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Diagnostics
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Novel Approaches for Artificial Intelligence in Neuroimaging Applications
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Novel Approaches for Artificial Intelligence in Neuroimaging Applications

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: 31 May 2025 | Viewed by 3675

Special Issue Editor

Dr. Kwang-Sig Lee

E-Mail Website
Guest Editor
AI Center, Korea University Anam Hospital, Seoul 02841, Republic of Korea
Interests: artificial intelligence; health informatics
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Emerging studies are employing artificial intelligence in neuroimaging for preprocessing, diagnosis, treatment and prognosis. The SHAP approach combines the results of all possible scenarios, which are ignored in linear or logistic regression, making the unrealistic assumption of ceteris paribus, i.e., “all the other variables staying constant”. This approach emphasizes important predictors for neuroimaging applications with numeric data. Likewise, Grad-CAM highlights regions of interest for neuroimaging using computed tomography and magnetic resonance imaging data. Moreover, generative artificial intelligence and multi-modal reinforcement learning are increasing in popularity. Given a sequence of text or image inputs, generative artificial intelligence generates a sequence of their probabilities based on Bidirectional Encoder Representations from Transformers (BERT) or Generative Pre-trained Transformers (GPT). This method’s impressive performance is due to its attention mechanism (in which different inputs receive different weights based on their similarity with their outputs). Few investigations have been carried out on these important issues. Therefore, this Special Issue invites authors to submit original and review articles on novel applications of artificial intelligence in neuroimaging, including preprocessing, diagnosis, treatment and prognosis.

Prof. Dr. Kwang-Sig Lee
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.

Keywords

  • neuroimaging
  • machine learning
  • deep learning
  • explainable artificial intelligence
  • SHAP
  • GradCAM
  • generative artificial intelligence
  • BERT
  • GPT
  • multi-modal reinforcement learning

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Published Papers (2 papers)

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Research

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14 pages, 1921 KiB  
Article
Machine Learning Models for 3-Month Outcome Prediction Using Radiomics of Intracerebral Hemorrhage and Perihematomal Edema from Admission Head Computed Tomography (CT)
by Fiona Dierksen, Jakob K. Sommer, Anh T. Tran, Huang Lin, Stefan P. Haider, Ilko L. Maier, Sanjay Aneja, Pina C. Sanelli, Ajay Malhotra, Adnan I. Qureshi, Jan Claassen, Soojin Park, Santosh B. Murthy, Guido J. Falcone, Kevin N. Sheth and Seyedmehdi Payabvash
Diagnostics 2024, 14(24), 2827; https://doi.org/10.3390/diagnostics14242827 - 16 Dec 2024
Viewed by 948
Abstract
Background: Intracerebral hemorrhages (ICH) and perihematomal edema (PHE) are respective imaging markers of primary and secondary brain injury in hemorrhagic stroke. In this study, we explored the potential added value of PHE radiomic features for prognostication in ICH patients. Methods: Using [...] Read more.
Background: Intracerebral hemorrhages (ICH) and perihematomal edema (PHE) are respective imaging markers of primary and secondary brain injury in hemorrhagic stroke. In this study, we explored the potential added value of PHE radiomic features for prognostication in ICH patients. Methods: Using a multicentric trial cohort of acute supratentorial ICH (n = 852) patients, we extracted radiomic features from ICH and PHE lesions on admission non-contrast head CTs. We trained and tested combinations of different machine learning classifiers and feature selection methods for prediction of poor outcome—defined by 4-to-6 modified Rankin Scale scores at 3-month follow-up—using five different input strategies: (a) ICH radiomics, (b) ICH and PHE radiomics, (c) admission clinical predictors of poor outcomes, (d) ICH radiomics and clinical variables, and (e) ICH and PHE radiomics with clinical variables. Models were trained on 500 patients, tested, and compared in 352 using the receiver operating characteristics Area Under the Curve (AUC), Integrated Discrimination Index (IDI), and Net Reclassification Index (NRI). Results: Comparing the best performing models in the independent test cohort, both IDI and NRI demonstrated better individual-level risk assessment by addition of PHE radiomics as input to ICH radiomics (both p < 0.001), but with insignificant improvement in outcome prediction (AUC of 0.74 versus 0.71, p = 0.157). The addition of ICH and PHE radiomics to clinical variables also improved IDI and NRI risk-classification (both p < 0.001), but with a insignificant increase in AUC of 0.85 versus 0.83 (p = 0.118), respectively. All machine learning models had greater or equal accuracy in outcome prediction compared to the widely used ICH score. Conclusions: The addition of PHE radiomics to hemorrhage lesion radiomics, as well as radiomics to clinical risk factors, can improve individual-level risk assessment, albeit with an insignificant increase in prognostic accuracy. Machine learning models offer quantitative and immediate risk stratification—on par with or more accurate than the ICH score—which can potentially guide patients’ selection for interventions such as hematoma evacuation. Full article
(This article belongs to the Special Issue Novel Approaches for Artificial Intelligence in Neuroimaging Applications)
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Review

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14 pages, 676 KiB  
Review
Predictive and Explainable Artificial Intelligence for Neuroimaging Applications
by Sekwang Lee and Kwang-Sig Lee
Diagnostics 2024, 14(21), 2394; https://doi.org/10.3390/diagnostics14212394 - 27 Oct 2024
Viewed by 1981
Abstract
Background: The aim of this review is to highlight the new advance of predictive and explainable artificial intelligence for neuroimaging applications. Methods: Data came from 30 original studies in PubMed with the following search terms: “neuroimaging” (title) together with “machine learning” (title) or [...] Read more.
Background: The aim of this review is to highlight the new advance of predictive and explainable artificial intelligence for neuroimaging applications. Methods: Data came from 30 original studies in PubMed with the following search terms: “neuroimaging” (title) together with “machine learning” (title) or ”deep learning” (title). The 30 original studies were eligible according to the following criteria: the participants with the dependent variable of brain image or associated disease; the interventions/comparisons of artificial intelligence; the outcomes of accuracy, the area under the curve (AUC), and/or variable importance; the publication year of 2019 or later; and the publication language of English. Results: The performance outcomes reported were within 58–96 for accuracy (%), 66–97 for sensitivity (%), 76–98 for specificity (%), and 70–98 for the AUC (%). The support vector machine and the convolutional neural network registered the best performance (AUC 98%) for the classifications of low- vs. high-grade glioma and brain conditions, respectively. Likewise, the random forest delivered the best performance (root mean square error 1) for the regression of brain conditions. The following factors were discovered to be major predictors of brain image or associated disease: (demographic) age, education, sex; (health-related) alpha desynchronization, Alzheimer’s disease stage, CD4, depression, distress, mild behavioral impairment, RNA sequencing; (neuroimaging) abnormal amyloid-β, amplitude of low-frequency fluctuation, cortical thickness, functional connectivity, fractal dimension measure, gray matter volume, left amygdala activity, left hippocampal volume, plasma neurofilament light, right cerebellum, regional homogeneity, right middle occipital gyrus, surface area, sub-cortical volume. Conclusion: Predictive and explainable artificial intelligence provide an effective, non-invasive decision support system for neuroimaging applications. Full article
(This article belongs to the Special Issue Novel Approaches for Artificial Intelligence in Neuroimaging Applications)
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