Nano Lett. 2025 Jan 4. doi: 10.1021/acs.nanolett.4c05120. Online ahead of print.

ABSTRACT

Topological plasmonics combines principles of topology and plasmonics to provide new methods for controlling light, analogous to topological edge states in photonics. However, designing such topological states remains challenging due to the complexity of the high-dimensional design space. We present a novel method that uses supervised, physics-informed deep learning and surrogate modeling to design topological devices for desired wavelengths. By embedding physical constraints in the neural network's training, our model efficiently explores the design space, significantly reducing simulation time. Additionally, we use nonplanar wavefront excitations to probe topologically protected plasmonic modes, making the design and training process nonlinear. Using this approach, we design a topological device with unidirectional edge modes in a ring resonator at specific operational frequencies. Our method reduces computational cost and time while maintaining high accuracy, highlighting the potential of combining machine learning and advanced techniques for photonic device innovation.

PMID:39754588 | DOI:10.1021/acs.nanolett.4c05120

Microb Biotechnol. 2025 Jan;18(1):e70072. doi: 10.1111/1751-7915.70072.

ABSTRACT

Antimicrobial peptides (AMPs) are promising candidates to combat multidrug-resistant pathogens. However, the high cost of extensive wet-lab screening has made AI methods for identifying and designing AMPs increasingly important, with machine learning (ML) techniques playing a crucial role. AI approaches have recently revolutionised this field by accelerating the discovery of new peptides with anti-infective activity, particularly in preclinical mouse models. Initially, classical ML approaches dominated the field, but recently there has been a shift towards deep learning (DL) models. Despite significant contributions, existing reviews have not thoroughly explored the potential of large language models (LLMs), graph neural networks (GNNs) and structure-guided AMP discovery and design. This review aims to fill that gap by providing a comprehensive overview of the latest advancements, challenges and opportunities in using AI methods, with a particular emphasis on LLMs, GNNs and structure-guided design. We discuss the limitations of current approaches and highlight the most relevant topics to address in the coming years for AMP discovery and design.

PMID:39754551 | DOI:10.1111/1751-7915.70072

Anal Chem. 2025 Jan 4. doi: 10.1021/acs.analchem.4c04448. Online ahead of print.

ABSTRACT

Cyclic voltammetry (CV) has been a powerful technique to provide impactful insights for electrochemical systems, including reaction mechanism, kinetics, diffusion coefficients, etc., in various fields of study, notably energy storage and energy conversion. However, the separation between the faradaic current component of CV and the nonfaradaic current contribution to extract useful information remains a major issue for researchers. Herein, we report a deep learning algorithm inspired by speech denoising that utilizes the theoretical faradaic current as a study target and predicts it from the overall current response from cyclic voltammograms. This deep neural network (DNN) is constructed from a series of fully connected layers, which apply a weight matrix to the inputs and transform it using an activation function to obtain the desired regression. Our model performed well with overall mean absolute percentage errors (MAPEs) of 6.36% between theoretical faradaic currents and the predicted responses from the total currents, with a peak position difference of 2.56 mV for anodic peaks and 2.44 mV for cathodic ones. Furthermore, the algorithm is also capable of extracting peak current values from experimental data with 3.37% MAPE and minimal peak position error (less than 0.75 mV). This innovative approach may be used as a tool to assist researchers in studying electrochemical systems using CV.

PMID:39754543 | DOI:10.1021/acs.analchem.4c04448

Biomed Tech (Berl). 2025 Jan 6. doi: 10.1515/bmt-2024-0299. Online ahead of print.

ABSTRACT

OBJECTIVES: Diabetic retinopathy (DR) is associated with long-term diabetes and is a leading cause of blindness if it is not diagnosed early. The rapid growth of deep learning eases the clinicians' DR diagnosing procedure. It automatically extracts the features and performs the grading. However, training the image toward the majority of background pixels can impact the accuracy and efficiency of grading tasks. This paper proposes an auto-thresholding algorithm that reduces the negative impact of considering the background pixels for feature extraction which highly affects the grading process.

METHODS: The PSO-based thresholding algorithm for retinal segmentation is proposed in this paper, and its efficacy is evaluated against the Otsu, histogram-based sigma, and entropy algorithms. In addition, the importance of retinal segmentation is analyzed using Explainable AI (XAI) to understand how each feature impacts the model's performance. For evaluating the accuracy of the grading, ResNet50 was employed.

RESULTS: The experiments were conducted using the IDRiD fundus dataset. Despite the limited data, the retinal segmentation approach provides significant accuracy than the non-segmented approach, with a substantial accuracy of 83.70 % on unseen data.

