(155 days)
EFAI CHESTSUITE XR MALPOSITIONED ETT ASSESSMENT SYSTEM (EFAI ETTXR) is a radiological computer-aided triage and notification software indicated for use in the analysis of chest X-ray (CXR) images in adults. The device is intended to assist hospital networks and appropriately trained medical specialists in workflow triage by flagging and communicating suspected positive cases of vertically malpositioned endotracheal tube (ETT) in relation to the carina. Findings are flagged when the ETT distal tip is assessed as being more than 7 cm above the carina, less than 3 cm above the carina, or when it is below the carina (i.e in the right or left mainstem bronchus). The device assesses solely the vertical position of the ETT distal tip relative to the carina, does not factor patient positioning, and cannot detect esophageal intubation. The device is tested in the single lumen endotracheal tube, while it may trigger a false prioritization alert in the case of properly positioned double lumen ETT.
EFAI ETTXR analyzes cases using algorithms to identify suspected malpositioned ETT findings. It makes case-level output available to a PACS/workstation for worklist prioritization or triage. EFAI ETTXR is not intended to direct attention to specific portions of an image or to 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 malpositioned ETT or otherwise preclude clinical assessment of chest radiographs.
EFAI CHESTSUITE XR MALPOSITIONED ETT ASSESSMENT SYSTEM (EFAI ETTXR) is a radiological computer-assisted triage and notification software system. The software uses deep learning techniques to automatically analyze chest radiographs and alerts the PACS/RIS workstation once images with features suggestive of malpositioned ETT are identified.
Through the use of EFAI ETTXR, a radiologist is able to review studies with features suggestive of malpositioned ETT 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 chest radiographs. The device aims to aid in prioritization and triage of radiological medical images only.
Here's a breakdown of the acceptance criteria and the study details for the EFAI ETTXR device, based on the provided document:
Acceptance Criteria and Reported Device Performance
| Acceptance Criteria | Reported Device Performance | Comments |
|---|---|---|
| Primary Endpoints | ||
| Sensitivity >= 80% | 0.890 (95% CI: 0.846-0.923) | Meets acceptance criteria. |
| Specificity >= 80% | 0.935 (95% CI: 0.909-0.954) | Meets acceptance criteria. |
| Secondary Endpoint | ||
| System processing time (less than pre-specified goal) | 2.49 minutes (95% CI: 2.43-2.56 minutes) on average | Meets acceptance criteria (significantly less than goal, though the goal itself is not explicitly stated in minutes). |
Study Details
1. Sample Size Used for the Test Set and Data Provenance:
* Sample Size: 940 studies (each patient included only one study).
* Data Provenance: Retrospective, consecutively collected from multiple clinical sites across the United States. None of the studies were used in model development or analytical validation.
2. Number of Experts Used to Establish the Ground Truth for the Test Set and Qualifications:
* Number of Experts: Three.
* Qualifications: U.S. board-certified radiologists.
3. Adjudication Method for the Test Set:
* Method: Majority agreement among the three U.S. board-certified radiologists.
* Resulting Ground Truth: 259 positive cases for malpositioned ETT, 681 negative cases (316 correctly positioned ETTs, 365 with no ETT).
4. If a Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study was Done:
* No, an MRMC comparative effectiveness study involving human readers with and without AI assistance was not explicitly described in this document. The study described is a standalone performance validation of the AI model.
5. If a Standalone (Algorithm Only) Performance Study Was Done:
* Yes, a standalone performance validation study was done. The document states: "The observed results of the standalone performance validation study demonstrated that EFAI ETTXR by itself, in the absence of any interaction with a clinician, can provide case-level notifications with features suggestive of malpositioned ETT with satisfactory results."
6. The Type of Ground Truth Used:
* Expert Consensus: The ground truth was established by the majority agreement of three U.S. board-certified radiologists.
7. The Sample Size for the Training Set:
* The document does not specify the exact sample size for the training set. It mentions that "None of the studies [in the test set] was used as part of the EFAI ETTXR model development or analytical validation testing," implying a separate training set was used, but its size is not provided.
8. How the Ground Truth for the Training Set Was Established:
* The document does not explicitly state how the ground truth for the training set was established. It only implies the use of "deep learning techniques" and a "database of images" for the algorithm. It's common in AI development studies for the training set ground truth to also be established by expert review, but this is not detailed for EFAI ETTXR's training data.
§ 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.