Modelling and finite time stability analysis of psoriasis pathogenesis

A new systems model of psoriasis is presented and analysed from the perspective of control theory. Cytokines are treated as actuators to the plant model that govern the cell population under the reasonable assumption that cytokine dynamics are faster than the cell population dynamics. The analysis of various equilibria is undertaken based on singular perturbation theory. Finite time stability and stabilisation has been studied in various engineering applications where the principal paradigm uses non-Lipschitz functions of the states. A comprehensive study of the finite time stability properties of the proposed psoriasis dynamics is carried out. It is demonstrated that the dynamics are finite time convergent to certain equilibrium points rather than asymptotically or exponentially convergent. This feature of finite time convergence motivates the development of a modi?ed version of the Michaelis-Menten function, frequently used in biology. This framework is used to model cytokines as fast finite time actuators

Man against Machine: Diagnostic performance of a deep learning convolutional neural network for dermoscopic melanoma recognition in comparison to 58 dermatologists

Background: Deep learning convolutional neural networks (CNN) May facilitate melanoma detection, but data comparing a CNN\u2019s diagnostic performance to larger groups of dermatologists are lacking. Methods: Google\u2019s 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\u2019 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\u2019s performance was compared with the top-five algorithms of the 2016 International Symposium on Biomedical Imaging (ISBI) challenge. Results: In level-I dermatologists achieved a mean (6standard deviation) sensitivity and specificity for lesion classification of 86.6% (69.3%) and 71.3% (611.2%), respectively. More clinical information (level-II) improved the sensitivity to 88.9% (69.6%, P \ubc 0.19) and specificity to 75.7% (611.7%, 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. Conclusions: For the first time we compared a CNN\u2019s diagnostic performance with a large international group of 58 dermatologists, including 30 experts. Most dermatologists were outperformed by the CNN. Irrespective of any physicians\u2019 experience, they May benefit from assistance by a CNN\u2019s image classification

Factors driving the use of dermoscopy in Europe: a pan-European survey

Background: When used correctly, dermoscopy is an essential tool for helping clinicians in the diagnosis of skin diseases and the early detection of skin cancers. Despite its proven benefits, there is a lack of data about how European dermatologists use dermoscopy in everyday practice. Objectives: To identify the motivations, obstacles and modifiable factors influencing the use of dermoscopy in daily dermatology practice across Europe. Methods: All registered dermatologists in 32 European countries were invited to complete an online survey of 20 questions regarding demographic and practice characteristics, dermoscopy training and self-confidence in dermoscopic skills, patterns of dermoscopy use, reasons for not using dermoscopy and attitudes relating to dermoscopy utility. Results: We collected 7480 valid answers, of which 89% reported use of dermoscopy. The main reasons for not using dermoscopy were lack of equipment (58% of nonusers) and lack of training (42%). Dermoscopy training during residency was reported by 41% of dermoscopy users and by 12% of nonusers (P &lt; 0·001). Dermatologists working in public hospitals were the least likely to use dermoscopy. High use of dermoscopy across the spectrum of skin diseases was reported by 62% of dermoscopy users and was associated with dermoscopy training during residency, the use of polarized light and digital dermoscopy devices, longer dermoscopy practice, younger age and female gender. Conclusions: Expanding access to dermoscopy equipment, especially in public healthcare facilities and establishing dermoscopy training during dermatology residency would further enhance the substantially high dermoscopy use across European countries. © 2016 British Association of Dermatologist