(188 days)
DeltaView is intended to generate a secondary residual image based on a current and prior chest x-ray image of the same patient, resulting in improved detection of lung nodules that have changed between the two examinations. The DeltaView image provides adjunctive information and is not a substitute for the original PA/AP image. This device is intended to be used by trained professionals, such as physicians and radiologists, on patients with risk of having lung nodules, and is not intended to be used on pediatric patients.
DeltaView is a dedicated post-processing application which registers current and prior chest exams to provide an image that shows areas of change.
Here's a breakdown of the acceptance criteria and study details for the DeltaView™ device based on the provided text:
Acceptance Criteria and Device Performance
| Acceptance Criteria (Study Objective) | Reported Device Performance |
|---|---|
| Radiologist's results when using DeltaView images are superior to results when using standard prior and current AP/PA x-ray image pairs alone. | Area under the Localized Receiver Operating Characteristic (LROC) curve (AUCLROC) increased from 0.477 (Unaided) to 0.536 (with DeltaView) (12.4% increase). |
| The upper 95% confidence limit on the differences in the area under the DeltaView LROC curve (AUCDV) subtracted from the area under the Unaided LROC curve (AUCUA) is greater than or equal to 0.0. | The text states "The research hypothesis indicating superiority was that the upper 95% confidence limit... is greater than or equal to 0.0," and the reported AUCLROC increase supports this. |
Study Details
-
Sample sizes used for the test set and the data provenance:
- The document does not explicitly state the sample size (number of cases or images) used for the test set.
- The document does not specify the data provenance (e.g., country of origin, retrospective or prospective).
-
Number of experts used to establish the ground truth for the test set and the qualifications of those experts:
- The document does not specify the number of experts or their qualifications for establishing ground truth.
-
Adjudication method (e.g., 2+1, 3+1, none) for the test set:
- The document does not specify an adjudication method for the test set.
-
If a multi-reader multi-case (MRMC) comparative effectiveness study was done, If so, what was the effect size of how much human readers improve with AI vs without AI assistance:
- Yes, an MRMC reader study was done.
- Effect size: The area under the LROC curve (AUCLROC) "increased from 0.477 to 0.536 (12.4%)" when radiologists were aided by DeltaView compared to unaided interpretation. This indicates a 12.4% improvement in the AUCLROC.
-
If a standalone (i.e., algorithm only without human-in-the loop performance) was done:
- The document does not state that a standalone (algorithm-only) performance study was conducted. The study described focuses on human-in-the-loop performance with the device as an adjunct.
-
The type of ground truth used (expert consensus, pathology, outcomes data, etc.):
- The type of ground truth used is not explicitly stated. However, the study evaluated "the detection of actionable lung nodules," implying that a determination of the presence or change of such nodules constituted the ground truth.
-
The sample size for the training set:
- The document does not provide information on the sample size used for the training set.
-
How the ground truth for the training set was established:
- The document does not provide information on how the ground truth for the training set was established.
§ 892.2050 Medical image management and processing system.
(a)
Identification. A medical image management and processing system is a device that provides one or more capabilities relating to the review and digital processing of medical images for the purposes of interpretation by a trained practitioner of disease detection, diagnosis, or patient management. The software components may provide advanced or complex image processing functions for image manipulation, enhancement, or quantification that are intended for use in the interpretation and analysis of medical images. Advanced image manipulation functions may include image segmentation, multimodality image registration, or 3D visualization. Complex quantitative functions may include semi-automated measurements or time-series measurements.(b)
Classification. Class II (special controls; voluntary standards—Digital Imaging and Communications in Medicine (DICOM) Std., Joint Photographic Experts Group (JPEG) Std., Society of Motion Picture and Television Engineers (SMPTE) Test Pattern).