Israeli Researchers Discover Hidden Health Clues in Human Gait
Translated & summarized from Cursorinfo by baba
Israeli scientists have discovered that artificial intelligence can detect hidden health indicators, including liver function and mental well-being, by analyzing subtle patterns in human gait. The study, involving over 3,300 participants and published in Nature Medicine, used AI to correlate movement data with medical information. While not yet a diagnostic tool, researchers believe the technology could be used in the future for patient assessment and health monitoring.
The story in 6 lines · by baba
- Israeli scientists used AI to find hidden health clues in human gait, including liver and mental health.
- The study analyzed movement patterns of 3,328 adults, correlating them with medical data.
- AI identified significant links between gait and 1887 indicators across 18 bodily systems.
- Movement analysis provided insights into liver elasticity and depressive states.
- Researchers believe the technology could aid in future patient assessment and health monitoring.
- The study's findings are based on statistical links and require further clinical validation.
Israeli scientists from the Weizmann Institute in Rehovot, led by Professor Eran Segal, have found that subtle aspects of human movement can reveal significant information about a person's health, including liver function, metabolism, and mental well-being. Their research, published on November 6 in Nature Medicine, utilized artificial intelligence to analyze gait patterns from video recordings.
The study involved 3,328 adults from the Israeli Human Phenotype Project. Participants performed five standard movements captured by a 3D motion-tracking camera, generating 17,589 recordings. An AI system analyzed the position of 26 joints, assessing not only speed and stride length but also less obvious details like coordination and body movements. This data was then correlated with participants' medical information, including blood tests and metabolic indicators.
The AI identified statistically significant links between movement patterns and 1887 out of 3210 studied indicators across 18 bodily systems. While some findings were expected, such as correlations with body mass and physical strength, others were surprising. The AI found connections between gait and liver elasticity, providing insights comparable to biochemical tests, and even detected links to depressive states, improving predictive models for mental health.
Furthermore, the study noted that different body parts convey distinct health information. Leg movements were particularly indicative of metabolic health and age-related weakness, while torso movements offered insights into sleep quality and lifestyle. These correlations persisted even after accounting for age, height, and weight.
Yuval Brodsky, a co-author and CEO of Newton Tech, stated that human movement contains more biological information than typically assessed. The researchers envision future applications in clinical settings for preliminary patient assessments and monitoring treatment effectiveness, noting the technology's advantage of using standard video analysis without complex equipment. However, they caution that this is a discovery of statistical links, not a diagnostic tool, and further validation in diverse populations and clinical settings is required.