(129 days)
fineSA software is intended for use as a non-invasive device that processes designated MR images using the 2-D pulse sequence incorporated on Siemens Medical 3T MAGNETOM systems running syngo MR software versions B17 and D13. This includes the Aera, Skyra, Avanto and Verio models.
fineSA software generates reports that provide information about the structural features in bone. The MR data, in the form of spectra or spectral images, reflect the spacing of structural features at a scale of approximately 500 microns or larger.
fineSA is a tool for the characterization of trabecular bone microarchitecture using magnetic resonance data. fineSA analyzes data acquired from a region of interest with a 2-D pulse sequence. The result is a spectrum that shows not chemical information, but the average spacing of the structural elements within the region of interest. fineSA provides the ability to identify repetitive structural features as small as 500 um.
Compatible MRI scanners, acquire one-dimensional data along the length of a defined region of interest in the anatomical region using a standard pulse sequence available on the OEM scanner. The additional acquisition time required to collect the fineSA compatible data is less than 2 minutes. The resulting DICOM data object is transferred to a network personal computer loaded with fineSA software for processing and report generation.
The DICOM input data is received from the MRI scanner via standard DICOM protocols. The processing performed is driven by the received data content, and at the completion of data processing and analysis, a PDF formatted report is generated and transferred to a designated DICOM destination as a DICOM encapsulated PDF document for viewing and interpretation.
fineSA is a software only image processing system that can be installed on any network computer that meets the specified minimum hardware and operating system requirements. The system can be deployed on the radiology internal network or at a remote location with secure access to radiology internal network.
The provided text is a 510(k) summary for the Fine Structure Analysis (fineSA) Software. It describes the device, its intended use, and its substantial equivalence to a predicate device, but it does not contain detailed information about specific acceptance criteria, study methodologies, or quantitative performance metrics typically found in a clinical study report.
Therefore, I cannot populate the table or answer all the requested questions with the specificity desired, as the document is a premarket notification summary, not a detailed study report.
Here's what can be extracted and inferred from the document:
1. A table of acceptance criteria and the reported device performance
The document mentions "defined specifications" and "formal requirements" but does not explicitly list them or provide quantitative performance results against those criteria. It states that "Formal test plans were executed to confirm that fineSA meets its formal requirements" and a "verification report confirming the fineSA meets its intended use and performance requirements" was provided. However, the details of these requirements and the actual performance metrics are not included in this summary.
2. Sample size used for the test set and the data provenance (e.g. country of origin of the data, retrospective or prospective)
The document mentions "phantom and clinical data" were used for testing. It does not specify:
- The sample size of the test set.
- The country of origin for the clinical data.
- Whether the clinical data was retrospective or 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)
This information is not provided in the document.
4. Adjudication method (e.g. 2+1, 3+1, none) for the test set
This information is not provided in the document.
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
The document does not describe an MRMC comparative effectiveness study where human readers' performance with and without AI assistance was evaluated. The fineSA software is described as generating reports "that provide information about the structural features in bone," which implies it provides data for interpretation, but its impact on human reader performance is not quantified.
6. If a standalone (i.e. algorithm only without human-in-the-loop performance) was done
The document states, "fineSA software generates reports that provide information about the structural features in bone." This implies a standalone capability for processing data and generating reports, which would be considered an algorithm-only performance aspect. However, the exact metrics of this standalone performance are not detailed. It's a "software only image processing system."
7. The type of ground truth used (expert consensus, pathology, outcomes data, etc)
The document mentions using "phantom and clinical data" for testing. For "clinical data," the type of ground truth against which the software's output was validated is not specified.
8. The sample size for the training set
The document does not specify a separate training set or its sample size. It mentions "Software verification and validation testing...using phantom and clinical data," but doesn't distinguish between data used for training/development and data used for independent testing.
9. How the ground truth for the training set was established
This information is not provided as a distinct training set detail.
§ 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).