(260 days)
Alignment System Cranial is intended to support the surgeon to plan and to achieve a trajectory with surgical instruments during cranial stereotactic procedures.
The indications for use are biopsy of intracranial lesions and placement of stereoelectroencephalography (SEGG) electrodes.
The subject device Alignment System Cranial is an image guided surgery system intended to support the surgeon to plan and to achieve a trajectory with surgical instruments during cranial stereotactic procedures using optical tracking technology.
For this purpose, the Alignment System Cranial consists of a combination of hardware and software. The Alignment Software Cranial with its sw components is installed on an Image Guided Surgery (IGS) platform (Curve, Curve Navigation 17700, Kick 2 Navigation Station or Buzz Navigation) consisting of a computer unit, a touch display and an infrared tracking camera. During surgery, the subject device tracks the position of instruments in relation to the patient anatomy and identifies this position on pre- or intraoperative images. The position of the surgical instruments is continuously updated on these images by optical tracking. This position information is used by the Alignment Software Cranial to align either passive or active positioning devices to a planned trajectory for subsequent surgical steps.
The Alignment System Cranial has different configurations of hardware devices depending on which positioning device is used and which indication is performed. The Alignment Software Cranial 2.0 supports the active positioning devices Surgical Base System 1.4 and Cirq Arm System 2.0 (+ Cirq Robotic Alignment Module + Cirq Robotic Disposable Kinematic Unit) as well as the passive positioning device Varioquide. Both types of positioning devices consist of articulated arms with different joints where additional devices and surgical instruments can be attached to for further robotic or manual alignment respectively to a defined trajectory.
In addition, the subject device offers a set of indication specific instruments to support biopsy and sEEG procedures. This instrumentation consists of instrument holders, tracking arrays, guide tubes, reduction tube, bone anchors, drill bits and depth stops. None of the instruments is delivered sterile. All patient contacting materials consist of different alloys of stainless steel.
With this submission, an already existing feature is now performed introducing a new algorithm using artificial intelligence and machine (AI/ML). This ML based functionality is used as an aid in the registration step (in surface matching) by allowing a pre-registration based on guide points which are delivered by this algorithm. This pre-registration step is not mandatory. The AI/ML algorithm is a Convolutional Network (CNN) developed using a Supervised Learning approach. The algorithm was developed using a controlled internal process that defines activities from the inspection of input data to the training and verification of the training process begins with the model observing, learning, and optimizing its parameters based on the training pool data. The model's prediction and performance are then evaluated against the test pool. The test pool data is set aside at the beginning of the project. This is a static algorithm (locked).
The Alignment Software Cranial has the following accessories:
- . Automatic Registration providing an automatic registration for subsequent use.
- . Automatic Registration iMRI providing an automatic image registration for intraoperatively acquired MR images.
Here's a breakdown of the acceptance criteria and study information based on the provided text:
1. Table of Acceptance Criteria and Reported Device Performance
| Acceptance Criteria | Reported Device Performance |
|---|---|
| Mean Positional Error (instrument's tip) ≤ 2 mm | Mean Positional Error (instrument's tip) ≤ 2 mm (fulfilled) |
| Mean Angular Error (instrument's axis) ≤ 2° | Mean Angular Error (instrument's axis) ≤ 2° (fulfilled) |
| Equivalent performance for landmark detection (AI/ML vs. conventional) to third predicate device | Achieved. Testing demonstrated no concerns regarding safety and effectiveness and equivalent performance. |
| Software Verification & Validation (Major concern level) | Conducted, documentation provided, product specifications, risk analysis/incremental test strategies included. |
| Usability according to IEC 62366-1 | Summative usability carried out in a simulated clinical environment. Final designs proven safe and effective. |
| Electrical safety according to IEC 60601-1 | Achieved |
| RFID according to AIM 7351731 | Achieved |
| EMC according to IEC 60601-1-2 | Achieved |
| Biocompatibility of instruments | Assessed, considering materials, manufacturing, test data, and history of safety/effectiveness. |
| Cleaning and disinfection evaluation/reprocessing validation for instruments | Conducted |
| Mechanical properties of instruments | Performed life cycle simulations and verification of clearance fits, material fatigue, functionality. |
| Stability performance testing for drill bits | Performed in selected worst-case situations to ensure load resistance. |
2. Sample Sizes Used for Test Set and Data Provenance
The document does not explicitly state the numerical sample size for the test set used for the AI/ML algorithm's performance evaluation or for the general system accuracy testing. However, it indicates:
- AI/ML Test Set: "The test pool data is set aside at the beginning of the project. This is a static algorithm (locked)." No further details on provenance or numerical size are provided.
- System Accuracy Testing: "evaluated considering a realistic clinical setup and representative worst case scenarios." No specific number of cases or data provenance (e.g., country of origin, retrospective/prospective) is mentioned.
3. Number of Experts Used to Establish Ground Truth for the Test Set and Qualifications
The document does not specify the number or qualifications of experts used to establish ground truth for the test set.
4. Adjudication Method for the Test Set
The document does not specify an adjudication method for the test set.
5. Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study
No mention of an MRMC comparative effectiveness study or human reader improvement with AI assistance is provided. The AI/ML component is described as an "aid in the registration step" by providing "pre-registration based on guide points."
6. Standalone (Algorithm Only) Performance Study
Yes, a standalone performance evaluation of the AI/ML algorithm was done. It focused on comparing its landmark detection capabilities to the conventional method: "For the landmark detection feature in the pre-registration step now implemented using AI/ML, performance testing comparing conventional to machine learning based landmark detection was performed showing equivalent performance as in the third predicate device."
7. Type of Ground Truth Used
The document does not explicitly state the type of ground truth (e.g., expert consensus, pathology, outcomes data) used for the AI/ML algorithm's test set or the system accuracy measurements. For the AI/ML, it implies that the ground truth for landmark detection was established against which the algorithm's performance was compared to the predicate's conventional method.
8. Sample Size for the Training Set
The document states: "The algorithm was developed using a controlled internal process that defines activities from the inspection of input data to the training and verification of the training process begins with the model observing, learning, and optimizing its parameters based on the training pool data." However, it does not provide the numerical sample size for the training set.
9. How the Ground Truth for the Training Set Was Established
The document states that the AI/ML algorithm is a "Convolutional Network (CNN) developed using a Supervised Learning approach." This implies that the training data was labeled with the correct ground truth for the landmarks. However, it does not explicitly describe how this ground truth was established (e.g., by experts, manually annotated, etc.).
§ 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).