Browsing by Author "Kittler, Harald"

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    Delphi Consensus Among International Experts on the Diagnosis, Management, and Surveillance for Lentigo Maligna
    (2023) Longo, Caterina; Navarrete-Dechent, Cristian; Tschandl, Philipp; Apalla, Zoe; Argenziano, Giuseppe; Braun, Ralph P.; Bataille, Veronique; Cabo, Horacio; Hoffmann-Wellhenhof, Rainer; Forsea, Ana Maria; Garbe, Claus; Guitera, Pascale; Raimond, Karls; Marghoob, Ashfaq A.; Malvehy, Josep; Del Marmol, Veronique; Moreno, David; Nehal, Kishwer S.; Nagore, Eduardo; Paoli, John; Pellacani, Giovanni; Peris, Ketty; Puig, Susana; Soyer, H. Peter; Swetter, Susan; Stratigos, Alexander; Stolz, Wilhelm; Thomas, Luc; Tiodorovic, Danica; Zalaudek, Iris; Kittler, Harald; Lallas, Aimilios
    Introduction: Melanoma of the lentigo maligna (LM) type is challenging. There is lack of consensus on the optimal diagnosis, treatment, and follow-up.
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    Dermatologist-like explainable AI enhances trust and confidence in diagnosing melanoma
    (2024) Chanda, Tirtha; Hauser, Katja; Hobelsberger, Sarah; Bucher, Tabea-Clara; Garcia, Carina Nogueira; Wies, Christoph; Kittler, Harald; Tschandl, Philipp; Navarrete-Dechent, Cristian; Podlipnik, Sebastian; Chousakos, Emmanouil; Crnaric, Iva; Majstorovic, Jovana; Alhajwan, Linda; Foreman, Tanya; Peternel, Sandra; Sarap, Sergei; Ozdemir, Irem; Barnhill, Raymond L.; Llamas-Velasco, Mar; Poch, Gabriela; Korsing, Soeren; Sondermann, Wiebke; Gellrich, Frank Friedrich; Heppt, Markus V.; Erdmann, Michael; Haferkamp, Sebastian; Drexler, Konstantin; Goebeler, Matthias; Schilling, Bastian; Utikal, Jochen S.; Ghoreschi, Kamran; Froehling, Stefan; Krieghoff-Henning, Eva; Brinker, Titus J.
    Artificial intelligence (AI) systems have been shown to help dermatologists diagnose melanoma more accurately, however they lack transparency, hindering user acceptance. Explainable AI (XAI) methods can help to increase transparency, yet often lack precise, domain-specific explanations. Moreover, the impact of XAI methods on dermatologists' decisions has not yet been evaluated. Building upon previous research, we introduce an XAI system that provides precise and domain-specific explanations alongside its differential diagnoses of melanomas and nevi. Through a three-phase study, we assess its impact on dermatologists' diagnostic accuracy, diagnostic confidence, and trust in the XAI-support. Our results show strong alignment between XAI and dermatologist explanations. We also show that dermatologists' confidence in their diagnoses, and their trust in the support system significantly increase with XAI compared to conventional AI. This study highlights dermatologists' willingness to adopt such XAI systems, promoting future use in the clinic.
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    Expert Agreement on the Presence and Spatial Localization of Melanocytic Features in Dermoscopy
    (2024) Liopyris, Konstantinos; Navarrete-Dechent, Cristian; Marchetti, Michael A.; Rotemberg, Veronica; Apalla, Zoe; Argenziano, Giuseppe; Blum, Andreas; Braun, Ralph P.; Carrera, Cristina; Codella, Noel C. F.; Combalia, Marc; Dusza, Stephen W.; Gutman, David A.; Helba, Brian; Hofmann-Wellenhof, Rainer; Jaimes, Natalia; Kittler, Harald; Kose, Kivanc; Lallas, Aimilios; Longo, Caterina; Malvehy, Josep; Menzies, Scott; Nelson, Kelly C.; Paoli, John; Puig, Susana; Rabinovitz, Harold S.; Rishpon, Ayelet; Russo, Teresa; Scope, Alon; Soyer, H. Peter; Stein, Jennifer A.; Stolz, Willhelm; Sgouros, Dimitrios; Stratigos, Alexander J.; Swanson, David L.; Thomas, Luc; Tschandl, Philipp; Zalaudek, Iris; Weber, Jochen; Halpern, Allan C.; Marghoob, Ashfaq A.
    Dermoscopy aids in melanoma detection; however, agreement on dermoscopic features, including those of high clinical relevance, remains poor. In this study, we attempted to evaluate agreement among experts on exemplar images not only for the presence of melanocytic-specific features but also for spatial localization. This was a cross-sectional, multicenter, observational study. Dermoscopy images exhibiting at least 1 of 31 melanocytic-specific features were submitted by 25 world experts as exemplars. Using a web-based platform that allows for image markup of specific contrast-defined regions (superpixels), 20 expert readers annotated 248 dermoscopic images in collections of 62 images. Each collection was reviewed by five independent readers. A total of 4,507 feature observations were performed. Good-to-excellent agreement was found for 14 of 31 features (45.2%), with eight achieving excellent agreement (Gwet's AC >0.75) and seven of them being melanomaspecific features. These features were peppering/granularity (0.91), shiny white streaks (0.89), typical pigment network (0.83), blotch irregular (0.82), negative network (0.81), irregular globules (0.78), dotted vessels (0.77), and blue-whitish veil (0.76). By utilizing an exemplar dataset, a good-to-excellent agreement was found for 14 features that have previously been shown useful in discriminating nevi from melanoma. All images are public (www.isic-archive.com) and can be used for education, scientific communication, and machine learning experiments.