CONCLUSIONS: The result shows that the proposed PSO-based approach helps automatically determine the threshold value and improves the model's accuracy.

PMID:39754503 | DOI:10.1515/bmt-2024-0299

Orthod Craniofac Res. 2025 Jan 4. doi: 10.1111/ocr.12895. Online ahead of print.

ABSTRACT

OBJECTIVE: This retrospective study aimed to evaluate morphometric changes in mandibular condyles of patients with skeletal Class III malocclusion following two-jaw orthognathic surgery planned using virtual surgical planning (VSP) and analysed with automated three-dimensional (3D) image analysis based on deep-learning techniques.

MATERIALS AND METHODS: Pre-operative (T1) and 12-18 months post-operative (T2) Cone-Beam Computed Tomography (CBCT) scans of 17 patients (mean age: 24.8 ± 3.5 years) were analysed using 3DSlicer software. Deep-learning algorithms automated CBCT orientation, registration, bone segmentation, and landmark identification. By utilising voxel-based superimposition of pre- and post-operative CBCT scans and shape correspondence, the overall changes in condylar morphology were assessed, with a focus on bone resorption and apposition at specific regions (superior, lateral and medial poles). The correlation between these modifications and the extent of actual condylar movements post-surgery was investigated. Statistical analysis was conducted with a significance level of α = 0.05.

RESULTS: Overall condylar remodelling was minimal, with mean changes of < 1 mm. Small but statistically significant bone resorption occurred at the condylar superior articular surface, while bone apposition was primarily observed at the lateral pole. The bone apposition at the lateral pole and resorption at the superior articular surface were significantly correlated with medial condylar displacement (p < 0.05).

CONCLUSION: The automated 3D analysis revealed distinct patterns of condylar remodelling following orthognathic surgery in skeletal Class III patients, with minimal overall changes but significant regional variations. The correlation between condylar displacements and remodelling patterns highlights the need for precise pre-operative planning to optimise condylar positioning, potentially minimising harmful remodelling and enhancing stability.

PMID:39754473 | DOI:10.1111/ocr.12895

NPJ Precis Oncol. 2025 Jan 3;9(1):2. doi: 10.1038/s41698-024-00789-2.

ABSTRACT

Recent progress in deep learning (DL) is producing a new generation of tools across numerous clinical applications. Within the analysis of brain tumors in magnetic resonance imaging, DL finds applications in tumor segmentation, quantification, and classification. It facilitates objective and reproducible measurements crucial for diagnosis, treatment planning, and disease monitoring. Furthermore, it holds the potential to pave the way for personalized medicine through the prediction of tumor type, grade, genetic mutations, and patient survival outcomes. In this review, we explore the transformative potential of DL for brain tumor care and discuss existing applications, limitations, and future directions and opportunities.

PMID:39753730 | DOI:10.1038/s41698-024-00789-2

Sci Rep. 2025 Jan 4;15(1):742. doi: 10.1038/s41598-024-84992-y.

ABSTRACT

Event-based surveillance is crucial for the early detection and rapid response to potential public health risks. In recent years, social networking services (SNS) have been recognized for their potential role in this domain. Previous studies have demonstrated the capacity of SNS posts for the early detection of health crises and affected individuals, including those related to infectious diseases. However, the reliability of such posts, being subjective and not clinically diagnosed, remains a challenge. In this study, we address this issue by assessing the classification performance of transformer-based pretrained language models to accurately classify Japanese tweets related to heat stroke, a significant health effect of climate change, as true or false. We also evaluated the efficacy of combining SNS and artificial intelligence for event-based public health surveillance by visualizing the data on correctly classified tweets and heat stroke emergency medical evacuees in time-space and animated video, respectively. The transformer-based pretrained language models exhibited good performance in classifying the tweets. Spatiotemporal and animated video visualizations revealed a reasonable correlation. This study demonstrates the potential of using Japanese tweets and deep learning algorithms based on transformer networks for event-based surveillance at high spatiotemporal levels to enable early detection of heat stroke risks.

PMID:39753702 | DOI:10.1038/s41598-024-84992-y

Sci Rep. 2025 Jan 3;15(1):675. doi: 10.1038/s41598-024-83944-w.

