(728 days)
This software is a medical device intended for the evaluation of DICOM images. It receives, stores, processes, and displays sequential DICOM images primarily obtained through low-dose chest fluoroscopy (e.g., RF and AX modalities).
This software is not intended to be used for primary diagnosis. Reference images such as scintigraphy or CT scans may be displayed for supplementary purposes.
The subject device is a software-only medical imaging system intended for installation on commercial off-the-shelf personal computers. It receives, stores, processes, and displays sequential DICOM images, primarily obtained from chest fluoroscopy (e.g., RF, AX modalities). The software is compatible with external systems such as hospital PACS via DICOM-compliant communication protocols.
The device operates as a standalone application, with all processing and visualization functionalities integrated into a single software package.
The provided FDA clearance letter and 510(k) summary for Mediott Inc.'s RW-1 device do not contain explicit acceptance criteria or results from a study that demonstrates the device meets specific performance criteria in the way typically expected for AI/ML-driven diagnostic devices.
Instead, the submission focuses on establishing substantial equivalence to a predicate device (KONICAMINOLTA DI-X1, K212685) based on technological characteristics and non-clinical performance testing.
Here's a breakdown of the information that can be extracted, and what is missing based on your requested format:
1. Table of Acceptance Criteria and Reported Device Performance
Acceptance Criteria:
The document does not define explicit, quantitative acceptance criteria for performance metrics like sensitivity, specificity, accuracy, or other clinical outcomes. The "acceptance criteria" are implied to be that the device performs its stated functions reliably and consistently, and that its differences from the predicate do not raise new questions of safety or effectiveness.
Reported Device Performance:
The document does not report quantitative performance metrics for the RW-1 device. The performance is described qualitatively as "functional correctness, repeatability, and robustness of the device functions."
| Acceptance Criteria | Reported Device Performance |
|---|---|
| Functional correctness, repeatability, and robustness of device functions consistent with industry standards for software-based medical devices. | Qualitative Statement: "The implemented software algorithms operate reliably and consistently under representative conditions. The primary focus was on ensuring functional correctness, repeatability, and robustness of the device functions, consistent with industry standards for software-based medical devices." |
| No new questions of safety or effectiveness compared to the predicate device. | Conclusion: "The observed differences do not raise new questions of safety or effectiveness and reflect reductions in scope or architectural simplification." |
2. Sample Size Used for the Test Set and Data Provenance
The document states: "Non-clinical performance testing was conducted as part of the comprehensive system-level verification and validation (V&V) activities for the subject device."
- Sample Size for Test Set: Not specified. The document implies that the testing was focused on the system's inherent functions rather than evaluation against a dataset of clinical cases with established ground truth.
- Data Provenance: Not specified.
3. Number of Experts Used to Establish Ground Truth for the Test Set and Their Qualifications
- Number of Experts: None explicitly mentioned.
- Qualifications of Experts: Not applicable, as there's no mention of expert-established ground truth for a test set.
4. Adjudication Method for the Test Set
- Adjudication Method: Not applicable, as no expert adjudication for a test set is mentioned.
5. If a Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study was done, and the Effect Size of Human Readers Improvement with AI vs. Without AI Assistance
- MRMC Study: No, an MRMC study was not conducted or reported. This type of study would typically be performed for AI/ML diagnostic aids to assess human reader performance with and without AI assistance.
- Effect Size: Not applicable, as no MRMC study was performed.
6. If a Standalone (Algorithm Only Without Human-in-the-Loop Performance) was done
The document states: "No separate standalone bench tests were performed beyond these system-level V&V activities, as the system-level testing was considered sufficient to evaluate all performance-critical features under anticipated use conditions."
This indicates that an "algorithm-only" or "standalone" performance evaluation (in the sense of quantitative clinical performance metrics on a clinical dataset) was not performed. The "standalone application" mentioned in the description refers to the software's architecture, not a standalone performance evaluation.
7. The Type of Ground Truth Used
- Type of Ground Truth: Not applicable. The V&V activities focused on functional correctness of the software's operations (e.g., displaying images, performing measurements) rather than clinical ground truth (e.g., diagnosis confirmed by pathology, expert consensus, or outcomes).
8. The Sample Size for the Training Set
- Sample Size for Training Set: Not applicable. The RW-1 is described as a medical image management and processing system with specific display and measurement functions. There is no indication that it is an AI/ML device that requires a training set in the conventional sense for learning-based tasks (e.g., disease detection, classification). The "implemented software algorithms" are deterministic.
9. How the Ground Truth for the Training Set Was Established
- Ground Truth for Training Set: Not applicable, as there is no indication of a training set or learning-based algorithms.
Summary of Device Nature:
Based on the provided text, the RW-1 device is primarily a medical image management and processing system. Its functions include receiving, storing, processing, and displaying DICOM images, with features like density/gradation adjustment, rotation, scaling, panning, cine display, comparison, and area measurement.
The key phrase "Statistical exhaustiveness was not required due to the deterministic nature of the implemented algorithms" strongly suggests that the RW-1 is developed using traditional, rule-based or deterministic algorithms for image manipulation and display, rather than machine learning algorithms that would typically require large training and test sets and extensive clinical performance evaluations with ground truth. The V&V focused on ensuring these deterministic functions worked correctly and reliably.
§ 892.2050 Medical image management and processing system.
(a)
Identification. A medical image management and processing system is a device that provides one or more capabilities relating to the review and digital processing of medical images for the purposes of interpretation by a trained practitioner of disease detection, diagnosis, or patient management. The software components may provide advanced or complex image processing functions for image manipulation, enhancement, or quantification that are intended for use in the interpretation and analysis of medical images. Advanced image manipulation functions may include image segmentation, multimodality image registration, or 3D visualization. Complex quantitative functions may include semi-automated measurements or time-series measurements.(b)
Classification. Class II (special controls; voluntary standards—Digital Imaging and Communications in Medicine (DICOM) Std., Joint Photographic Experts Group (JPEG) Std., Society of Motion Picture and Television Engineers (SMPTE) Test Pattern).