(25 days)
VisAble.IO is a Computed Tomography (CT) and Magnetic Resonance (MR) image processing software package available for use with liver ablation procedures.
VisAble.IO is controlled by the user via a user interface.
VisAble.IO imports images from CT and MR scanners and facility PACS systems for display and processing during liver ablation procedures.
VisAble.IO is used to assist physicians in planning ablation procedures, including identifying ablation targets and virtual ablation needle placement. VisAble.IO is used to assist physicians in confirming ablation zones.
The software is not intended for diagnosis. The software is not intended to predict ablation volumes or predict ablation success.
VisAble.IO is a stand-alone software application with tools and features designed to assist users in planning ablation procedures as well as tools for treatment confirmation. The use environment for VisAble.IO is the Operating Room and the hospital healthcare environment such as interventional radiology control room.
VisAble.IO has five distinct workflow steps:
- Data Import
- . Anatomic Structures Segmentation (Liver, Hepatic Vein, Portal Vein, Ablation Target)
- . Instrument Placement (Needle Planning)
- Ablation Zone Segmentation
- . Treatment Confirmation (Registration of Pre- and Post-Interventional Images; Quantitative Analysis)
Of these workflow steps, two (Anatomic Segmentation, and Instrument Placement) make use of the planning image. These workflow steps contain features and tools designed to support the planning of ablation procedures. The other two (Ablation Zone Segmentation, and Treatment Confirmation) make use of the confirmation image volume. These workflow steps contain features and tools designed to support the evaluation of the ablation procedure's technical performance in the confirmation image volume.
Key features of the VisAble.IO Software include:
- . Workflow steps availability
- Manual and automated tools for anatomic structures and ablation zone segmentation
- Overlaying and positioning virtual instruments (ablation needles) and user-selected estimates of the ablation regions onto the medical images
- . Image fusion and registration
- . Compute achieved margins and missed volumes to help the user assess the coverage of the ablation target by the ablation zone
- . Data saving and secondary capture generation
The software components provide functions for performing operations related to image display, manipulation, analysis, and quantification, including features designed to facilitate segmentation of the ablation target and ablation zones.
The software system runs on a dedicated computer and is intended for display and processing, of a Computed Tomography (CT) and Magnetic Resonance (MR), including contrast enhanced images.
The system can be used on patient data for any patient demographic chosen to undergo the ablation treatment.
VisAble.IO uses several algorithms to perform operations to present information to the user in order for them to evaluate the planned and post ablation zones. These include:
- . Segmentation
- . Image Registration
- . Measurement and Quantification
VisAble.IO is intended to be used for ablations with the following ablation instruments:
For needle planning, the software currently supports the following needle models:
- Medtronic: Emprint Antenna 15CM, 20CM, 30CM -
- -NeuWave Medical: PR Probe 15CM, 20CM; PR XT Probe 15CM, 20CM; LK Probe 15CM, 20CM; LK XT Probe 15CM, 20CM
- -H.S. Hospital Service: AMICA Probe 15 CM, 20 CM, 27 CM.
For treatment confirmation (including segmentation and registration), the software is compatible with all ablation devices as these functions are independent from probes/power settings.
Here's a summary of the acceptance criteria and study details for the Techsomed VisAble.IO device, based on the provided text:
1. Table of Acceptance Criteria and Reported Device Performance
| Algorithm | Performance Goal (Acceptance Criteria) | Reported Performance |
|---|---|---|
| CT Processing | ||
| Liver Segmentation | Mean DICE = 0.92 | Mean DICE = 0.98 |
| Ablation Target Segmentation | Mean DICE = 0.70 | Mean DICE = 0.82 |
| Ablation Zone Segmentation | Mean DICE = 0.70 | Mean DICE = 0.88 |
| Liver Vessels Segmentation | Mean DICE = 0.70 | Mean DICE = 0.72 |
| MR Processing | ||
| Liver Segmentation | Mean DICE = 0.92 | Mean DICE = 0.93 |
| Ablation Target Segmentation | Mean DICE = 0.70 | Mean DICE = 0.76 |
| Image Registration | ||
| Pre-ablation CT to Post Ablation CT Image Registration | MCD* = 6.06 mm | MCD* = 4.09 mm |
| Pre-ablation MR to Post-ablation CT Image Registration | MCD* = 6.06 mm | MCD* = 4.72 mm |
| Pre-ablation MR to Pre-ablation CT Image Registration | MCD* = 7.90 mm | MCD* = 5.10 mm |
*MCD = Mean Corresponding Distance
Note on Segmentation and Registration Accuracy: The document explicitly states:
- "The use of the segmentation tools to achieve a satisfactory delineation of ablation target or ablation zone is a user operation and the clinical accuracy of segmentation is the responsibility of the user and not a VisAble.IO function."
