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Innovations in Clinical Artificial Intelligence: Image Analysis, Prediction, and Treatment...
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Innovations in Clinical Artificial Intelligence: Image Analysis, Prediction, and Treatment Optimization

A special issue of Journal of Clinical Medicine (ISSN 2077-0383).

Deadline for manuscript submissions: closed (25 May 2025) | Viewed by 1150

Special Issue Editor

Dr. Shahriar Faghani

E-Mail Website
Guest Editor
Artificial Intelligence Lab, Department of Radiology, Mayo Clinic, Rochester, MN, USA
Interests: radiology; medical imaging; neuroimaging; deep learning; machine learning

Special Issue Information

Dear Colleagues,

This Special Issue aims to explore recent advancements in clinical AI, focusing on innovations in image analysis, predictive modeling, and treatment optimization. It invites contributions on key topics such as AI-driven imaging, radiogenomics, and predictive analytics for disease progression. Special emphasis will be placed on developing trustworthy AI systems, uncertainty quantification, and ensuring reliable integration into clinical workflows. By addressing these topics, this Special Issue aims to enhance personalized medicine and improve patient outcomes through the adoption of cutting-edge AI technologies in clinical practice.

Dr. Shahriar Faghani
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. Journal of Clinical Medicine 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

  • artificial intelligence
  • clinical practice
  • prediction
  • diagnosis
  • treatment

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Published Papers (1 paper)

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Research

14 pages, 1135 KB  
Article
Is Personalized Mechanical Thrombectomy Based on Clot Characteristics Feasible? A Radiomics Model Using NCECT to Predict FPE in AIS Patients Undergoing Thromboaspiration
by Jacobo Porto-Álvarez, Javier Martínez Fernández, Antonio Jesús Mosqueira Martínez, Miguel Blanco Ulla, Susana Arias Rivas, Emilio Rodríguez Castro, Ramón Iglesias Rey, José M. Pumar, Roberto García-Figueiras and Miguel Souto Bayarri
J. Clin. Med. 2025, 14(12), 4027; https://doi.org/10.3390/jcm14124027 - 6 Jun 2025
Viewed by 734
Abstract
Background/Objectives: In patients with acute ischemic stroke (AIS), the first pass effect (FPE) refers to the complete recanalization of an occluded vessel (TICI = 2C/3) with a single thrombectomy attempt. Achieving complete vessel recanalization is associated with better functional outcomes compared to [...] Read more.
Background/Objectives: In patients with acute ischemic stroke (AIS), the first pass effect (FPE) refers to the complete recanalization of an occluded vessel (TICI = 2C/3) with a single thrombectomy attempt. Achieving complete vessel recanalization is associated with better functional outcomes compared to lower reperfusion rates (TICI < 2B). There is no consensus on which thrombectomy technique provides the best recanalization results for AIS patients. Furthermore, there is a paucity of tools available to predict FPE prior to mechanical thrombectomy (MT). The objective of this study is to develop a radiomics model based on brain NCECT to predict which patients are more likely to achieve a FPE with thromboaspiration MT. Methods: The thrombi of 91 patients were semi-automatically segmented on NCECT. A total of 1167 radiomic features (RFs) were extracted for each patient. Some clinical data (age, gender, cardiovascular risk factors, smoking or alcohol abuse, clot density and clot laterality) were also collected. Results: A LASSO regression analysis identified nine RFs with nonzero coefficients. A logistic regression model for FPE prediction was developed with nine RFs and eight clinical variables. A total of six RFs were found to be statistically associated with FPE. The clinical variables did not demonstrate a statistically significant association with the likelihood of achieving FPE (p > 0.05). The prediction of which patients are likely to achieve FPE obtained an AUC, accuracy, sensitivity and specificity of 0.890, 0.813, 0.815 and 0.811, respectively (p < 0.05). Conclusions: Radiomics can help identify patients who are more likely to achieve FPE with thromboaspiration. Full article
(This article belongs to the Special Issue Innovations in Clinical Artificial Intelligence: Image Analysis, Prediction, and Treatment Optimization)
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