angiography and mammography.
This device represents a new combination of already cleared solid state digital x-ray acquisition panels and already cleared software. It is suitable for use with adult and pediatric populations. This is an upgrade kit for existing or new systems.
Here's an analysis of the provided text regarding the acceptance criteria and supporting study for the MasterX 800 Series, structured according to your request:
Based on the provided FDA 510(k) Summary, the MasterX 800 Series device is primarily an upgrade kit consisting of a new combination of already cleared digital X-ray acquisition panels and existing software. The submission focuses on demonstrating substantial equivalence to a predicate device rather than presenting a de novo study with strict acceptance criteria for novel performance claims.
Therefore, the "acceptance criteria" here are implicitly tied to demonstrating safety and effectiveness compared to the predicate, and the "study" is primarily non-clinical validation and integration testing.
1. A table of acceptance criteria and the reported device performance
Since this is a 510(k) submission for an existing technology combination, explicit quantitative acceptance criteria for new clinical performance aren't stated as they would be for a novel AI device with specific performance metrics. Instead, the "performance" shown is its equivalence to the predicate device and the compliance of its components with relevant standards.
| Acceptance Criteria (Implicit from Substantial Equivalence Goal) | Reported Device Performance | Comments |
|---|---|---|
| Indications for Use: | The MasterX 800 Series has the same Indications for Use as the predicate, with an added mention of "adult and pediatric patients" for clarity. | "Patient population added" is the only comment, implying functional equivalence. |
| Technological Characteristics: | ||
| - Configuration of Digital Panels | Exactly the same as predicate. | |
| - Power Source | Same as predicate, with some models now "AC Line only." | |
| - Digital Panel Models | 8 new models from InnoCare are used, which are "Newer models" but are individually 510(k) cleared. | This is the primary technological difference, but panels are pre-cleared. |
| - Panel Performance (DQE @ 1lp/mm, MTF @ 1lp/mm) | DQE @ 1lp/mm: 50% (same as predicate) | |
| MTF @ 1lp/mm: 63% (predicate was 60%) | "Similar" performance. The new device shows slightly better MTF, which is not a concern for equivalence. | |
| - Interface | Same as predicate. | |
| - Panel Sizes | Same sizes available as predicate. | |
| - X-ray Conversion Layer | Csl or GOS (predicate was Csl only). | GOS is described as "lower cost but has nearly comparable performance", indicating acceptable equivalence. |
| - Active Areas (pixels) | "Nearly identical" to predicate. | Minor variations in pixel counts are presented as substantially equivalent. |
| - Pixel Pitch | 140 μm (same as predicate). | |
| - A/D Conversion | 16 bits (predicate was 14 bits). | "Better" implies an improvement that does not raise new questions of safety or effectiveness. |
| - Image Acquisition Software | "Voyance" (same as predicate). | |
| - DICOM Compliance | DICOM 3 (same as predicate). | |
| Safety and Effectiveness: | Summarized through software validation, risk management, integration testing, and compliance with FDA guidances and standards. | The conclusion states the device is "as safe and effective as the predicate device" and has "insignificant technological differences." No new safety/effectiveness issues were identified. |
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 states: "Summary of clinical testing: Not required."
- Therefore, there is no clinical test set, sample size, or data provenance from a clinical study. The evaluation relies on non-clinical testing.
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)
- Not applicable as no clinical test set or ground truth established by experts was used for performance evaluation in a clinical context. The evaluation was primarily engineering and regulatory in nature, comparing an assembled system to a predicate device.
4. Adjudication method (e.g., 2+1, 3+1, none) for the test set
- Not applicable as no clinical test set requiring adjudication was used.
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
- No MRMC comparative effectiveness study was done. This device is an X-ray acquisition system, not an AI-powered diagnostic or assistive tool.
6. If a standalone (i.e., algorithm only without human-in-the-loop performance) was done
- Not applicable as this is an X-ray acquisition system, not an algorithm being evaluated for standalone performance. The "software" referred to is image acquisition software, which is part of the system operation, not an AI for interpretation.
7. The type of ground truth used (expert consensus, pathology, outcomes data, etc.)
- No clinical ground truth (expert consensus, pathology, outcomes data) was used, as no clinical study was performed. The "ground truth" for the non-clinical testing was defined by engineering specifications, regulatory standards compliance, and comparison to the predicate device's established performance parameters (e.g., DQE, MTF).
8. The sample size for the training set
- No training set is mentioned or applicable, as this device is an X-ray acquisition system and not an AI/machine learning algorithm that requires training data.
9. How the ground truth for the training set was established
- Not applicable, as there is no training set mentioned or used.
Ask a specific question about this device
Page 1 of 1