Diagnosing acute bilirubin encephalopathy in neonates using MRI-based deep learning model.
Authors
Affiliations (6)
Affiliations (6)
- School of Biomedical Engineering, Shenzhen University Medical School, Shenzhen University, Shenzhen, 518060, China.
- The Second Affiliated Hospital, Shantou University Medical College, 69 Dongxia North Road, Jinping District, Shantou, Guangdong Province, 515041, China.
- School of Biomedical Engineering, Shenzhen University Medical School, Shenzhen University, Shenzhen, 518060, China. [email protected].
- Shenzhen Key Laboratory of Precision Medicine for Hematological Malignancies, Shenzhen, 518060, China. [email protected].
- Marshall Laboratory of Biomedical Engineering, Shenzhen, 518060, China. [email protected].
- The Second Affiliated Hospital, Shantou University Medical College, 69 Dongxia North Road, Jinping District, Shantou, Guangdong Province, 515041, China. [email protected].
Abstract
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. 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. 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. 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.