ABSTRACT

Land Surface Temperature (LST) is widely recognized as a sensitive indicator of climate change, and it plays a significant role in ecological research. The ERA5-Land LST dataset, developed and managed by the European Centre for Medium-Range Weather Forecasts (ECMWF), is extensively used for global or regional LST studies. However, its fine-scale application is limited by its low spatial resolution. Therefore, to improve the spatial resolution of ERA5-Land LST data, this study proposes an Attention Mechanism U-Net (AMUN) method, which combines data acquisition and preprocessing on the Google Earth Engine (GEE) cloud computing platform, to downscale the hourly monthly mean reanalysis LST data of ERA5-Land across China's territory from 0.1° to 0.01°. This method comprehensively considers the relationship between the LST and surface features, organically combining multiple deep learning modules, includes the Global Multi-Factor Cross-Attention (GMFCA) module, the Feature Fusion Residual Dense Block (FFRDB) connection module, and the U-Net module. In addition, the Bayesian global optimization algorithm is used to select the optimal hyperparameters of the network in order to enhance the predictive performance of the model. Finally, the downscaling accuracy of the network was evaluated through simulated data experiments and real data experiments and compared with the Random Forest (RF) method. The results show that the network proposed in this study outperforms the RF method, with RMSE reduced by approximately 32-51%. The downscaling method proposed in this study can effectively improve the accuracy of ERA5-Land LST downscaling, providing new insights for LST downscaling research.

PMID:39753651 | DOI:10.1038/s41598-024-83944-w

Nat Commun. 2025 Jan 2;16(1):349. doi: 10.1038/s41467-024-55228-4.

ABSTRACT

Identifying transitional states is crucial for understanding protein conformational changes that underlie numerous biological processes. Markov state models (MSMs), built from Molecular Dynamics (MD) simulations, capture these dynamics through transitions among metastable conformational states, and have demonstrated success in studying protein conformational changes. However, MSMs face challenges in identifying transition states, as they partition MD conformations into discrete metastable states (or free energy minima), lacking description of transition states located at the free energy barriers. Here, we introduce Transition State identification via Dispersion and vAriational principle Regularized neural networks (TS-DAR), a deep learning framework inspired by out-of-distribution (OOD) detection in trustworthy artificial intelligence (AI). TS-DAR offers an end-to-end pipeline that can simultaneously detect all transition states between multiple free minima from MD simulations using the regularized hyperspherical embeddings in latent space. The key insight of TS-DAR lies in treating transition state structures as OOD data, recognizing that they are sparsely populated and exhibit a distributional shift from metastable states. We demonstrate the power of TS-DAR by applying it to a 2D potential, alanine dipeptide, and the translocation of a DNA motor protein on DNA, where it outperforms previous methods in identifying transition states.

PMID:39753544 | DOI:10.1038/s41467-024-55228-4

Acad Radiol. 2025 Jan 2:S1076-6332(24)00997-8. doi: 10.1016/j.acra.2024.12.029. Online ahead of print.

ABSTRACT

RATIONALE AND OBJECTIVES: Accurate preoperative pathological staging of gastric cancer is crucial for optimal treatment selection and improved patient outcomes. Traditional imaging methods such as CT and endoscopy have limitations in staging accuracy.

METHODS: This retrospective study included 691 gastric cancer patients treated from March 2017 to March 2024. Enhanced venous-phase CT and endoscopic images, along with postoperative pathological results, were collected. We developed three modeling approaches: (1) nine deep learning models applied to CT images (DeepCT), (2) 11 machine learning algorithms using handcrafted radiomic features from CT images (HandcraftedCT), and (3) ResNet-50-extracted deep features from endoscopic images followed by 11 machine learning algorithms (DeepEndo). The two top-performing models from each approach were combined into the Integrated Multi-Modal Model using a stacking ensemble method. Performance was assessed using ROC-AUC, sensitivity, and specificity.

RESULTS: The Integrated Multi-Modal Model achieved an ROC-AUC of 0.933 (95% CI, 0.887-0.979) on the test set, outperforming individual models. Sensitivity and specificity were 0.869 and 0.840, respectively. Various evaluation metrics demonstrated that the final fusion model effectively integrated the strengths of each sub-model, resulting in a balanced and robust performance with reduced false-positive and false-negative rates.

CONCLUSION: The Integrated Multi-Modal Model effectively integrates radiomic and deep learning features from CT and endoscopic images, demonstrating superior performance in preoperative pathological staging of gastric cancer. This multimodal approach enhances predictive accuracy and provides a reliable tool for clinicians to develop individualized treatment plans, thereby improving patient outcomes.

DATA AVAILABILITY: The data presented in this study are available on request from the corresponding author. The data are not publicly available due to ethical reasons. All code used in this study is based on third-party libraries and all custom code developed for this study is available upon reasonable request from the corresponding author.

PMID:39753481 | DOI:10.1016/j.acra.2024.12.029

BMC Cancer. 2025 Jan 3;25(1):10. doi: 10.1186/s12885-024-13402-3.

