Computer-aided diagnosis of neonatal acute bilirubin encephalopathy with multi-modal MRI images and convolutional neural networks

Acute bilirubin encephalopathy (ABE) is a serious complication of hyperbilirubinemia (HB). Efficiently detecting ABE in non-ABE neonates with HB by magnetic resonance imaging (MRI) techniques remains a great challenge in clinical practice, as both groups may exhibit similar MRI features of T1 hyperintensity in specific brain regions. To this end, a computer-aided (CAD) diagnosis system based on multi-modal MRI images and convolutional neural networks (CNNs) was proposed in this study. A total of 150 patients were included in the study, half of whom were ABE neonates, and the other half were non-ABE neonates with HB. During the hospitalization, each patient underwent a 3 Tesla whole-brain MRI examination, generating a T1-weighted image (T1WI), a T2-weighted image (T2WI), and an apparent diffusion coefficient map (ADC), respectively. These 3 types of MRI images and their combinations were fed into 2 CNNs, i.e., EfficientNetB0 and InceptionV3. A traditional machine learning method, as a baseline, named support vector machine was also applied and its performance was then compared with CNNs. The classifiers’ performance with multi-modal MRI images was superior to that with any single-modal MRI image. Both 2 CNNs outperformed SVM. The best performance was achieved by InceptionV3 with the fusion images of T1WI, T2WI, and ADC, with an accuracy of 0.9622 AUC of 0.9978, and F1-score of 0.9617. The proposed CAD system for automatically diagnosing neonatal ABE based on multi-modal MRI images and CNNs showed an outstanding performance and is potentially applicable in actual clinical practice.