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510(k) Data Aggregation
(152 days)
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(168 days)
Acorn 3D Software is a modular image processing software intended for use as an interface for visualization of medical images, segmentation, treatment planning, and production of an output file.
The Acorn 3D Segmentation module is intended for use as a software interface and image segmentation system for the transfer of CT or CTA medical images to an output file. Acorn 3D Segmentation is also intended for measuring and treatment planning. The Acorn 3D Segmentation output can also be used for the fabrication of physical replicas of the output file using additive manufacturing methods, Acorn 3DP Models. The physical replica can be used for diagnostic purposes in the field of musculoskeletal and craniomaxillofacial applications.
The Acorn 3D Trajectory Automation module may be used to plan pedicle screw placement in the thoracic and lumbar regions of the spine in pediatric and adult patients.
Acorn 3D Software and 3DP Models should be used in conjunction with expert clinical judgment.
Acorn 3D Software is an image processing software that allows the user to import, visualize and segment medical images, check and correct the segmentations, and create digital 3D models. The models can be used in Acorn 3D Software for measuring, treatment planning and producing an output file to be used for additive manufacturing (3D printing). Acorn 3D Software is structured as a modular package.
This includes the following functionality:
- Importing medical images in DICOM format
- Viewing images and DICOM data
- Selecting a region of interest using generic segmentation tools
- Segmenting specific anatomy using dedicated semi-automatic tools or fully automatic algorithms
- Verifying and editing a region of interest
- Calculating a digital 3D model and editing the model
- Measuring on images and 3D models
- Exporting 3D models to third-party packages
- Planning pedicle screw placement
The Acorn 3D Segmentation module contains both machine learning based auto segmentation as well as semi-automatic and manual segmentation tools. The auto-segmentation tool is only intended to be used for thoracic and lumbar regions of the spine (T1-T12 and L1-L5) and the pelvis (sacrum). Semi-automatic and manual segmentation tools are intended to be used for all musculoskeletal anatomy.
| Automatic | Semi-Automatic | Manual | |
|---|---|---|---|
| Definition | Algorithmic with little or no direct human control | A combination of algorithmic and direct human control | Directly controlled by a human |
| Tool Type | Machine Learning algorithm used to automatically segment individual vertebrae and the pelvis | Algorithmic based tools that do not incorporate machine learning. | Manual tools requiring user input. |
| Anatomical Location(s) | Spinal anatomy:• Thoracic (T1-T12)• Lumbar (L1-L5)• Sacrum | Musculoskeletal & craniomaxillofacial bone:• Short• Long• Flat• Sesamoid• Irregular | Musculoskeletal & craniomaxillofacial bone:• Short• Long• Flat• Sesamoid• Irregular |
Acorn 3DP Model is an additively manufactured physical replica of the virtual 3D model generated in Acorn 3D Segmentation. The output file from Acorn 3D Segmentation is used to additively manufacture the Acorn 3DP Model.
The Acorn 3D Trajectory Automation module contains dedicated fully automatic algorithms for planning pedicle screw trajectories. The algorithms are only intended to be used for the thoracic and lumbar regions of the spine (T1-T12 and L1-L5). The output file from Acorn 3D Trajectory Automation contains information relevant to pedicle screw placement surgery, including entry points, end points, and screw sizes of planned screws.
The provided FDA 510(k) clearance letter describes the Acorn 3D Software, specifically focusing on the new Acorn 3D Trajectory Automation module. However, the document is quite sparse on detailed descriptions of the acceptance criteria and the specifics of the study proving the device meets these criteria. The information below is extracted from the provided text, and where details are missing, it's explicitly stated.
1. Table of Acceptance Criteria and Reported Device Performance
The document mentions "deviations were within the acceptance criteria" without specifying the numerical acceptance criteria themselves. It also doesn't provide specific numerical results of the device's performance, only a qualitative statement of accuracy.
| Acceptance Criteria (Not explicitly stated in numerical terms) | Reported Device Performance |
|---|---|
| Accuracy of pedicle screw geometry: Deviations within acceptance criteria | Deviations were within the acceptance criteria. |
| Accuracy of pedicle screw trajectories: Deviations within acceptance criteria | Deviations were within the acceptance criteria. |
| Substantial equivalence to predicate device for planning pedicle screws and trajectories | Performance testing demonstrated substantial equivalence to the predicate device. |
2. Sample Size Used for the Test Set and Data Provenance
- Sample Size for Test Set: Not specified. The document only mentions "clinical data" was used.
- Data Provenance: Not specified. It's unclear if the clinical data was retrospective or prospective, or the country of origin.
3. Number of Experts Used to Establish Ground Truth for the Test Set and Their Qualifications
- The document does not specify the number of experts used or their qualifications for establishing ground truth for the test set.
4. Adjudication Method for the Test Set
- The document does not specify any adjudication method (e.g., 2+1, 3+1, none) used for the test set.
5. Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study
- The document does not mention that a Multi-Reader Multi-Case (MRMC) comparative effectiveness study was done. Therefore, no effect size of human readers improving with AI vs. without AI assistance is provided.
6. Standalone Performance Study
- Yes, a standalone performance study was done for the Acorn 3D Trajectory Automation module. The document states:
- "The accuracy of pedicle screw geometry as well as pedicle screw trajectories created in the subject device, Acorn 3D Trajectory Automation, was assessed via bench testing."
- This implies the algorithm's performance was evaluated independently without human intervention during the trajectory planning process itself, as it's a "fully automatic algorithm."
