(380 days)
XmaruView V1 software is to make the processing and administration of medical X-ray images as efficient as possible. Functions to be carried out using XmaruView V1 is, for example, but not limited to, adjustment of window leveling, rotation, zoom, and measurements. XmaruView V1 software can control X-ray generator acquisition settings. XmaruView V1 is not approved for the acquisition of mammographic image data and it cannot be used to interpret mammographic image date either. XmaruView V1 is meant to be used by qualified medical personnel only. All users must be qualified to create and diagnose radiological image data. XmaruView V1 is complying with DICOM standards to assure optimum communications between network systems.
XmaruView V1 is a radiographic image capture program in charge of variety of image related works, including photographing Digital Radiography, acquiring and processing the images, and the image management. XmaruView V1 provides integrated controls the flat-panel detector and the Xray generator of a radiographic system to perform image acquisitions and image processing. Its own database storage capability enables a user the convenient management of patient images and data. In addition, it supports DICOM output for a seamless integration with the operating environment where PACS is installed, XmaruView V1 has been developed to meet the requirements of hospitals to provide a seamless work flow in the heavy work load environment and thus is equipped with a number of relevant convenience functions.
This 510(k) summary for the XmaruView V1 device does not contain the detailed information required to describe acceptance criteria and a study proving the device meets those criteria.
The document discusses the device's substantial equivalence to a predicate device (dicomPACS® DX-R 1.6) for its intended use and general functionalities. It lists compliance with certain standards (NEMA, IEC, ISO) but does not provide specific acceptance criteria or performance metrics in the format requested.
Therefore, most of the requested information cannot be extracted from the provided text.
Here is what can be extracted, along with an explanation of why other sections cannot be completed:
1. Table of Acceptance Criteria and Reported Device Performance
| Acceptance Criteria | Reported Device Performance |
|---|---|
| Not specified in document | Not specified in document |
Explanation: The document does not define explicit acceptance criteria or provide specific performance metrics for the XmaruView V1. It focuses on demonstrating substantial equivalence to a predicate device based on shared functionalities and intended use, rather than presenting a performance study against predefined criteria.
2. Sample size used for the test set and the data provenance
- Sample Size: Not specified.
- Data Provenance: Not specified.
Explanation: The document does not describe a clinical or performance study involving a test set of data.
3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts
- Number of Experts: Not specified.
- Qualifications of Experts: Not specified.
Explanation: No test set or ground truth establishment process is described.
4. Adjudication method for the test set
- Adjudication Method: Not specified.
Explanation: No test set or adjudication process is described.
5. If a multi-reader multi-case (MRMC) comparative effectiveness study was done
- MRMC Study Done: No, an MRMC study is not mentioned or described in this 510(k) summary.
- Effect Size of Human Reader Improvement: Not applicable, as no MRMC study was described.
Explanation: The 510(k) focuses on substantial equivalence based on technical characteristics and intended use, not a clinical comparative effectiveness study with human readers.
6. If a standalone (i.e., algorithm only without human-in-the-loop performance) was done
- Standalone Performance Study Done: No, a standalone performance study of the algorithm is not mentioned or described.
Explanation: The device is described as a "Radiological Image Processing System" that provides tools for "qualified medical personnel." The submission emphasizes its role in image acquisition, processing, and management under human control, not as an autonomous diagnostic algorithm.
7. The type of ground truth used
- Type of Ground Truth: Not specified.
Explanation: No study requiring ground truth is described.
8. The sample size for the training set
- Sample Size for Training Set: Not specified.
Explanation: The document does not describe the development or training of an AI algorithm, so a "training set" is not relevant to the information provided.
9. How the ground truth for the training set was established
- Ground Truth Establishment for Training Set: Not applicable, as no training set or AI model development is described.
Explanation: As in point 8, the document does not describe AI development and therefore no training set or its ground truth establishment.
Summary of what the document does provide regarding "Safety and Performance Data":
The document lists compliance with the following standards:
- NEMA PS 3.1-3.18 Digital Imaging and Communications in Medicine (DICOM) set (2008)
- IEC 62304 Medical device software – Software life-cycle processes : 2006
- ISO 14971 Medical Devices – Application of risk management to medical device : 2007
This indicates that the device's software development process followed recognized standards for medical device software lifecycle and risk management, and that its image communication capabilities adhere to DICOM standards. However, these are process and interoperability standards, not direct measures of diagnostic or clinical performance.
§ 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).