(202 days)
ImmersiveTouch is intended for use as a software interface and image segmentation system for the transfer of medical imaging information to an output file. ImmersiveTouch is also intended for measuring and treatment planning. ImmersiveTouch output can be used for the fabrication of physical replicas of the output file using traditive manufacturing methods. The physical replicas generated from digital output files are not for diagnostic purpose.
ImmersiveTouch should be used in conjunction with expert clinical judgment.
ImmersiveTouch is a stand-alone modular software package that allows user to import, visualize and segment medical images to create accurate 3D representations. The 3D models can be utilized in ImmersiveTouch for measuring, treatment planning and output file to be further used as an input for additive manufacturing.
This modular package includes, but is not limited to the following functions:
- Importing medical images in DICOM format for visualization, segmentation, and analysis.
- Viewing of medical imaging data in the axial. coronal and sagittal views.
- Calculating a digital 3D model and editing the model.
- Measurements on 3D models.
- Treatment Planning on 3D models with cutting planes and the ability to move cut objects.
- File export for 3D Printing.
Here's a breakdown of the acceptance criteria and study information for ImmersiveTouch, based on the provided text:
1. Table of Acceptance Criteria and Reported Device Performance
The document does not explicitly present a table of acceptance criteria with numerical targets. Instead, it describes compliance with "set acceptance criteria" and "pre-established specifications."
| Acceptance Criteria Category | Reported Device Performance |
|---|---|
| Software Verification & Validation | Conformity to pre-established specifications and acceptance criteria. All independent software subsystems, interfaces, and integrated systems verified and validated against defined requirements and user needs. |
| Measurements Study (Inter-user variability) | All measurements fell within the set acceptance criteria (demonstrating consistency between different users). |
| Segmentation Study (Visual Comparison) | Similarity in all models (between subject and predicate device, validated by subject matter experts). |
| Output Study (Exported Model Comparison) | All measurements fell within the set acceptance criteria (demonstrating consistency between exported models from subject and predicate device). |
2. Sample Size Used for the Test Set and Data Provenance
The document does not specify the exact sample size for the test sets in any of the studies (Measurements, Segmentation, or Output). It also does not explicitly state the data provenance (e.g., country of origin, retrospective or prospective) for the test data.
3. Number of Experts Used to Establish the Ground Truth for the Test Set and Qualifications of Those Experts
- Segmentation Study: Ground truth was "validated by subject matter experts." The number and qualifications of these experts are not provided.
4. Adjudication Method for the Test Set
The document does not specify an adjudication method (e.g., 2+1, 3+1, none) for any of the studies. For the segmentation study, it only mentions validation by subject matter experts without detailing the process.
5. If a Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study was done, and the effect size of how much human readers improve with AI vs without AI assistance
There is no mention of an MRMC comparative effectiveness study involving human readers and AI assistance. The studies described are focused on device performance in terms of measurements, segmentation, and output consistency, primarily comparing the ImmersiveTouch device to its predicate.
6. If a Standalone (i.e. algorithm only without human-in-the-loop performance) was done
The provided text focuses on the standalone performance of the ImmersiveTouch software system (e.g., segmentation, measurements, output generation). The clinical use of ImmersiveTouch is stated to be "in conjunction with expert clinical judgment," implying it's an aid rather than a fully autonomous diagnostic tool for clinical decision-making. The studies describe the inherent performance of the software without directly evaluating the human-AI interaction.
7. The Type of Ground Truth Used
- Segmentation Study: The ground truth for the segmentation study was established by expert visual validation against the predicate device's models.
- Measurements Study: The "ground truth" for the measurements study appears to be internal consistency, comparing inter and intra-user measurements to "set acceptance criteria," implying a defined range of acceptable variability.
- Output Study: The "ground truth" for the output study again seems to be internal consistency, comparing exported models from both software systems to "set acceptance criteria."
8. The Sample Size for the Training Set
The document does not provide any information regarding the sample size used for training for any algorithms within the ImmersiveTouch software. Given that the software focuses on "image segmentation system," it's plausible it uses machine learning models, but training data details are absent.
9. How the Ground Truth for the Training Set Was Established
Since no information on training sets is provided, there is no information on how the ground truth for any potential 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).