(136 days)
M'Ath® software is a Windows based application program running on a personal computer that is intended to aid the physician in the organisation of patient data relating to the ultrasound images or video acquired during vascular examination by sonography, and including patient characteristics. Additionally the software allows making measurements to determine the intima media thickness of the carotid artery from the accurate image and store them with the patient file.
M'Ath®Std is a software running on a stand alone computer under Microsoft Windows operating system. Images are captured from any ultrasound device. Proprietary algorithms are used to measure Intima Media Thickness ( IMT).Storage of patient measurements values can be performed during the examination. These measurements help to detect early atherosclerosis in the carotid vascular bed.
The provided text reports a 510(k) summary for the M'Ath® Std software, which is intended to aid physicians in measuring Intima Media Thickness (IMT) from ultrasound images. However, the document explicitly states that no performance standards are required for this device class for determining substantial equivalence, and therefore, no specific acceptance criteria or detailed study data proving the device meets acceptance criteria are provided.
The submission focuses on establishing substantial equivalence to predicate devices (K021966, Q Lab Software; K030223, Sonocalc) based on similar intended use, technological characteristics, features, specifications, and mode of operation, rather than presenting a performance study with defined acceptance criteria.
The relevant sections state:
- "Performance data: There are no section 514 performance standards for this class of device for assisting in the determination of its substantial equivalence."
- "Conclusions drawn from clinical and non clinical test data: Not required for determination of substantial equivalence for this class of device, though publication of some clinical data are contained in this premarket submission."
Therefore, based solely on the provided text, it's not possible to populate all the requested information columns regarding acceptance criteria and study details.
However, I can extract the information that is present:
1. Table of Acceptance Criteria and Reported Device Performance
| Acceptance Criteria | Reported Device Performance |
|---|---|
| Not specified/Not required for substantial equivalence | Substantially equivalent to predicate devices (Q Lab Software and Sonocalc) in intended use, technological characteristics, features, specifications, and mode of operation. |
2. Sample size used for the test set and the data provenance
- Sample size: Not specified.
- Data provenance: Not specified (the document mentions "publication of some clinical data are contained in this premarket submission" but does not detail the nature or origin of this data, nor does it link it to a specific test set for performance).
3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts
- Not specified.
4. Adjudication method for the test set
- Not specified.
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 specified. The document does not describe a comparative effectiveness study of this nature.
6. If a standalone (i.e. algorithm only without human-in-the-loop performance) was done
- Not specified. The device is described as "intended to aid the physician," implying human-in-the-loop, but no standalone performance metrics are provided.
7. The type of ground truth used
- Not specified.
8. The sample size for the training set
- Not specified.
9. How the ground truth for the training set was established
- Not specified.
§ 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).