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27 results on '"Hidalgo, Leonel"'

4. Lesions on the Feet

Author

Deck, Dominga Peirano, Vargas, Sebastián, Abarzúa, Álvaro, Navarrete Dechent, Cristián, Uribe, Pablo, Hidalgo, Leonel, Tiodorovic, Danica, Anca, Ilut Paula, Saa, Sonia Rodriguez, Cabo, Horacio, Cabo, Horacio, editor, and Lallas, Aimilios, editor

Published
2023
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5. On Skin Lesion Recognition Using Deep Learning: 50 Ways to Choose Your Model

Author

Mery, Domingo, Romero, Pamela, Garib, Gabriel, Pedro, Alma, Salinas, Maria Paz, Sepulveda, Javiera, Hidalgo, Leonel, Prieto, Claudia, Navarrete-Dechent, Cristian, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Wang, Han, editor, Lin, Wei, editor, Manoranjan, Paul, editor, Xiao, Guobao, editor, Chan, Kap Luk, editor, Wang, Xiaonan, editor, Ping, Guiju, editor, and Jiang, Haoge, editor

Published
2023
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11. Clinical role of PRAME expression in melanocytic tumours: A single‐centre real‐world retrospective study

Author

Hidalgo, Leonel, primary, Peirano, Dominga, additional, Donoso, Francisca, additional, Villagrán, Sofía, additional, Carrasco, Karina, additional, Rossi, Anthony M., additional, Villarroel, Alejandra, additional, Del Puerto, Constanza, additional, Villaseca‐Hernández, Miguel, additional, and Navarrete‐Dechent, Cristian, additional

Published
2023
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15. Accuracy in anatomical location on dermatological surgery: A multi‐centre retrospective study

Author

Donoso, Francisca, primary, Hidalgo, Leonel, additional, Cowen, Emily A., additional, Villagran, Sofía, additional, Villablanca, Paula, additional, Del Puerto, Constanza, additional, Silva‐Valenzuela, Sergio, additional, Galimany, Lucas, additional, Majerson, Daniela, additional, Andino, Romina, additional, Uribe, Pablo, additional, Droppelmann, Katherine, additional, Cárdenas, Consuelo, additional, Abarzúa‐Araya, Álvaro, additional, Castro‐Ayala, Juan C., additional, Kurtansky, Nicholas R., additional, Halpern, Allan C., additional, Molenda, Matthew A., additional, Rotemberg, Veronica, additional, and Navarrete‐Dechent, Cristian, additional

Published
2022
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19. Multiple aggregated yellow‐white globules, a dermoscopic sign to be considered in the presurgical evaluation in Mohs surgery

Author

Hidalgo, Leonel, primary, Donoso, Francisca, additional, Guzmán, Mariana, additional, Millán, Rocío, additional, Curi, Maximiliano, additional, Misad‐Saide, Carlos, additional, Cárdenas, Consuelo, additional, Droppelmann, Katherine, additional, Abarzúa, Álvaro, additional, Uribe, Pablo, additional, and Navarrete‐Dechent, Cristian, additional

Published
2022
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21. Accuracy in anatomical location on dermatological surgery: A multi‐centre retrospective study.

Author

Donoso, Francisca, Hidalgo, Leonel, Cowen, Emily A., Villagran, Sofía, Villablanca, Paula, Del Puerto, Constanza, Silva‐Valenzuela, Sergio, Galimany, Lucas, Majerson, Daniela, Andino, Romina, Uribe, Pablo, Droppelmann, Katherine, Cárdenas, Consuelo, Abarzúa‐Araya, Álvaro, Castro‐Ayala, Juan C., Kurtansky, Nicholas R., Halpern, Allan C., Molenda, Matthew A., Rotemberg, Veronica, and Navarrete‐Dechent, Cristian

Subjects
*SURGICAL errors, *MEDICAL photography, *DELPHI method, *SKIN imaging, *RETROSPECTIVE studies
Abstract

While laterality was recorded in a higher proportion in MSKCC than in PUC (92.8% vs. 57.5% biopsies, I p i < 0.001), PUC was slightly more accurate in DCL registration (Delphi > 4; 23.8% vs. 19.1%, I p i = 0.008). Dear Editor, Lack of precision in recording anatomical sites can have several downstream effects that may jeopardize correct clinical communication. Consecutive biopsy records obtained from two tertiary centres - 10 dermatologists from Pontificia Universidad Catolica (PUC) in Santiago, Chile and 22 dermatology practitioners from Memorial Sloan Kettering Cancer Center (MSKCC) in New York - from 1 January 2021 to 31 October 2021 were reviewed. [Extracted from the article]

Published
2023
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22. How well can we predict Mohs micrographic surgery number of stages and reconstruction type? Reply to Shoham et al.

Author

Vargas, Sebastian, Peirano, Dominga, Hidalgo, Leonel, Droppelmann, Katherine, Cardenas, Consuelo, and Navarrete‐Dechent, Cristian

Subjects
MOHS surgery, MACHINE learning, CONVOLUTIONAL neural networks, RESEARCH personnel
Abstract

In a recent study published in Experimental Dermatology, researchers investigated the use of machine learning (ML) to predict the number of stages and type of reconstruction in Mohs micrographic surgery (MMS). The study found that the ML model had an accuracy of 77% in predicting the number of stages and 75% in predicting wound reconstruction complexity. However, the model was not externally validated. In an external validation using a cohort of 100 MMS patients, the researchers found poor predictions for both the number of stages and reconstruction type. They suggest that larger and more diverse datasets, including different populations and tumor subtypes, are needed to improve the efficacy of the ML model. The researchers also propose the inclusion of photographs and the use of convolutional neural networks to enhance the accuracy of predictions. [Extracted from the article]

Published
2023
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27. The role of ultraviolet-induced fluorescence dermatoscopy for the detection of multiple aggregated yellow-white globules in basal cell carcinoma.

Author

Navarrete-Dechent C, Pietkiewicz P, Astronave G, Marghoob NG, Dusza SW, Lorenzoni J, Boleira M, Cristopher M, Aguero R, Bustos S, Jaimes N, Kurpis M, Hidalgo L, Abarzua-Araya A, Zoroquiain P, Uribe P, Cárdenas C, Droppelmann K, and Marghoob AA

Abstract

Competing Interests: Conflicts of interest None disclosed.

Published
2024
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