(161 days)
EFAI PNXXR is a software workflow tool designed to aid the clinical assessment of adult (22 years of age or older) Posteroanterior (PA) view Chest X-Ray cases with features suggestive of pneumothorax in the medical care environment. EFAI PNXXR analyzes cases using an artificial intelligence algorithm to identify suspected findings. It makes case-level output available to a PACS/workstation for worklist prioritization or triage. EFAI PNXXR is not intended to direct attention to specific portions or anomalies of an image. Its results are not intended to be used on a stand-alone basis for clinical decision-making nor is it intended to rule out pneumothorax or otherwise preclude clinical assessment of X-Ray cases.
EFAI ChestSuite XR Pneumothorax Assessment System, herein referred to as EFAI PNXXR, is a radiological computer-assisted triage and notification software system. The software uses deep learning techniques to automatically analyze PA chest x-rays and sends notification messages to the picture archiving and communication system (PACS)/workstation to allow suspicious findings of pneumothorax to be identified.
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 chest X-ray. The device aims to aid in prioritization and triage of radiological medical images only.
The deployment environment is recommended to be in a local network with an existing hospitalgrade IT system in place. EFAI PNXXR should be installed on a specialized server supporting deep learning processing. The configurations are only being operated by the manufacturer:
- · Local network setting of input and output destinations;
Here's a breakdown of the acceptance criteria and study details for EFAI ChestSuite XR Pneumothorax Assessment System, based on the provided FDA 510(k) summary:
Acceptance Criteria and Device Performance
1. Acceptance Criteria Table and Reported Device Performance
| Metric | Acceptance Criteria | Reported Device Performance (EFAI PNXXR) |
|---|---|---|
| Sensitivity | Lower bound of 95% CI should exceed 0.8 | 0.97 (95% CI=0.94-0.99) |
| Specificity | Lower bound of 95% CI should exceed 0.8 | 0.98 (95% CI=0.96-0.99) |
| AUC | Not explicitly stated as an acceptance criterion threshold, but reported for effectiveness comparison. | 0.99 (95% CI=0.98-1.00) |
| Processing Time | Comparable with the predicate device (red dot™) | 23.3 seconds (95% CI=[23.2, 23.4]) compared to predicate's 29.3 seconds |
2. Sample Size and Data Provenance for Test Set
- Sample Size: 800 anonymized Chest X-ray images.
- Data Provenance: Retrospective, multi-center study. Data was collected from 3 institutions in the US and 1 institution outside the US (OUS). The dataset was explicitly stated as not being used for model development or analytical validation.
3. Number of Experts and Qualifications for Ground Truth
- Number of Experts: Three.
- Qualifications of Experts: US board-certified radiologists.
4. Adjudication Method for Test Set
- Adjudication Method: Majority agreement among the three board-certified radiologists. (This generally implies a 2-out-of-3 consensus.)
5. Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study
- Was an MRMC study done? No, an MRMC comparative effectiveness study was not reported. The study focused on the standalone performance of the algorithm against a defined ground truth and compared its performance metrics (sensitivity, specificity, AUC, processing time) to a predicate device.
6. Standalone Performance (Algorithm Only)
- Was a standalone performance study done? Yes, a retrospective, blinded, multicenter study was performed to establish the standalone performance of EFAI PNXXR. The study compared the device's pneumothorax classification performance and processing time against the predicate device (red dot™).
7. Type of Ground Truth Used
- Type of Ground Truth: Expert consensus. Specifically, the reference standard (ground truth) was generated by the majority agreement between the three board-certified radiologists.
8. Sample Size for Training Set
- The sample size for the training set is not specified in the provided document. The document mentions that the test set was "Neither of the datasets were used as part of the EFAI PNXXR model development or analytical validation testing," implying a separate (and unquantified) training dataset.
9. How Ground Truth for Training Set Was Established
- The method for establishing ground truth for the training set is not specified in the provided document. It only states that the test set (from section 2 above) was not part of the model development.
§ 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.