(196 days)
AutoSeg-H is medical imaging software that is intended to professionals with tools to aid them in reading, interpreting, reporting, and treatment planning. AutoSeg-H accepts DICOM compliant medical images acquired from imaging device (Computed Tomography).
AutoSeg-H provides the tools for specific analysis applications which provide custom UI, targeted measurements and reporting functions including:
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Coronary Artery Analysis for CT coronary arteriography images: which is intended for the qualitative and quantitative analysis of coronary arteries.
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Valve Analysis: which is intended for automatic extraction of the heart and aorta regions, automatic detection of the contour of the aorta and valves, measurement of the vicinity of the valves.
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4-Chamber Analysis: which is intended for automatic extraction of the left atrium, and right ventricle from CT.
AutoSeg-H(V1.0.0.01) is software that is intended to provide trained medical professionals with tools to aid them in reading, interpreting, and treatment planning. AutoSeg-H(V1.0.0.01) runs on Windows standalone installed on a commercial general-purpose Windows-compatible computer and accepts DICOM compliant medical images acquired from a CT. The AutoSeg-H is not connected to a PACS system directly but retrieves saved image data from a computer. Image data obtained from the computer are used for display, image processing, analysis, etc. AutoSeg-H cannot be used to interpret Mammography images.
The main functions of AutoSeg-H are shown below.
- Coronary Artery Analysis (CT) -
- Aortic Valve Analysis (CT) -
- -4-Chamber Analysis (CT)
The provided text details the 510(k) summary for the AutoSeg-H device. As per the document, no clinical studies were performed. The non-clinical performance testing focused on verifying that the software met performance test criteria, functioned without errors, and met standards for estimating quantitative measurement and segmentation algorithm errors. The details regarding acceptance criteria, study design, and ground truth establishment for a standalone algorithm performance or comparative effectiveness study are not explicitly provided in the furnished document.
However, based on the Non-Clinical Test Summary, we can infer some aspects of the performance evaluation.
Here's an attempt to answer your questions based only on the provided text, noting where information is explicitly stated or absent:
1. A table of acceptance criteria and the reported device performance
The document states: "Through the performance test, it was confirmed that AutoSeg-H meets all performance test criteria and that all functions work without errors. Test results support the conclusion that actual device performance satisfies the design intent and is equivalent to its predicate device."
- Acceptance Criteria: While specific numerical acceptance criteria (e.g., minimum accuracy, sensitivity, specificity, or error tolerances) are not explicitly detailed in the provided text, the criteria are broadly described as:
- Meeting "all performance test criteria."
- Ensuring "all functions are operating without errors."
- Meeting "test standards which estimation of the quantitative measurement error."
- Meeting "test standards which estimation of segmentation algorithm error."
- Reported Device Performance: The reported performance is a qualitative statement of meeting these unquantified criteria. No specific metrics or numerical results are provided in this summary.
| Acceptance Criterion (Inferred from text) | Reported Device Performance |
|---|---|
| Meets all performance test criteria | Confirmed to meet. |
| All functions operate without errors | Confirmed to work without errors. |
| Meets standards for quantitative measurement error estimation | Confirmed to meet. |
| Meets standards for segmentation algorithm error estimation | Confirmed to meet. |
| Satisfies design intent and is equivalent to predicate device | Confirmed to satisfy design intent and equivalent predicate. |
2. Sample size used for the test set and the data provenance (e.g. country of origin of the data, retrospective or prospective)
- Sample Size: The document does not specify the sample size (number of cases or images) used for the non-clinical performance testing.
- Data Provenance: The document does not specify the country of origin of the data or whether it was retrospective or prospective. It only states that the device accepts "DICOM compliant medical images acquired from imaging device (Computed Tomography)."
3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts (e.g. radiologist with 10 years of experience)
The document does not provide information on the number or qualifications of experts used to establish ground truth for the non-clinical test set. The testing appears to be primarily software verification against pre-defined performance and error estimation standards.
4. Adjudication method (e.g. 2+1, 3+1, none) for the test set
The document does not describe any adjudication method for establishing ground truth, as it does not mention human readers or expert consensus for ground truth generation in the non-clinical testing. The "Test results were reviewed by designated technical professionals," but this refers to reviewing the software test outcomes, not establishing medical ground truth for image interpretation.
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
The document explicitly states: "No clinical studies were considered necessary and performed." Therefore, no MRMC comparative effectiveness study was done.
6. If a standalone (i.e. algorithm only without human-in-the-loop performance) was done
Yes, the non-clinical performance testing described seems to be a standalone (algorithm only) evaluation. The tests were conducted to "confirm that AutoSeg-H meets all performance test criteria and all functions are operating without errors," and to "confirm that AutoSeg-H meets test standards which estimation of the quantitative measurement error" and "segmentation algorithm error." This suggests an evaluation of the algorithm's output against defined standards or computational accuracy, rather than human-in-the-loop performance.
7. The type of ground truth used (expert consensus, pathology, outcomes data, etc)
The document largely discusses testing against "performance test criteria" and "test standards which estimation of the quantitative measurement error" and "segmentation algorithm error." This implies that the 'ground truth' for the non-clinical tests would involve computational benchmarks, reference measurements, or predefined correct segmentations, rather than medical ground truth established by expert consensus, pathology, or outcomes data. The core technology is described as an "advanced active contour algorithm with entropy regularization," suggesting validation against known segmentation accuracy targets.
8. The sample size for the training set
The document does not provide any information regarding the training set's sample size.
9. How the ground truth for the training set was established
The document does not provide any information regarding 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).