- "Final accuracy of registration is dependent on user assessment and manual modification of the registration prior to acceptance, and not a VisAble.IO function."
This suggests that while the algorithms perform well against the statistical metrics, the final clinical accuracy is attributed to the user.
2. Sample Sizes Used for the Test Set and Data Provenance
| Algorithm | N (Sample Size) | Data Provenance (Countries/Regions) | Retrospective/Prospective |
|---|---|---|---|
| CT Processing | |||
| Liver Segmentation | 50 | US: 32, OUS: 18 | Not specified (implied retrospective from clinical sites) |
| Ablation Target Segmentation | 59 | US: 30, OUS: 29 | Not specified (implied retrospective from clinical sites) |
| Ablation Zone Segmentation | 59 | US: 30, OUS: 29 | Not specified (implied retrospective from clinical sites) |
| Liver Vessels Segmentation | 100 | US: 72, OUS: 28 | Not specified (implied retrospective from clinical sites) |
| MR Processing | |||
| Liver Segmentation | 25 | US: 25 | Not specified (implied retrospective from clinical sites) |
| Ablation Target Segmentation | 50 | US: 46, OUS: 4 | Not specified (implied retrospective from clinical sites) |
| Image Registration | |||
| Pre-ablation CT to Post Ablation CT Image Registration | 46 | US: 13, OUS: 33 | Not specified (implied retrospective from clinical sites) |
| Pre-ablation MR to Post-ablation CT Image Registration | 25 | US: 25 | Not specified (implied retrospective from clinical sites) |
| Pre-ablation MR to Pre-ablation CT Image Registration | 18 | US: 14, OUS: 4 | Not specified (implied retrospective from clinical sites) |
3. Number of Experts Used to Establish Ground Truth for the Test Set and Their Qualifications
The document does not explicitly state the "number of experts used to establish the ground truth for the test set" or their specific "qualifications." It generally refers to "performance data demonstrate that the VisAble.IO (V 1.4) is as safe and effective as the cleared VisAble.IO (K223693)," but does not detail the specific ground truth generation process for the reported performance metrics.
4. Adjudication Method for the Test Set
The document does not specify an adjudication method (e.g., 2+1, 3+1, none) for the test set.
5. If a Multi Reader Multi Case (MRMC) Comparative Effectiveness Study Was Done, and Effect Size
No MRMC comparative effectiveness study is mentioned in the provided text, nor is there any discussion of human reader improvement with or without AI assistance.
6. If a Standalone (Algorithm Only Without Human-in-the-Loop Performance) Was Done
Yes, the performance data presented in the table (DICE scores, MCD) are for the algorithms themselves, indicating a standalone performance evaluation. The document highlights that "VisAble.IO uses several algorithms to perform operations to present information to the user in order for them to evaluate the planned and post ablation zones," and then presents the algorithmic validation results. However, it also clarifies that the final clinical accuracy of segmentations and registrations is dependent on user actions.
7. The Type of Ground Truth Used
The ground truth for the algorithmic performance (e.g., DICE scores for segmentation, MCD for registration) is implicitly expert-derived segmentation and registration. While the document doesn't explicitly detail the process, DICE scores and Mean Corresponding Distances are calculated by comparing algorithmic outputs to a pre-established "true" segmentation or correspondence, which in medical imaging is typically generated by human experts (e.g., radiologists, experienced technicians).
8. The Sample Size for the Training Set
- CT Processing - Liver Segmentation Algorithm: N = 1091 contrast-enhanced CT images
- CT Processing - Liver Vessel Segmentation Algorithm: N = 393 contrast-enhanced CT images
- MR Processing - Liver Segmentation AI algorithm: N = 418 MR images
9. How the Ground Truth for the Training Set Was Established
The document provides details on the characteristics of the training datasets but does not explicitly state how the ground truth for these training sets was established. It describes the data as "contrast-enhanced CT images taken for diagnostic reading" or "MR images taken for diagnostic reading," suggesting that these were real-world clinical images, but the manual annotation or expert review process for creating the ground truth for training is not 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).