(175 days)
The IMAGEnet 6 Ophthalmic Data System is a software program that is intended for use in the collection, storage and management of digital images, patient data, diagnostic data and clinical information from Topcon devices without controlling or altering the functions and parameters of any medical devices or through computerized networks. It is intended for processing and displaying ophthalmic images and optical coherence tomography data.
The IMAGEnet 6 Ophthalmic Data System uses the same algorithms and reference databases from the original data capture device as a quantitative tool for the comparison of posterior ocular measurements to a database of known normal subjects.
IMAGEnet 6 Ophthalmic Data System is a Web application that allows management of patient information, exam information and image information. It is installed on a server PC and operated via a web browser of a client PC.
IMAGEnet 6 Ophthalmic Data System receives information from Topcon ophthalmological medical devices and saves the information including the patient information. The saved data can be displayed for diagnosis. In addition, it can save patient information, exam information, and image information as digital data to a database. These data can also be exported as digital data.
IMAGEnet 6 Ophthalmic Data System does not control or alter the functions or parameters of any medical device. IMAGEnet 6 Ophthalmic Data System is used in cooperation with the capture software designated for each capture device to retrieve image data such as an OCT image or a fundus image. IMAGEnet 6 Ophthalmic Data System receives, displays, and saves the image data captured with the capture software.
It also allows to send/receive patient information and image information, etc. to/from an external system via communication conforming to the DICOM standard.
This document, a 510(k) Summary for the IMAGEnet 6 Ophthalmic Data System, outlines the device's substantial equivalence to predicate devices, focusing on its function as a picture archiving and communication system (PACS) for ophthalmic data.
Here's an analysis of the requested information, based only on the provided text:
Acceptance Criteria and Reported Device Performance
The document states that "Software verification and validation testing was conducted" and that "the IMAGEnet 6 Ophthalmic Data System was tested to demonstrate that the measurement and analysis functions are equivalent to the predicate devices and been found equivalent to the predicate devices."
However, the document does not explicitly define specific numerical acceptance criteria (e.g., sensitivity, specificity, accuracy thresholds) or provide a table listing these criteria alongside reported device performance metrics. Instead, it broadly claims equivalency to predicate devices.
| Acceptance Criteria (Explicitly Stated in Document) | Reported Device Performance (Explicitly Stated in Document) |
|---|---|
| Equivalence of measurement and analysis functions to predicate devices. | Measurement and analysis functions found equivalent to predicate devices. |
Missing Information: Specific quantitative metrics for acceptance criteria and device performance are not provided. The document relies on a qualitative statement of equivalence.
Study Details
2. Sample size used for the test set and the data provenance (e.g. country of origin of the data, retrospective or prospective)
- Sample size for test set: Not specified.
- Data provenance: Not specified (country of origin, retrospective/prospective).
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 specified. The document focuses on software verification and validation and equivalence to predicate devices, not on diagnostic performance against a ground truth established by experts.
4. Adjudication method (e.g., 2+1, 3+1, none) for the test set
- Not specified. This is typically relevant for studies involving human readers or expert consensus, which is not detailed here.
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
- Not explicitly stated that an MRMC comparative effectiveness study was done. The focus of the performance data section is on validating the software and its equivalence to predicate devices, not on human-AI collaboration or improvement with AI assistance. The device is described as a data system, not an AI diagnostic tool.
- Effect size of human reader improvement: Not mentioned, as an MRMC study is not detailed.
6. If a standalone (i.e. algorithm only without human-in-the-loop performance) was done
- The document implies that "measurement and analysis functions" were tested for equivalence to predicate devices in a standalone manner (i.e., the software's performance itself), but it does not provide standalone performance metrics beyond a claim of equivalence. The device is a data management system, not a diagnostic algorithm in the sense of AI.
7. The type of ground truth used (expert consensus, pathology, outcomes data, etc)
- The document states that the device "uses the same algorithms and reference databases from the original data capture device as a quantitative tool for the comparison of posterior ocular measurements to a database of known normal subjects." This suggests the ground truth for these comparisons is derived from these "reference databases of known normal subjects." However, for general software functionality, ground truth would typically be defined by engineering specifications and expected output.
8. The sample size for the training set
- Not applicable/Not specified. This device is described as a data system that uses algorithms and reference databases from predicate devices. It is not presented as an AI/ML model that would have its own training set in a traditional sense. The algorithms and databases are inherited from already cleared devices.
9. How the ground truth for the training set was established
- Not applicable/Not specified. As above, this information would be relevant if the IMAGEnet 6 itself encompassed novel AI/ML algorithms requiring a training set. The document indicates it reuses established algorithms and reference databases.
§ 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).