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Literature DB >> 29864435

Automated Dermatological Diagnosis: Hype or Reality?

Cristian Navarrete-Dechent¹, Stephen W Dusza², Konstantinos Liopyris², Ashfaq A Marghoob², Allan C Halpern², Michael A Marchetti³.

Abstract

Entities: Disease Gene Species

Mesh：

Year: 2018 PMID： 29864435 PMCID： PMC7701995 DOI： 10.1016/j.jid.2018.04.040

Source DB: PubMed Journal: J Invest Dermatol ISSN： 0022-202X Impact factor: 8.551

× No keyword cloud information.

7 in total

1. The performance of SolarScan: an automated dermoscopy image analysis instrument for the diagnosis of primary melanoma.

Authors: Scott W Menzies; Leanne Bischof; Hugues Talbot; Alex Gutenev; Michelle Avramidis; Livian Wong; Sing Kai Lo; Geoffrey Mackellar; Victor Skladnev; William McCarthy; John Kelly; Brad Cranney; Peter Lye; Harold Rabinovitz; Margaret Oliviero; Andreas Blum; Alexandra Varol; Alexandra Virol; Brian De'Ambrosis; Roderick McCleod; Hiroshi Koga; Caron Grin; Ralph Braun; Robert Johr
Journal: Arch Dermatol Date: 2005-11

2. Can we open the black box of AI?

Authors: Davide Castelvecchi
Journal: Nature Date: 2016-10-06 Impact factor: 49.962

3. Potential overdiagnosis of basal cell carcinoma in older patients with limited life expectancy.

Authors: Eleni Linos; Steven A Schroeder; Mary-Margaret Chren
Journal: JAMA Date: 2014-09-10 Impact factor: 56.272

4. Classification of the Clinical Images for Benign and Malignant Cutaneous Tumors Using a Deep Learning Algorithm.

Authors: Seung Seog Han; Myoung Shin Kim; Woohyung Lim; Gyeong Hun Park; Ilwoo Park; Sung Eun Chang
Journal: J Invest Dermatol Date: 2018-02-08 Impact factor: 8.551

5. Dermatologist-level classification of skin cancer with deep neural networks.

Authors: Andre Esteva; Brett Kuprel; Roberto A Novoa; Justin Ko; Susan M Swetter; Helen M Blau; Sebastian Thrun
Journal: Nature Date: 2017-01-25 Impact factor: 49.962

6. Computer-aided classification of melanocytic lesions using dermoscopic images.

Authors: Laura K Ferris; Jan A Harkes; Benjamin Gilbert; Daniel G Winger; Kseniya Golubets; Oleg Akilov; Mahadev Satyanarayanan
Journal: J Am Acad Dermatol Date: 2015-09-19 Impact factor: 11.527

7. Results of the 2016 International Skin Imaging Collaboration International Symposium on Biomedical Imaging challenge: Comparison of the accuracy of computer algorithms to dermatologists for the diagnosis of melanoma from dermoscopic images.

Authors: Michael A Marchetti; Noel C F Codella; Stephen W Dusza; David A Gutman; Brian Helba; Aadi Kalloo; Nabin Mishra; Cristina Carrera; M Emre Celebi; Jennifer L DeFazio; Natalia Jaimes; Ashfaq A Marghoob; Elizabeth Quigley; Alon Scope; Oriol Yélamos; Allan C Halpern
Journal: J Am Acad Dermatol Date: 2017-09-29 Impact factor: 11.527

7 in total

23 in total

1. Computer algorithms show potential for improving dermatologists' accuracy to diagnose cutaneous melanoma: Results of the International Skin Imaging Collaboration 2017.

Authors: Michael A Marchetti; Konstantinos Liopyris; Stephen W Dusza; Noel C F Codella; David A Gutman; Brian Helba; Aadi Kalloo; Allan C Halpern
Journal: J Am Acad Dermatol Date: 2019-07-12 Impact factor: 11.527

2. Fully Automated Approach for Early Detection of Pigmented Skin Lesion Diagnosis Using ABCD.

Authors: Mai S Mabrouk; Ahmed Y Sayed; Heba M Afifi; Mariam A Sheha; Amr Sharwy
Journal: J Healthc Inform Res Date: 2020-03-03

3. Performance of a deep neural network in teledermatology: a single-centre prospective diagnostic study.

Authors: C Muñoz-López; C Ramírez-Cornejo; M A Marchetti; S S Han; P Del Barrio-Díaz; A Jaque; P Uribe; D Majerson; M Curi; C Del Puerto; F Reyes-Baraona; R Meza-Romero; J Parra-Cares; P Araneda-Ortega; M Guzmán; R Millán-Apablaza; M Nuñez-Mora; K Liopyris; C Vera-Kellet; C Navarrete-Dechent
Journal: J Eur Acad Dermatol Venereol Date: 2020-11-22 Impact factor: 6.166

Review 4. The Importance of Incorporating Human Factors in the Design and Implementation of Artificial Intelligence for Skin Cancer Diagnosis in the Real World.

Authors: Claire M Felmingham; Nikki R Adler; Zongyuan Ge; Rachael L Morton; Monika Janda; Victoria J Mar
Journal: Am J Clin Dermatol Date: 2021-03 Impact factor: 7.403

Automated Dermatological Diagnosis: Hype or Reality?

1. The performance of SolarScan: an automated dermoscopy image analysis instrument for the diagnosis of primary melanoma.

2. Can we open the black box of AI?

3. Potential overdiagnosis of basal cell carcinoma in older patients with limited life expectancy.

4. Classification of the Clinical Images for Benign and Malignant Cutaneous Tumors Using a Deep Learning Algorithm.

5. Dermatologist-level classification of skin cancer with deep neural networks.

6. Computer-aided classification of melanocytic lesions using dermoscopic images.

7. Results of the 2016 International Skin Imaging Collaboration International Symposium on Biomedical Imaging challenge: Comparison of the accuracy of computer algorithms to dermatologists for the diagnosis of melanoma from dermoscopic images.

1. Computer algorithms show potential for improving dermatologists' accuracy to diagnose cutaneous melanoma: Results of the International Skin Imaging Collaboration 2017.

2. Fully Automated Approach for Early Detection of Pigmented Skin Lesion Diagnosis Using ABCD.

3. Performance of a deep neural network in teledermatology: a single-centre prospective diagnostic study.

Review 4. The Importance of Incorporating Human Factors in the Design and Implementation of Artificial Intelligence for Skin Cancer Diagnosis in the Real World.

5. Multiclass Artificial Intelligence in Dermatology: Progress but Still Room for Improvement.

Review 6. Machine Learning in Dermatology: Current Applications, Opportunities, and Limitations.

Review 7. Buruli Ulcer: a Review of the Current Knowledge.

8. Skin Cancer Classification Using Convolutional Neural Networks: Systematic Review.

Review 9. The Possibility of Deep Learning-Based, Computer-Aided Skin Tumor Classifiers.

Review 10. Artificial Intelligence Applications in Dermatology: Where Do We Stand?