An Image-Based Device for Accurate Rotational Alignment of the Femoral Component in Total Knee Arthroplasty
Bibliographic record
Abstract
INTRODUCTION Rotational malalignment of the components in total knee arthroplasty has been linked to patellar maltracking, improper soft tissue balance, abnormal kinematics, premature wear of the polyethylene inlay, and subsequent clinical complications such as anterior knee pain (Barrack et al., 2001; Zihlmann et al., 2005; Lakstein at al., 2010). This study investigates an innovative image-based device that is designed to be used along with an intraoperative Isocentric (ISO-C) 3D imaging C-arm, and the conventional surgical instruments for positioning the femoral component at accurate rotational alignment angles. METHODS The new device was tested on 5 replica models of the femur (Sawbones). Zimmer NexGen total knee replacement instruments were used to prepare the bones. After making the distal transverse cut on the femurs, the trans-epicondylar-axis (TEA) were defined by a line connecting the medial and lateral epicondyles which were marked by holes on the bone models. The 4-in-1 cutting jig was placed and pinned to the bones with respect to the TEA considering 5 different planned rotational alignments: −10°, −5°, 0°, +5°, and +10° (minus sign indicating external and plus sign internal rotation). At this point, the jig was replaced by the alignment device using the head-less pins as the reference, and subsequently an Iso-c 3D image of the bone was acquired using Siemens ARCADIS Orbic C-arm. The image was automatically analyzed using custom software that determined the angle between the TEA and the reference pins (Fig 1). The difference between the angle read from the device and the planned angle was then used to correct the locations of the reference pins through a custom protractor device. Preparation of the bone was continued by placing the 4-in-1 jigs on the newly placed pins. Three-dimensional images of the bones after completion of the cuts were acquired, and the angle between the final cut surface and the TEA was determined. RESULTS The results are listed in Fig 2. The rotational angle read from the image-based device showed misalignments in the range of 0.53° to 5.94° (RMS error=3.67°). After alignments were corrected, the final cut accuracy was in the range of 0.3° to 0.74° (RMS error=0.5°). DISCUSSION The introduced device was very accurate (0.5°) in correcting the rotational alignment of the femoral component. The range of errors for defining the boney landmarks through palpation and visualization is expected to be much larger than was observed in this work (RMS error =3.67°), due to soft tissue obstructions and time pressure during surgery. This would highlight the value of the device even more. The introduced technology is expected to add about 5 to 10 minutes to the surgery at a safe radiation dose comparable to a round transatlantic flight. The surgeon and staff can keep a safe distance during the short imaging time. CONCLUSION The introduced device provides a fast and safe tool for improving component alignments in total knee arthroplasty.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".