Analysis of surgical outcome after upper eyelid surgery by computer vision algorithm using face and facial landmark detection

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 blep...

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Bibliographic Details
Published in:Graefe's archive for clinical and experimental ophthalmology Vol. 259; no. 10; pp. 3119 - 3125
Main Authors: Bahçeci Şimşek, İlke, Şirolu, Can
Format: Journal Article
Language:English
Published: Berlin/Heidelberg Springer Berlin Heidelberg 01.10.2021
Springer Nature B.V
Subjects:
Algorithms
Artificial Intelligence
Blepharoplasty
Blepharoptosis - diagnosis
Blepharoptosis - surgery
Computer vision
Computers
Eyelid
Eyelids - surgery
Face
Humans
Image processing
Medicine
Medicine & Public Health
Oculoplastics and Orbit
Ophthalmology
Patients
Pattern recognition
Prohibitins
Retrospective Studies
Surgery
Treatment Outcome
Vision
Computer vision algorithm
Ptosis surgery
Upper eyelid blepharoplasty
Müller’s muscle-conjunctival resection
Artificial intelligence
Face and facial landmark detection
ISSN:0721-832X, 1435-702X, 1435-702X
Online Access:Get full text
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Summary: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.
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ISSN:0721-832X
1435-702X
1435-702X
DOI:10.1007/s00417-021-05219-8