(91 days)
LVivo platform is intended for non-invasive processing of ultrasound images to detect, measure, and calculate relevant medical parameters of structures and function of patients with suspected disease and Age >18. In addition, it has the ability to provide Quality Score feedback.
The LVivo platform is a software system for automated analysis of ultrasound examinations. Automated analysis of echocardiographic examinations is done using DICOM movies. The LVivo platform supports global and segmental evaluation of the left ventricle (LV) of the heart utilizing the apical views. Global LV function evaluation by ejection fraction (EF) is done based on two of the apical views: four-chamber (4CH) and two-chamber (2CH). Segmental LV function evaluation is done from three apical views 4CH, 2CH and three chamber (3CH) and supports segmental wall motion evaluation and strain. The LVivo platform supports also LV function evaluation from the parasternal views including global and segmental LV function analysis from the Short Axis (SAX) view and the global LV function analysis form the Parasternal Long Axis (PLAX) views.
In addition to the LV analysis, the cardiology toolbox includes a module for automated evaluation of the Right Ventricular function. The LVivo platform includes one additional non-cardiac module for the measurement of the bladder volume.
The LVivo Platform includes also two additional configurations: LVivo Seamless for offline analysis based on automatically selected views and LVivo IQS for real-time quality feedback during image acquisition
Here's an analysis of the provided text to fulfill your request, broken down by the specified information points:
1. A table of acceptance criteria and the reported device performance
The document does not explicitly state "acceptance criteria" for the performance metrics (Specificity, Sensitivity, Accuracy, Correlation) in a pass/fail format prior to presenting the results. Instead, it presents the achieved performance. However, we can infer that the reported values are what the manufacturer considers acceptable for equivalence.
Inferred Acceptance Criteria (Based on Study Results):
| Parameter | Acceptance Criteria (Inferred) | Reported Performance (Main Study) | Reported Performance (Second Validation Set) |
|---|---|---|---|
| Specificity | > 79% | 79% | 82% |
| Sensitivity | > 82% | 82% | 82% |
| Accuracy | > 82% | 82% | 82% |
| Correlation (Pearson) | > 0.89 | 0.89 | 0.856 |
| ICC (GT vs LVivo SWM) | > 0.85 | 0.85 | Not reported for this set |
| Automated Analysis Success Rate | > 84% | 84% (139 out of 166 exams) | Not explicitly stated, 'n=78' implies successful processing |
2. Sample sizes used for the test set and the data provenance (e.g. country of origin of the data, retrospective or prospective)
- Main Test Set:
- Sample Size: 170 echo exams.
- Data Provenance:
- 101 exams from a medical center in the US.
- 69 exams from two medical centers in Israel.
- Nature of Data: Retrospective (implied by the description "The exams were collected..." and subsequent analysis; not explicitly stated as prospective).
- Second Validation Set:
- Sample Size: 101 patients.
- Data Provenance: From a hospital in Taiwan.
- Nature of Data: Retrospective (implied).
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)
- Number of Experts: Three cardiologists.
- Qualifications of Experts: Specializing in echo. (No further details on specific experience or certifications are provided).
4. Adjudication method (e.g. 2+1, 3+1, none) for the test set
- Adjudication Method: "WMSI average across three cardiologists specializing in echo." This indicates a consensus approach where the average of the three experts' interpretations formed the ground truth (e.g., if one expert was an outlier, their reading would still contribute to the average).
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
- MRMC Study: No, a multi-reader multi-case (MRMC) comparative effectiveness study comparing human readers with AI assistance versus without AI assistance was not conducted or reported.
- The study focuses on the standalone performance of the AI algorithm compared to expert-defined ground truth. It also shows the inter-expert variability (ICC between experts).
6. If a standalone (i.e. algorithm only without human-in-the-loop performance) was done
- Standalone Performance: Yes, the reported performance metrics (Specificity, Sensitivity, Accuracy, Pearson Correlation, ICC) are for the standalone algorithm's performance against the established ground truth, without human intervention or assistance during the assessment.
7. The type of ground truth used (expert consensus, pathology, outcomes data, etc)
- Type of Ground Truth: Expert consensus. Specifically, "WMSI average across three cardiologists specializing in echo."
8. The sample size for the training set
- Training Set Sample Size: The document does not provide the sample size for the training set. It only describes the validation sets.
9. How the ground truth for the training set was established
- Training Set Ground Truth: The document does not provide information on how the ground truth for the training set was established. It only details the ground truth establishment for the test/validation sets.
This analysis is based solely on the provided text. Any information not explicitly stated in the document cannot be inferred.
§ 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).