Comparative Analysis of AI Models for Atypical Pigmented Facial Lesion Diagnosis by Alessandra Cartocci, Alessio Luschi, Linda Tognetti, Elisa Cinotti, Francesca Farnetani, Aimilios Lallas, John Paoli, Caterina Longo, Elvira Moscarella, Danica Tiodorovic, Ignazio Stanganelli, Mariano Suppa, Emi Dika, Iris Zalaudek, Maria Antonietta Pizzichetta, Jean Luc Perrot, Gabriele Cevenini, Ernesto Iadanza, Giovanni Rubegni, Harald Kittler, Philipp Tschandl, Pietro Rubegni

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Multimodal neuroimaging investigation of post-stroke fatigue in middle-aged and older adults: combining resting-state fMRI and DTI-ALPS analysis by Jingjing Sun, Yanlin Tang, Wenni Wang, Yong Zhang, Hengshan Ji, Peng Wang

“…This study aimed to identify imaging markers for PSF in middle-aged and older adults using a multimodal imaging approach.MethodsThis retrospective case–control study analyzed data from patients with first ischemic stroke aged 50 years and above who were treated from January 2021 to June 2022 at the First Hospital of the University of Science and Technology of China. …”
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Synergizing advanced algorithm of explainable artificial intelligence with hybrid model for enhanced brain tumor detection in healthcare by Kamini Lamba, Shalli Rani, Mohammad Shabaz

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Non-Invasive Localization of Epileptogenic Zone in Drug-Resistant Epilepsy Based on Time–Frequency Analysis and VGG Convolutional Neural Network by Yaqing Liu, Yalin Wang, Tiancheng Wang

“…Previous researchers have proposed a range of methods for this purpose, but these suffer from limits such as unclear post-operative outcomes, invasiveness, limited data volume, and single DRE type. This study constructed a non-invasive epilepsy localization method, integrating sLORETA source imaging, time–frequency analysis, and Visual Geometry Group (VGG-16) deep learning. …”
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A combination of Sentinel-1 RADAR and Sentinel-2 multispectral data improves classification of morphologically similar savanna woody plants by Emmanuel Fundisi, Solomon G. Tesfamichael, Fethi Ahmed

“…The fused image recorded a higher overall classification accuracy (76%) than the sole use of Sentinel-2 (72%) and Sentinel-1 RADAR data (71%). …”
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Advancements in deep learning for early diagnosis of Alzheimer’s disease using multimodal neuroimaging: challenges and future directions by Muhammad Liaquat Raza, Muhammad Liaquat Raza, Muhammad Liaquat Raza, Syed Tawassul Hassan, Subia Jamil, Noorulain Hyder, Kinza Batool, Sajidah Walji, Muhammad Khizar Abbas

“…The review process involved a comprehensive search of relevant databases (PubMed, Embase, Google Scholar and ClinicalTrials.gov), selection of pertinent studies, and critical analysis of findings. We employed a best-evidence approach, prioritizing high-quality studies and identifying consistent patterns across the literature.ResultsDeep learning architectures, including convolutional neural networks, recurrent neural networks, and transformer-based models, have shown remarkable potential in analyzing multimodal neuroimaging data. …”
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Predictive Deep Learning for High‐Dimensional Inverse Modeling of Hydraulic Tomography in Gaussian and Non‐Gaussian Fields by Quan Guo, Ming Liu, Jian Luo

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Functional Connectivity Assessment in Alzheimer's Disease: A Comparative Study of Linear and Non-linear fMRI Analysis Approaches by Hessam Ahmadi, Emad Fatemizadeh, Ali Motie Nasrabadi

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Hubs, influencers, and communities of executive functions: a task-based fMRI graph analysis by Alexandra T. Davis

“…IntroductionThis study investigates four subdomains of executive functioning—initiation, cognitive inhibition, mental shifting, and working memory—using task-based functional magnetic resonance imaging (fMRI) data and graph analysis.MethodsWe used healthy adults’ functional magnetic resonance imaging (fMRI) data to construct brain connectomes and network graphs for each task and analyzed global and node-level graph metrics.ResultsThe bilateral precuneus and right medial prefrontal cortex emerged as pivotal hubs and influencers, emphasizing their crucial regulatory role in all four subdomains of executive function. …”
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Measurement of Fracture Networks in Rock Sample by X-Ray Tomography, Convolutional Filtering and Deep Learning by Alessia Caputo, Maria Teresa Calcagni, Giovanni Salerno, Elisa Mammoliti, Paolo Castellini

