⚡ Quick Summary
This study evaluated the use of computer-assisted surgery (CAS) for analyzing outcomes of zygomatic fractures, revealing significant asymmetries post-surgery and proposing a reproducible workflow for surgical outcome assessment. The findings highlight the potential of CAS to enhance surgical precision and planning in complex cases.
🔍 Key Details
- 📊 Study Population: 16 patients with unilateral zygomatic fractures
- 🧩 Inclusion Criteria: ZMC fractures classified as Zingg B or C, treated via open reduction and internal fixation (ORIF)
- ⚙️ Technology Used: Computer-assisted surgery (CAS) for pre- and post-operative CT scans
- 📏 Assessment Methods: Two- and three-dimensional analyses using cephalometric landmarks
🔑 Key Takeaways
- 🔍 Significant Findings: 69% of patients were male, with a mean age of 48.1 years.
- ⚖️ Fracture Side: 81% of fractures occurred on the right side.
- 📉 Asymmetries Detected: Notable discrepancies along X and Y axes, particularly in fronto-zygomatic suture and zygo-maxillary points.
- 📈 CAS Advantages: CAS-optimized reductions showed measurable improvements over traditional methods.
- 🔄 Workflow Proposal: A reproducible workflow for surgical outcome assessment was proposed for future studies.
- 🤖 Future Directions: Emphasis on integrating artificial intelligence to automate processes for broader clinical applicability.
📚 Background
Zygomatico-maxillary complex (ZMC) fractures are common facial injuries that can lead to significant functional and aesthetic challenges. Traditional surgical methods may not always achieve ideal symmetry, prompting the exploration of computer-assisted surgery (CAS) as a means to enhance surgical planning and outcome evaluation.
🗒️ Study
Conducted at the Maxillofacial Surgery Unit of Siena University Hospital from 2017 to 2024, this retrospective cohort study focused on 16 patients who underwent treatment for unilateral ZMC fractures. The study aimed to assess the effectiveness of CAS in evaluating surgical outcomes and to propose a standardized workflow for future assessments.
📈 Results
The analysis revealed that CAS comparisons between post-operative outcomes and the contralateral side showed significant asymmetries, particularly in critical areas such as the fronto-zygomatic suture and zygo-maxillary points. Furthermore, CAS-optimized reductions demonstrated statistically significant improvements compared to traditional surgical methods, underscoring the challenges in achieving ideal symmetry.
🌍 Impact and Implications
The findings from this study suggest that CAS technology can significantly enhance the precision of evaluating ZMC fracture outcomes. By allowing for detailed comparisons between actual surgical results and virtual simulations, CAS has the potential to improve surgical planning and execution, particularly in complex cases. This could lead to better patient outcomes and more consistent aesthetic results in facial reconstructive surgery.
🔮 Conclusion
This study highlights the transformative potential of computer-assisted surgery in the evaluation of zygomatic fracture outcomes. By proposing a reproducible workflow and demonstrating measurable improvements in surgical precision, the research paves the way for future advancements in surgical techniques. Continued exploration of CAS, particularly with the integration of artificial intelligence, could further enhance its clinical applicability and effectiveness.
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Computer-Assisted Evaluation of Zygomatic Fracture Outcomes: Case Series and Proposal of a Reproducible Workflow.
Abstract
BACKGROUND: Zygomatico-maxillary complex (ZMC) fractures are prevalent facial injuries with significant functional and aesthetic implications. Computer-assisted surgery (CAS) offers precise surgical planning and outcome evaluation. The study aimed to evaluate the application of CAS in the analysis of ZMC fracture outcomes and to propose a reproducible workflow for surgical outcome assessment using cephalometric landmarks.
METHODS: A retrospective cohort study was conducted on 16 patients treated for unilateral ZMC fractures at the Maxillofacial Surgery Unit of Siena University Hospital (2017-2024). Inclusion criteria included ZMC fractures classified as Zingg B or C, treated via open reduction and internal fixation (ORIF). Pre- and post-operative CT scans were processed for two- and three-dimensional analyses. Discrepancies between CAS-optimized reduction and achieved surgical outcomes were quantified using cephalometric landmarks and volumetric assessments.
RESULTS: Out of the 16 patients (69% male, mean age 48.1 years), fractures were predominantly on the right side (81%). CAS comparison between the post-operative and the contralateral side revealed significant asymmetries along the X and Y axes, particularly in the fronto-zygomatic suture (FZS), zygo-maxillary point (MP), and zygo-temporal point (ZT). Computer-assisted comparison between the post-operative and the CAS-simulated reductions showed statistical differences along all three orthonormal axes, highlighting the challenges in achieving ideal symmetry despite advanced surgical techniques. CAS-optimized reductions demonstrated measurable improvements compared to traditional methods, underscoring their utility in outcome evaluation.
CONCLUSIONS: CAS technology enhances the precision of ZMC fracture outcome evaluation, allowing for detailed comparison between surgical outcomes and virtual simulations. Its application underscores the potential for improved surgical planning and execution, especially in complex cases. Future studies should focus on expanding sample size, refining workflows, and integrating artificial intelligence to automate processes for broader clinical applicability.
Author: [‘Benedetti S’, ‘Frosolini A’, ‘Cascino F’, ‘Pignataro LV’, ‘Franz L’, ‘Marioni G’, ‘Gabriele G’, ‘Gennaro P’]
Journal: Tomography
Citation: Benedetti S, et al. Computer-Assisted Evaluation of Zygomatic Fracture Outcomes: Case Series and Proposal of a Reproducible Workflow. Computer-Assisted Evaluation of Zygomatic Fracture Outcomes: Case Series and Proposal of a Reproducible Workflow. 2025; 11:(unknown pages). doi: 10.3390/tomography11020019