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        <title>Frontiers in Imaging | Imaging Applications section | New and Recent Articles</title>
        <link>https://www.frontiersin.org/journals/imaging/sections/imaging-applications</link>
        <description>RSS Feed for Imaging Applications section in the Frontiers in Imaging journal | New and Recent Articles</description>
        <language>en-us</language>
        <generator>Frontiers Feed Generator,version:1</generator>
        <pubDate>2026-08-19T15:50:13.944+00:00</pubDate>
        <ttl>60</ttl>
        <item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fimag.2026.1845480</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fimag.2026.1845480</link>
        <title><![CDATA[Wavelet-based features to improve cross-forgery generalization in deepfake detection]]></title>
        <pubdate>2026-08-05T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Niccolò Marini</author><author>Stefano Berretti</author><author>Roberto Caldelli</author>
        <description><![CDATA[Deepfake detection remains a challenging problem due to the ever-evolving techniques used to synthesize realistic fake images and videos. Traditional detection methods primarily operate in the pixel domain, often struggling to generalize across different forgery types. In this work, we explore an approach that integrates wavelet-based frequency analysis with deep learning to enhance deepfake detection. By decomposing input images using the Discrete Wavelet Transform (DWT), we extract multi-resolution features that capture both global structures and fine-grained manipulation artifacts; such features are then input to a neural network. Our results show that level-1 DWT features mainly induce a sensitivity-specificity tradeoff: they improve fake-class sensitivity and F1 in cross-forgery settings, while RGB models retain stronger specificity and in-forgery performance. This suggests that wavelet sub-bands expose manipulation cues that are useful for detecting unseen fake classes, but they should not be interpreted as a uniform robustness improvement.]]></description>
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        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fimag.2026.1836077</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fimag.2026.1836077</link>
        <title><![CDATA[A deep learning-based decision fusion framework for image forgery localization]]></title>
        <pubdate>2026-07-16T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Pengpeng Yang</author><author>Daniele Baracchi</author><author>Dasara Shullani</author><author>Marco Fontani</author><author>Rongrong Ni</author><author>Yao Zhao</author><author>Alessandro Piva</author>
        <description><![CDATA[As an answer to the need to prove the authenticity of digital images, several forensic tools for image forgery localization have been proposed in the past. However, authenticity analysis remained a challenging task, which requires an expert's knowledge to correctly interpret each forensic algorithm's output. Moreover, since different tools look for different manipulation traces, a thorough analysis requires the joint interpretation of the maps produced by several tools, which is nontrivial since each tool's reliability is possibly affected by different elements. Recently, deep learning-based forgery localization schemes were proposed, allowing for more automated reasoning; however, their accuracy significantly decreases when they are tested on forgeries that deviate from those used for the training phase. This work proposes a deep learning-based framework that merges the forgery localization maps provided by model-based image forensics tools based on the U-Net architecture. The experiments show that the proposed approach improves the quality of forgery localization maps compared to those produced by single tools and by state-of-the-art fusion frameworks while simultaneously achieving a desirable generalization capability.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fimag.2026.1844310</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fimag.2026.1844310</link>
        <title><![CDATA[Comprehensive guideline for X-ray microtomography-guided bone analysis and histology applied to the assessment of defect repair in long and flat bones]]></title>
        <pubdate>2026-07-10T00:00:00Z</pubdate>
        <category>Methods</category>
        <author>Bruno Henrique Costa</author><author>Isabela Louise Pereira Lopes</author><author>Michelle de Oliveira</author><author>Tamires Aparecida Jaques de Resende</author><author>Murilo Leone Miranda Fajardo</author><author>Anna Beatriz Modesto Pires</author><author>Anderson Camargo Moreira</author><author>Iara Frangiotti Mantovani</author><author>Rodrigo Nagata</author><author>Talita Martins</author><author>Eduardo Henrique Martins Nunes</author><author>Taíse Matte Manhabosco</author><author>Erika Costa de Alvarenga</author>
