(159 days)
Lunit INSIGHT CXR Triage 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). Lunit INSIGHT CXR Triage 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, Lunit INSIGHT CXR Triage does not send a proactive alert directly to the appropriately trained medical specialists. Lunit INSIGHT CXR Triage is not intended to direct attention to specific portions of an image. Its results are not intended to be used on a standalone basis for clinical decision-making.
Lunit INSIGHT CXR Triage is a radiological computer-assisted prioritization software that utilizes AI-based image analysis algorithms to identify pre-specified critical findings (pleural effusion and/or pneumothorax) on frontal chest X-ray images and flag the images in the PACS/workstation to enable prioritized review by the appropriately trained medical specialists who are qualified to interpret chest radiographs. The software does not alter the order or remove cases from the reading queue.
Chest radiographs are automatically received from the user's image system (e.g. Picture Archiving and Communication System (PACS)) or other radiological imaging equipment (e.g. X-ray systems) and processed by the Lunit INSIGHT CXR Triage for analysis. Following receipt of chest radiographs, the software device de-identifies a copy of each chest radiographs in DICOM format (.dcm) and automatically analyzes each image to identify features suggestive of pleural effusion and/or pneumothorax. Based on the analysis result, the software notifies PACS/workstation for the presence of the critical findings as indicating either "flag" or "(blank)". This would allow the appropriately trained medical specialists to group suspicious exams together that may potentially benefit for their prioritization. Chest radiographs without an identified anomaly are placed in the worklist for routine review, which is the current standard of care. Lunit INSIGHT CXR Triage can flag more than one critical finding per radiograph and the user may select the option to turn on and off notification of critical findings (pleural effusion and pneumothorax).
When deployed on other radiological imaging equipment, Lunit INSIGHT CXR Triage automatically runs after image acquisition. It prioritizes and displays the analysis result through the worklist interface of PACS/workstation. Moreover, the analysis result can also be provided in the form of DICOM files containing information on the presence of suspicious radiologic findings. In parallel, the algorithms produce an on-device notification indicating which cases were prioritized by Lunit INSIGHT CXR Triage in PACS. The on-device notification does not provide any diagnostic information and it is not intended to inform any clinical decision, prioritization, or action to the technologist.
Lunit INSIGHT CXR Triage works in parallel to and in conjunction with the standard care of workflow; therefore, the user enables to review the study containing critical findings earlier than others. As a passive notification for prioritization-only software tool within standard of care workflow, the software does not send a proactive alert directly to the appropriately trained medical specialists who are qualified to interpret chest radiographs. Lunit INSIGHT CXR Triage is not intended to direct attention to specific portions or anomalies of an image and it should not be used on a stand-alone basis for clinical decision-making.
In parallel, an on-device, technologist notification is generated 15 minutes after interpretation by the user, indicating which cases were prioritized by Lunit INSIGHT CXR Triage in PACS. The technologist notification is contextual and does not provides any diagnostic information. The ondevice, technologist notification is not intended to inform any clinical decision, prioritization, or action.
