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Diagnostics
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Artificial Intelligence and Imaging in Brain Diseases
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Artificial Intelligence and Imaging 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 (30 June 2022) | Viewed by 4029

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. There have been famous publicly accessible datasets provided 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 (2 papers)

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Research

16 pages, 2200 KiB  
Article
Domain-Specific Cognitive Prosthesis for Face Memory and Recognition
by Jowy Tani, Yao-Hua Yang, Chao-Min Chen, Co Yih Siow, Tsui-San Chang, Kai Yang, Jack Yao, Chaur-Jong Hu and Jia-Ying Sung
Diagnostics 2022, 12(9), 2242; https://doi.org/10.3390/diagnostics12092242 - 16 Sep 2022
Viewed by 1860
Abstract
The present study proposes a cognitive prosthesis device for face memory impairment as a proof-of-concept for the domain-specific cognitive prosthesis. Healthy subjects (n = 6) and a patient with poor face memory were enrolled. An acquaintance face recognition test with and without [...] Read more.
The present study proposes a cognitive prosthesis device for face memory impairment as a proof-of-concept for the domain-specific cognitive prosthesis. Healthy subjects (n = 6) and a patient with poor face memory were enrolled. An acquaintance face recognition test with and without the use of cognitive prosthesis for face memory impairment, face recognition tests, quality of life, neuropsychological assessments, and machine learning performance of the cognitive prosthesis were followed-up throughout four weeks of real-world device use by the patient. The healthy subjects had an accuracy of 92.38 ± 4.41% and reaction time of 1.27 ± 0.12 s in the initial attempt of the acquaintance face recognition test, which changed to 80.48 ± 6.23% (p = 0.06) and 2.11 ± 0.20 s (p < 0.01) with prosthesis use. The patient had an accuracy of 74.29% and a reaction time of 6.65 s, which improved to 94.29% and 3.28 s with prosthesis use. After four weeks, the patient’s unassisted accuracy and reaction time improved to 100% and 1.23 s. Functional MRI study revealed activation of the left superior temporal lobe during face recognition task without prosthesis use and activation of the right precentral motor area with prosthesis use. The prosthesis could improve the patient’s performance by bypassing the brain area inefficient for facial recognition and employing the area more efficiently for the cognitive task. Full article
(This article belongs to the Special Issue Artificial Intelligence and Imaging in Brain Diseases)
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17 pages, 2000 KiB  
Article
An Analysis of Vocal Features for Parkinson’s Disease Classification Using Evolutionary Algorithms
by Son V. T. Dao, Zhiqiu Yu, Ly V. Tran, Phuc N. K. Phan, Tri T. M. Huynh and Tuan M. Le
Diagnostics 2022, 12(8), 1980; https://doi.org/10.3390/diagnostics12081980 - 16 Aug 2022
Cited by 10 | Viewed by 1734
Abstract
Parkinson’s Disease (PD) is a brain disorder that causes uncontrollable movements. According to estimation, roughly ten million individuals worldwide have had or are developing PD. This disorder can have severe consequences that affect the patient’s daily life. Therefore, several previous works have worked [...] Read more.
Parkinson’s Disease (PD) is a brain disorder that causes uncontrollable movements. According to estimation, roughly ten million individuals worldwide have had or are developing PD. This disorder can have severe consequences that affect the patient’s daily life. Therefore, several previous works have worked on PD detection. Automatic Parkinson’s Disease detection in voice recordings can be an innovation compared to other costly methods of ruling out examinations since the nature of this disease is unpredictable and non-curable. Analyzing the collected vocal records will detect essential patterns, and timely recommendations on appropriate treatments will be extremely helpful. This research proposed a machine learning-based approach for classifying healthy people from people with the disease utilizing Grey Wolf Optimization (GWO) for feature selection, along with Light Gradient Boosted Machine (LGBM) to optimize the model performance. The proposed method shows highly competitive results and has the ability to be developed further and implemented in a real-world setting. Full article
(This article belongs to the Special Issue Artificial Intelligence and Imaging in Brain Diseases)
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