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Background: Dermatology is an ideal specialty for artificial intelligence (AI)-driven image recognition to improve diagnostic accuracy and patient care. Lack of dermatologists in many parts of the world and the high frequency of cutaneous disorders and malignancies highlight the increasing need for AI-aided diagnosis. Although AI-based applications for the identification of dermatological conditions are widely available, research assessing their reliability and accuracy is lacking., Objective: The aim of this study was to analyze the efficacy of the Aysa AI app as a preliminary diagnostic tool for various dermatological conditions in a semiurban town in India., Methods: This observational cross-sectional study included patients over the age of 2 years who visited the dermatology clinic. Images of lesions from individuals with various skin disorders were uploaded to the app after obtaining informed consent. The app was used to make a patient profile, identify lesion morphology, plot the location on a human model, and answer questions regarding duration and symptoms. The app presented eight differential diagnoses, which were compared with the clinical diagnosis. The model's performance was evaluated using sensitivity, specificity, accuracy, positive predictive value, negative predictive value, and F 1 -score. Comparison of categorical variables was performed with the χ 2 test and statistical significance was considered at P<.05., Results: A total of 700 patients were part of the study. A wide variety of skin conditions were grouped into 12 categories. The AI model had a mean top-1 sensitivity of 71% (95% CI 61.5%-74.3%), top-3 sensitivity of 86.1% (95% CI 83.4%-88.6%), and all-8 sensitivity of 95.1% (95% CI 93.3%-96.6%). The top-1 sensitivities for diagnosis of skin infestations, disorders of keratinization, other inflammatory conditions, and bacterial infections were 85.7%, 85.7%, 82.7%, and 81.8%, respectively. In the case of photodermatoses and malignant tumors, the top-1 sensitivities were 33.3% and 10%, respectively. Each category had a strong correlation between the clinical diagnosis and the probable diagnoses (P<.001)., Conclusions: The Aysa app showed promising results in identifying most dermatoses., (©Shiva Shankar Marri, Warood Albadri, Mohammed Salman Hyder, Ajit B Janagond, Arun C Inamadar. Originally published in JMIR Dermatology (http://derma.jmir.org), 02.07.2024.)

This investigation explored relationships between biological maturation, physical and academic performance in young male soccer players. Thirty-eight players (age: 9.79 ± 1.21 years; body mass index (BMI): 20.4 ± 2.39 kg/m2; body fat: 16.8 ± 2.21%) participated. Measures of anthropometry used for body mass, body fat percentage (%BF), and BMI as well. Postural control, 15 m sprint, squat jumps and counter-movement jumps (SJ, CMJ), and T-half test for change-of-direction (CoD) were parameters of physical performance. The grade point average (GPA) of mathematics determined academic attainment. Moore's equations were used to estimate their maturity status (PHV). Biological maturation was highly correlated with most (not 15 m sprint) physical and academic performance parameters, especially CMJ (r = -0.812) and mathematics (r = -0.781). Academic performance showed the largest relations to the jumping performance (CMJ: r = 0.771; SJ: r = 0.723). In contrast, anthropometric and fatness parameters were not relevantly (r ≥ 0.5) correlated with any other parameters. The largest correlations were calculated for sitting height vs. SJ (r = -0.408), sitting height vs. postural control (r = -0.355), leg length vs. postural control (r = -0.339). As a result, it is essential to take biological maturation inconsideration while assessing the physical and academic achievement of young soccer players. In consequence, soccer coaches and physical education (PE) teachers should be cognizant of the impact of biological maturity on physical and academic performance to assist fair and equal opportunities for achievement in young players., Competing Interests: NO authors have competing interests., (Copyright: © 2024 Hermassi et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

Resin molecules play a crucial role in the stability of colloidal asphaltene particles in petroleum reservoirs. De-stabilization of the asphaltene/resin interaction due to changes in thermodynamic parameters can cause asphaltene precipitation, thus leading to petroleum field problems such as decreased in situ permeability, as well as severe plugging problems in production facilities. One remedial technology used in the oil industry involves developing synthetic resins with enhanced chemical potential to increase the stability of asphaltene in the oil phase. However, accurately predicting what synthetic resin structures are compatible with asphaltenes in this context can be difficult and ineffective. Here, we introduce a method that enhances the stability of colloidal asphaltene in petroleum fluid by increasing the concentrations of natural-state oil resins and increases reservoir oil recovery by increasing the oil's aromatic power solvency. The stability of colloidal asphaltene and improvements in oil reservoir recovery were investigated by using an oil prefractionation process and a solvent deasphalting technology based on the residuum oil supercritical extraction process to develop three types of deasphalted oils derived from Kuwait Marrat oil. Using these methods, we found that resin concentration by volume in Marrat oil increased with the removal of more oil fractions. Asphaltene stability in the oil phase was strongly influenced by resin concentration. The deasphalted oils' aromatic power solvency increased the oil reservoir permeability by twofold. No formation damage was observed for all DAO products in core flooding tests., Competing Interests: The authors declare no competing financial interest., (© 2023 The Authors. Published by American Chemical Society.)

Background: Artificial intelligence (AI) aims to create programs that reproduce human cognition and processes involved in interpreting complex data. Dermatology relies on morphological features and is ideal for applying AI image recognition for assisted diagnosis. Tibot is an AI app that analyzes skin conditions and works on the principle of a convolutional neural network. Appropriate research analyzing the accuracy of such apps is necessary., Objective: This study aims to analyze the predictability of the Tibot AI app in the identification of dermatological diseases as compared to a dermatologist., Methods: This is a cross-sectional study. After taking informed consent, photographs of lesions of patients with different skin conditions were uploaded to the app. In every condition, the AI predicted three diagnoses based on probability, and these were compared with that by a dermatologist. The ability of the AI app to predict the actual diagnosis in the top one and top three anticipated diagnoses (prediction accuracy) was used to evaluate the app's effectiveness. Sensitivity, specificity, and positive predictive value were also used to assess the app's performance. Chi-square test was used to contrast categorical variables. P<.05 was considered statistically significant., Results: A total of 600 patients were included. Clinical conditions included alopecia, acne, eczema, immunological disorders, pigmentary disorders, psoriasis, infestation, tumors, and infections. In the anticipated top three diagnoses, the app's mean prediction accuracy was 96.1% (95% CI 94.3%-97.5%), while for the exact diagnosis, it was 80.6% (95% CI 77.2%-83.7%). The prediction accuracy (top one) for alopecia, acne, pigmentary disorders, and fungal infections was 97.7%, 91.7%, 88.5%, and 82.9%, respectively. Prediction accuracy (top three) for alopecia, eczema, and tumors was 100%. The sensitivity and specificity of the app were 97% (95% CI 95%-98%) and 98% (95% CI 98%-99%), respectively. There is a statistically significant association between clinical and AI-predicted diagnoses in all conditions (P<.001)., Conclusions: The AI app has shown promising results in diagnosing various dermatological conditions, and there is great potential for practical applicability., (©Shiva Shankar Marri, Arun C Inamadar, Ajit B Janagond, Warood Albadri. Originally published in JMIR Dermatology (http://derma.jmir.org), 01.03.2023.)