Zachary M Bodnar1, Michael Neimkin2, John B Holds1. 1. Department of Ophthalmology, Saint Louis University, St Louis, Missouri. 2. Department of Ophthalmology and Visual Sciences, Washington University in St Louis, St Louis, Missouri.
Abstract
IMPORTANCE: Measurements of the margin reflex distances 1 and 2 are crucial for the surgical planning of ptosis repair and blepharoplasty. Facial photographs annotated with automated measurements of eyelid position could provide objective, accurate, and reproducible documentation of these features. OBJECTIVES: To describe a software algorithm for determining the margin reflex distances 1 and 2 from facial photographs and to evaluate its agreement with manual measurements of the margin reflex distances 1 and 2. DESIGN, SETTING, AND PARTICIPANTS: Observational study at a single-surgeon oculoplastic private practice among 55 eyes of 28 adult volunteers. The study dates were July 30, 2014, to September 12, 2014. The dates of our analysis were October 12, 2014, to June 18, 2015. MAIN OUTCOMES AND MEASURES: Agreement between manual and automated measurements of the margin reflex distances 1 and 2. RESULTS: Among 55 eyes of 28 participants, automated margin reflex distance 1 measurements were strongly correlated with manual measurements (r = 0.97; 95% CI, r = 0.95 to r = 0.98; P < .001). The bias of automated margin reflex distance 1 measurements was 0.03 mm (95% CI, -0.06 to 0.12 mm), with 95% confidence limits of -0.66 and 0.71 mm. Automated margin reflex distance 2 measurements were strongly correlated with manual measurements (r = 0.96; 95% CI, r = 0.93 to r = 0.98; P < .001). The bias of automated margin reflex distance 2 measurements was 0.13 mm (95% CI, 0.03-0.22 mm), with 95% confidence limits of -0.54 and 0.80 mm. CONCLUSIONS AND RELEVANCE: Automated ptosis measurements produced by our software algorithm compare favorably with manually performed clinical measurements. An automated, photography-based system could provide an archival and highly reproducible means for obtaining the margin reflex distances 1 and 2 and other facial morphometric data.
IMPORTANCE: Measurements of the margin reflex distances 1 and 2 are crucial for the surgical planning of ptosis repair and blepharoplasty. Facial photographs annotated with automated measurements of eyelid position could provide objective, accurate, and reproducible documentation of these features. OBJECTIVES: To describe a software algorithm for determining the margin reflex distances 1 and 2 from facial photographs and to evaluate its agreement with manual measurements of the margin reflex distances 1 and 2. DESIGN, SETTING, AND PARTICIPANTS: Observational study at a single-surgeon oculoplastic private practice among 55 eyes of 28 adult volunteers. The study dates were July 30, 2014, to September 12, 2014. The dates of our analysis were October 12, 2014, to June 18, 2015. MAIN OUTCOMES AND MEASURES: Agreement between manual and automated measurements of the margin reflex distances 1 and 2. RESULTS: Among 55 eyes of 28 participants, automated margin reflex distance 1 measurements were strongly correlated with manual measurements (r = 0.97; 95% CI, r = 0.95 to r = 0.98; P < .001). The bias of automated margin reflex distance 1 measurements was 0.03 mm (95% CI, -0.06 to 0.12 mm), with 95% confidence limits of -0.66 and 0.71 mm. Automated margin reflex distance 2 measurements were strongly correlated with manual measurements (r = 0.96; 95% CI, r = 0.93 to r = 0.98; P < .001). The bias of automated margin reflex distance 2 measurements was 0.13 mm (95% CI, 0.03-0.22 mm), with 95% confidence limits of -0.54 and 0.80 mm. CONCLUSIONS AND RELEVANCE: Automated ptosis measurements produced by our software algorithm compare favorably with manually performed clinical measurements. An automated, photography-based system could provide an archival and highly reproducible means for obtaining the margin reflex distances 1 and 2 and other facial morphometric data.
Authors: Alexandra Van Brummen; Julia P Owen; Theodore Spaide; Colin Froines; Randy Lu; Megan Lacy; Marian Blazes; Emily Li; Cecilia S Lee; Aaron Y Lee; Matthew Zhang Journal: Am J Ophthalmol Date: 2021-05-16 Impact factor: 5.258