(136 days)
BrainInsight is intended for automatic labeling, spatial measurement, and volumetric quantification of brain structures from a set of low-field MR images and returns annotated images, color overlays, and reports.
BrainInsight is a fully automated MR imaging post-processing medical software that image alignment, whole brain segmentation, ventricle segmentation, and midline shift measurements of brain structures from a set of MR images from patients aged 18 or older. The output annotated and segmented images are provided in a standard image format using segmented color overlays and reports that can be displayed on third-party workstations and FDA cleared Picture Archive and Communications Systems (PACS). The high throughput capability makes the software suitable for use in routine patient care as a support tool for clinicians ir assessment of low-field (64mT) structural MRIs. BrainInsight provides overlays and reports based on 64mT 3D MRI series of a T1 and T2-weighted sequence. The outputs of the software are DICOM images which include volumes that have been annotated with color overlays, with each color representing a particular segmented region, spatial measurement of anatomical structures, and information reports computed from the image data, segmentations, and measurements. The BrainInsight processing architecture includes a proprietary automated internal pipeline that performs whole brain segmentation, ventricle segmentation, and midline shift measurements based on machine learning tools. Additionally, the system's automated safety measures include automated quality control functions, such as tissue contrast check and scan protocol verification. The system is installed on a standard computing platform, e.g. server that may be in the cloud, and is designed to support file transfer for input and output of results.
The provided text describes the BrainInsight device and references its 510(k) summary (K202414). However, it does not contain specific acceptance criteria or a detailed study description with performance metrics, sample sizes, or ground truth establishment relevant to those criteria. The "Non-clinical Performance Data" section lists areas of evaluation but doesn't provide the results against specific criteria.
Therefore, I cannot fulfill the request to provide a table of acceptance criteria and reported device performance based solely on the provided text.
However, I can extract information related to the studies mentioned and other requested points:
1. Table of Acceptance Criteria and Reported Device Performance
- Not explicitly provided in the text. The document lists areas of non-clinical performance data (Cybersecurity and PHI protection, Midline shift, 3D Coordinates and alignment, Segmentation, Data Quality Control, Audit trail, User Manual information, Software control, Ventricle segmentation, Midline shift measurement, Skull stripping). However, it does not state specific acceptance criteria (e.g., "midline shift accuracy > X%") or the actual performance achieved against such criteria.
2. Sample Size Used for the Test Set and Data Provenance
- Test Set Sample Size: Not specified in the provided text.
- Data Provenance: Not specified in the provided text. The device processes MRI scans from "Hyperfine FSE MRI scans acquired with specified protocols." Whether these were retrospective or prospective, or from specific countries, is not mentioned.
3. Number of Experts Used to Establish Ground Truth for the Test Set and Qualifications
- Not explicitly provided in the text. The document states that "Results must be reviewed by a trained physician," implying human review, but does not detail how ground truth for a test set was established (e.g., number of experts, their qualifications, or their role in defining ground truth for segmentation or measurement accuracy).
4. Adjudication Method for the Test Set
- Not explicitly provided in the text.
5. Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study
- No MRMC study mentioned. The document focuses on the device's standalone capabilities and its equivalence to a predicate. There is no mention of a study involving human readers with and without AI assistance or effect sizes.
6. Standalone (Algorithm Only Without Human-in-the-loop) Performance
- Yes, a standalone evaluation was performed. The "Non-clinical Performance Data" section describes software evaluations conducted to confirm various aspects like midline shift, 3D coordinates and alignment, segmentation, ventricle segmentation, and skull stripping. This indicates an assessment of the algorithm's performance independent of human input during the processing phase.
7. Type of Ground Truth Used
- Not explicitly provided in the text. The document describes the device's function (automatic labeling, spatial measurement, volumetric quantification, segmentation, midline shift measurements) and states "Performance data was limited to software evaluations to confirm...". While this implies comparison to some form of truth, the type of ground truth (e.g., expert consensus, manual tracings, pathology, outcomes data) for the segmentation, measurements, and other evaluated features is not detailed.
8. Sample Size for the Training Set
- Not explicitly provided in the text. The device uses "machine learning tools" for its processing architecture, indicating the use of a training set, but its size is not disclosed.
9. How the Ground Truth for the Training Set Was Established
- Not explicitly provided in the text. While it states machine learning is used, the process for establishing the ground truth labels or segmentations used to train these models is not detailed.
§ 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).