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Diagnostics
Special Issues
Artificial Intelligence in Brain Diseases
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Artificial Intelligence in Brain Diseases

A special issue of Diagnostics (ISSN 2075-4418). This special issue belongs to the section "Machine Learning and Artificial Intelligence in Diagnostics".

Deadline for manuscript submissions: closed (31 July 2023) | Viewed by 1506

Special Issue Editor

Dr. Yan Liu

E-Mail Website
Guest Editor
The College of Electrical Engineering, Sichuan University, Chengdu 610065, China
Interests: machine learning; brain; LSTM; artificial intelligence; load forecasting; image segmentation
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The application of artificial intelligence in brain disease diagnosis and treatment planning is developing with increasing speed and has proved to be an effective tool for the early screening of brain tumors and cerebral hemorrhage as well as for the PTV delineation of radio therapy and radiosurgery. Notable publicly accessible datasets are available for the design, validation, and comparison of corresponding algorithms. Some of them act as a benchmark in the field—for example, the dataset from the MICCAI Brain Tumor Segmentation (BraTS) Challenge and the dataset from Ischemic Stroke Lesion Segmentation (ISLES). Additionally, the Alzheimer’s Disease Neuroimaging Initiative (ADNI) dataset provides researchers a way to apply their methods to functional brain image analysis.

Inspired by these exciting achievements, we are organizing this Special Issue on Artificial Intelligence and Imaging in Brain Diseases. We warmly invite researchers worldwide to submit their original research articles in the field.

Dr. Yan Liu
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Diagnostics is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Published Papers (1 paper)

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Research

10 pages, 1391 KiB  
Article
Deep Learning Network with Spatial Attention Module for Detecting Acute Bilirubin Encephalopathy in Newborns Based on Multimodal MRI
by Huan Zhang, Yi Zhuang, Shunren Xia and Haoxiang Jiang
Diagnostics 2023, 13(9), 1577; https://doi.org/10.3390/diagnostics13091577 - 28 Apr 2023
Viewed by 1169
Abstract
Background: Acute bilirubin encephalopathy (ABE) is a significant cause of neonatal mortality and disability. Early detection and treatment of ABE can prevent the further development of ABE and its long-term complications. Due to the limited classification ability of single-modal magnetic resonance imaging (MRI), [...] Read more.
Background: Acute bilirubin encephalopathy (ABE) is a significant cause of neonatal mortality and disability. Early detection and treatment of ABE can prevent the further development of ABE and its long-term complications. Due to the limited classification ability of single-modal magnetic resonance imaging (MRI), this study aimed to validate the classification performance of a new deep learning model based on multimodal MRI images. Additionally, the study evaluated the effect of a spatial attention module (SAM) on improving the model’s diagnostic performance in distinguishing ABE. Methods: This study enrolled a total of 97 neonates diagnosed with ABE and 80 neonates diagnosed with hyperbilirubinemia (HB, non-ABE). Each patient underwent three types of multimodal imaging, which included T1-weighted imaging (T1WI), T2-weighted imaging (T2WI), and an apparent diffusion coefficient (ADC) map. A multimodal MRI classification model based on the ResNet18 network with spatial attention modules was built to distinguish ABE from non-ABE. All combinations of the three types of images were used as inputs to test the model’s classification performance, and we also analyzed the prediction performance of models with SAMs through comparative experiments. Results: The results indicated that the diagnostic performance of the multimodal image combination was better than any single-modal image, and the combination of T1WI and T2WI achieved the best classification performance (accuracy = 0.808 ± 0.069, area under the curve = 0.808 ± 0.057). The ADC images performed the worst among the three modalities’ images. Adding spatial attention modules significantly improved the model’s classification performance. Conclusion: Our experiment showed that a multimodal image classification network with spatial attention modules significantly improved the accuracy of ABE classification. Full article
(This article belongs to the Special Issue Artificial Intelligence in Brain Diseases)
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