        <description><![CDATA[Bone regeneration studies require reliable analytical tools capable of assessing both microstructural and cellular aspects of tissue repair. This article presents a comprehensive methodological guide that integrates microcomputed tomography (micro-CT) and histological analyses for standardized evaluation of bone defect repair in rodent models, focusing on the tibia (long bone) and calvaria (flat bone). We describe detailed surgical protocols for defect creation, imaging workflows for micro-CT acquisition, reconstruction, and morphometric quantification, alongside histological processing and semi-quantitative scoring of tissue parameters. Illustrative results demonstrate how micro-CT enables three-dimensional, non-destructive quantification of mineralized tissue, while histology provides complementary insights into non-mineralized structures, vascularization, and cellular organization. The combined use of these techniques enhances reproducibility, ensures methodological consistency, and allows for robust comparisons across studies. By establishing a standardized workflow, this protocol supports translational research in bone repair and offers a valuable resource for evaluating regenerative strategies and biomaterials.]]></description>
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        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fimag.2026.1846377</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fimag.2026.1846377</link>
        <title><![CDATA[Facial attribute-aware DeepFake detection through semi-supervised facial attribute labeling]]></title>
        <pubdate>2026-06-24T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Vittorio Stile</author><author>Roberto Caldelli</author><author>Sara Balderas-Díaz</author><author>Gabriel Guerrero-Contreras</author><author>Inmaculada Medina-Bulo</author>
        <description><![CDATA[This study investigates the correlation between misclassifications in DeepFake detection and high-level facial attributes. A pre-trained frame-level classifier is used to distinguish manipulated from authentic video content, and its wrong predictions are analyzed in detail. To enrich the dataset, we automatically annotate each video with additional labels, including gender, hair color, hair length, ear visibility, and ethnicity, using a semi-supervised facial-attribute recognition pipeline. We extend this analysis with controlled training-time exclusions on FaceForensics++, keeping a unified test set to isolate generalization bias. Compared to the no-exclusion baseline (Accuracy = 0.806, AUC = 0.823), excluding samples with ears visible yields the largest degradation (Accuracy = 0.741, AUC = 0.763), while excluding non-visible ears has a milder effect (Accuracy = 0.813, AUC = 0.832). Hair length shows a moderate but consistent impact that interacts with ear visibility. We also explain the observed confusion-matrix asymmetry as a consequence of fixed score thresholds and video-level k-of-n aggregation. The results demonstrate that ear visibility is a critical factor for robust FAKE vs. REAL discrimination and motivate attribute-aware training, including targeted data curation, attribute-specific augmentation, and threshold calibration. The proposed framework provides actionable guidance for bias-aware training strategies and supports the development of more interpretable and operationally reliable DeepFake detection systems.]]></description>
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        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fimag.2026.1846329</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fimag.2026.1846329</link>
        <title><![CDATA[Motion representations for privacy-aware cross-domain action recognition]]></title>
        <pubdate>2026-06-03T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Pascal Benschop</author><author>Jan van Gemert</author><author>Jelte P. Mense</author><author>Justin Dauwels</author>
        <description><![CDATA[Video captured for action recognition often contains sensitive appearance cues such as faces, skin color, and clothing. Models trained on such data may exploit these cues rather than the underlying motion, raising privacy concerns in real-world deployment. In this work, we study action recognition under a motion-focused constraint: the model receives only motion representations that capture pixel displacement over time, while reducing appearance cues that expose identity or scene context. We focus on motion-history images and optical flow as learning-free representations that reduce identifiable appearance information while retaining action recognition accuracy. Our motion I3D model achieves approximately 31% and 52% zero-shot top-1 accuracy on HMDB-51 and UCF-101, respectively, outperforming non-CLIP direct-transfer baselines trained on Kinetics-400 despite operating without any appearance input. In 16-shot adaptation, the same model reaches 52% and 83% top-1 accuracy. In the domain adaptation setting on TP-HMDB↔TP-UCF, our motion-focused models achieve higher action recognition accuracy than prior privacy-preserving methods. Sensitive attribute predictability is reduced relative to RGB by a comparable margin, without requiring a learned privacy filter. On PA-HMDB51, optical flow is the strongest motion representation for privacy preservation, approaching chance level for skin-color prediction and remaining below RGB on most privacy attributes, indicating that motion representations retain useful action information while exposing less personal information.