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

Non-randomized Protocols and Methods (ehealth) (540) Skin Cancer and Melanoma Prevention (110) Machine Learning (1385) Artificial Intelligence (1198) Imaging Informatics (187) Image Recognition and AI in Dermatology (9) Machine Learning from Digital Images in Dermatology (8)

Published on in Vol 11, No 3 (2022): March

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/34896, first published .
Leveraging Artificial Intelligence to Improve the Diversity of Dermatological Skin Color Pathology: Protocol for an Algorithm Development and Validation Study

Leveraging Artificial Intelligence to Improve the Diversity of Dermatological Skin Color Pathology: Protocol for an Algorithm Development and Validation Study

Leveraging Artificial Intelligence to Improve the Diversity of Dermatological Skin Color Pathology: Protocol for an Algorithm Development and Validation Study

Authors of this article:

Eman Rezk1 Author Orcid Image ;   Mohamed Eltorki2 Author Orcid Image ;   Wael El-Dakhakhni1 Author Orcid Image
Article Authors Cited by (15) Tweetations (7) Metrics

Journals

  1. Rezk E, Eltorki M, El-Dakhakhni W. Improving Skin Color Diversity in Cancer Detection: Deep Learning Approach. JMIR Dermatology 2022;5(3):e39143 View
  2. Baig A, Abbas Q, Almakki R, Ibrahim M, AlSuwaidan L, Ahmed A. Light-Dermo: A Lightweight Pretrained Convolution Neural Network for the Diagnosis of Multiclass Skin Lesions. Diagnostics 2023;13(3):385 View
  3. Ghaith M, Yosri A, El-Dakhakhni W. Synchronization-Enhanced Deep Learning Early Flood Risk Predictions: The Core of Data-Driven City Digital Twins for Climate Resilience Planning. Water 2022;14(22):3619 View
  4. Kushimo O, Salau A, Adeleke O, Olaoye D. Deep learning model to improve melanoma detection in people of color. Arab Journal of Basic and Applied Sciences 2023;30(1):92 View
  5. Rezk E, Haggag M, Eltorki M, El-Dakhakhni W. A comprehensive review of artificial intelligence methods and applications in skin cancer diagnosis and treatment: Emerging trends and challenges. Healthcare Analytics 2023;4:100259 View
  6. Jiminez V, Chung M, Saleem M, Yusuf N. Use of Artificial Intelligence in Skin Aging. OBM Geriatrics 2023;07(02):1 View
  7. Patel R, Foltz E, Witkowski A, Ludzik J. Analysis of Artificial Intelligence-Based Approaches Applied to Non-Invasive Imaging for Early Detection of Melanoma: A Systematic Review. Cancers 2023;15(19):4694 View
  8. Sengupta D. Artificial Intelligence in Diagnostic Dermatology: Challenges and the Way Forward. Indian Dermatology Online Journal 2023;14(6):782 View
  9. Cho S, Navarrete-Dechent C, Daneshjou R, Cho H, Chang S, Kim S, Na J, Han S. Generation of a Melanoma and Nevus Data Set From Unstandardized Clinical Photographs on the Internet. JAMA Dermatology 2023;159(11):1223 View
  10. Ongoro G, Avestruz Z, Stover S. Skin Inclusion: Addressing Deficits in Medical Education to Promote Diversity in Dermatological Diagnosis and Treatment. Clinical, Cosmetic and Investigational Dermatology 2023;Volume 16:3481 View
  11. Primiero C, Rezze G, Caffery L, Carrera C, Podlipnik S, Espinosa N, Puig S, Janda M, Soyer H, Malvehy J. A Narrative Review: Opportunities and Challenges in Artificial Intelligence Skin Image Analyses Using Total Body Photography. Journal of Investigative Dermatology 2024;144(6):1200 View
  12. Foltz E, Witkowski A, Becker A, Latour E, Lim J, Hamilton A, Ludzik J. Artificial Intelligence Applied to Non-Invasive Imaging Modalities in Identification of Nonmelanoma Skin Cancer: A Systematic Review. Cancers 2024;16(3):629 View
  13. Fliorent R, Fardman B, Podwojniak A, Javaid K, Tan I, Ghani H, Truong T, Rao B, Heath C. Artificial intelligence in dermatology: advancements and challenges in skin of color. International Journal of Dermatology 2024;63(4):455 View
  14. Hartmann L, Langhans D, Eggarter V, Freisenich T, Hillenmayer A, König S, Vounotrypidis E, Wolf A, Wertheimer C. Keratoconus Progression Determined at the First Visit: A Deep Learning Approach With Fusion of Imaging and Numerical Clinical Data. Translational Vision Science & Technology 2024;13(5):7 View