Analysis of surgical outcome after upper eyelid surgery by computer vision algorithm using face and facial landmark detection - Bahçeci Şimşek, İlke; Şirolu, Can (Graefe's archive for clinical and experimental ophthalmology, 2021)
Purpose To evaluate the postoperative changes with a computer vision algorithm for anterior full-face photographs of patients who have undergone upper eyelid blepharoplasty surgery with, or without, a Müller’s muscle-conjunctival resection (MMCR). Methods All patients who underwent upper eyelid blepharoplasty surgery (Group I), or upper eyelid blepharoplasty with MMCR (Group II) were included. Both preoperative and 6-month postoperative anterior full-face photographs of 55 patients were analyzed. Computer vision and image processing technologies were used to measure the palpebral distance (PD), eye-opening area (EA), and average eyebrow height (AEBH) for both eyes. Preoperative and postoperative measurements were calculated and compared between the two groups. Results In Group II, change in postoperative Right PD, Left PD, Right EA, Left EA was significantly higher than in Group I ( p = 0.004 for REPD; p = 0.001 for LEPD; p = 0.004 for REA; p = 0.002 for LEA, p < 0.05). In Group II, the postoperative change in Right AEBH, Left AEBH was significantly higher than in Group I ( p = 0.001 for RABH and LABH, p < 0.05). Conclusion Eyelid surgery for esthetic purposes requires artistic judgment and objective evaluation. Because of the slight differences in photograph sizes and dynamic factors of the face due to head movements and facial expressions, it is hard to compare and make a truly objective evaluation of the eyelid operations. With a computer vision algorithm, using the face and facial landmark detection system, the photographs are normalized and calibrated. This system offers a simple, standardized, objective, and repeatable method of patient assessment. This can be the first step of Artificial Intelligence algorithm to evaluate the patients who had undergone eyelid operations.