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fimag.2026.1817515</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fimag.2026.1817515</link>
        <title><![CDATA[Adaptive-LwF: continual training of morphing attack detector without forgetting]]></title>
        <pubdate>2026-05-27T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Lorenzo Pellegrini</author><author>Guido Borghi</author><author>Annalisa Franco</author><author>Davide Maltoni</author>
        <description><![CDATA[In Biometrics, the presence of privacy restrictions on personal data transfer and storage poses significant challenges in creating a sufficiently comprehensive and varied dataset by leveraging various data sources for traditional batch-based training procedures. This is particularly true in the Morphing Attack Detection (MAD) task, in which data involves facial images and a limited number of public datasets of well-controlled images are available. In this context, MAD systems generally suffer from limited generalization capabilities, with low performance on new and unseen data. Therefore, in this paper, we propose Adaptive-LwF, adopting the recent paradigm of Continual Learning (CL) as a viable solution to enable incremental training across multiple sites. Indeed, CL assumes that once a model has been trained, previous data cannot be utilized in subsequent training iterations and can be deleted. In particular, we investigate the performance of different methods in this new scenario, where a model is updated each time a new chunk of data, of variable size, becomes available. We focus our attention on the well-known Learning without Forgetting (LwF) algorithm, proposing a novel adaptive approach able to automatically fine-tune its parameters in relation to the variable size of the specific input chunks. Experimental results confirm that our approach is capable of mitigating the catastrophic forgetting effects, and the superior performance of the Adaptive-LwF algorithm with respect to alternative solutions.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fimag.2026.1795278</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fimag.2026.1795278</link>
        <title><![CDATA[Deep learning based thermal foot segmentation with probability inversion post-processing for automated epidural block assessment]]></title>
        <pubdate>2026-05-14T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Sungjoon Hong</author><author>Milan Toma</author>
        <description><![CDATA[IntroductionEpidural analgesia represents the gold standard for labor pain management, yet clinical failure occurs in approximately 9 to 12 percent of cases, adversely affecting maternal well-being and necessitating additional interventions. Current assessment methods rely on subjective sensory tests that depend on patient feedback, which may be unreliable during active labor. Infrared thermography offers objective monitoring of sympathetic blockade through peripheral temperature changes, but clinical adoption has been limited by the absence of automated analysis tools requiring manual region-of-interest delineation by clinicians.MethodsThis study introduces an automated deep learning pipeline utilizing a U-Net convolutional neural network trained on thermal images from 30 pregnant women to segment foot regions and quantify surface temperatures for epidural analgesia assessment. A principal technical challenge emerged from severe class imbalance, where the foot region occupies substantially less image area than background space. To address this limitation, a probability inversion strategy was implemented during post-processing, optimizing the model to prioritize complete thermal region capture over geometric boundary precision.ResultsThe system achieved a global accuracy of 82.08 percent with foot-class sensitivity of 71.83 percent, successfully segmenting foot regions despite indistinct thermal gradients characteristic of infrared imagery. Automated temperature quantification enabled objective measurement of mean surface temperature within the segmented region.ConclusionsThe proposed automated pipeline offers a non-invasive, objective, and real-time alternative to subjective manual assessment methods, with potential to improve clinical decision-making in obstetric analgesia monitoring and pain management settings. This approach addresses the critical need for standardized, operator-independent evaluation of neuraxial blockade efficacy in high-volume clinical environments.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fimag.2026.1758694</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fimag.2026.1758694</link>
