A Validation Study of a Deep Learning-Based Doping Drug Text Recognition System to Ensure Safe Drug Use among Athletes.

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.

Machine learning-based obesity classification considering 3D body scanner measurements.

Obesity can cause various diseases and is a serious health concern. BMI, which is currently the popular measure for judging obesity, does not accurately classify obesity; it reflects the height and weight but ignores the characteristics of an individual's body type. In order to overcome the limitations of classifying obesity using BMI, we considered 3-dimensional (3D) measurements of the human body. The scope of our study was limited to Korean subjects. In order to expand 3D body scan data clinically, 3D body scans, Dual-energy X-ray absorptiometry, and Bioelectrical Impedance Analysis data was collected pairwise for 160 Korean subjects. A machine learning-based obesity classification framework using 3D body scan data was designed, validated through Accuracy, Recall, Precision, and F1 score, and compared with BMI and BIA. In a test dataset of 40 people, BMI had the following values: Accuracy: 0.529, Recall: 0.472, Precision: 0.458, and F1 score: 0.462, while BIA had the following values: Accuracy: 0.752, Recall: 0.742, Precision: 0.751, and F1 score: 0.739. Our proposed model had the following values: Accuracy: 0.800, Recall: 0.767, Precision: 0.842, and F1 score: 0.792. Thus, our accuracy was higher than BMI as well as BIA. Our model can be used for obesity management through 3D body scans.

The effects of fluid absorption and plasma volume changes in athletes following consumption of various beverages.

BACKGROUND: To verify the hydration effects of oral rehydration solution (ORS) on athletes by comparing the degrees of fluid absorption and plasma volume changes following beverage consumption, including ORS. METHODS: Thirty-one participants visited the testing laboratory 4 times at 1-week intervals to consume 1 L of beverage (e.g., water, ORS, and two sports drinks [SpD]) for 30 min on each visit. The urine output was measured 4 times at 1 h, 2 h, 3 h, and 4 h after beverage consumption. A blood sample was collected 3 times at 1 h, 2 h, and 3 h after beverage consumption. Body weight was measured once in 4 h after beverage consumption. RESULTS: Body weight change was smaller for ORS than for water, SpD1, and SpD2 (p < 0.05). Cumulative urine output in 4 h was lower for ORS, SpD1, and SpD2 than for water (p < 0.05), and it was lower for ORS than for SpD2 (p < 0.05). BHI in 4 h was higher for ORS, SpD1, and SpD2 than for water (p < 0.05), and it was higher for ORS than for SpD2 (p < 0.05). There was no significant difference in PVC for different beverages at all test times, i.e.., 1 h, 2 h, and 3 h. CONCLUSIONS: We evaluated the hydration effects of the consumption of beverages, such as water, SpD, and ORS in athletes. ORS and SpD were more effective than water. A comparison between ORS and SpD showed that the result could vary depending on the type of SpD.