(187 days)
The HealthPPT device is a software workflow tool designed to aid the clinical assessment of adult frontal Chest X-Ray cases with features suggestive of pneumoperitoneum in the medical care environment. HealthPPT 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. HealthPPT is not intended to direct attention to anomalies other than pneumoperitoneum. Notifications include compressed preview images that are meant for informational purposes only and not intended for diagnostic use beyond notification. The device does not alter the original medical image and is not intended to be used as a diagnostic device. Its results are not intended to be used on a stand-alone basis for clinical decision-making nor is it intended to rule out pneumoperitoneum or otherwise preclude clinical assessment of X-Ray cases.
The HealthPPT solution is a software product that automatically identifies suspected findings on chest x-rays (e.g. pneumoperitoneum) and notifies PACS/workstation of the presence of this critical finding in the scan. This notification allows for prioritization of the identified scan and assists clinicians in viewing the prioritized scan before others. The device aim is to aid in prioritization and triage of radiological medical images only.
The software is automatic and is capable of analyzing PA or AP chest x-rays. If a suspected finding is found in a scan, the alert is automatically sent to the PACS/workstation used by the radiologist or to a standalone desktop application in parallel with the ongoing standard of care. The PACS/workstation prioritizes and displays the study through its worklist interface. The ZebrAInsight standalone application includes a compressed preview image meant for informational purposes only and is not intended for diagnostic use.
The HealthPPT device works in parallel to and in conjunction with the standard care of workflow. After a chest x-ray has been performed, a copy of the study is automatically retrieved and processed by the HealthPPT device performs the analysis of the study and returns a notification about the relevant pathology to the PACS/workstation for prioritization. The clinician is then able to review the study earlier than in standard of care workflow.
The software does not recommend treatment or provide a diagnosis. It is meant as a tool to assist in improved workload prioritization of critical cases. The final diagnosis is provided by a radiologist after reviewing the scan itself.
The following modules compose the HealthPPT software for Pneumoperitoneum:
Data input and validation: Following retrieval of a study, the validation feature assessed the input data (i.e. age, modality, view) to ensure compatibility for processing by the algorithm.
Pneumoperitoneum algorithm: Once a study has been validated, the algorithm analyzes the frontal chest x-ray for detection of suspected finding suggestive of pneumoperitoneum.
IMA Integration feature: The study analysis and the results of a successful study analysis is provided to IMA, to then be sent to the PACS/workstation for prioritization.
Error codes feature: In the case of a study failure during data validation or the analysis by the algorithm, an error is provided to the system.
Here's a breakdown of the acceptance criteria and study details for the HealthPPT device, based on the provided document:
1. Table of Acceptance Criteria and Reported Device Performance
| Metric | Acceptance Criterion (implicitly met if "reached performance goal") | Reported Device Performance |
|---|---|---|
| Overall Accuracy | Comparable to predicate device & exceeds technical method | AUC: 96.75% (95% CI: [94.28%, 99.21%]) |
| Operating Point 1 (Balanced Sensitivity & Specificity) | Reached performance goal | Sensitivity: 92.52% (95% CI: [85.94%;96.16%]), Specificity: 92.66% (95% CI: [86.18%;96.23%]) |
| Operating Point 2 (High Specificity) | Reached performance goal | Sensitivity: 80.37% (95% CL: [71.85%;86.79%]), Specificity: 97.25% (95% CI: [92.22%;99.06%]) |
| Processing Time | Lower than predicate device | Average performance time: 4.78 seconds |
2. Sample Size and Data Provenance for Test Set
- Sample Size: 216 anonymized Chest X-ray cases.
- Data Provenance: Retrospective cohort from the USA and OUS (Outside the US).
- 107 cases positive for Pneumoperitoneum.
- 109 cases negative for Pneumoperitoneum, including confounding imaging factors.
3. Number of Experts and Qualifications for Ground Truth
- Number of Experts: Three.
- Qualifications: US Board-Certified Radiologists.
4. Adjudication Method
The document states the validation data set was "trued (ground truth) by three US Board-Certified Radiologists." It does not explicitly mention an adjudication method like 2+1 or 3+1, but implies consensus among the three radiologists to establish the ground truth.
5. Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study
No, a Multi-Reader Multi-Case (MRMC) comparative effectiveness study was not explicitly mentioned or presented in the document. The study focused on the standalone performance of the HealthPPT device.
6. Standalone Performance Study
Yes, a standalone (algorithm only without human-in-the-loop performance) study was done. The document explicitly states: "The stand-alone detection accuracy was measured on this cohort respective to the ground truth."
7. Type of Ground Truth Used
Expert consensus. The ground truth was established by "three US Board-Certified Radiologists."
8. Sample Size for the Training Set
The document does not specify the sample size used for the training set. It only details the test/validation set.
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. It only details the establishment of ground truth for the validation data set.
§ 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.