7. Type of Ground Truth Used
- The ground truth for the "accuracy of pedicle screw geometry" and "pedicle screw trajectories" was established by comparing the device's output to "clinical data." While the nature of this "clinical data" is not explicitly defined (e.g., expert consensus on images, surgical outcomes, or pathology reports), it serves as the reference standard.
8. Sample Size for the Training Set
- The document does not specify the sample size used for the training set of the machine learning algorithms. It mentions "Using a collection of images and masks as a training dataset for machine-learning segmentation algorithm" but no numbers.
9. How the Ground Truth for the Training Set Was Established
- The document states that the machine learning segmentation algorithm uses "a collection of images and masks as a training dataset." It doesn't explicitly describe how these "masks" (which represent the ground truth segmentations) were created. It can be inferred that these masks would have been generated by human experts, likely through manual or semi-automatic segmentation, but this is not confirmed in the text.
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(206 days)
Acorn Segmentation is intended for use as a software interface and image segmentation system for the transfer of CT or CTA medical images to an output file. Acorn Segmentation is also intended for measuring and treatment planning. The Acorn Segmentation output can also be used for the fabrication of the output file using additive manufacturing methods, Acorn 3DP Models. The physical replica can be used for diagnostic purposes in the field of musculoskeletal and craniomaxillofacial applications.
Acorn Segmentation and 3DP Models should be used in conjunction with expert clinical judgment.
Acorn Segmentation is an image processing software that allows the user to import, visualize and segment medical images, check and correct the segmentations, and create digital 3D models. The models can be used in Acorn Segmentation for measuring, treatment planning and producing an output file to be used for additive manufacturing (3D printing). Acorn Segmentation is structured as a modular package. This includes the following functionality:
- Importing medical images in DICOM format
- Viewing images and DICOM data
- Selecting a region of interest using generic segmentation tools
- Segmenting specific anatomy using dedicated semi-automatic tools or automatic algorithms
- Verifying and editing a region of interest
- Calculating a digital 3D model and editing the model
- Measuring on images and 3D models
- Exporting 3D models to third-party packages
Acorn Segmentation contains both machine learning based auto-segmentation as well as semi-automatic and manual segmentation tools. The auto-segmentation tool is only intended to be used for thoracic and lumbar regions of the spine (T1-T12 and L1-L5). Semi-automatic and manual segmentation tools are intended to be used for all musculoskeletal and craniomaxillofacial anatomy.
Acorn 3DP Model is an additively manufactured physical replica of the digital 3D model generated in Acorn Segmentation. The output file from Acorn Segmentation is used to additively manufacture the Acorn 3DP Model.
The provided text describes the acceptance criteria and the study that proves the device meets those criteria for the Acorn 3D Software (AC-SEG-4009) and Acorn 3DP Model (AC-101-XX).
Here's the breakdown:
1. A table of acceptance criteria and the reported device performance
| Acceptance Criteria (Metric) | Target Acceptance Criteria | Reported Device Performance |
|---|---|---|
| Geometric accuracy of digital models (Dice-Sorensen Coefficient) - Automatic Segmentation (Thoracic and Lumbar spine) | Average Dice-Sorensen Coefficient ≥ 0.93 | Average Dice-Sorensen Coefficient > 0.93 |
| Geometric accuracy of digital models (Dice-Sorensen Coefficient) - Semi-automatic and Manual Segmentation (Musculoskeletal & Craniomaxillofacial bone) | Average Dice-Sorensen Coefficient ≥ 0.93 | Average Dice-Sorensen Coefficient > 0.93 |
| Geometric accuracy of physical replicas (Mean Deviation) | Mean Deviation < 1mm | Mean Deviation < 1mm |
2. Sample size(s) used for the test set and the data provenance
The document does not specify the exact sample size for the test set nor the data provenance (e.g., country of origin, retrospective or prospective). It only states that software verification and validation included bench testing for geometric accuracy.
3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts
The document does not explicitly state the number of experts or their qualifications used to establish the ground truth for the test set.
4. Adjudication method for the test set
The document does not mention any specific adjudication method (e.g., 2+1, 3+1) for the test set.
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
A Multi-Reader, Multi-Case (MRMC) comparative effectiveness study was not explicitly mentioned or described. The study primarily focused on the algorithmic performance against predicate devices and physical models, rather than human reader improvement with AI assistance.
6. If a standalone (i.e. algorithm only without human-in-the loop performance) was done
Yes, a standalone (algorithm only) performance study was conducted. The "Accuracy of automatic segmentation" was evaluated, which by definition means the algorithm's performance without direct human control. The bench testing for geometric accuracy directly assesses the algorithm's output against a reference.
7. The type of ground truth used (expert consensus, pathology, outcomes data, etc.)
The ground truth for the geometric accuracy of digital models was established by comparing the Acorn Segmentation output against "predicate device segmentations." This implies that the segmentations generated by previously cleared and accepted devices served as the reference standard. For physical replicas, the ground truth was the "digital models" themselves.
8. The sample size for the training set
The document states that the "Acorn Segmentation contains both machine learning based auto-segmentation... Using a collection of images and masks as a training dataset for machine-learning segmentation algorithm". However, the specific sample size for the training set is not provided in this document.
9. How the ground truth for the training set was established
The document states that the ground truth for the training set was established by "using a collection of images and masks as a training dataset for machine-learning segmentation algorithm." This implies that pre-existing segmented images (masks) were used as the ground truth for training the machine learning model. The method by which these initial "masks" were generated (e.g., manual segmentation by experts, semi-automatic segmentation, etc.) is not detailed in this document.
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