An ambiguity-aware classifier of lumbar disc degeneration

Diagnostic ambiguity is prevalent in medicine, and may lead to undesirable medical consequences. In this paper, diagnostic ambiguity for lumber disc degeneration (LDD) is quantitatively expressed as a label distribution, based on which, a classifier that can perceive and predict the diagnostic ambiguity is modeled by integrating the discal metabolomics, subjective probability quantification, and label distribution learning. An algorithm of subjective probability quantification is designed to optimally estimate the label distribution of each disc. A novel metric is proposed to evaluate the performance of the classifiers in predicting the label ranking of sample. Numerical experiments verify that the proposed classifiers are feasible to perceive and predict the ambiguous grading of LDD.(c) 2022 Elsevier B.V. All rights reserved.

Automated summary

This paper proposes a classifier that can perceive and predict the diagnostic ambiguity of lumber disc degeneration by integrating discal metabolomics, subjective probability quantification, and label distribution learning, demonstrating its feasibility in numerical experiments.