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510(k) Data Aggregation
(15 days)
The Quantum DiRex System provides diagnostic quality images to aid the physician with diagnosis. The DiRex can be used to perform radiographic exposures of the skeleton (including skull, spinal column and extremities), chest, abdomen and other body parts. The DiRex is not indicated for use in mammography.
The Quantum DiRex System is an integrated digital imaging system that combines the currently marketed Quantum Q-Rad Radiographic System with the currently marketed Agfa DX-S CR System (digitizer with NX workstation). The Quantum DiRex System is a combination of these previously cleared systems that have been combined and will be marketed as a single system.
Here's an analysis of the provided text regarding the acceptance criteria and study for the Quantum DiRex System:
Based on the provided text, the Quantum DiRex System is an integrated digital imaging system that combines the Quantum Q-Rad Radiographic System with the Agfa DX-S CR System. The primary acceptance criteria for this submission revolve around demonstrating substantial equivalence to predicate devices and meeting pre-determined performance criteria.
Here's a breakdown of the requested information:
1. A table of acceptance criteria and the reported device performance
| Acceptance Criteria | Reported Device Performance |
|---|---|
| Substantial Equivalence to Predicate Devices | "The Quantum DiRex System is substantially equivalent to the predicate devices and/or met pre-determined acceptance criteria." |
| Meet Pre-determined Performance Criteria | "Performance data demonstrated that the Quantum DiRex System... met pre-determined acceptance criteria." and "Performance testing was successfully completed on the proposed system in accordance with predetermined protocols based on the system design inputs." |
| Acceptable Risks | "The risks associated with use of the new device were found acceptable when evaluated by standardized risk/hazard analysis techniques." |
| Biocompatibility | "No biocompatibility testing was conducted... all patient-contacting materials... have been previously cleared for similar devices." (No new testing required as materials were pre-cleared) |
| Technological Characteristics Identity | "The technological characteristics are the same in the proposed and predicate devices." |
| Safety and Effectiveness Confirmation | "The Quantum DiRex System meets all the pre-determined acceptance criteria of the testing performed to confirm safety and effectiveness..." |
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 does not specify a sample size for any test set or data provenance. The descriptions are general statements about "performance data" and "performance testing."
3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts
The document does not mention any expert review committee, experts used to establish ground truth, or their qualifications. The testing appears to be focused on technical performance and equivalence, rather than clinical efficacy studies requiring expert reader consensus on diagnostic images.
4. Adjudication method (e.g. 2+1, 3+1, none) for the test set
The document does not mention any 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
No, an MRMC comparative effectiveness study was not conducted. This submission is for a digital X-ray system, not an AI-powered diagnostic device, hence, there is no mention of human readers improving with AI assistance.
6. If a standalone (i.e. algorithm only without human-in-the loop performance) was done
This question is not applicable as the device is a medical imaging system (hardware and associated software for image acquisition), not a standalone diagnostic algorithm. The performance described relates to the system's ability to produce diagnostic quality images, not an automated reading of those images.
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. Given the nature of the device (an imaging system, not a diagnostic algorithm), the "performance testing" likely involved evaluating image quality metrics, dose, system functionality, and compliance with pre-determined technical specifications (e.g., spatial resolution, contrast resolution, noise, consistency) rather than clinical ground truth diagnoses.
8. The sample size for the training set
The document does not mention a training set sample size. This is expected as the device is an imaging system, not a machine learning model that requires a training set.
9. How the ground truth for the training set was established
This question is not applicable as the device is an imaging system and not an AI/ML model requiring a training set with established ground truth.
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