The use of artificial intelligence in blepharoplasty surgery for dermatochalasis cases

Objective: The aim of this study is to analyze before and after periorbital images of patients who underwent blepharoplasty surgery due to dermatochalasis using artificial intelligence (AI), to minimize subjective evaluation and enhance accuracy. Materials and Methods: 50 periorbital photographs were obtained from 25 patients who underwent blepharoplasty at the Department of Ophthalmology, Afyonkarahisar Health Sciences University, between April-November 2024. Under standardized conditions, a computer-aided analysis was conducted to quantitatively evaluate the effectiveness of the surgery. The measurements included maximum reflex distance-1 (MRD-1), tarsal platform show (TPS), brow fat span (BFS), and palpebral fissure height (PFH). The MediaPipe platform and the OpenSource Computer Vision Library (OpenCV) was used. Using a hybrid approach, segmentation and measurement procedures were conducted to identify dermatochalasis and evaluate its changes. Results: Digital images taken before and after surgery were included and the deep learning models were supported by advanced algorithms. According to statistical analyses conducted with IBM SPSS version 29 using the Wilcoxon Signed-Rank Test, a statistically significant increase was observed (p < 0.001). These findings support that AI-assisted image processing approach provides a reliable and valid method for postoperative follow-up after blepharoplasty. Conclusion: AI-supported applications for facial landmark detection algorithms in blepharoplasty eliminate differences and enable more reliable measurements.