(158 days)
EFAI NEUROSUITE CT MIDLINE SHIFT ASSESSMENT SYSTEM (EFAI MLSCT) is a software workflow tool designed to aid in prioritizing the clinical assessment of non-contrast head CT cases with features suggestive of midline shift (MLS) in individuals aged 18 years and above. EFAI MLSCT analyzes cases using deep learning algorithms to identify suspected MLS findings. It makes case-level output available to a PACS/workstation for worklist prioritization or triage.
EFAI MLSCT is not intended to direct attention to specific portions of an image or to anomalies other than MLS. Its results are not intended to be used on a stand-alone basis for clinical decision-making nor is it intended to rule out MLS or otherwise preclude clinical assessment of CT studies.
EFAI NEUROSUITE CT MIDLINE SHIFT ASSESSMENT SYSTEM (EFAI MLSCT) is a radiological computer-assisted triage and notification software system. The software uses deep learning techniques to automatically analyze non-contrast head CTs and alerts the PACS/RIS workstation once images with features suggestive of MLS are identified.
Through the use of EFAI MLSCT, a radiologist is able to review studies with features suggestive of MLS earlier than in standard of care workflow.
The device is intended to provide a passive notification through the PACS/workstation to the radiologists indicating the existence of a case that may potentially benefit from the prioritization. It does not mark, highlight, or direct users' attention to a specific location on the original non-contrast head CT. The device aims to aid in prioritization and triage of radiological medical images only.
Here's an analysis of the acceptance criteria and study details for the EFAI Neurosuite CT Midline Shift Assessment System (MLS-CT-100), based on the provided text:
Acceptance Criteria and Device Performance
1. Table of Acceptance Criteria and Reported Device Performance
| Metric | Acceptance Criteria (Lower Bound of 95% CI) | Reported Device Performance (95% CI) |
|---|---|---|
| Sensitivity | > 0.8 | 0.961 (0.903-0.985) |
| Specificity | > 0.8 | 0.955 (0.916-0.973) |
| AUROC | Not explicitly stated (but reported) | 0.983 (0.967-0.996) |
| Processing Time | Significantly less than pre-specified goal | 62.04 seconds (60.65-63.44) |
2. Sample Size and Data Provenance
- Test Set Sample Size: 300 cases (102 positive for MLS, 198 negative for MLS). Each case included only one CT study.
- Data Provenance: Retrospective, consecutively collected from multiple clinical sites across the United States (U.S.). The U.S. cases were solely collected for this study.
3. Number and Qualifications of Experts for Ground Truth (Test Set)
- Number of Experts: Three (3)
- Qualifications: U.S. board-certified radiologists.
4. Adjudication Method (Test Set)
- Adjudication Method: Majority agreement between the three experts established the reference standard (ground truth).
5. Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study
- Was it done? No. The document describes a "standalone performance validation study" and mentions "Reader comparison analysis" for overall safety & effectiveness, but does not detail an MRMC comparative effectiveness study where human readers' performance with and without AI assistance was evaluated for an effect size. The study described focuses on the standalone performance of the AI.
6. Standalone Performance Study
- Was it done? Yes. The document explicitly states: "The observed results of the standalone performance validation study demonstrated that EFAI MLSCT by itself, in the absence of any interaction with a clinician, can provide case-level notifications with features suggestive of MLS with satisfactory results."
7. Type of Ground Truth Used
- Type of Ground Truth: Expert consensus (majority agreement of three U.S. board-certified radiologists).
8. Sample Size for the Training Set
- The document states that the "model development and validation utilized cases from Taiwan," but it does not specify the sample size for the training set. It only mentions that the U.S. validation cases were not used for model development or analytical validation testing.
9. How the Ground Truth for the Training Set Was Established
- The document indicates that the model was developed and validated using cases from Taiwan, but it does not describe how the ground truth for these training cases was established.
§ 892.2080 Radiological computer aided triage and notification software.
(a)
Identification. Radiological computer aided triage and notification software is an image processing prescription device intended to aid in prioritization and triage of radiological medical images. The device notifies a designated list of clinicians of the availability of time sensitive radiological medical images for review based on computer aided image analysis of those images performed by the device. The device does not mark, highlight, or direct users' attention to a specific location in the original image. The device does not remove cases from a reading queue. The device operates in parallel with the standard of care, which remains the default option for all cases.(b)
Classification. Class II (special controls). The special controls for this device are:(1) Design verification and validation must include:
(i) A detailed description of the notification and triage algorithms and all underlying image analysis algorithms including, but not limited to, a detailed description of the algorithm inputs and outputs, each major component or block, how the algorithm affects or relates to clinical practice or patient care, and any algorithm limitations.
(ii) A detailed description of pre-specified performance testing protocols and dataset(s) used to assess whether the device will provide effective triage (
e.g., improved time to review of prioritized images for pre-specified clinicians).(iii) Results from performance testing that demonstrate that the device will provide effective triage. The performance assessment must be based on an appropriate measure to estimate the clinical effectiveness. The test dataset must contain sufficient numbers of cases from important cohorts (
e.g., subsets defined by clinically relevant confounders, effect modifiers, associated diseases, and subsets defined by image acquisition characteristics) such that the performance estimates and confidence intervals for these individual subsets can be characterized with the device for the intended use population and imaging equipment.(iv) Stand-alone performance testing protocols and results of the device.
(v) Appropriate software documentation (
e.g., device hazard analysis; software requirements specification document; software design specification document; traceability analysis; description of verification and validation activities including system level test protocol, pass/fail criteria, and results).(2) Labeling must include the following:
(i) A detailed description of the patient population for which the device is indicated for use;
(ii) A detailed description of the intended user and user training that addresses appropriate use protocols for the device;
(iii) Discussion of warnings, precautions, and limitations must include situations in which the device may fail or may not operate at its expected performance level (
e.g., poor image quality for certain subpopulations), as applicable;(iv) A detailed description of compatible imaging hardware, imaging protocols, and requirements for input images;
(v) Device operating instructions; and
(vi) A detailed summary of the performance testing, including: test methods, dataset characteristics, triage effectiveness (
e.g., improved time to review of prioritized images for pre-specified clinicians), diagnostic accuracy of algorithms informing triage decision, and results with associated statistical uncertainty (e.g., confidence intervals), including a summary of subanalyses on case distributions stratified by relevant confounders, such as lesion and organ characteristics, disease stages, and imaging equipment.