Stellenwert der künstlichen Intelligenz zur Ausbreitungsdiagnostik und Verlaufsbeurteilung von Dermatosen

Maul, Lara Valeska; Meienberger, Nina; Kaufmann, L

doi:10.1007/s00105-020-04657-5

Cited by 5 publications

(3 citation statements)

References 23 publications

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“…Based on the corresponding histopathology of lesions in the dataset, the computer is "taught" which lesions are malignant or benign [1]. Additionally, algorithms continue to be developed via computerbased image analysis to assess the extent and progression of various dermatoses [7]. This not only lends interest to the diagnostic capability of AI systems, but also to their ability to evaluate disease progression.…”

Section: Artificial Intelligence and Machine Learningmentioning

confidence: 99%

Use of Artificial Intelligence in Skin Aging

Chung¹,

Jiminez²,

Saleem³

et al. 2023

OBM Geriatr

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Skin aging is a complex process that involves several extrinsic and intrinsic factors and skin health is an indicator of the well-being of an individual. In recent years, there have been numerous developments using computerized systems to aid in finding solutions and treatments to skin aging processes. Tools like artificial intelligence (AI) can aid in finding solutions and treatments for skin aging. AI can also help in monitoring or identifying early signs of skin aging. Within the field of skin aging, several innovations utilize AI to provide better patient care. There is a gap in knowledge within this field concerning current and future directions concerning skin aging and AI. In this review, we aim to highlight current and prospective applications of AI in skin aging and provide insights into future modalities in this field. Models for AI can serve to increase patient participation in skin-care decisions and eventually enhance the patient-provider experience.

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Section: Artificial Intelligence and Machine Learningmentioning

confidence: 99%

Use of Artificial Intelligence in Skin Aging

Chung¹,

Jiminez²,

Saleem³

et al. 2023

OBM Geriatr

View full text Add to dashboard Cite

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“…Recently, the combination of AI and human intelligence was promoted for the detection of skin cancer in parallel with a growing acceptance of AI in medicine among patients 7,8 . AI may therefore soon become part of day‐to‐day dermatological practice and clinical routine 8–10 . All technical terminology that is made use of can be found in Table S1.…”

Section: Introductionmentioning

confidence: 99%

“…7,8 AI may therefore soon become part of day-to-day dermatological practice and clinical routine. [8][9][10] All technical terminology that is made use of can be found in Table S1.…”

Section: Introductionmentioning

confidence: 99%

Outlier detection in dermatology: Performance of different convolutional neural networks for binary classification of inflammatory skin diseases

Schielein

Christl

Sitaru

et al. 2023

Acad Dermatol Venereol

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Background Artificial intelligence (AI) and convolutional neural networks (CNNs) represent rising trends in modern medicine. However, comprehensive data on the performance of AI practices in clinical dermatologic images are non‐existent. Furthermore, the role of professional data selection for training remains unknown. Objectives The aims of this study were to develop AI applications for outlier detection of dermatological pathologies, to evaluate CNN architectures' performance on dermatological images and to investigate the role of professional pre‐processing of the training data, serving as one of the first anchor points regarding data selection criteria in dermatological AI‐based binary classification tasks of non‐melanoma pathologies. Methods Six state‐of‐the‐art CNN architectures were evaluated for their accuracy, sensitivity and specificity for five dermatological diseases and using five data subsets, including data selected by two dermatologists, one with 5 and the other with 11 years of clinical experience. Results Overall, 150 CNNs were evaluated on up to 4051 clinical images. The best accuracy was reached for onychomycosis (accuracy = 1.000), followed by bullous pemphigoid (accuracy = 0.951) and lupus erythematosus (accuracy = 0.912). The CNNs InceptionV3, Xception and ResNet50 achieved the best accuracy in 9, 8 and 6 out of 25 data sets, respectively (36.0%, 32.0% and 24.0%). On average, the data set provided by the senior physician and the data set provided in accordance with both dermatologists performed the best (accuracy = 0.910). Conclusions This AI approach for the detection of outliers in dermatological diagnoses represents one of the first studies to evaluate the performance of different CNNs for binary decisions in clinical non‐dermatoscopic images of a variety of dermatological diseases other than melanoma. The selection of images by an experienced dermatologist during pre‐processing had substantial benefits for the performance of the CNNs. These comparative results might guide future AI approaches to dermatology diagnostics, and the evaluated CNNs might be applicable for the future training of dermatology residents.

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