Medida da distância reflexo margem por meio de processamento computadorizado de imagens em usuários de lentes de contato rígidas

Burmann, Tiana Gabriela; Valiatti, Fabiana Borba; Corrêa, Zélia M.; Bayer, Marcia Cristina; Marcon, Ítalo Mundialino

doi:10.1590/s0004-27492008000100007

Cited by 4 publications

(4 citation statements)

References 8 publications

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“…Furthermore, small variations may impact clinical value, research, and audit value, which require a more precise and accurate method. The computer-assisted analysis of facial photographs for the measurement of MRD1, MRD2, eyelid contour, and palpebral fissure has been previously described [13][14][15][16]. However, these methods rely on significant user and computer interaction after image acquisition and depend on an observer to identify the edges and facial features.…”

Section: Discussionmentioning

confidence: 99%

Introduction of Deep Learning-Based Infrared Image Analysis to Marginal Reflex Distance1 Measurement Method to Simultaneously Capture Images and Compute Results: Clinical Validation Study

Song,

Kwon,

Kim

et al. 2023

JCM

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Marginal reflex distance1 (MRD1) is a crucial clinical tool used to evaluate the position of the eyelid margin in relation to the cornea. Traditionally, this assessment has been conducted manually by plastic surgeons, ophthalmologists, or trained technicians. However, with the advancements in artificial intelligence (AI) technology, there is a growing interest in the development of automated systems capable of accurately measuring MRD1. In this context, we introduce novel MRD1 measurement methods based on deep learning algorithms that can simultaneously capture images and compute the results. This prospective observational study involved 154 eyes of 77 patients aged over 18 years who visited Chungnam National University Hospital between 1 January 2023 and 29 July 2023. We collected four different MRD1 datasets from patients using three distinct measurement methods, each tailored to the individual patient. The mean MRD1 values, measured through the manual method using a penlight, the deep learning method, ImageJ analysis from RGB eye images, and ImageJ analysis from IR eye images in 56 eyes of 28 patients, were 2.64 ± 1.04 mm, 2.85 ± 1.07 mm, 2.78 ± 1.08 mm, and 3.07 ± 0.95 mm, respectively. Notably, the strongest agreement was observed between MRD1_deep learning (DL) and MRD1_IR (0.822, p < 0.01). In a Bland–Altman plot, the smallest difference was observed between MRD1_DL and MRD1_IR ImageJ, with a mean difference of 0.0611 and ΔLOA (limits of agreement) of 2.5162, which was the smallest among all of the groups. In conclusion, this novel MRD1 measurement method, based on an IR camera and deep learning, demonstrates statistical significance and can be readily applied in clinical settings.

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Section: Discussionmentioning

confidence: 99%

Introduction of Deep Learning-Based Infrared Image Analysis to Marginal Reflex Distance1 Measurement Method to Simultaneously Capture Images and Compute Results: Clinical Validation Study

Song,

Kwon,

Kim

et al. 2023

JCM

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“…Os diversos modos disponíveis para quantificar a fenda palpebral sugerem que não existe um método padrão universal. Atualmente, as medidas com uma régua, em posição primária do olhar (PPO), da fenda palpebral e da distância margem reflexo (MRD) -que corresponde à distância medida em milímetros entre a margem palpebral superior e o reflexo corneano forma- do por uma fonte luminosa puntiforme fixada em PPO -, têm sido amplamente utilizadas (10,16) . No entanto, como a régua é um meio subjetivo, as fotografias tornam a avaliação mais precisa.…”

Section: Discussionunclassified

“…Em 51,8% dos olhos estudados, houve aumento da fenda palpebral, dado estatisticamente significante. Tais achados tornam-se mais confiáveis devido à utilização de um método preciso e objetivo de avaliação da fenda palpebral (10) .…”

Section: Discussionunclassified

“…Após aplicação dessa toxina na região periocular, em alguns casos têm sido observadas complicações, como dor, edema e hematoma locais, diminuição da sensibilidade local e fotofobia, além de ectrópio e ptose palpebral ipsilaterais (8,9) . A alteração do posicionamento palpebral é decorrente da ação desta toxina nos músculos retratores (músculo elevador da pálpebra superior, músculo de Muller, músculo frontal) ou protatores (músculos orbicular e secundários) (10,11) . A distância entre as margens palpebrais superior e infeFigura 1: Técnica inner orbital de aplicação de toxina botulínica tipo A em pacientes com BEB -bilateral totalizando 30U(A) e EHF -unilateral totalizando 20U (B).…”

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Avaliação da fenda palpebral após aplicação de toxina botulínica tipo A em pacientes com distonias faciais

Cunial

Fonseca

Rehder

2012

Rev. bras.oftalmol.

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PeriorbitAI: Artificial Intelligence Automation of Eyelid and Periorbital Measurements

Brummen

Owen

Spaide³

et al. 2021

American Journal of Ophthalmology

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Medida da distância reflexo margem por meio de processamento computadorizado de imagens em usuários de lentes de contato rígidas

Cited by 4 publications

References 8 publications

Introduction of Deep Learning-Based Infrared Image Analysis to Marginal Reflex Distance1 Measurement Method to Simultaneously Capture Images and Compute Results: Clinical Validation Study

Introduction of Deep Learning-Based Infrared Image Analysis to Marginal Reflex Distance1 Measurement Method to Simultaneously Capture Images and Compute Results: Clinical Validation Study

Avaliação da fenda palpebral após aplicação de toxina botulínica tipo A em pacientes com distonias faciais

PeriorbitAI: Artificial Intelligence Automation of Eyelid and Periorbital Measurements

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