This study aimed to develop an English version of a doping drug-recognition system using deep learning-based optical character recognition (OCR) technology. A database of 336 banned substances was built based on the World Anti-Doping Agency’s International Standard Prohibited List and the Korean Pharmaceutical Information Center’s Drug Substance Information. For accuracy and validity analysis, 886 drug substance images, including 152 images of prescriptions and drug substance labels collected using data augmentation, were used. The developed hybrid system, based on the Tesseract OCR model, can be accessed by both a smartphone and website. A total of 5379 words were extracted, and the system showed character recognition errors regarding 91 words, showing high accuracy (98.3%). The system correctly classified all 624 images for acceptable substances, 218 images for banned substances, and incorrectly recognized 44 of the banned substances as acceptable. The validity analysis showed a high level of accuracy (0.95), sensitivity (1.00), and specificity (0.93), suggesting system validity. The system has the potential of allowing athletes who lack knowledge about doping to quickly and accurately check whether they are taking banned substances. It may also serve as an efficient option to support the development of a fair and healthy sports culture.

Humans share a similar body structure, but each individual possesses unique characteristics, which we define as one’s body type. Various classification methods have been devised to understand and assess these body types. Recent research has applied artificial intelligence technology utilizing noninvasive measurement tools, such as 3D body scanner, which minimize physical contact. The purpose of this study was to develop an artificial intelligence somatotype system capable of predicting the three body types proposed by Heath-Carter’s somatotype theory using 3D body images collected using a 3D body scanner. To classify body types, measurements were taken to determine the three somatotype components (endomorphy, mesomorphy, and ectomorphy). MobileNetV2 was utilized as the transfer learning model. The results of this study are as follows: first, the AI somatotype model showed good performance, with a training accuracy around 91% and a validation accuracy around 72%. The respective loss values were 0.26 for the training set and 0.69 for the validation set. Second, validation of the model’s performance using test data resulted in accurate predictions for 18 out of 21 new data points, with prediction errors occurring in three cases, indicating approximately 85% classification accuracy. This study provides foundational data for subsequent research aiming to predict 13 detailed body types across the three body types. Furthermore, it is hoped that the outcomes of this research can be applied in practical settings, enabling anyone with a smartphone camera to identify various body types based on captured images and predict obesity and diseases.

Background/Objectives: Exercise performance and recovery depend on interactions between energy metabolism and redox regulation. SLAB51, a multi-strain probiotic, may influence oxygen-related metabolism and antioxidant responses, but evidence in elite athletes remains limited. This study aimed to evaluate the effects of SLAB51 supplementation on aerobic exercise capacity and oxidative stress responses in elite athletes. Methods: This randomized, double-blind, placebo-controlled crossover trial evaluated the effects of SLAB51 supplementation on aerobic exercise performance and oxidative stress in elite athletes. Participants received SLAB51 and placebo for one week each, separated by a two-week washout period. Assessments were performed at baseline, after a single intake, and after one week of intake. Exercise performance was evaluated using a treadmill-based Bruce protocol, and physiological and oxidative stress markers were measured. Data were analyzed using two-way repeated-measures analysis of variance (ANOVA), supplemented by post hoc linear mixed-effects models (LMMs) accounting for the crossover design. Results: The original ANOVA showed significant treatment × time interactions for exercise duration, duration above a respiratory exchange ratio (RER) of 1.0, and pre-exercise superoxide dismutase (SOD) activity, but not for maximal oxygen uptake, lactate removal, or malondialdehyde concentration. The additional post hoc LMM analysis also identified a significant treatment difference in exercise duration after one week (mean difference = 33.32 s, 95% CI: 17.42–49.22, p < 0.001), with significant differences in duration above RER 1.0 and SOD activity also observed after one week. Conclusions: These findings support the potential benefits of one-week SLAB51 supplementation for exercise duration in elite athletes, consistent with the additional crossover-adjusted analysis. Favorable differences in duration above RER 1.0 and pre-exercise antioxidant enzyme activity warrant further investigation.