(37 days)
The NAI Tech Products Medical Digital Recorder is intended for use in Radiology applications to capture and digitize images and record them to single or multiple in the form of CD (Compact Disc) or DVD (Digital Video Disc).
Medical Digital Video Recorder
The provided text is a 510(k) clearance letter from the FDA for a "Medical Digital Video Recorder." It does not contain the specific technical details about acceptance criteria or a study proving those criteria are met for the device itself.
The document states that the device is "intended for use in Radiology applications of capture and digitize video images and record them to single or multiple media in the form of CD (Compact Disc) or DVD (Digital Video Disc)." This describes the function, but not performance metrics or how performance was validated.
Therefore, I cannot provide the requested information based solely on the text provided. The acceptance criteria and the study results would typically be contained within the 510(k) submission document itself, which is not fully included here.
However, I can deduce what types of information would typically be present in such a study for this kind of device, if it were available:
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Acceptance Criteria: For a medical digital video recorder, common acceptance criteria would likely revolve around image quality, recording accuracy, data integrity, and compliance with relevant standards. Examples might include:
- Resolution preservation (e.g., recorded image resolution matches input resolution).
- Frame rate integrity (e.g., no dropped frames during recording).
- Color accuracy (e.g., recorded colors are perceptually similar to input).
- Absence of artifacts (e.g., compression artifacts, noise introduction).
- Successful recording and playback rate (e.g., 99.9% successful recordings).
- Data permanence on recorded media (e.g., ability to reliably retrieve data after X years).
- Compliance with DICOM or other relevant medical imaging standards for interoperability.
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Study Design (Hypothetical, based on device type):
- Device Performance: Assessment would involve recording various medical video sources (e.g., fluoroscopy, endoscopy, ultrasound, etc.) and evaluating the output against the input.
- Ground Truth: For a device that digitizes and records, the "ground truth" would likely be the original analog or digital video signal itself, as observed on a calibrated monitor or measured electronically.
- "Experts": For image quality assessment, radiologists, clinicians, or imaging scientists might subjectively evaluate recorded media, or objective metrics could be used. For data integrity, technical experts would verify bit-for-bit accuracy.
- Sample Size: This would depend on the statistical power needed to demonstrate compliance with the acceptance criteria. For a variety of medical video types and recording durations.
- Data Provenance: Could be a mix of synthetic data, clinical data from various institutions (potentially retrospective), and test patterns.
- Adjudication Method: If subjective image quality assessment was used, a consensus method (e.g., 2+1 or 3+1) among experts would be common.
- MRMC Comparative Effectiveness: Highly unlikely for this type of device. An MRMC study assesses the impact of a diagnostic tool on human reader performance. A video recorder's primary function is data capture and storage, not diagnostic interpretation. It doesn't "improve" human readers in the same way an AI diagnostic algorithm might.
- Standalone Performance: Yes, the performance would be standalone – the algorithm / device would be judged on its ability to accurately capture and store the video, without direct human-in-the-loop interpretation being part of its primary function.
- Training Set: For a digital video recorder, extensive "training" in the AI sense is unlikely. The device likely relies on established digital signal processing, compression algorithms, and hardware. If there were any adaptive components, the "training data" would be technical specifications and various video inputs used for calibration and testing during development.
- How Ground Truth for Training Set was Established: Again, not applicable in the AI sense. Ground truth for calibration and testing would be established by comparing processed video to known input video signals, using objective metrics.
In summary, the provided document is a regulatory clearance letter, not a technical report detailing the performance study. To get the specific information requested, one would need access to the actual 510(k) submission and its supporting technical documentation.
§ 892.2030 Medical image digitizer.
(a)
Identification. A medical image digitizer is a device intended to convert an analog medical image into a digital format. Examples include Iystems employing video frame grabbers, and scanners which use lasers or charge-coupled devices.(b)
Classification. Class II (special controls; voluntary standards—Digital Imaging and Communications in Medicine (DICOM) Std., Joint Photographic Experts Group (JPEG) Std.). The device is exempt from the premarket notification procedures in subpart E of part 807 of this chapter subject to the limitations in § 892.9.