Elegbede, Adekunle

Background/Objectives: Facial analysis is critical for preoperative planning in facial plastic surgery, but traditional methods can be time consuming and subjective. This study investigated the potential of Artificial Intelligence (AI) for objective and efficient facial analysis in plastic surgery, with a specific focus on Multimodal Large Language Models (MLLMs). We evaluated their ability to analyze facial skin quality, volume, symmetry, and adherence to aesthetic standards such as neoclassical facial canons and the golden ratio. Methods: We evaluated four MLLMs—ChatGPT-4o, ChatGPT-4, Gemini 1.5 Pro, and Claude 3.5 Sonnet—using two evaluation forms and 15 diverse facial images generated by a Generative Adversarial Network (GAN). The general analysis form evaluated qualitative skin features (texture, type, thickness, wrinkling, photoaging, and overall symmetry). The facial ratios form assessed quantitative structural proportions, including division into equal fifths, adherence to the rule of thirds, and compatibility with the golden ratio. MLLM assessments were compared with evaluations from a plastic surgeon and manual measurements of facial ratios. Results: The MLLMs showed promise in analyzing qualitative features, but they struggled with precise quantitative measurements of facial ratios. Mean accuracy for general analysis were ChatGPT-4o (0.61 ± 0.49), Gemini 1.5 Pro (0.60 ± 0.49), ChatGPT-4 (0.57 ± 0.50), and Claude 3.5 Sonnet (0.52 ± 0.50). In facial ratio assessments, scores were lower, with Gemini 1.5 Pro achieving the highest mean accuracy (0.39 ± 0.49). Inter-rater reliability, based on Cohen’s Kappa values, ranged from poor to high for qualitative assessments (κ > 0.7 for some questions) but was generally poor (near or below zero) for quantitative assessments. Conclusions: Current general purpose MLLMs are not yet ready to replace manual clinical assessments but may assist in general facial feature analysis. These findings are based on testing models not specifically trained for facial analysis and serve to raise awareness among clinicians regarding the current capabilities and inherent limitations of readily available MLLMs in this specialized domain. This limitation may stem from challenges with spatial reasoning and fine-grained detail extraction, which are inherent limitations of current MLLMs. Future research should focus on enhancing the numerical accuracy and reliability of MLLMs for broader application in plastic surgery, potentially through improved training methods and integration with other AI technologies such as specialized computer vision algorithms for precise landmark detection and measurement.

Summary: Facial buttresses are supportive bony structures of the facial skeleton that form a thick, strong, and protective framework for the face. Surgical fixation may be required to restore morphology and function when damage to these buttresses occurs. The authors sought to determine whether, similar to buttresses of the facial skeleton, buttresses of the internal orbit exist. They analyzed 10 human cadaver skulls imaged by microcomputed tomography. Image processing, and thickness and heat mapping, were performed using Avizo and ImageJ software. After identifying the orbital buttresses, the authors reviewed computed tomography scans of patients who had orbital fractures over a 3-year period to determine the frequency of fracture of the orbital buttresses. The authors identified 5 buttresses of the internal orbit: superomedial fronto-ethmoidal strut with the deep orbital buttress, inferomedial strut with the posterior ledge, inferior orbital fissure, sphenoid-frontal superolateral strut, and the sphenoid lip. The mean (SD) threshold orbital buttress thickness was 1.36 (0.25) mm. A total of 1186 orbits of 593 individuals were analyzed for orbital buttress involvement. Orbital buttresses were spared in 770 (65%) orbits. The inferomedial strut with the posterior ledge was the most commonly fractured buttress in 14.4% of orbits (n = 171), followed by the sphenoid strut and lip (66 [5.6%]). To the authors’ knowledge, this is the first description of the buttresses of the internal orbit. Orbital reconstruction for fracture repair or oncologic purposes requires the support of orbital buttresses. Understanding the anatomy of orbital buttresses is crucial for successful surgical planning, proper implant positioning, and restoration of function and appearance.