        <title><![CDATA[Feasibility in the detection of sentinel lymph node-associated blood vessels using intravital microscopy in patients undergoing sentinel lymph node biopsy for melanoma]]></title>
        <pubdate>2026-03-09T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Emmanuel Gabriel</author><author>Daniel T. Fisher</author><author>Minhyung Kim</author><author>Kristopher Attwood</author><author>Xiaoyi Ma</author><author>Valerie Francescutti</author><author>John M. Kane</author><author>Sharon S. Evans</author><author>Joseph J. Skitzki</author>
        <description><![CDATA[While the clinical focus on the sentinel lymph node biopsy (SLNB) is the presence of intra- or extra-nodal metastases, preclinical studies suggest that tumor-draining SLNB-associated vascular architecture and adhesion properties are altered regardless of SLNB positivity. Human intravital microscopy (HIVM) has defined blood vessel abnormalities that may impact lymphocyte adhesion and systemic drug delivery at primary melanoma sites. In this pilot study of HIVM during melanoma SLNB, we sought to determine the feasibility of obtaining HIVM observations of SLNB-associated vessels. We successfully performed HIVM in all 20 SLNB patients, and 7 were found to have nodal micrometastases by standard pathology. HIVM was capable of identifying both functional and non-functional SLNB-associated vessels based on the presence or absence of fluorescent dye uptake, respectively. Comparing vessel characteristics as a secondary exploratory objective, no statistically significant differences were noted in the diameter, flow rate, functionality, or shear stress of SLNB-associated blood vessels between positive and negative SLNBs, which may likely have been a reflection of the minimal disease burden. Nonetheless, these initial observations provide the framework to optimize future trials of HIVM in cancer patients.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fimag.2025.1694840</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fimag.2025.1694840</link>
        <title><![CDATA[Cardiac adipose tissue, imaging segmentation, and quantification for cardiovascular disease assessment]]></title>
        <pubdate>2026-01-08T00:00:00Z</pubdate>
        <category>Review</category>
        <author>Julian Rene Cuellar Buritica</author><author>Mukul Bhattarai</author><author>Pedro Carrillo</author><author>Manjula Burri</author><author>Jon Klingensmith</author>
        <description><![CDATA[Cardiac adipose tissue (CAT) has emerged as a critical and clinically relevant factor in cardiovascular disease (CVD), yet its full impact remains largely overlooked. The amount of fat surrounding the heart can influence major blood vessels by promoting plaque formation. In conditions such as cardiac steatosis or fatty heart disease, fat infiltration or accumulation within the heart muscle compromises its function may play a role in heart failure (HF) and coronary artery disease (CAD). This review explores the different types of fat deposits surrounding the heart, focusing on the potential contribution of CAT to cardiovascular disease (CVD). Three main imaging modalities for assessing cardiac fat are discussed, including magnetic resonance imaging (MRI), computed tomography (CT), and echocardiography. The segmentation and quantification of the fat for each imaging modality are also presented, correlating these measurements with CVD risk. Each imaging modality offers distinct advantages and limitations in segmenting and quantifying fat. Despite its clinical significance, quantification and characterization of CAT remain challenging, requiring advanced imaging techniques for precise assessment. Future research should focus on unlocking the mechanistic pathways that link CAT to adverse cardiovascular outcomes, ultimately enhancing our ability to predict, prevent, and treat heart disease with greater precision. As imaging technology advances, there is a need for refined segmentation methods and consensus-driven guidelines to establish CAT as a key biomarker in CVD risk stratification.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fimag.2025.1761718</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fimag.2025.1761718</link>
        <title><![CDATA[Editorial: Deep learning for medical imaging applications]]></title>
        <pubdate>2026-01-06T00:00:00Z</pubdate>
        <category>Editorial</category>
        <author>Simone Bonechi</author><author>Monica Bianchini</author><author>Paolo Andreini</author><author>Sandeep Kumar Mishra</author>
        <description></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fimag.2025.1610258</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fimag.2025.1610258</link>
