Objectifying aesthetic outcomes following face transplantation – the AI research metrics model (CAARISMA ® ARMM)

Abstract

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.