Machine Learning-Powered fNIRS Detection of Idiopathic Central Precocious Puberty via Prefrontal Cortex Activation

TL;DR

This study uses machine learning and brain imaging to develop a noninvasive way to diagnose a type of early puberty in children.

Contribution

A novel machine learning model with data augmentation improves noninvasive diagnosis of idiopathic central precocious puberty using fNIRS.

Findings

Normal children showed more prefrontal cortex activation than those with ICPP during mental tasks.

A decision tree model achieved 86.57% accuracy in classifying ICPP and normal children.

Synthetic data generated by C-DDPM improved classifier performance.

Abstract

Objective and Impact Statement: This study examines prefrontal cortex (PFC) hemodynamic responses in children with idiopathic central precocious puberty (ICPP) versus normals and constructs a noninvasive diagnostic model using functional near-infrared spectroscopy (fNIRS) augmented by machine learning. Introduction: Current ICPP diagnosis relies on invasive and time-consuming gonadotropin-releasing hormone stimulation tests. While fNIRS offers a noninvasive alternative, the neural mechanisms underlying ICPP remain unclear, and reliable automated diagnostic tools distinguishing patients from healthy peers are lacking. Methods: fNIRS data were acquired from 167 participants (82 ICPP and 85 normal) during a mental arithmetic (MA) task. General linear models and statistical tests were employed to analyze group and gender-specific activation patterns. Multidimensional features were extracted from hemodynamic signals, and a conditional denoising diffusion probabilistic model (C-DDPM) was introduced for data augmentation. Results: Analysis revealed gender-specific disparities, with the normal group exhibiting more extensive PFC activation than the ICPP group. In classification, a decision tree model using features from key negatively correlated channels achieved 86.57% accuracy. Notably, integrating C-DDPM-generated synthetic data further improved classifier performance metrics. Conclusion: The study elucidates the mechanisms of PFC activation in both normative and ICPP-affected cohorts during MA tasks and validates the effectiveness of machine learning in distinguishing between normal and ICPP children. This study provides a scientific basis for the development of automated, noninvasive rapid diagnostic tools for ICPP.

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