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Deep learning algorithm reveals probabilities of stage‐specific time to conversion in individuals with neurodegenerative disease LATE
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Deep learning algorithm reveals probabilities of stage‐specific time to conversion in individuals with neurodegenerative disease LATE
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Deep learning algorithm reveals probabilities of stage‐specific time to conversion in individuals with neurodegenerative disease LATE
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Journal Article
Deep learning algorithm reveals probabilities of stage‐specific time to conversion in individuals with neurodegenerative disease LATE
2022
Overview
Introduction
Limbic‐predominant age‐related TAR DNA‐binding protein 43 (TDP‐43) encephalopathy (LATE) is a recently defined neurodegenerative disease. Currently, there is no effective way to make a prognosis of time to stage‐specific future conversions at an individual level.
Methods
After using the Kaplan–Meier estimation and log‐rank test to confirm the heterogeneity of LATE progression, we developed a deep learning–based approach to assess the stage‐specific probabilities of time to LATE conversions for different subjects.
Results
Our approach could accurately estimate the disease incidence and transition to next stages: the concordance index was at least 82% and the integrated Brier score was less than 0.14. Moreover, we identified the top 10 important predictors for each disease conversion scenario to help explain the estimation results, which were clinicopathologically meaningful and most were also statistically significant.
Discussion
Our study has the potential to provide individualized assessment for future time courses of LATE conversions years before their actual occurrence.
Publisher
John Wiley & Sons, Inc,John Wiley and Sons Inc,Wiley
