(246 days)
Aid-U is a software solution intended to be used for storing, zoom-out, retrieving, transmitting, and processing medical images, or outputting them. Aid-U is intended for use as a web- based application and is networked with a PACS server. It enables the display, comparison of CT, MR, PT, US, XA, NM, DX, and SC from other DICOM compliant modalities. Typical users are radiologists, technologists, and clinicians.
Aid-U is a medical image transmission device software, of SaaS type (Software as a Service, provided as a service, not as a software installation) or an On-Premise Software type web application (Companies install software in data centers managed by their own facilities) that allows patient medical image data generated by medical imaging devices to be transmitted, stored, managed, and retrieved using standard internet protocols (HTML5).
The provided text is a 510(k) Summary for the device Aid-U. It primarily focuses on demonstrating substantial equivalence to predicate devices based on functional and technical characteristics, as well as adherence to various medical device software standards. However, it does NOT contain specific information regarding clinical performance studies, such as multi-reader multi-case (MRMC) studies, standalone algorithm performance, or detailed acceptance criteria for clinical efficacy.
The document states that "software validation and verification tests" were performed and that "all software specifications meet the acceptance criteria," but it does not define these acceptance criteria in terms of clinical performance metrics (e.g., sensitivity, specificity, AUC) or provide reported device performance against such metrics. It also does not elaborate on the specific study design, sample sizes, ground truth establishment, or expert involvement for clinical evaluation.
Therefore,Based on the provided text, I cannot fulfill most of the requested information regarding acceptance criteria, study details, and performance metrics as these are not present in the document.
Here's what can be extracted and what information is missing:
1. A table of acceptance criteria and the reported device performance
- Acceptance Criteria (General Statement): The document states that "all software specifications meet the acceptance criteria." However, it does not specify what these criteria are in terms of clinical performance (e.g., sensitivity, specificity, accuracy for detecting a condition).
- Reported Device Performance: No specific clinical performance metrics (e.g., sensitivity, specificity, AUC) are reported. The document focuses on showing technical equivalence and compliance with software standards.
2. Sample sized used for the test set and the data provenance (e.g. country of origin of the data, retrospective or prospective)
- Information Not Provided: The document does not mention any test set sample size for clinical performance evaluation, nor does it describe data provenance (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)
- Information Not Provided: The text does not refer to the use of experts for establishing ground truth in a performance study.
4. Adjudication method (e.g. 2+1, 3+1, none) for the test set
- Information Not Provided: There is no mention of an adjudication method for a test set.
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
- Information Not Provided: The document does not describe an MRMC study or any information about human reader improvement with or without AI assistance. The device Aid-U is described as a "Medical Image Management and Processing System," not explicitly an AI diagnostic aid that would typically undergo such studies.
6. If a standalone (i.e. algorithm only without human-in-the-loop performance) was done
- Information Not Provided: No standalone algorithm performance is reported. The device is described as a system for storing, viewing, and processing medical images.
7. The type of ground truth used (expert consensus, pathology, outcomes data, etc)
- Information Not Provided: There is no mention of the type of ground truth used, as no clinical performance study is detailed.
8. The sample size for the training set
- Information Not Provided: No information about a training set size is provided. The document highlights the device's technical specifications and substantial equivalence to existing PACS systems, not a machine learning model developed with a training set.
9. How the ground truth for the training set was established
- Information Not Provided: No information about ground truth establishment for a training set is provided.
Summary of Device and Study (Based on Provided Text):
- Device Name: Aid-U
- Device Type: Medical Image Management and Processing System (SaaS or On-Premise Software web application)
- Intended Use: Storing, displaying (zoom-in/out), retrieving, transmitting, and processing medical images from various DICOM-compliant modalities (CT, MR, PT, US, XA, NM, DX, SC). Intended for use by radiologists, technologists, and clinicians.
- Regulatory Pathway: 510(k) Premarket Notification (K231662)
- Basis for Clearance: Substantial Equivalence to predicate devices (Agfa HealthCare's ICIS View K143397 and Novarad Corporation's NovaPACS K171754).
- Non-Clinical Tests Mentioned:
- Compliance with DICOM (NEMA PS 3.1 - 3.15, 3.18)
- JPEG 2000 (ISO/IEC 15444-1:2016)
- Software life-cycle processes (IEC 62304:2015)
- Risk management (ISO 14971:2019, IEC TR 80002-1:2009)
- Medical device security (AAMI TIR57:2016)
- Conclusion from Non-Clinical Tests: "All software specifications meet the acceptance criteria. It complies with cybersecurity requirements... The results of the hazard analysis, combined with the appropriate preventive measures Taken, indicate the device is of moderate level of concern..."
This documentation is typical for a 510(k) submission for an image management system, which primarily relies on demonstrating functional equivalence and adherence to established standards rather than clinical performance studies focused on diagnostic accuracy or AI assistance.
§ 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).