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For: Maier T, Kulichova D, Schotten K, Astrid R, Ruzicka T, Berking C, Udrea A. Accuracy of a smartphone application using fractal image analysis of pigmented moles compared to clinical diagnosis and histological result. J Eur Acad Dermatol Venereol 2015;29:663-7. [PMID: 25087492 DOI: 10.1111/jdv.12648] [Cited by in Crossref: 55] [Cited by in F6Publishing: 37] [Article Influence: 6.9] [Reference Citation Analysis]
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18 Udrea A, Mitra GD, Costea D, Noels EC, Wakkee M, Siegel DM, de Carvalho TM, Nijsten TEC. Accuracy of a smartphone application for triage of skin lesions based on machine learning algorithms. J Eur Acad Dermatol Venereol 2020;34:648-55. [PMID: 31494983 DOI: 10.1111/jdv.15935] [Cited by in Crossref: 20] [Cited by in F6Publishing: 13] [Article Influence: 6.7] [Reference Citation Analysis]
19 Fujisawa Y, Otomo Y, Ogata Y, Nakamura Y, Fujita R, Ishitsuka Y, Watanabe R, Okiyama N, Ohara K, Fujimoto M. Deep-learning-based, computer-aided classifier developed with a small dataset of clinical images surpasses board-certified dermatologists in skin tumour diagnosis. Br J Dermatol. 2019;180:373-381. [PMID: 29953582 DOI: 10.1111/bjd.16924] [Cited by in Crossref: 93] [Cited by in F6Publishing: 66] [Article Influence: 23.3] [Reference Citation Analysis]
20 Gallay C, Girardet A, Viviano M, Catarino R, Benski AC, Tran PL, Ecabert C, Thiran JP, Vassilakos P, Petignat P. Cervical cancer screening in low-resource settings: a smartphone image application as an alternative to colposcopy. Int J Womens Health 2017;9:455-61. [PMID: 28790867 DOI: 10.2147/IJWH.S136351] [Cited by in Crossref: 16] [Cited by in F6Publishing: 9] [Article Influence: 3.2] [Reference Citation Analysis]
21 Jurczyszyn K, Kozakiewicz M. Application of Texture and Fractal Dimension Analysis to Estimate Effectiveness of Oral Leukoplakia Treatment Using an Er:YAG Laser-A Prospective Study. Materials (Basel) 2020;13:E3614. [PMID: 32824196 DOI: 10.3390/ma13163614] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 0.5] [Reference Citation Analysis]
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26 Rubagumya F, Nyagabona SK, Longombe AN, Manirakiza A, Ngowi J, Maniragaba T, Sabushimike D, Urusaro S, Ndoli DA, Dharsee N, Mwaiselage J, Mavura D, Hanna TP, Hammad N. Feasibility Study of a Smartphone Application for Detecting Skin Cancers in People With Albinism. JCO Glob Oncol 2020;6:1370-5. [PMID: 32903120 DOI: 10.1200/GO.20.00264] [Reference Citation Analysis]
27 Ngoo A, Finnane A, Mcmeniman E, Tan J, Janda M, Soyer HP. Efficacy of smartphone applications in high-risk pigmented lesions. Australas J Dermatol 2018;59:e175-82. [DOI: 10.1111/ajd.12599] [Cited by in Crossref: 25] [Cited by in F6Publishing: 21] [Article Influence: 5.0] [Reference Citation Analysis]
28 Chung Y, van der Sande AAJ, de Roos KP, Bekkenk MW, de Haas ERM, Kelleners-Smeets NWJ, Kukutsch NA. Poor agreement between the automated risk assessment of a smartphone application for skin cancer detection and the rating by dermatologists. J Eur Acad Dermatol Venereol 2020;34:274-8. [PMID: 31423673 DOI: 10.1111/jdv.15873] [Cited by in Crossref: 5] [Cited by in F6Publishing: 5] [Article Influence: 1.7] [Reference Citation Analysis]
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39 Chuchu N, Takwoingi Y, Dinnes J, Matin RN, Bassett O, Moreau JF, Bayliss SE, Davenport C, Godfrey K, O'Connell S, Jain A, Walter FM, Deeks JJ, Williams HC; Cochrane Skin Cancer Diagnostic Test Accuracy Group. Smartphone applications for triaging adults with skin lesions that are suspicious for melanoma. Cochrane Database Syst Rev 2018;12:CD013192. [PMID: 30521685 DOI: 10.1002/14651858.CD013192] [Cited by in Crossref: 31] [Cited by in F6Publishing: 22] [Article Influence: 7.8] [Reference Citation Analysis]