An Outperforming Artificial Intelligence Model to Identify Referable Blepharoptosis for General Practitioners
Journal
Journal of Personalized Medicine
Journal Volume
12
Journal Issue
2
Pages
283
Date Issued
2022
Author(s)
Hung J.-Y.
Chen K.-W.
Perera C.
Chiu H.-K.
Hsu C.-R.
Myung D.
Luo A.-C.
Kossler A.L.
Abstract
The aim of this study is to develop an AI model that accurately identifies referable ble-pharoptosis automatically and to compare the AI model’s performance to a group of non-ophthalmic physicians. In total, 1000 retrospective single-eye images from tertiary oculoplastic clinics were labeled by three oculoplastic surgeons as having either ptosis, including true and pseudoptosis, or a healthy eyelid. A convolutional neural network (CNN) was trained for binary classification. The same dataset was used in testing three non-ophthalmic physicians. The CNN model achieved a sensitivity of 92% and a specificity of 88%, compared with the non-ophthalmic physician group, which achieved a mean sensitivity of 72% and a mean specificity of 82.67%. The AI model showed better performance than the non-ophthalmic physician group in identifying referable blepharoptosis, including true and pseudoptosis, correctly. Therefore, artificial intelligence-aided tools have the potential to assist in the diagnosis and referral of blepharoptosis for general practitioners. © 2022 by the authors. Licensee MDPI, Basel, Switzerland.
Subjects
Artificial intelligence; Blepharoptosis; Computer-aided diagnosis (CAD); General practitioners
SDGs
Other Subjects
Adam optimizer; adult; area under the curve; Article; artificial intelligence; binary classification; blepharochalasis; computer assisted diagnosis; convolutional neural network; cornea; dermatochalasis; diagnostic procedure; eye disease; eyebrow ptosis; eyelid; general practitioner; Gradient weighted class activation mapping; human; imaging and display; mathematical phenomena; neurologist; photography; ptosis (eyelid); receiver operating characteristic; sensitivity and specificity; single eye image; surgeon
Publisher
MDPI
Type
journal article