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Objectifying aesthetic outcomes following face transplantation - the AI research metrics model (CAARISMA® ARMM)
Leonard Knoedler1, Cosima C Hoch2, Samuel Knoedler3
1Department of Oral and Maxillofacial Surgery, Charité - Universitätsmedizin Berlin, Berlin, Germany.
Background:
Face transplantation (FT) offers a reconstructive option for patients with severe facial disfigurements by restoring both function and appearance. Aesthetic outcomes, which are crucial to psychological well-being and social reintegration, have historically been evaluated subjectively. This study introduces the AI Research Metrics Model (CAARISMA ® ARMM), a machine learning-based medical device designed to objectively assess aesthetic outcomes in FT patients.
Methods:
Overall, 14 FT patients were analyzed using CAARISMA ® ARMM, which evaluates 3 key aesthetic indices: the Facial Youthfulness Index (FYI), Facial Aesthetic Index (FAI), and Skin Quality Index (SQI). Preoperative, postoperative, and pre-trauma images were processed to assess improvements in facial aesthetics. Statistical analysis was performed to compare changes in these indices across the different time points.
Results:
Postoperative scores for FYI, FAI, and SQI were significantly higher than preoperative scores (p < 0.0001), indicating substantial aesthetic improvements. No significant differences were found between postoperative and pre-trauma images, suggesting that FT can effectively restore a patient's pre-injury appearance. Aesthetic improvements were consistent across different age and gender groups, with no notable disparities in outcomes.
Conclusion:
CAARISMA ® ARMM offers a reliable and objective framework for objectifying aesthetic outcomes following FT, allowing for more standardized assessments. This medical device can potentially improve patient-surgeon communication, enhance surgical planning, and serve as a benchmark for evaluating long-term aesthetic success in FT patients. Future research should focus on expanding CAARISMA ® ARMM's application to larger and more diverse patient populations.
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