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AIAS Cephalon is an image-processing software indicated to assist in the positioning of femur fracture implant components for adult patients. It is intended to assist in precisely positioning femur fracture implant components intraoperatively by measuring their positions relative to the bone structures of interest provided that the points of interest can be identified from radiology images (C-arm images). Clinical judgement and experience are required to properly use the device. The device is not for primary image interpretation. The software is not for use on mobile phones.

AIAS Cephalon is a fully automated software as a medical device (SaMD) that provides image analysis features and intraoperative instructions for proximal femur fracture surgery supporting the positioning of the Depuy Synthes TFN-ADVANCED Proximal femoral nail (TFNA) implant.

The instructions are based on the intraoperative X-ray images acquired. The system automatically detects anatomy, implants, and tools in the X-ray images. Based on what it sees in the X-ray image, it provides different kinds of information. Every new X-ray image acquired triggers a new system response. This means updated information is only available when a new X-ray image is acquired.

AIAS Cephalon supports all of the following surgical steps of the procedure: determining the angle of anteversion and the Caput Collum Diaphyseal (CCD)/neck-shaft angle, finding the entry point at the greater trochanter, nail insertion, determination of length of head element, insertion of head element, distal locking of long TFNA nails, and determination of lengths of distal locking screws.

Here's a summary of the acceptance criteria and the study that proves the device meets them, based on the provided FDA 510(k) clearance letter for AIAS Cephalon:

1. Table of Acceptance Criteria and Reported Device Performance

Note: The document states that the evaluations "have demonstrated that the acceptance criteria for the median error and 95% error quantiles of the measurements provided by the device (anteversion angle, CCD angle, head element length, tip-apex-distance, and distal locking screw length) are met." It does not provide the specific numerical thresholds for these criteria or the exact numerical results, but rather confirms that the criteria were satisfied.

2. Sample Size Used for the Test Set and Data Provenance

• Test Set Description: "Comprehensive digitally reconstructed radiographic (DRR) datasets, which cover a wide range of real-world imaging scenarios, as well as real X-ray image data acquired from a study with human specimen."
• DRR Test Set Size: At least 11,000 DRRs were computed from CT scans of 18 patients.
• DRR Test Set Provenance: The CT scans were from patients with diverse demographic groups (56% male, 44% female; average age 72.3 and 71.1 years respectively; 56% White, 28% Asian, 11% Hispanic or Latino, and 6% Black or African American). The country of origin is not explicitly stated, but the mention of "real-world imaging scenarios" and diverse demographics suggests a broad representation. The data appears to be retrospective as it's CT scans used to generate DRRs for testing a device.
• Real X-ray Image Data: "real X-ray image data acquired from a study with human specimen." The sample size for this specific real X-ray data is not explicitly given, nor is its provenance.

3. Number of Experts Used to Establish Ground Truth for the Test Set and Qualifications

• Number of Experts: One senior orthopedic surgeon.
• Qualifications: "senior orthopaedic surgeon." No further details on years of experience or specific subspecialty are provided.

4. Adjudication Method for the Test Set

• The document states, "For all tests, the reference standard [ground truth] was validated by a senior orthopaedic surgeon." This implies a single expert validation of the ground truth, rather than a multi-expert adjudication method like 2+1 or 3+1.

5. Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study

• No MRMC study was done. The clearance letter states, "Substantial equivalence was not based on an assessment of clinical performance data." The device is intended to assist in positioning, and the focus of the performance testing was on the accuracy of its measurements and detections in a standalone capacity.

6. Standalone Performance Study

• Yes, a standalone study was done. The performance of the deep neural networks was tested both stand-alone as well as integrated into the device. The "dedicated validation was performed on image processing accuracy" using DRR datasets and real X-ray data demonstrates standalone algorithm performance. This study evaluated the "measurements provided by the device," indicating the algorithm's direct output.

7. Type of Ground Truth Used

• Expert Consensus (single expert validation): "For all tests, the reference standard was validated by a senior orthopaedic surgeon." This indicates expert-defined measurements or annotations as the ground truth.

8. Sample Size for the Training Set

• The document states, "The patients whose CT scans were used to compute the DRRs for testing were not used for training the neural networks nor for tuning of any image processing algorithms." However, the sample size for the training set is not provided in the given text.

9. How Ground Truth for the Training Set Was Established

• The document does not explicitly state how the ground truth for the training set was established. It only mentions that the ground truth for the test set was validated by a senior orthopedic surgeon.

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