(87 days)
The iASSIST Knee System is a computer assisted stereotaxic surgical instrument system to assist the surgeon in the positioning of orthopedic implant system components intra-operatively. It involves surgical instruments and position sensors to determine alignment axes in relation to anatomical landmarks and to precisely position alignments and implant components relative to these axes.
Example orthopedic surgical procedures include but are not limited to: Total Knee Arthroplasty.
As in the predicate, the iASSIST Knee System consists of tracking sensors ('pods'), a computer system, software, and surgical instruments designed to assist the surgeon in the placement of Total Knee Replacement components. The pods combined with the surgical instruments provide positional information to help orient and locate the main femoral and tibial cutting planes as required in knee replacement surgery. This includes means for the surgeon to determine and thereafter track each of the bones' alignment axes relative to which the cutting planes are set.
The provided document is a 510(k) Pre-Market Notification for the iASSIST™ Knee System. It details the device, its intended use, and comparisons to a predicate device. However, it does not explicitly provide a table of acceptance criteria with reported device performance statistics in the way that would typically be seen for AI/ML device performance.
Instead, the document states that "Non-clinical tests were performed to assess that no new safety and efficacy issues were raised in the device." The performance data section describes the types of tests conducted, rather than specific quantitative acceptance criteria and results against those criteria.
Therefore, I cannot fulfill all parts of your request with the provided information. I will, however, extract all available information about the study and acceptance criteria as described.
Here's an analysis based on the provided text:
1. Table of Acceptance Criteria and Reported Device Performance
As noted above, a detailed table with specific quantitative acceptance criteria and corresponding reported device performance (e.g., accuracy, sensitivity, specificity, or specific error margins with numerical results) is not provided in this document. The document focuses on demonstrating that modifications to an existing device (predicate) did not introduce new safety or efficacy issues and that the device still meets its intended functionality.
The closest to "acceptance criteria" are implied by the types of tests described, indicating that the system must maintain its required functionality, robust performance, and compatibility.
| Acceptance Criteria (Implied) | Reported Device Performance (Summary from text) |
|---|---|
| Required functionalities maintained or correctly updated without hazardous anomalies | Software system tests performed to ensure functionalities were maintained/updated correctly. |
| Performance of bone registration related functionalities verified | Performance tests performed under simulated bench test conditions and analyses. |
| Robustness and compatibility of added/modified instruments verified | Bench test and analyses performed. |
| Resistance of pods to electro-static discharges verified | Bench test and analyses performed. |
| Sufficiency of pod's battery expected lifetime verified | Bench test and analyses performed. |
| Overall system performance, usage, surgical flow, and instrument ergonomics validated | Full use simulations tests using sawbones performed. |
| Electrical safety certification (IEC 60601-1:2005) met | Electrical certification tests related to the update performed. |
2. Sample Size Used for the Test Set and Data Provenance
The document describes "Non-clinical tests" and "simulated bench test conditions," and "Full use simulations tests using sawbones." This indicates the tests were conducted in a controlled, non-human, and likely retrospective or simulated environment, rather than on patient data.
- Sample Size: Not explicitly mentioned.
- Data Provenance: Simulated bench tests and sawbone simulations. This is not patient data; therefore, "country of origin" is not applicable in the typical sense. It implies lab or manufacturing environment testing.
- Retrospective/Prospective: Not applicable, as it's not patient-level data. The tests would have been performed prospectively during the development and modification phases.
3. Number of Experts Used to Establish Ground Truth for the Test Set and Qualifications
This information is not provided. Since the tests were primarily engineering and functional verification on simulated or bench setups (e.g., software, hardware, mechanical components, sawbones), "ground truth" would likely be established by engineering specifications, calibration standards, and comparison to the predicate device's known performance, rather than clinical expert consensus.
4. Adjudication Method for the Test Set
Not applicable, as "adjudication" typically refers to resolving discrepancies between human readers or ground truth experts for clinical data. The tests described are engineering validations.
5. If a Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study Was Done
No, an MRMC comparative effectiveness study is not mentioned. The document primarily focuses on verifying that device modifications do not introduce new safety or efficacy concerns compared to its own predicate, rather than comparing its performance against humans or quantifying human improvement with AI assistance.
6. If a Standalone (i.e., algorithm only without human-in-the-loop performance) Was Done
The device is described as a "computer assisted stereotaxic surgical instrument system to assist the surgeon." This inherently implies a "human-in-the-loop" system. While the software and hardware components were tested individually ("Software system tests," "Performance tests were performed...to verify the implementation of the performance of the bone registration related functionalities"), these tests were to ensure the components functioned correctly for the purpose of assisting a surgeon. A standalone performance without a human interaction is not the intended use and therefore not explicitly evaluated in isolation as a primary performance metric in the way an AI diagnostic tool might be.
7. The Type of Ground Truth Used
For the engineering tests:
- Software tests: Likely against defined software requirements and specifications.
- Performance tests (bone registration): Likely against known, calibrated physical measurements or established mathematical models for bone alignment.
- Robustness/Compatibility/Battery life/ESD: Against engineering specifications, industry standards, and predicate device performance.
- Sawbone simulations: Likely against established surgical techniques and expected outcomes for total knee arthroplasty, possibly with objective measurements of alignment.
8. The Sample Size for the Training Set
This device is not described as an AI/ML device that undergoes "training" in the typical sense of a deep learning model. It's a computer-assisted surgical instrument system using predefined algorithms and sensors. Therefore, a "training set" as understood in machine learning is not applicable here. The software development would involve traditional software engineering and testing cycles.
9. How the Ground Truth for the Training Set Was Established
As explained above, there isn't a "training set" in the AI/ML context for this device. The algorithms are likely based on biomechanical principles, geometry, and surgical protocols, with "ground truth" derived from engineering specifications and clinical understanding of proper implant positioning.
§ 882.4560 Stereotaxic instrument.
(a)
Identification. A stereotaxic instrument is a device consisting of a rigid frame with a calibrated guide mechanism for precisely positioning probes or other devices within a patient's brain, spinal cord, or other part of the nervous system.(b)
Classification. Class II (performance standards).