        <title><![CDATA[Advances in magnetic particle imaging: evaluating magnetic microspheres and optimized acquisition parameters for high sensitivity cell tracking]]></title>
        <pubdate>2025-07-24T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Samantha N. Flood</author><author>Paula J. Foster</author>
        <description><![CDATA[IntroductionThe sensitivity and resolution of magnetic particle imaging (MPI) depend on the choice of tracer and specific imaging parameters. For cell tracking applications with MPI, both the superparamagnetic iron oxide (SPIO) tracer and the cell labeling efficiency have a significant impact on MPI sensitivity and vary for different tracers.MethodsThis study compared three commercially available SPIO tracers (VivoTrax, Synomag-D and ProMag) and SPIO-labeled cells using magnetic particle relaxometry (MPR) and imaging. Further, the effect of imaging parameters (high and low gradient field strength and drive field amplitude) on MPI signal strength, resolution, and cell detection limits, was evaluated.ResultsThe peak MPI signal measured by MPR was much higher for Synomag-D compared to VivoTrax and ProMag. However, the signal for intracellular Synomag-D was significantly reduced. In contrast, the signal for ProMag, a micron-sized iron oxide (MPIO) particle, was not significantly different for free and intracellular particles. The cellular iron loading was higher for ProMag compared to Synomag-D. The total MPI signal measured from images of free and intracellular SPIOs was highest for ProMag. Varying imaging parameters confirmed that a lower gradient field strength and higher drive field amplitude improved tracer and cellular sensitivity.DiscussionThese results, in addition to prior work from our lab, suggest that MPIOs are a good option for cell tracking with MPI. In conclusion, the evaluation of tracers by MPR is not sufficient to predict the performance of all SPIO tracers; in particular, not for larger, polymer-encapsulated iron particles such as ProMag, or for SPIO tracers internalized in cells. Improvements in MPI sensitivity through lower gradient field strength and higher drive field amplitudes are associated with a trade-off in image resolution.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fimag.2025.1476377</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fimag.2025.1476377</link>
        <title><![CDATA[Template recovery attack on encrypted face recognition systems with unprotected decision using synthetic faces]]></title>
        <pubdate>2025-05-12T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Amina Bassit</author><author>Florian Hahn</author><author>Zohra Rezgui</author><author>Hatef Otroshi Shahreza</author><author>Raymond Veldhuis</author><author>Andreas Peter</author>
        <description><![CDATA[IntroductionHomomorphic encryption (HE) enables privacy-preserving face recognition by allowing encrypted facial embeddings to be compared without decryption. While efficient, these systems often reveal comparison scores in plaintext, introducing a security risk. Revealing these scores can potentially allow adversaries to reconstruct sensitive facial embeddings and infer demographic attributes, thus compromising user privacy.MethodsThis work proposes a training-less face template recovery attack leveraging the Lagrange multiplier optimization method. The attack requires only a small set of randomly generated synthetic facial images and their associated comparison scores with a target template. The method assumes attackers use spoofed synthetic faces and lack direct access to the face recognition system, aligning with real-world threat models.ResultsExperimental evaluation demonstrates the feasibility and effectiveness of the proposed attack. It shows that between 50 and 192 comparison scores and synthetic images are sufficient to recover the target face template with 100% success under strict system thresholds. The recovered templates closely resemble the original and retain identifiable soft biometric traits.DiscussionThe findings reveal a critical vulnerability in face recognition systems employing inner product similarity measures under homomorphic encryption. Even without system access or training data, attackers can exploit leaked comparison scores to compromise facial privacy. The study underscores the need to reassess how score leakage is handled in encrypted recognition systems and explore stronger protection mechanisms against template reconstruction.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fimag.2025.1504551</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fimag.2025.1504551</link>
        <title><![CDATA[High-quality deepfakes have a heart!]]></title>
        <pubdate>2025-04-30T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Clemens Seibold</author><author>Eric L. Wisotzky</author><author>Arian Beckmann</author><author>Benjamin Kossack</author><author>Anna Hilsmann</author><author>Peter Eisert</author>
