(85 days)
The AlphaPoint software is a device that allows review, analysis, and interchange of CT chest images. It is intended for use with CT Chest images to assist medical professionals in image analysis. It is not intended to be the primary interpretation. The software provides segmentation and Hounsfield numerical analysis values which are indicative of various substances (i.e., air, lung, soft tissue, fat, water, transudate, exudate, blood, muscle and bone). The user can review, verify and correct the results of the system and generate a report of the findings.
The AlphaPoint system provides a full application framework with integration to PACS using DICOM. The system has the following functions:
- Communicates with PACS to get imaging studies for processing; .
- Activates one or more applications that process the imaging data . and use segmentation and Hounsfield measurements algorithms to find and measure various attributes in the images, and also identify particular slices as references images for the findings ;
- Formats the processing results for each study into a Preliminary . Findings Report
- Sends the results to PACS. .
- The software is written in C++, C# and Matlab. .
The provided text does not contain the detailed performance study results, acceptance criteria, or specific information about the test set (sample size, data provenance, ground truth establishment, or expert qualifications) that would allow for a comprehensive answer to your request.
The document is a 510(k) summary and FDA clearance letter for the AlphaPoint Imaging Software. It primarily focuses on:
- Device Description and Intended Use: What the device does and what it's for.
- Substantial Equivalence: How it compares to a predicate device.
- Verification and Validation Statement: A general statement that the software underwent V&V processes according to company procedures and FDA guidance, including DICOM compliance testing and internal software testing (unit tests, system testing). It mentions a Software Test Description (STD) specifying acceptance criteria and a Software Test Report (STR) documenting results, but these documents themselves are not included or summarized in detail in the provided text.
Therefore, I cannot populate the table or answer most of your specific questions about the performance study.
Here's what can be extracted and what information is missing:
Missing Information:
- Specific Acceptance Criteria: The document states that the STD "describes the test cases for the device, along with its acceptance criteria," but it does not list these criteria or the associated reported device performance.
- Detailed Device Performance: Beyond DICOM compliance, there are no specific metrics (e.g., accuracy, precision, sensitivity, specificity for segmentation or Hounsfield analysis) reported.
- Sample Size for Test Set: Not mentioned.
- Data Provenance (country, retrospective/prospective): Not mentioned.
- Number of Experts/Qualifications for Ground Truth: Not mentioned.
- Adjudication Method: Not mentioned.
- MRMC Comparative Effectiveness Study: No mention of such a study.
- Standalone Performance: While the device acts as an algorithm (software), specific standalone performance metrics (e.g., accuracy of its segmentation compared to ground truth) are not provided.
- Type of Ground Truth: Not mentioned how ground truth was established for "segmentation and Hounsfield numerical analysis" beyond general V&V.
- Sample Size for Training Set: Not mentioned.
- How Ground Truth for Training Set was Established: Not mentioned.
Available Information (or lack thereof, based on your questions):
| Information Point | Details from Provided Text |
|---|---|
| 1. Table of Acceptance Criteria and Reported Device Performance | Cannot be provided. The text states that the "Software Test Description (STD) for the Alphapoint System describes the test cases for the device, along with its acceptance criteria, and the detailed test procedure." It also mentions DICOM compliance testing, which "passed the six DICOM specific test cases." However, no specific performance metrics (e.g., accuracy for segmentation) or detailed acceptance criteria for the core functions (segmentation, Hounsfield analysis) are present in this summary. |
| 2. Sample size used for the test set and the data provenance (e.g. country of origin of the data, retrospective or prospective) | Not specified. The document mentions "test cases for the device" and "validation test runs" but does not detail the size or nature of the dataset used for these tests. |
| 3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts | Not specified. There is no information regarding the establishment of ground truth by experts for any dataset. |
| 4. Adjudication method (e.g. 2+1, 3+1, none) for the test set | Not specified. |
| 5. 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 | No indication that an MRMC comparative effectiveness study was done. The device's intended use states it "is not intended to be the primary interpretation" and "assist medical professionals," suggesting an assistive role, but no study comparing human performance with and without the device is mentioned. |
| 6. If a standalone (i.e. algorithm only without human-in-the loop performance) was done | Implicitly, yes, for certain functional aspects, but no performance metrics are given. The document states the system "Activates one or more applications that process the imaging data and use segmentation and Hounsfield measurements algorithms to find and measure various attributes in the images." Testing for DICOM compliance and internal "unit tests and system testing" would evaluate the algorithm's functionality in a standalone manner. However, specific standalone performance (e.g., accuracy of algorithm segmentation vs. expert ground truth) is not reported. |
| 7. The type of ground truth used (expert consensus, pathology, outcomes data, etc) | Not specified. While the software performs "segmentation and Hounsfield numerical analysis," the method for establishing the "correct" segmentation or Hounsfield values for validation is not described. |
| 8. The sample size for the training set | Not applicable/Not specified. The document does not describe the device as a machine learning/AI model that undergoes a "training" phase with a separate training set. It describes algorithms for segmentation and Hounsfield measurements. If these algorithms are more traditional image processing rather than learned AI models, a training set might not exist in the conventional sense. Even if it were an AI model, the training set size is not mentioned. |
| 9. How the ground truth for the training set was established | Not applicable/Not specified. As above, a "training set" is not mentioned in the context of this device's development as described. |
§ 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).