(234 days)
EmbedMed is intended for use as a software system and image segmentation system for the transfer of imaging information from a medical scanner such as a CT based system. The is processed by the system, and the result is an output data file. This file may then be provided as digital models or used an input to an additive manufacturing portion of the system. The additive manufacturing portion of the system produces physical outputs including anatomical models and surgical guides for use in maxillofacial surgeries. EmbedMed is also intended as a pre-operative software tool for simulating/evaluating surgical treatment options.
EmbedMed utilizes Commercial Off-The-Shelf (COTS) software to manipulate 3D medical images to create digital and additive manufactured, patient-specific physical anatomical models and surgical guides for use in surgical procedures. Imaging data files are obtained from the surgeons for treatment planning and various patient-specific products that are manufactured with biocompatible photopolymer resins using additive manufacturing (stereolithography).
The provided text describes the 3D LifePrints UK Ltd. EmbedMed device (K220366), an image segmentation software and additive manufacturing system for creating patient-specific anatomical models and surgical guides for maxillofacial surgeries.
Here's an analysis of the acceptance criteria and the study conducted:
1. Table of Acceptance Criteria and Reported Device Performance
The FDA 510(k) summary does not explicitly present a table of quantitative acceptance criteria with corresponding performance metrics for the EmbedMed device in terms of clinical accuracy (e.g., sensitivity, specificity, or deviation). Instead, the performance data presented is focused on demonstrating the physical and functional aspects of the manufactured outputs and their compliance with general medical device standards.
However, based on the provided text, we can infer some "acceptance criteria" through the verification and validation testing performed. These are more general compliance points rather than precise numerical performance targets for the AI component's diagnostic accuracy.
| Acceptance Criteria Category | Stated Verification/Validation/Performance |
|---|---|
| Biocompatibility | EmbedMed meets the requirements of ISO 10993-1:2018, ISO 14971:2019, and FDA Guidance Document Use of International Standard ISO 10993-1:2016 for short term (≤ 24 hours) contact with tissue and bone. Tested endpoints: cytotoxicity, sensitization, acute systemic toxicity, material-mediated pyrogenicity. |
| Sterilization Validation (End User) | Sterilization process validated to a sterility assurance level (SAL) of 10-6 using the over-kill method according to ISO 17665-1:2006. Drying time validation also conducted. |
| Functional/System Performance (Software & Manufacturing) | Installation, Operational, and Performance Qualification (IO/PQ) confirmed. |
| Dimensional Accuracy (Physical Outputs) | Verified that physical outputs (anatomical models, surgical guides) meet dimensional accuracy requirements across the range of possible patient-specific devices. |
| Feature Accuracy (Physical Outputs) | Verified that physical outputs meet feature accuracy requirements across the range of possible patient-specific devices. |
| Simulated Use Testing | Performed to confirm EmbedMed physical outputs meet requirements. |
2. Sample Size Used for the Test Set and Data Provenance
The document does not explicitly state a specific "test set" for evaluating the performance of the image segmentation system in terms of diagnostic accuracy or clinical utility. The testing described focuses on the physical outputs and system performance rather than the AI's ability to accurately segment anatomical structures on a test dataset.
- Sample Size for Test Set: Not explicitly stated for the image segmentation component. The "Verification and Validation Testing" indicates testing was performed "across the range of possible patient-specific devices," implying a variety of cases were used for dimensional and feature accuracy, but not a specific count or dataset description.
- Data Provenance: Not specified. The input imaging information is stated to come from "a medical scanner such as a CT based system." There is no mention of country of origin or whether data was retrospective or prospective for any internal testing of the image segmentation.
3. Number of Experts Used to Establish the Ground Truth for the Test Set and Qualifications of Those Experts
This information is not provided in the document. The study described does not involve establishing ground truth for image segmentation using expert consensus for a test set, nor does it quantify the performance of the image segmentation algorithm in terms of accuracy against such a ground truth. The "ground truth" for the physical outputs (dimensional and feature accuracy) would likely be based on the digital models generated by the system and engineering specifications, not expert clinical interpretation.
4. Adjudication Method for the Test Set
This information is not provided. As no specific test set for image segmentation performance against expert ground truth is described, an adjudication method is not mentioned.
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 done. The document explicitly states under "5.8.4. Clinical Studies": "Clinical testing was not necessary for the demonstration of substantial equivalence." The focus was on the substantial equivalence of the system and the physical outputs, not on comparing reader performance with and without AI assistance.
6. If a Standalone (i.e. algorithm only without human-in-the-loop performance) was done
A standalone performance evaluation of the image segmentation algorithm in terms of its accuracy (e.g., Dice score, Hausdorff distance, etc.) against a clinical ground truth is not explicitly described or provided. The system is described as a "software system and image segmentation system," but its performance metrics as an algorithm by itself are not detailed. The digital output is "reviewed and approved by the prescribing clinician prior to delivery of the final outputs," indicating a human-in-the-loop workflow.
7. The Type of Ground Truth Used
For the "Feature Accuracy" and "Dimensional Accuracy" validation, the ground truth would likely be the digital design files generated by the EmbedMed software itself, against which the physical 3D-printed outputs are compared. For the biocompatibility and sterilization validation, the ground truth is established by international standards (ISO) and FDA guidance documents.
There is no mention of ground truth derived from expert consensus, pathology, or outcomes data for the performance of the image segmentation component of the software.
8. The Sample Size for the Training Set
The document does not provide any information about a training set size for the image segmentation software. Given that the software is described as utilizing "Commercial Off-The-Shelf (COTS) software to manipulate 3D medical images," it is possible that the underlying segmentation algorithms were developed and trained externally or are based on traditional image processing techniques rather than a large, custom-trained deep learning model.
9. How the Ground Truth for the Training Set Was Established
Since information regarding a specific training set is not provided, how its ground truth was established is also not mentioned.
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