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Background <p>Neonatal acute bilirubin encephalopathy (ABE) severely endangers the neonatal health. However, early clinical symptoms of ABE are nonspecific, often leading to missed diagnoses. The current study endeavors to establish a computer-assisted integrated model for clinical assessment and diagnosis of ABE.</p> Methods <p>Diagnostic data from the ABE group and the hyperbilirubinemia without concurrent ABE (non-ABE) group were retrospectively analyzed. Patients were divided into a pre-training cohort, a training cohort, and two test cohorts. The training cohort and test cohort 1 were used to train and test a deep learning (DL) model integrating multimodal, self-supervised, and multi-instance learning. Test cohort 2 was used to compare the DL model with the radiologists.</p> Results <p>A total of 1048 magnetic resonance images from 262 patients were analyzed. The accuracy of the DL model and the area under the curve were 86.3% and 91.2% and 91.1% and 89.3% in test cohorts 1 and 2, respectively.</p> Conclusions <p>This study integrated clinical and radiological data into DL models to accurately diagnose ABE, close to the proficiency level of senior radiologists. It provides a convenient, low-cost evaluation model for patient management decisions and physician diagnoses.</p>

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Diagnosing acute bilirubin encephalopathy in neonates using MRI-based deep learning model

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年10月21日

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  • Kun Huang,
  • Jin Wang,
  • Qinzhu Yang,
  • Gengbiao Zhang,
  • Hongyi Zheng,
  • Yi Gao,
  • Wenbin Zheng

摘要

Background

Neonatal acute bilirubin encephalopathy (ABE) severely endangers the neonatal health. However, early clinical symptoms of ABE are nonspecific, often leading to missed diagnoses. The current study endeavors to establish a computer-assisted integrated model for clinical assessment and diagnosis of ABE.

Methods

Diagnostic data from the ABE group and the hyperbilirubinemia without concurrent ABE (non-ABE) group were retrospectively analyzed. Patients were divided into a pre-training cohort, a training cohort, and two test cohorts. The training cohort and test cohort 1 were used to train and test a deep learning (DL) model integrating multimodal, self-supervised, and multi-instance learning. Test cohort 2 was used to compare the DL model with the radiologists.

Results

A total of 1048 magnetic resonance images from 262 patients were analyzed. The accuracy of the DL model and the area under the curve were 86.3% and 91.2% and 91.1% and 89.3% in test cohorts 1 and 2, respectively.

Conclusions

This study integrated clinical and radiological data into DL models to accurately diagnose ABE, close to the proficiency level of senior radiologists. It provides a convenient, low-cost evaluation model for patient management decisions and physician diagnoses.