A machine learning-based, decision support, mobile phone application for diagnosis of common dermatological diseases.

BACKGROUND: The integration of machine learning algorithms in decision support tools for physicians is gaining popularity. These tools can tackle the disparities in healthcare access as the technology can be implemented on smartphones. We present the first, large-scale study on patients with skin of colour, in which the feasibility of a novel mobile health application (mHealth app) was investigated in actual clinical workflows. OBJECTIVE: To develop a mHealth app to diagnose 40 common skin diseases and test it in clinical settings. METHODS: A convolutional neural network-based algorithm was trained with clinical images of 40 skin diseases. A smartphone app was generated and validated on 5014 patients, attending rural and urban outpatient dermatology departments in India. The results of this mHealth app were compared against the dermatologists' diagnoses. RESULTS: The machine-learning model, in an in silico validation study, demonstrated an overall top-1 accuracy of 76.93 ± 0.88% and mean area-under-curve of 0.95 ± 0.02 on a set of clinical images. In the clinical study, on patients with skin of colour, the app achieved an overall top-1 accuracy of 75.07% (95% CI = 73.75-76.36), top-3 accuracy of 89.62% (95% CI = 88.67-90.52) and mean area-under-curve of 0.90 ± 0.07. CONCLUSION: This study underscores the utility of artificial intelligence-driven smartphone applications as a point-of-care, clinical decision support tool for dermatological diagnosis for a wide spectrum of skin diseases in patients of the skin of colour.

Effect of temperature on Pi54-mediated leaf blast resistance in rice.

Assessment of temperature effect on plant resistance against diseases has become essential under climate change scenario as temperature rise is anticipated to modify host resistance. To determine temperature influence on resistance gene, a pair of near-isogenic rice lines differing for the Pi54 resistance gene was assessed against leaf blast. Blast resistance was determined as the extent of infection efficiency (IE) and sporulation (SP) at suboptimal (22 °C and 32 °C) and optimal temperature (27 °C) of pathogen aggressiveness. Relative resistance for IE and SP was higher at suboptimal temperature as compared to that of optimal temperature. Maximum level of resistance was at 22 °C where higher levels of expression of Pi54 and defence-regulatory transcription factor WRKY45 were also noted. At 32 °C, although some level of resistance noted, but level of Pi54 and WRKY45 expression was too low, suggesting that resistance recorded at higher temperature was due to reduced pathogen aggressiveness. At the optimal temperature for pathogen aggressiveness, comparatively lower levels of Pi54 and WRKY45 expression suggest possible temperature-induced interruption of the defence processes. The variation in resistance patterns modulated by temperature is appeared to be due to pathogen's sensitivity to temperature that leads to varying levels of Pi54 gene activation. Quick and violent activity of the pathogen at optimal temperature came into sight for the interruption of defence process activated by Pi54 gene. Evaluation of blast resistance genes under variable temperature conditions together with weather data could be applied in screening rice genotypes for selection of resistance having resilience to temperature rise.