Lagophthalmos as a presenting sign in dermatomyositis with muscle involvement limited to the ocular muscles

Roesler, Jordanna; Jenkins, Donald H.

doi:10.1016/j.jdcr.2021.01.035

Cited by 3 publications

(1 citation statement)

References 9 publications

(2 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In patients suffering from CL, NL, and PL, the eye is not adequately wetted with tear fluid and, therefore, dries out. Such xerophthalmia and prolonged corneal exposure can trigger keratopathy and keratitis, ultimately progressing to corneal ulceration, reduction of visual acuity, or even blindness 2 , 3 . In order to preemptively avoid such sequelae, lagophthalmos must be diagnosed at an early stage and immediate targeted therapy should be initiated 4 .…”

Section: Introductionmentioning

confidence: 99%

Diagnosing lagophthalmos using artificial intelligence

Knoedler,

Alfertshofer,

Simon

et al. 2023

Sci Rep

View full text Add to dashboard Cite

Lagophthalmos is the incomplete closure of the eyelids posing the risk of corneal ulceration and blindness. Lagophthalmos is a common symptom of various pathologies. We aimed to program a convolutional neural network to automatize lagophthalmos diagnosis. From June 2019 to May 2021, prospective data acquisition was performed on 30 patients seen at the Department of Plastic, Hand, and Reconstructive Surgery at the University Hospital Regensburg, Germany (IRB reference number: 20-2081-101). In addition, comparative data were gathered from 10 healthy patients as the control group. The training set comprised 826 images, while the validation and testing sets consisted of 91 patient images each. Validation accuracy was 97.8% over the span of 64 epochs. The model was trained for 17.3 min. For training and validation, an average loss of 0.304 and 0.358 and a final loss of 0.276 and 0.157 were noted. The testing accuracy was observed to be 93.41% with a loss of 0.221. This study proposes a novel application for rapid and reliable lagophthalmos diagnosis. Our CNN-based approach combines effective anti-overfitting strategies, short training times, and high accuracy levels. Ultimately, this tool carries high translational potential to facilitate the physician’s workflow and improve overall lagophthalmos patient care.

show abstract

Section: Introductionmentioning

confidence: 99%