Here's a summary of the acceptance criteria and study details for the Lunit INSIGHT CXR Triage device:
1. Table of Acceptance Criteria and Reported Device Performance
| Feature/Metric | Acceptance Criteria | Reported Device Performance (Clinical Study) |
|------------------------------------------------|--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
| Pleural Effusion Detection | | |
| ROC AUC | > 0.95 | 0.9686 (95% CI: 0.9547 - 0.9824) |
| Sensitivity (Lower Bound) | > 0.85 | 89.86% (95% CI: 86.72 - 93.00) |
| Specificity (Lower Bound) | > 0.85 | 93.48% (95% CI: 91.06 - 95.91) |
| Pneumothorax Detection | | |
| ROC AUC | > 0.95 | 0.9630 (95% CI: 0.9521 - 0.9739) |
| Sensitivity (Lower Bound) | > 0.85 | 88.92% (95% CI: 85.60 - 92.24) |
| Specificity (Lower Bound) | > 0.85 | 90.51% (95% CI: 88.18 - 92.83) |
| Device Performance Time (average) | Comparable to cleared commercial products (HealthCXR (Zebra, K192320) and red dot™ (Behold.AI, K161556)) Considering the primary predicate (HealthCXR) had a delay of ~22 seconds for image transfer, computation, and results transfer, and Critical Care Suite had an average of 42 seconds for acquisition, annotation, processing, and transfer, comparably shorter times would be favorable. | Pleural Effusion: 20.76 seconds (95% CI: 20.23 - 21.28) |
| | | Pneumothorax: 20.45 seconds (95% CI: 19.99 - 20.92) |
| Nonclinical Testing (Standalone Performance) | ROC AUC > 0.95, Sensitivity > 0.80, Specificity > 0.80 | Pleural Effusion: ROC AUC: 0.9864 (95% CI: 0.9815 - 0.9913), Sensitivity: 94.29%, Specificity: 95.72% |
| | | Pneumothorax: ROC AUC: 0.9973 (95% CI: 0.9955 - 0.9992), Sensitivity: 96.08%, Specificity: 99.14% |
2. Sample Size Used for the Test Set and Data Provenance
-
Clinical Studies (Pivotal Studies):
- Total Sample Size: 1,708 anonymized chest radiographs.
- 754 cases for pleural effusion.
- 954 cases for pneumothorax.
- Data Provenance:
- NIH chest X-ray dataset (represents the US population).
- India dataset collected from multiple institutions in India (6 sites for pleural effusion and 3 sites for pneumothorax).
- Type: Retrospective (implied by "anonymized chest radiographs collected from datasets").
- Total Sample Size: 1,708 anonymized chest radiographs.
-
Nonclinical Internal Validation Test (for standalone performance validation):
- Total Sample Size: 1,385 images.
- Data Provenance: Not explicitly stated, but performed "internally" by the company. It's likely a mix of internal data or data acquired for internal development.
- Type: Retrospective (implied by "images were collected").
3. Number of Experts Used to Establish the Ground Truth for the Test Set and Qualifications
- Clinical Studies (Pivotal Studies): Not explicitly stated how many experts established the ground truth for the clinical pivotal studies.
- Nonclinical Internal Validation Test: "highly experienced board-certified radiologists" were used to classify images into positive and negative groups. The specific number of radiologists is not mentioned.
4. Adjudication Method for the Test Set
- The document does not explicitly describe an adjudication method (e.g., 2+1, 3+1). For the Nonclinical Internal Validation Test, it states "classified into positive and negative groups by highly experienced board-certified radiologists," which might imply a consensus or single-reader approach, but an explicit adjudication method is not detailed.
5. If a Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study Was Done
- No, a Multi-Reader Multi-Case (MRMC) comparative effectiveness study was not done or reported in this document. The studies primarily focused on the standalone performance of the Lunit INSIGHT CXR Triage device and comparison to predicate device performance metrics rather than human reader improvement with AI assistance.
6. If a Standalone (i.e., algorithm only without human-in-the-loop performance) Was Done
- Yes, standalone performance was evaluated:
- An internal nonclinical validation test was conducted specifically "to assess the standalone performance of Lunit INSIGHT CXR Triage."
- The clinical pivotal studies also reported standalone performance metrics (ROC AUC, sensitivity, specificity) for the device.
7. The Type of Ground Truth Used
- For both the nonclinical internal validation test and the clinical pivotal studies, the ground truth was established by expert consensus/interpretation, specifically by "highly experienced board-certified radiologists" for the internal validation. For the clinical studies, "positive" cases contained at least one target finding, and "negative" cases did not.
8. The Sample Size for the Training Set
- The document does not provide the sample size for the training set. It only discusses the validation/test sets used for performance evaluation.
9. How the Ground Truth for the Training Set Was Established
- The document does not specify how the ground truth for the training set was established, as it does not detail the training process or dataset.
§ 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.