Hung J.-Y.Chen K.-W.Perera C.Chiu H.-K.Hsu C.-R.Myung D.Luo A.-C.CHIOU-SHANN FUHSHU-LANG LIAOKossler A.L.2022-09-132022-09-1320222075-4426https://www.scopus.com/inward/record.uri?eid=2-s2.0-85125051873&doi=10.3390%2fjpm12020283&partnerID=40&md5=7b6d4dee7ad283d2ab2d5042989752fbhttps://scholars.lib.ntu.edu.tw/handle/123456789/619304The 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.Artificial intelligence; Blepharoptosis; Computer-aided diagnosis (CAD); General practitioners[SDGs]SDG5Adam 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; surgeonjournal article10.3390/jpm120202832-s2.0-85125051873