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510(k) Data Aggregation
(140 days)
DIGIMATCH ORTHODOC ROBODOC ENCORE SURGICAL SYSTEM
The DigiMatch™ ROBODOC®/ORTHODOC® Encore Surgical System is intended for use as a device which uses diagnostic images of the patient acquired specifically to assist the physician with presurgical planning and to provide orientation and reference information during intraoperative procedures. The robotic surgical tool, under the direction of the surgeon, precisely implements the presurgical software plan.
The preoperative planning software and robotic surgical tool is used as an alternative to manual planning and broaching/reaming techniques for femoral canal preparation in primary total hip arthroplasty (THA).
The DigiMatch™ ROBODOC®/ORTHODOC® Encore Surgical System is indicated for orthopedic procedures in which the broaching/reaming in primary total hip arthroplasty (THA) may be considered to be safe and effective and where references to rigid anatomical structures may be made.
The DigiMatch™ ORTHODOC®/ROBODOC® Encore System is a three-dimensional, graphical, preoperative planner and implementation tool for treatment of patients who require a total hip arthroplasty (THA) procedure. This device is intended as an alternative to manual template planning, broaching, and reaming techniques for the preparation of bone for patients requiring a THA procedure. The system consists of the ORTHODOC® Preoperative Planning Workstation and ROBODOC® , a robotic system composed of an electromechanical arm, electronics control cabinet, computer, display monitor, and miscellaneous accessories such as cutters, drapes, irrigation sets, probes, and markers. ORTHODOC® and ROBODOC® when used according to the instructions for use, make precision bone preparation possible before and during THA surgical procedures.
The provided text describes a 510(k) summary for the DigiMatch™ ORTHODOC®/ROBODOC® Encore Surgical System, a robotic system for Total Hip Arthroplasty (THA). This submission focuses on demonstrating substantial equivalence to a predicate device (DigiMatch™ ROBODOC® Surgical System, K072629) rather than establishing novel performance metrics or conducting a new clinical study. Therefore, some of the requested information, such as detailed quantitative acceptance criteria for a new device's performance, a standalone algorithm study, or a multi-reader, multi-case study, is not explicitly provided in the summary.
However, based on the information available, here's a breakdown of the requested points:
1. Table of Acceptance Criteria and Reported Device Performance
The acceptance criteria here are based on demonstrating substantial equivalence to the predicate device, meaning the new device performs at least as well as, and presents no new safety or effectiveness concerns compared to, the predicate. The "performance" is implicitly that the modified features function as intended and do not degrade the overall system's safety or effectiveness.
Acceptance Criteria Category | Specific Criteria (Implied) | Reported Device Performance (Implied) |
---|---|---|
Technological Characteristics & Principles of Operation | - Similar intended use and indications. |
- Similar technological characteristics (e.g., CT scan input, presurgical planning, robotic arm driven by validated software, point-to-surface registration). | - The table clearly shows identical technological characteristics and principles of operation between the new device and the predicate.
- "Any minor differences...raise no new questions of safety or effectiveness nor change the device's intended therapeutic effect." |
| Performance Data | - Modifications and improvements do not adversely affect safety or effectiveness. - Functional verification of software and hardware.
- Simulated clinical use performs as expected. | - Non-clinical performance testing for specific modifications (e.g., ORTHODOC® host computer, CT file checks, elimination of pin patient/robot registration, ROBODOC® electromechanical arm, Smart Bone Motion Monitor, etc.).
- Bench and simulated use tests conducted, including functional software testing and simulated clinical use. |
| Safety and Effectiveness | - No new questions of safety or effectiveness. | - Implicitly confirmed by the FDA's substantial equivalence determination. |
2. Sample Size Used for the Test Set and Data Provenance
The document does not specify a "test set" in the traditional sense of a patient-based clinical study for performance evaluation against a gold standard for a diagnostic or AI device. Instead, the testing described is primarily non-clinical performance testing and simulated use tests.
- Sample Size: Not applicable in terms of patient numbers for a test set. The "samples" would be the modified components, software modules, and system configurations that underwent bench and simulated use tests. The number of such items or test runs is not specified.
- Data Provenance: Not applicable in terms of country of origin or retrospective/prospective for patient data. The testing was laboratory-based, focusing on the functionality of the device's modifications.
3. Number of Experts Used to Establish Ground Truth for the Test Set and Qualifications of Those Experts
This information is not provided in the summary. For non-clinical, bench, and simulated use testing, "ground truth" would likely be defined by engineering specifications, software design documents, and expected performance under controlled conditions, rather than expert-established ground truth from medical data.
4. Adjudication Method for the Test Set
This information is not provided and is generally not applicable for non-clinical and simulated use testing as described. Adjudication methods (like 2+1 or 3+1) are typically reserved for clinical studies where multiple human readers assess medical images or findings to establish a consensus ground truth.
5. If a Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study was Done
No, a Multi-Reader Multi-Case (MRMC) comparative effectiveness study was not done or reported in this summary. The submission is focused on demonstrating substantial equivalence of a modified surgical robotic system, not on assessing the improvement in human reader performance with or without AI assistance.
6. If a Standalone (i.e. algorithm only without human-in-the-loop performance) was Done
The device is a robotic surgical system that works with human intervention (under the direction of the surgeon). While the ORTHODOC® planning software and ROBODOC® robotic arm have algorithms, the performance evaluation described is for the integrated system, including its hardware, software, and simulated use. Therefore, a standalone "algorithm only" performance study in the context of, for example, a diagnostic AI device is not applicable or reported here. The "algorithm" here controls a physical robot and forms part of a surgical aid, not an independent diagnostic tool.
7. The Type of Ground Truth Used
For the non-clinical and simulated use testing, the "ground truth" would be based on:
- Engineering specifications and design requirements: Ensuring that modified components meet their intended design outputs.
- Functional correctness: Verifying that software and hardware perform as programmed and expected during various operational scenarios simulated in the lab.
- Expected outcomes of simulated clinical scenarios: Ensuring the robotic system accurately plans and executes simulated bone preparations within specified tolerances.
This is distinct from "expert consensus," "pathology," or "outcomes data" which are typically used for diagnostic or predictive AI devices involving patient data.
8. The Sample Size for the Training Set
This information is not provided. The document describes modifications to an existing system, rather than the development of a new AI model that requires a "training set" in the machine learning sense. The "training" for such a system would typically refer to the extensive development and validation cycles of the underlying control software and robotic components, not a patient data training set.
9. How the Ground Truth for the Training Set was Established
As no "training set" in the machine learning context is mentioned, the method for establishing its ground truth is not applicable or discussed. The "ground truth" for the development of the surgical system (both the predicate and the modified version) would be established through principles of mechanical engineering, software engineering, and surgical accuracy requirements, validated through extensive bench testing and cadaver studies (though not explicitly detailed in this summary).
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