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: Most biomarkers for predicting DSP have demonstrated suboptimal predictive value, and many lack established accuracy in forecasting DSP progression. Consequently, the diagnostic utility of any single biomarker remains limited. A comprehensive combination of biomarkers from various categories may hold incredible promise for accurate detection. As artificial intelligence (AI) techniques, especially machine learning, rapidly advance, these technologies may offer significant potential for developing diagnostic platforms to integrate and interpret complex biomarker data for DSP.
Chong et al. (Sat,) studied this question.