| 2. |
- Haenssle, H A, et al.
(författare)
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Man against machine: diagnostic performance of a deep learning convolutional neural network for dermoscopic melanoma recognition in comparison to 58 dermatologists.
- 2018
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Ingår i: Annals of Oncology. - : Elsevier BV. - 1569-8041 .- 0923-7534. ; 29:8, s. 1836-1842
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Tidskriftsartikel (refereegranskat)abstract
- Deep learning convolutional neural networks (CNN) may facilitate melanoma detection, but data comparing a CNN's diagnostic performance to larger groups of dermatologists are lacking.Google's Inception v4 CNN architecture was trained and validated using dermoscopic images and corresponding diagnoses. In a comparative cross-sectional reader study a 100-image test-set was used (level-I: dermoscopy only; level-II: dermoscopy plus clinical information and images). Main outcome measures were sensitivity, specificity and area under the curve (AUC) of receiver operating characteristics (ROC) for diagnostic classification (dichotomous) of lesions by the CNN versus an international group of 58 dermatologists during level-I or -II of the reader study. Secondary end points included the dermatologists' diagnostic performance in their management decisions and differences in the diagnostic performance of dermatologists during level-I and -II of the reader study. Additionally, the CNN's performance was compared with the top-five algorithms of the 2016 International Symposium on Biomedical Imaging (ISBI) challenge.In level-I dermatologists achieved a mean (±standard deviation) sensitivity and specificity for lesion classification of 86.6% (±9.3%) and 71.3% (±11.2%), respectively. More clinical information (level-II) improved the sensitivity to 88.9% (±9.6%, P = 0.19) and specificity to 75.7% (±11.7%, P < 0.05). The CNN ROC curve revealed a higher specificity of 82.5% when compared with dermatologists in level-I (71.3%, P < 0.01) and level-II (75.7%, P < 0.01) at their sensitivities of 86.6% and 88.9%, respectively. The CNN ROC AUC was greater than the mean ROC area of dermatologists (0.86 versus 0.79, P < 0.01). The CNN scored results close to the top three algorithms of the ISBI 2016 challenge.For the first time we compared a CNN's diagnostic performance with a large international group of 58 dermatologists, including 30 experts. Most dermatologists were outperformed by the CNN. Irrespective of any physicians' experience, they may benefit from assistance by a CNN's image classification.This study was registered at the German Clinical Trial Register (DRKS-Study-ID: DRKS00013570; https://www.drks.de/drks_web/).
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| 3. |
- Knobler, R., et al.
(författare)
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Guidelines on the use of extracorporeal photopheresis
- 2014
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Ingår i: Journal of the European Academy of Dermatology and Venereology. - : Wiley-Blackwell. - 0926-9959 .- 1468-3083. ; 28:s1, s. 1-37
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Tidskriftsartikel (refereegranskat)abstract
- BackgroundAfter the first investigational study on the use of extracorporeal photopheresis for the treatment of cutaneous T-cell lymphoma was published in 1983 with its subsequent recognition by the FDA for its refractory forms, the technology has shown significant promise in the treatment of other severe and refractory conditions in a multi-disciplinary setting. Among the major studied conditions are graft versus host disease after allogeneic bone marrow transplantation, systemic sclerosis, solid organ transplant rejection and inflammatory bowel disease. Materials and methodsIn order to provide recognized expert practical guidelines for the use of this technology for all indications the European Dermatology Forum (EDF) proceeded to address these questions in the hands of the recognized experts within and outside the field of dermatology. This was done using the recognized and approved guidelines of EDF for this task. Results and conclusionThese guidelines provide at present the most comprehensive available expert recommendations for the use of extracorporeal photopheresis based on the available published literature and expert consensus opinion.
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