(144 days)
qXR-PTX-PE is a radiological computer-assisted triage and notification software that analyzes adult chest X-ray images for the presence of pre-specified suspected critical findings (pleural effusion and/or pneumothorax). qXR-PTX-PE uses an artificial intelligence algorithm to analyze images for features suggestive of critical findings and provides case-level output available in the PACS/workstation for worklist prioritization or triage.
As a passive notification for prioritization-only software tool within standard of care workflow, qXR-PTX-PE does not send a proactive alert directly to the appropriately trained medical specialists. qXR-PTX-PE is not intended to direct attention to specific portions of an image or to anomalies other than pleural effusion and/or pneumothorax. Its results are not intended to be used on a stand-alone basis for clinical decision-making.
qXR-PTX-PE is a radiological computer aided triage and notification software that analyses adult frontal (AP or PA views) CXR images for the presence of pre-specified suspected target conditions (pleural effusion and/or pneumothorax). The algorithm was trained on training data from across the world. The training dataset consisted of 74% of the data from India, 20.04% from the EU, 3.9% from the US, 1.4% from Brazil and 0.63% from Vietnam. The input for qXR-PTX-PE is a frontal chest X-ray (AP and PA view) in digital imaging and communications in medicine (DICOM) format
Chest X-rays are sent to qXR-PTX-PE by the means of transmission within the user's image storage system (e.g., Picture Archiving and Communication System (PACS)) or other radiological imaging equipment (e.g., X-ray systems) and processed by the qXR-PTX-PE for analysis. Following receipt of chest radiographs, the software device automatically analyses each image to detect features suggestive of pneumothorax and/or pleural effusion.
This would allow the appropriately trained medical specialists to group suspicious exams together that may potentially benefit for their prioritization. Chest radiographs without the suspicious findings are placed in the worklist for routine review, which is the standard of care at present. A secondary capture is available for the information on presence of the suspicious findings.
qXR-PTX-PE does not provide any proactive alerts. qXR-PTX-PE is not intended to direct attention to specific portions of the image. The results are not intended to be used on a standalone basis for clinical decision-making nor is it intended to rule out the target conditions or otherwise preclude clinical assessment of X-Ray cases.
Here's a breakdown of the acceptance criteria and the study proving the device meets them, based on the provided text:
1. Table of Acceptance Criteria and Reported Device Performance
The acceptance criteria are implicitly defined by the statement: "The device shows > 95% AUC". The reported performance significantly exceeds this threshold.
| Metric | Acceptance Criteria | qXR-PTX-PE Performance (Pneumothorax) | qXR-PTX-PE Performance (Pleural Effusion) |
|---|---|---|---|
| ROC AUC | > 0.95 | 0.9894 (95% CI: 0.9829 - 0.9980) | 0.9890 (95% CI: 0.9847 - 0.9944) |
| Sensitivity | Not explicitly defined beyond AUC | 94.53% (95% CI: 90.42-97.24) | 96.22% (95% CI: 93.62-97.97) |
| Specificity | Not explicitly defined beyond AUC | 96.36% (95% CI: 94.07-97.95) | 94.90% (95% CI: 93.04-96.39) |
| Performance Time (Notification) | Not explicitly defined, but compared to predicate and other cleared products | 10 seconds (average) | 10 seconds (average) |
2. Sample Size Used for the Test Set and Data Provenance
- Pneumothorax Test Set: 613 scans
- 201 scans with pneumothorax
- 412 scans without pneumothorax
- Pleural Effusion Test Set: 1070 scans
- 344 scans with pleural effusion
- 726 scans without pleural effusion
- Data Provenance: Retrospective. All test data was obtained from various hospitals across the US. Specific regions mentioned include Midwest, Northeast, and West. The test set was intentionally obtained from sites different from the training data sites to ensure independence.
3. Number of Experts Used to Establish Ground Truth for the Test Set and Qualifications
- Number of Experts: 3
- Qualifications: ABR (American Board of Radiology) thoracic radiologists with a minimum of 10 years of experience.
4. Adjudication Method for the Test Set
The provided text states that "The ground truth was established by 3 ABR thoracic radiologists with a minimum of 10 years of experience." It does not specify a particular adjudication method (e.g., 2+1, 3+1, or simple consensus). Without further detail, it's assumed to be a consensus among these three experts, but the exact process of resolving discrepancies is not described.
5. 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 reported. The study focuses purely on the standalone performance of the AI algorithm (qXR-PTX-PE) and compares it to the standalone performance of a predicate device.
6. If a Standalone (i.e., algorithm only without human-in-the-loop performance) was done
Yes, a standalone performance study was done. The reported AUC, sensitivity, and specificity metrics are for the qXR-PTX-PE algorithm acting independently.
7. The Type of Ground Truth Used
The ground truth used was expert consensus (3 ABR thoracic radiologists with a minimum of 10 years of experience).
8. The Sample Size for the Training Set
The exact total sample size for the training set is not specified. However, the text provides the geographical distribution of the training data:
- 74% from India
- 20.04% from the EU
- 3.9% from the US
- 1.4% from Brazil
- 0.63% from Vietnam
9. 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 mentions that the algorithm was "trained on training data from across the world." It can be inferred that expert labeling or clinical diagnoses were likely used, given the nature of a medical imaging AI, but specific details about the process (e.g., number of readers, their qualifications, adjudication) are not provided for the 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.