Final DXA-nation

<h2>Key Points, TLDR:</h2> <ul> <li>The combination of body composition imaging and meta-data (e.g. age, sex, grip strength, walking speed, etc) resulted in the best 10 year mortality predictions</li> <li>Longitudinal or sequential models overall performed better than single record models, highlighting the importance of modeling change and time dependencies in health data.</li> <li>Longitudinal models have the potential to provide a more comprehensive assessment of one&rsquo;s health</li> <li><a href="https://www.nature.com/articles/s43856-022-00166-9" rel="noopener ugc nofollow" target="_blank"><strong>Read the paper</strong></a></li> </ul> <p>Artificial intelligence (AI) and machine learning (ML) are revolutionizing healthcare, driving us toward the era of precision medicine. The motivation to develop AI health models is to reduce deaths and disease as well as prolong a high quality of life. Well trained models have the ability to more thoroughly analyze data that is presented which offers a more comprehensive assessment of one&rsquo;s health.</p> <h1>Single Record vs Longitudinal Models</h1> <p>Image-based medical AI/ML models have now reached a maturity where they often rival or even surpass human performance, adeptly identifying patterns and anomalies that could easily elude the human eye. However, the majority of these models still operate on single time-point data, providing an isolated snapshot of health at one specific instance. Whether these are uni-modal or multi-modal models, they tend to work with data gathered within a relatively similar timeframe, forming the foundation of a prediction.</p> <p><a href="https://towardsdatascience.com/final-dxa-nation-f0309d718980"><strong>Visit Now</strong></a></p>