Automated Deep Learning Estimation of Anthropometric Measurements for Preparticipation Cardiovascular Screening
PositiveArtificial Intelligence
- A new study presents a fully automated deep learning approach to estimate key anthropometric measurements from 2D synthetic human body images, aimed at enhancing preparticipation cardiovascular screening. The method, utilizing a dataset of 100,000 images, achieved sub-centimeter accuracy with the ResNet50 model performing the best, indicating a significant advancement in the field of sports medicine.
- This development is crucial as it addresses the limitations of traditional manual measurement methods, which are labor-intensive and difficult to scale. By automating the estimation process, the study promises to improve the efficiency and accuracy of cardiovascular risk assessments in athletes, potentially preventing sudden cardiac deaths.
- The integration of deep learning in medical assessments reflects a broader trend towards leveraging artificial intelligence in healthcare. Similar advancements in areas such as health insurance prediction from chest X-rays and medical image classification underscore the growing reliance on AI technologies to enhance diagnostic accuracy and operational efficiency across various medical fields.
— via World Pulse Now AI Editorial System