        <description><![CDATA[IntroductionDeepfakes have become ubiquitous in our modern society, with both their quantity and quality increasing. The current evolution of image generation techniques makes the detection of manipulated content through visual inspection increasingly difficult. This challenge has motivated researchers to analyze heart-beat-related signal to distinguish deep fakes from genuine videos.MethodsIn this study, we analyze deepfake videos of faces generated with novel methods regarding their heart-beat-related signals using remote photoplethysmography (rPPG). The rPPG signal describes the blood flow based, or rather local blood volume changes, and thus reflects the pulse signal. For our analysis, we present a pipeline that extracts rPPG signals and investigate the origin of the extracted signals in deepfake videos using correlation analyses. To validate our rPPG extraction pipeline and analyze rPPG signals of deepfakes, we captured a dataset of facial videos synchronized with an electrocardiogram (ECG) as a ground-truth pulse signal. Additionally, we generated high-quality deepfakes and incorporated publicly available datasets into our evaluation.ResultsWe prove that our heart rate extraction pipeline produces valid estimates for genuine videos by comparing the estimated results with ECG reference data. Our high-quality deepfakes exhibit valid heart rates and their rPPG signals show a significant correlation with the corresponding driver video that was used to generate them. Furthermore, we show that this also holds for deepfakes from a publicly available dataset.DiscussionPrevious research assumed that the subtle heart-beat-related signals get lost during the deepfake generation process, making them useful for deepfake detection. However, this paper shows that this assumption is no longer valid for current deepfake methods. Nevertheless, preliminary experiments indicate that analyzing spatial distribution of bloodflow regarding its plausibility can still help to detect high quality deepfakes.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fimag.2025.1538533</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fimag.2025.1538533</link>
        <title><![CDATA[Novel imaging approach for simultaneous tracking of cell dynamics in distinct tissue layers reveals cells involved in colonic peristalsis]]></title>
        <pubdate>2025-04-03T00:00:00Z</pubdate>
        <category>Methods</category>
        <author>Salah A. Baker</author><author>Peter J. Blair</author><author>Sharif Amit Kamran</author><author>Kenton M. Sanders</author>
        <description><![CDATA[We have developed a novel approach for high-resolution confocal imaging across multiple tissue planes simultaneously. By combining confocal microscopy, piezo actuators, and optogenetic sensors, we can simultaneously capture images of dynamic fluorescence signals from various cell populations in different tissue layers (Z planes). This enables the decoding of cell-to-cell communication through complex tissues, offering a significant advancement in understanding how cells in distinct layers of tissue communicate and coordinate their functions and produce integrated behaviors. For example, our technique sheds light on myogenic coordination underlying colonic motility. Examining various cell types, such as interstitial cells of Cajal (ICC) and smooth muscle cells (SMC), distributed through the thickness of muscle layers, we demonstrate distinct Ca2+ signaling patterns and organization that underlie complex colonic motor activities.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fimag.2025.1547166</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fimag.2025.1547166</link>
        <title><![CDATA[Continuous patient monitoring with AI: real-time analysis of video in hospital care settings]]></title>
        <pubdate>2025-03-10T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Paolo Gabriel</author><author>Peter Rehani</author><author>Tyler Troy</author><author>Tiffany Wyatt</author><author>Michael Choma</author><author>Narinder Singh</author>
        <description><![CDATA[IntroductionThis study introduces an AI-driven platform for continuous and passive patient monitoring in hospital settings, developed by LookDeep Health. Leveraging advanced computer vision, the platform provides real-time insights into patient behavior and interactions through video analysis, securely storing inference results in the cloud for retrospective evaluation.MethodsThe AI system detects key components in hospital rooms, including individuals' presence and roles, furniture location, motion magnitude, and boundary crossings. Inference results are securely stored in the cloud for retrospective evaluation. The dataset, compiled with 11 hospital partners, includes over 300 high-risk fall patients and spans more than 1,000 days of inference. An anonymized subset is publicly available to foster innovation and reproducibility at lookdeep/ai-norms-2024.ResultsPerformance evaluation demonstrates strong accuracy in object detection (macro F1-score = 0.92) and patient-role classification (F1-score = 0.98). The system reliably tracks the “patient alone” metric (mean logistic regression accuracy = 0.82 ± 0.15), enabling detection of patient isolation, wandering, and unsupervised movement-key indicators for fall risk and adverse events.DiscussionThis work establishes benchmarks for AI-driven patient monitoring, highlighting the platform's potential to enhance patient safety through continuous, data-driven insights into patient behavior and interactions.