This paper is in the following e-collection/theme issue:

mHealth for Symptom and Disease Monitoring, Chronic Disease Management (1317) Artificial Intelligence (1214) Imaging Informatics (187) Mobile Health (mhealth) (2777) Tools, Programs and Algorithms (245) New Methods (911)

Published on in Vol 9, No 10 (2021): October

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/32444, first published .
Smartphone-Based Artificial Intelligence–Assisted Prediction for Eyelid Measurements: Algorithm Development and Observational Validation Study

Smartphone-Based Artificial Intelligence–Assisted Prediction for Eyelid Measurements: Algorithm Development and Observational Validation Study

Smartphone-Based Artificial Intelligence–Assisted Prediction for Eyelid Measurements: Algorithm Development and Observational Validation Study

Authors of this article:

Hung-Chang Chen1, 2 Author Orcid Image ;   Shin-Shi Tzeng1, 2 Author Orcid Image ;   Yen-Chang Hsiao1, 2 Author Orcid Image ;   Ruei-Feng Chen3 Author Orcid Image ;   Erh-Chien Hung1, 2 Author Orcid Image ;   Oscar K Lee4, 5 Author Orcid Image
Article Authors Cited by (12) Tweetations (2) Metrics

Journals

  1. Bao X, Sun Y, Zhan X, Li G. Orbital and eyelid diseases: The next breakthrough in artificial intelligence?. Frontiers in Cell and Developmental Biology 2022;10 View
  2. Xu J, Yan Z, Liu Q. Smartphone-Based Electrochemical Systems for Glucose Monitoring in Biofluids: A Review. Sensors 2022;22(15):5670 View
  3. Chen K, Yang C, Wang H, Ma H, Lee O. Artificial Intelligence–Assisted Diagnosis of Anterior Cruciate Ligament Tears From Magnetic Resonance Images: Algorithm Development and Validation Study. JMIR AI 2022;1(1):e37508 View
  4. Hui S, Dong L, Zhang K, Nie Z, Jiang X, Li H, Hou Z, Ding J, Wang Y, Li D. Noninvasive identification of Benign and malignant eyelid tumors using clinical images via deep learning system. Journal of Big Data 2022;9(1) View
  5. Yang Y, Xu F, Chen J, Tao C, Li Y, Chen Q, Tang S, Lee H, Shen W. Artificial intelligence-assisted smartphone-based sensing for bioanalytical applications: A review. Biosensors and Bioelectronics 2023;229:115233 View
  6. Bhattacharjee K, Rehman O, Sarkar A. The gamut of artificial intelligence in oculoplasty. Journal of Ophthalmic Research and Practice 2023;1:5 View
  7. Abascal Azanza C, Barrio-Barrio J, Ramos Cejudo J, Ybarra Arróspide B, Devoto M. Development and validation of a convolutional neural network to identify blepharoptosis. Scientific Reports 2023;13(1) View
  8. Mahmoudi T, Riazi-Esfahani H, Montazeriani Z, Yaseri M, Mehdipour Namdar Z, Jamali M, Rafizadeh S, Khalili Pour E. Sector area index: a novel supporting marker for blepharoptosis screening and grading. International Ophthalmology 2023;43(12):4967 View
  9. Lootus M, Beatson L, Atwood L, Bourdais T, Steyaert S, Sarabu C, Framroze Z, Dickinson H, Steels J, Lewis E, Shah N, Rinaldo F. Development and Assessment of an Artificial Intelligence-Based Tool for Ptosis Measurement in Adult Myasthenia Gravis Patients Using Selfie Video Clips Recorded on Smartphones. Digital Biomarkers 2023:63 View
  10. Cai Y, Zhang X, Cao J, Grzybowski A, Ye J, Lou L. Application of artificial intelligence in oculoplastics. Clinics in Dermatology 2024;42(3):259 View
  11. Jin K, Li Y, Wu H, Tham Y, Koh V, Zhao Y, Kawasaki R, Grzybowski A, Ye J. Integration of smartphone technology and artificial intelligence for advanced ophthalmic care: A systematic review. Advances in Ophthalmology Practice and Research 2024;4(3):120 View
  12. Büchner T, Mothes O, Guntinas-Lichius O, Denzler J. JeFaPaTo - A joint toolbox for blinking analysis and facial features extraction. Journal of Open Source Software 2024;9(97):6425 View