Browsing by Author "Donoso, Francisca"

Now showing 1 - 5 of 5
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    Accuracy in anatomical location on dermatological surgery: a multi‐centre retrospective study
    (2023) Donoso, Francisca; Hidalgo, Leonel; Cowen, Emily A.; Villagran, Sofía; Villablanca, Paula; Puerto, Constanza del; 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; Navarrete-Dechent, Cristian
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    Multiple aggregated yellow‐white globules, a dermoscopic sign to be considered in the presurgical evaluation in Mohs surgery
    (2022) Hidalgo, Leonel; Donoso, Francisca; Guzmán, Mariana; Millán, Rocío; Curi, Maximiliano; Misad‐Saide, Carlos; Cárdenas, Consuelo; Droppelmann, Katherine; Abarzúa, Álvaro; Uribe, Pablo; Navarrete-Dechent, Cristian
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    The degradation of performance of a state-of-the-art skin image classifier when applied to patient-driven internet search
    (2022) Han, Seung Seog; Navarrete-Dechent, Cristian; Liopyris, Konstantinos; Kim, Myoung Shin; Park, Gyeong Hun; Woo, Sang Seok; Park, Juhyun; Shin, Jung Won; Kim, Bo Ri; Kim, Min Jae; Donoso, Francisca; Villanueva, Francisco; Ramirez, Cristian; Chang, Sung Eun; Halpern, Allan; Kim, Seong Hwan; Na, Jung-Im
    Model Dermatology (; Build2021) is a publicly testable neural network that can classify 184 skin disorders. We aimed to investigate whether our algorithm can classify clinical images of an Internet community along with tertiary care center datasets. Consecutive images from an Internet skin cancer community ('RD' dataset, 1,282 images posted between 25 January 2020 to 30 July 2021; ) were analyzed retrospectively, along with hospital datasets (Edinburgh dataset, 1,300 images; SNU dataset, 2,101 images; TeleDerm dataset, 340 consecutive images). The algorithm's performance was equivalent to that of dermatologists in the curated clinical datasets (Edinburgh and SNU datasets). However, its performance deteriorated in the RD and TeleDerm datasets because of insufficient image quality and the presence of out-of-distribution disorders, respectively. For the RD dataset, the algorithm's Top-1/3 accuracy (39.2%/67.2%) and AUC (0.800) were equivalent to that of general physicians (36.8%/52.9%). It was more accurate than that of the laypersons using random Internet searches (19.2%/24.4%). The Top-1/3 accuracy was affected by inadequate image quality (adequate = 43.2%/71.3% versus inadequate = 32.9%/60.8%), whereas participant performance did not deteriorate (adequate = 35.8%/52.7% vs. inadequate = 38.4%/53.3%). In this report, the algorithm performance was significantly affected by the change of the intended settings, which implies that AI algorithms at dermatologist-level, in-distribution setting, may not be able to show the same level of performance in with out-of-distribution settings.
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    Use of game-based learning strategies for dermatology and dermoscopy education: a cross-sectional survey of members of the International Dermoscopy Society
    (2024) Donoso, Francisca; Peirano, Dominga; Aguero, Rosario; Longo, Caterina; Apalla, Zoe; Lallas, Aimilios; Jaimes, Natalia; Navarrete-Dechent, Cristian
    Background Dermoscopy is a valuable tool in the diagnosis of various skin conditions. It increases sensitivity and specificity in skin cancer diagnosis, as well as in infectious, inflammatory and hair diseases. However, mastering the intricacies of dermoscopy can be challenging. In this context, innovative educational methods are sought, including game-based learning (GBL) strategies. Objectives To describe current perceptions, knowledge and use of GBL strategies in dermoscopy education, and identify strengths and challenges to enhance their use. Methods A web-based cross-sectional survey with 25 questions was distributed to members of the International Dermoscopy Society between October 2022 and April 2023. Responses were collected and analysed using frequency analysis and graphical representation. Results In total, 801 responses were received. Of these, 46.6% of respondents were unfamiliar with gamification and serious games. Among those acquainted with these concepts, 56.3% reported using GBL strategies for education. Younger participants were more likely to use GBL strategies (P = 0.02). Participants familiar with GBL believed it enhanced medical education (78.5%) but should not entirely replace traditional teaching methods (96.0%). For dermoscopy education specifically, 22.1% of respondents had used GBL strategies, with Kahoot! (35.5%) and YOUdermoscopy (24.1%) being the most commonly used platforms. Respondents found gaming strategies to be fun (95.5%), motivating (91.0%) and valuable for e-learning (94.4%). Conclusions Results from this survey demonstrate a favourable perception of GBL strategies in dermatology education, including dermoscopy. While there are ongoing challenges in validation, GBL strategies are promising and valuable tools that can aid the learning and teaching experience. Addressing implementation barriers and validating existing games could optimize the impact of GBL on dermatology education.
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    Visualizing Touton Giant Cells Under Reflectance Confocal Microscopy in Two Cases of Juvenile Xanthogranuloma
    (2023) Peirano, Dominga; Donoso, Francisca; Hidalgo, Leonel; Feuerhake, Teo; Scope, Alon; Longo, Caterina; Navarrete-Dechent, Cristian