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fimag.2025.1502613</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fimag.2025.1502613</link>
        <title><![CDATA[Electroanatomic mapping reconstruction with photogrammetry across different mapping systems]]></title>
        <pubdate>2025-02-24T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Giacomo Talevi</author><author>Luigi Pannone</author><author>Domenico Giovanni Della Rocca</author><author>Antonio Sorgente</author><author>Rani Kronenberger</author><author>Ingrid Overeinder</author><author>Gezim Bala</author><author>Alexandre Almorad</author><author>Erwin Ströker</author><author>Juan Sieira</author><author>Mark La Meir</author><author>Andrea Sarkozy</author><author>Pedro Brugada</author><author>Gian Battista Chierchia</author><author>Ali Gharaviri</author><author>Carlo de Asmundis</author>
        <description><![CDATA[BackgroundAutomatic digital photogrammetry produces digital reproductions of objects using photographs. The aim of this study is to analyze feasibility of photogrammetry for electroanatomic map (EAM) reconstruction from different mapping systems. Furthermore, the possibility to import the reconstructed EAMs in a common working space is evaluated.MethodsAll consecutive patients undergoing EAM with one of the following EAM systems were screened for the study: (1) CARTO™; (2) Ensite™ X; (3) Rhythmia™; (4) Affera™ PRISM-3. All patient geometries were reconstructed from a video acquisition within the source EAM software. The video obtained was processed with Zephyr software and a dense point cloud was obtained. An image or sequence of images was selected to build a 3D mesh. At the end, the mesh was imported in the 3D graphics software Blender.ResultA total of 24 EAMs from 24 patients were included in the study. All EAMs were reconstructed with success using photogrammetry from all 4 mapping systems assessed. The process time was ≈ 25 min. In particular, EAMs were as follows: left atrium (2 Carto; 2 Ensite; 5 Rhythmia; 2 Affera), right atrium (1 Carto; 6 Ensite; 3 Affera) and left ventricles (1 Carto; 2 Ensite). All the reconstructed EAMs were imported in Blender with success. They could be visualized in Blender and all the operations were allowed including moving EAMs in a common working space and EAMs overlap.ConclusionThis study demonstrated for the first time the possibility of realizing 3-D objects from digital video formats of different EAMs.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fimag.2025.1542128</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fimag.2025.1542128</link>
        <title><![CDATA[Vision transformers for automated detection of diabetic peripheral neuropathy in corneal confocal microscopy images]]></title>
        <pubdate>2025-02-03T00:00:00Z</pubdate>
        <category>Brief Research Report</category>
        <author>Chaima Ben Rabah</author><author>Ioannis N. Petropoulos</author><author>Rayaz A. Malik</author><author>Ahmed Serag</author>
        <description><![CDATA[Early detection and management of diabetic peripheral neuropathy (DPN) are critical to reducing associated morbidity and mortality. Corneal Confocal Microscopy (CCM) facilitates the imaging of corneal nerves to detect early and progressive nerve damage in DPN. However, its wider adoption has been limited by the subjectivity and time-intensive nature of manual nerve fiber quantification. This study investigates the diagnostic utility of state-of-the-art Vision Transformer (ViT) models for the binary classification of CCM images to distinguish between healthy controls and individuals with DPN. The ViT model's performance was also compared to ResNet50, a convolutional neural network (CNN) previously applied for DPN detection using CCM images. Using a dataset of approximately 700 CCM images, the ViT model achieved an AUC of 0.99, a sensitivity of 98%, a specificity of 92%, and an F1-score of 95%, outperforming previously reported methods. These findings highlight the potential of the ViT model as a reliable tool for CCM-based DPN diagnosis, eliminating the need for time-consuming manual image segmentation. Moreover, the results reinforce CCM's value as a non-invasive and precise imaging modality for detecting nerve damage, particularly in neuropathy-related conditions such as DPN.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fimag.2024.1530335</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fimag.2024.1530335</link>