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    International Skin Imaging Collaboration-Designated Diagnoses (ISIC-DX): Consensus terminology for lesion diagnostic labeling
    (2024) Scope, Alon; Liopyris, Konstantinos; Weber, Jochen; Barnhill, Raymond L.; Braun, Ralph P.; Curiel-Lewandrowski, Clara N.; Elder, David E.; Ferrara, Gerardo; Grant-Kels, Jane M.; Jeunon, Thiago; Lallas, Aimilios; Lin, Jennifer Y.; Marchetti, Michael A.; Marghoob, Ashfaq A.; Navarrete-Dechent, Cristian; Pellacani, Giovanni; Soyer, Hans Peter; Stratigos, Alexander; Thomas, Luc; Kittler, Harald; Rotemberg, Veronica; Halpern, Allan C.
    Background: A common terminology for diagnosis is critically important for clinical communication, education, research and artificial intelligence. Prevailing lexicons are limited in fully representing skin neoplasms. Objectives: To achieve expert consensus on diagnostic terms for skin neoplasms and their hierarchical mapping. Methods: Diagnostic terms were extracted from textbooks, publications and extant diagnostic codes. Terms were hierarchically mapped to super-categories (e.g. 'benign') and cellular/tissue-differentiation categories (e.g. 'melanocytic'), and appended with pertinent-modifiers and synonyms. These terms were evaluated using a modified-Delphi consensus approach. Experts from the International-Skin-Imaging-Collaboration (ISIC) were surveyed on agreement with terms and their hierarchical mapping; they could suggest modifying, deleting or adding terms. Consensus threshold was >75% for the initial rounds and >50% for the final round. Results: Eighteen experts completed all Delphi rounds. Of 379 terms, 356 (94%) reached consensus in round one. Eleven of 226 (5%) benign-category terms, 6/140 (4%) malignant-category terms and 6/13 (46%) indeterminate-category terms did not reach initial agreement. Following three rounds, final consensus consisted of 362 terms mapped to 3 super-categories and 41 cellular/tissue-differentiation categories. Conclusions: We have created, agreed upon, and made public a taxonomy for skin neoplasms and their hierarchical mapping. Further study will be needed to evaluate the utility and completeness of the lexicon.
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    Lentigo maligna and lentigo maligna melanoma in patients younger than 50 years: a multicentre international clinical-dermoscopic study
    (2024) Longo, Caterina; Sticchi, Alberto; Curti, Alex; Kaleci, Shaniko; Moscarella, Elvira; Argenziano, Giuseppe; Thomas, Luc; Guitera, Pascale; Huang, Chen; Tiodorovic, Danica; Apalla, Zoe; Peris, Ketty; del Regno, Laura; Guida, Stefania; Lallas, Aimilios; Kittler, Harald; Pellacani, Giovanni; Navarrete-Dechent, Cristian
    Background Lentigo maligna/lentigo maligna melanoma (LM/LMM) is usually diagnosed in older patients, when lesions are larger. However, it is important to detect it at an earlier stage to minimize the area for surgical procedure.
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    Position statement of the EADV Artificial Intelligence (AI) Task Force on AI-assisted smartphone apps and web-based services for skin disease
    (2024) Sangers, Tobias E.; Kittler, Harald; Blum, Andreas; Braun, Ralph P.; Barata, Catarina; Cartocci, Alessandra; Combalia, Marc; Esdaile, Ben; Guitera, Pascale; Haenssle, Holger A.; Kvorning, Niels; Lallas, Aimilios; Navarrete-Dechent, Cristian; Navarini, Alexander A.; Podlipnik, Sebastian; Rotemberg, Veronica; Soyer, H. Peter; Tognetti, Linda; Tschandl, Philipp; Malvehy, Josep
    Background: As the use of smartphones continues to surge globally, mobile applications (apps) have become a powerful tool for healthcare engagement. Prominent among these are dermatology apps powered by Artificial Intelligence (AI), which provide immediate diagnostic guidance and educational resources for skin diseases, including skin cancer.Objective: This article, authored by the EADV AI Task Force, seeks to offer insights and recommendations for the present and future deployment of AI-assisted smartphone applications (apps) and web-based services for skin diseases with emphasis on skin cancer detection.Methods: An initial position statement was drafted on a comprehensive literature review, which was subsequently refined through two rounds of digital discussions and meticulous feedback by the EADV AI Task Force, ensuring its accuracy, clarity and relevance.Results: Eight key considerations were identified, including risks associated with inaccuracy and improper user education, a decline in professional skills, the influence of non-medical commercial interests, data security, direct and indirect costs, regulatory approval and the necessity of multidisciplinary implementation. Following these considerations, three main recommendations were formulated: (1) to ensure user trust, app developers should prioritize transparency in data quality, accuracy, intended use, privacy and costs; (2) Apps and web-based services should ensure a uniform user experience for diverse groups of patients; (3) European authorities should adopt a rigorous and consistent regulatory framework for dermatology apps to ensure their safety and accuracy for users.Conclusions: The utilisation of AI-assisted smartphone apps and web-based services in diagnosing and treating skin diseases has the potential to greatly benefit patients in their dermatology journeys. By prioritising innovation, fostering collaboration and implementing effective regulations, we can ensure the successful integration of these apps into clinical practice.