(171 days)
SixFix™ Hexapod Fixator and Deformity Analysis and Correction Software (DACS) is intended to be used for posttraumatic joint contracture which has resulted in loss of range of motion; fractures and disease which generally may result in joint contractures or loss of range of motion and fractures requiring distraction; open and closed fracture fixation; pseudo-arthrosis of long bones; limb lengthening by epiphyseal or metaphyseal distraction; correction of bony or soft tissue deformities; correction of bony or soft tissue defects; joint arthrodesis; infected fractures or nonunions.
The SixFix™ web application for the DACS software can be used with the SixFix™ Hexapod fixator. Use of the software is optional for clinicians using the SixFix™ Hexapod fixator.
The SixFix™ Deformity Analysis and Correction Software (DACS) can be used with the SixFix™ Hexapod fixator (K190069), otherwise known as a spatial frame external fixator. Use of the software is optional for clinicians using the SixFix™ Hexapod, which is a circular external fixator based on Illizarov principles. The software utilizes radiographs, along with surgeon inputs, to develop a patient prescription to correct the deformity.
The provided FDA 510(k) clearance letter and summary for the SixFix™ Hexapod Fixator and Deformity Analysis and Correction Software (DACS) contain details about the device's intended use, description, and comparison to a predicate device. However, it does not provide the specific quantitative acceptance criteria or detailed study results typically found in a comprehensive study report for software performance.
The document states:
- "Functional testing used the same approach as testing for the predicate device and included 43 test cases representing a range of clinical scenarios to ensure the device performed as intended."
- "Test results demonstrate that the device is as safe, as effective, and performs as well as or better than the legally marketed device predicates."
This indicates that testing was performed, but the precise acceptance criteria, the reported device performance against those criteria, or the detailed methodology (sample size for test set, data provenance, expert involvement, ground truth establishment, MRMC study, etc.) are not included in this summary.
Therefore, many of your requested details cannot be extracted from the provided text. I will provide a table and describe what information is available and what is missing based on the provided document.
Acceptance Criteria and Device Performance (Based on provided document)
Since the document does not explicitly state quantitative acceptance criteria or detailed performance metrics, the "acceptance criteria" can be inferred as successful completion of the functional tests and demonstrating equivalence to the predicate. The "reported device performance" is a general statement of successful testing.
| Acceptance Criteria (Inferred from document) | Reported Device Performance (From document) |
|---|---|
| Software performs as intended across a range of clinical scenarios (implying accurate deformity analysis and correction prescription generation). | "ensure the device performed as intended." |
| Correct implementation of software requirements and risk mitigations. | "Test cases also ensured correct implementation of software requirements and risk mitigations." |
| Device is as safe, as effective, and performs as well as or better than the legally marketed predicate device. | "Test results demonstrate that the device is as safe, as effective, and performs as well as or better than the legally marketed device predicates." |
| Functional equivalence to the predicate device (user inputs, device outputs, principle of operations, indications for use). | "The indications for use, user inputs, device outputs, and principle of operations of the subject device are identical to those of the predicate." |
| Support for updated operating systems and web browsers. | "The subject device runs on updated operating systems and includes support for up to date web browsers." |
| Minor changes in image formats and an additional (optional) user role are correctly implemented. | "It includes minor changes in image formats and an additional (optional) user role." |
Detailed Study Information (Based on provided document)
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Sample size used for the test set and the data provenance:
- Sample Size for Test Set: The document states "43 test cases representing a range of clinical scenarios." It does not specify if these "test cases" refer to patient datasets or defined functional tests. Assuming "test cases" are distinct clinical scenarios/data points used to validate the software, the sample size for the test set is 43.
- Data Provenance: Not specified. It does not mention the country of origin of the data or whether it was retrospective or prospective.
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Number of experts used to establish the ground truth for the test set and the qualifications of those experts:
- Not specified. The document broadly mentions "surgeon inputs" but does not detail how ground truth for the test set was established or the number/qualifications of experts involved in that process.
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Adjudication method (e.g. 2+1, 3+1, none) for the test set:
- Not specified. This detail is not provided in the document.
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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:
- No MRMC study is mentioned. The document focuses on the software itself and its equivalence to a predicate, not on human-in-the-loop performance improvement. The "Use of the software is optional for clinicians using the SixFix™ Hexapod fixator" suggests it's a tool, but its impact on human reader performance is not discussed.
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If a standalone (i.e. algorithm only without human-in-the-loop performance) was done:
- Yes, implicitly. The "Functional testing" involving 43 test cases likely refers to standalone software performance. The software "utilizes radiographs, along with surgeon inputs, to develop a patient prescription to correct the deformity." The testing would have evaluated the accuracy of these generated prescriptions based on input data.
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The type of ground truth used (expert consensus, pathology, outcomes data, etc.):
- Not explicitly stated for the test set. Given the nature of the software (deformity analysis and correction), the ground truth for the test cases would likely involve pre-defined, clinically correct "prescriptions" or measurements established by expert opinion or theoretical calculations validated by experts. It's not pathology or outcomes data in this context.
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The sample size for the training set:
- Not specified. The document mentions testing and validation but provides no information about a training set size, implying either it's not a machine learning model that requires a distinct, labeled training set of this type, or the information is simply not included in this summary. Given it's a "Deformity Analysis and Correction Software" and not explicitly termed an AI/ML device in the modern sense (though it performs "analysis"), it might be a rules-based or algorithmic system that doesn't rely on a separate training dataset in the same way a deep learning model would.
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How the ground truth for the training set was established:
- Not applicable/Not specified, as no training set information is provided.
§ 888.3030 Single/multiple component metallic bone fixation appliances and accessories.
(a)
Identification. Single/multiple component metallic bone fixation appliances and accessories are devices intended to be implanted consisting of one or more metallic components and their metallic fasteners. The devices contain a plate, a nail/plate combination, or a blade/plate combination that are made of alloys, such as cobalt-chromium-molybdenum, stainless steel, and titanium, that are intended to be held in position with fasteners, such as screws and nails, or bolts, nuts, and washers. These devices are used for fixation of fractures of the proximal or distal end of long bones, such as intracapsular, intertrochanteric, intercervical, supracondylar, or condylar fractures of the femur; for fusion of a joint; or for surgical procedures that involve cutting a bone. The devices may be implanted or attached through the skin so that a pulling force (traction) may be applied to the skeletal system.(b)
Classification. Class II.