        <title><![CDATA[Editorial: Horizons in imaging]]></title>
        <pubdate>2024-12-20T00:00:00Z</pubdate>
        <category>Editorial</category>
        <author>Alessandro Piva</author><author>Lifu Zhang</author><author>Jinchang Ren</author>
        <description></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fimag.2024.1478783</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fimag.2024.1478783</link>
        <title><![CDATA[Presentation Attack Detection using iris periocular visual spectrum images]]></title>
        <pubdate>2024-12-18T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Andrés Valenzuela</author><author>Juan E. Tapia</author><author>Violeta Chang</author><author>Christoph Busch</author>
        <description><![CDATA[In this work, we analyse the comparison between using the periocular area instead of the full face area for Presentation Attack Detection (PAD) in the visual spectrum (RGB). The analysis was carried out by evaluating the performance of five Convolutional Neural Networks (CNN) using both facial and periocular iris images for PAD with two different attack instruments. Additionally, we improved the CNN results by integrating the ArcFace loss function instead of the traditional categorical cross-entropy loss, highlighting that the ArcFace function enhances the performance of the models for both regions of interest, facial and iris periocular areas. We conducted Binary and Multiclass comparisons, followed by cross-database validation to assess the generalization capabilities of the trained models. Our study also addresses some of the current challenges in PAD research, such as the limited availability of high-quality face datasets in the desired spectrum (RGB), which impacts the quality of Presentation Attack Instruments (PAI) examples used in training and evaluation. Our goal was to address the challenge of detecting Iris periocular presentation attacks by leveraging the ArcFace function. The results demonstrate the effectiveness of our approach and provide valuable insights for improving PAD systems using periocular areas in the visual spectrum.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fimag.2024.1443142</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fimag.2024.1443142</link>
        <title><![CDATA[Video tracking of single cells to identify clustering behavior]]></title>
        <pubdate>2024-12-02T00:00:00Z</pubdate>
        <category>Methods</category>
        <author>Mónica Suárez Korsnes</author><author>Håkon André Ramberg</author><author>Kristin Austlid Taskén</author><author>Reinert Korsnes</author>
        <description><![CDATA[Cancer cell clustering is a critical factor in metastasis, with cells often believed to migrate in groups as they establish themselves in new environments. This study presents preliminary findings from an in vitro experiment, suggesting that co-culturing cells provides an effective method for observing this phenomenon, even though the cells are grown as monolayers. We introduce a novel single-cell tracking approach based on graph theory to identify clusters in PC3 cells cultivated in both monoculture and co-culture with PC12 cells, using 66-h time-lapse recordings. The initial step consists of defining “linked” pairs of PC3 cells, laying the foundation for the application of graph theory. We propose two alternative definitions for cell pairings. The first method, Method 1, defines cells as “linked” at a given time t if they are close together within a defined time period before and after t. A second potential alternative method, Method 2, pairs cells if there is an overlap between the convex hulls of their respective tracks during this time period. Pairing cells enables the application of graph theory for subsequent analysis. This framework represents a cell as a vertex (node) and a relation between two cells as an edge. An interconnected set of high-degree nodes (nodes with many connections or edges) forms a subgraph, or backbone, that defines a patch (cluster) of cells. All nodes connected to this backbone are part of the subgraph. The backbone of high-degree nodes functions as a partition (or cut) of the initial graph. Two consecutive clusters in the video are considered to share the same identity if the following cluster contains at least p = 75 % of the cells from the preceding cluster, and the mean positions of their cells are within △r = 75μm. PC3 cells grown in co-culture appear to form persistent clusters exceeding 10 cells after 40–50 h incubation following seeding. In contrast, PC3 cells cultured alone (mono-culture) did not exhibit this behavior. This approach is experimental and requires further validation with a broader dataset.]]></description>
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