“…By combining convolution-based image processing techniques with advanced neural network-based segmentation, the proposed approach achieves high precision in identifying complex fracture networks. …”
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Artificial neural network analysis of natural convection of Casson fluid flow over a curved stretching surface with viscous dissipation by Sami Ul Haq, Muhammad Bilal Ashraf, Arooj Tanveer

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FedRSC: A Federated Learning Analysis for Multi-Label Road Surface Classifications by Ioannis V. Vondikakis, Ilias E. Panagiotopoulos, George J. Dimitrakopoulos

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Neural Oscillatory Mechanisms Underlying Step Accuracy: Integrating Microstate Segmentation with eLORETA-Independent Component Analysis by Kohei Okuyama, Kota Maeda, Ryosuke Yamauchi, Daichi Harada, Takayuki Kodama

“…This study investigated the cortical networks underlying stepping accuracy using mobile brain/body imaging with electroencephalography (EEG)-based exact low-resolution electromagnetic tomography-independent component analysis (eLORETA-ICA) and microstate segmentation analysis (MSA). …”
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Comparison of ResNet-50, EfficientNet-B1, and VGG-16 Algorithms for Cataract Eye Image Classification by Ilham Santoso, Ayub Michaelangelo Manurung, Egia Rosi Subhiyakto

“…This study evaluates the capabilities of three widely-used deep learning models—ResNet-50, EfficientNet-B1, and VGG-16—in classifying visual data. The analysis was conducted on a dataset of 2,112 images, comprising 1,074 normal cases and 1,038 cataract cases. …”
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Leveraging Thermal Infrared Imaging for Pig Ear Detection Research: The TIRPigEar Dataset and Performances of Deep Learning Models by Weihong Ma, Xingmeng Wang, Simon X. Yang, Lepeng Song, Qifeng Li

“…By labeling pig ears within these images, a total of 69,567 labeled files were generated, which can be directly used for training pig ear detection models and enabling the analysis of pig temperature information by integrating the corresponding thermal imaging data. …”
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Women’s attitudes to the use of AI image readers: a case study from a national breast screening programme by Yan Chen, Niamh Lennox-Chhugani, Veronica Pearson, Bernadette Trzcinski, Jonathan James

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Implicit Is Not Enough: Explicitly Enforcing Anatomical Priors inside Landmark Localization Models by Simon Johannes Joham, Arnela Hadzic, Martin Urschler

“…The task of localizing distinct anatomical structures in medical image data is an essential prerequisite for several medical applications, such as treatment planning in orthodontics, bone-age estimation, or initialization of segmentation methods in automated image analysis tools. …”
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Voxel-based versus network-analysis of changes in brain states in patients with auditory verbal hallucinations using the Eriksen Flanker task. by Lydia Brunvoll Sandøy, Katarzyna Kazimierczak, Frank Riemer, Alexander R Craven, Lars Ersland, Lin Lilleskare, Erik Johnsen, Kenneth Hugdahl, Renate Grüner

“…A network connectivity analysis of the fMRI data showed that both groups recruited similar networks related to task-present and task-absent conditions. …”
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Alterations of white matter functional networks in pediatric drug-resistant temporal lobe epilepsy: A graph theory analysis study by Kexin Huang, Yuxin Xie, Haifeng Ran, Jie Hu, Yulun He, Gaoqiang Xu, Guiqin Chen, Qiane Yu, Xuhong Li, Junwei Liu, Heng Liu, Tijiang Zhang

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The Robust Vessel Segmentation and Centerline Extraction: One-Stage Deep Learning Approach by Rostislav Epifanov, Yana Fedotova, Savely Dyachuk, Alexandr Gostev, Andrei Karpenko, Rustam Mullyadzhanov

“…In addition, we conducted a robustness analysis to evaluate the model stability under small rigid and deformable transformations of the input data, and benchmarked its robustness against the widely used VMTK toolkit.…”
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