ABSTRACT

OBJECTIVE: Rapid on-site evaluation (ROSE) of respiratory cytology specimens is a critical technique for accurate and timely diagnosis of lung cancer. However, in China, limited familiarity with the Diff-Quik staining method and a shortage of trained cytopathologists hamper utilization of ROSE. Therefore, developing an improved deep learning model to assist clinicians in promptly and accurately evaluating Diff-Quik stained cytology samples during ROSE has important clinical value.

METHODS: Retrospectively, 116 digital images of Diff-Quik stained cytology samples were obtained from whole slide scans. These included 6 diagnostic categories - carcinoid, normal cells, adenocarcinoma, squamous cell carcinoma, non-small cell carcinoma, and small cell carcinoma. All malignant diagnoses were confirmed by histopathology and immunohistochemistry. The test image set was presented to 3 cytopathologists from different hospitals with varying levels of experience, as well as an artificial intelligence system, as single-choice questions.

RESULTS: The diagnostic accuracy of the cytopathologists correlated with their years of practice and hospital setting. The AI model demonstrated proficiency comparable to the humans. Importantly, all combinations of AI assistance and human cytopathologist increased diagnostic efficiency to varying degrees.

CONCLUSIONS: This deep learning model shows promising capability as an aid for on-site diagnosis of respiratory cytology samples. However, human expertise remains essential to the diagnostic process.

PMID:39754166 | DOI:10.1186/s12885-024-13402-3

BMC Med Imaging. 2025 Jan 3;25(1):5. doi: 10.1186/s12880-024-01546-4.

ABSTRACT

A bone fracture is a medical condition characterized by a partial or complete break in the continuity of the bone. Fractures are primarily caused by injuries and accidents, affecting millions of people worldwide. The healing process for a fracture can take anywhere from one month to one year, leading to significant economic and psychological challenges for patients. The detection of bone fractures is crucial, and radiographic images are often relied on for accurate assessment. An efficient neural network method is essential for the early detection and timely treatment of fractures. In this study, we propose a novel transfer learning-based approach called MobLG-Net for feature engineering purposes. Initially, the spatial features are extracted from bone X-ray images using a transfer model, MobileNet, and then input into a tree-based light gradient boosting machine (LGBM) model for the generation of class probability features. Several machine learning (ML) techniques are applied to the subsets of newly generated transfer features to compare the results. K-nearest neighbor (KNN), LGBM, logistic regression (LR), and random forest (RF) are implemented using the novel features with optimized hyperparameters. The LGBM and LR models trained on proposed MobLG-Net (MobileNet-LGBM) based features outperformed others, achieving an accuracy of 99% in predicting bone fractures. A cross-validation mechanism is used to evaluate the performance of each model. The proposed study can improve the detection of bone fractures using X-ray images.

PMID:39754038 | DOI:10.1186/s12880-024-01546-4

Med Biol Eng Comput. 2025 Jan 4. doi: 10.1007/s11517-024-03273-y. Online ahead of print.

ABSTRACT

This study presents an advanced methodology for 3D heart reconstruction using a combination of deep learning models and computational techniques, addressing critical challenges in cardiac modeling and segmentation. A multi-dataset approach was employed, including data from the UK Biobank, MICCAI Multi-Modality Whole Heart Segmentation (MM-WHS) challenge, and clinical datasets of congenital heart disease. Preprocessing steps involved segmentation, intensity normalization, and mesh generation, while the reconstruction was performed using a blend of statistical shape modeling (SSM), graph convolutional networks (GCNs), and progressive GANs. The statistical shape models were utilized to capture anatomical variations through principal component analysis (PCA), while GCNs refined the meshes derived from segmented slices. Synthetic data generated by progressive GANs enabled augmentation, particularly useful for congenital heart conditions. Evaluation of the reconstruction accuracy was performed using metrics such as Dice similarity coefficient (DSC), Chamfer distance, and Hausdorff distance, with the proposed framework demonstrating superior anatomical precision and functional relevance compared to traditional methods. This approach highlights the potential for automated, high-resolution 3D heart reconstruction applicable in both clinical and research settings. The results emphasize the critical role of deep learning in enhancing anatomical accuracy, particularly for rare and complex cardiac conditions. This paper is particularly important for researchers wanting to utilize deep learning in cardiac imaging and 3D heart reconstruction, bringing insights into the integration of modern computational methods.

PMID:39753994 | DOI:10.1007/s11517-024-03273-y

Sci Rep. 2025 Jan 3;15(1):714. doi: 10.1038/s41598-025-85189-7.

ABSTRACT

To assess the choroidal vessels in healthy eyes using a novel three-dimensional (3D) deep learning approach. In this cross-sectional retrospective study, swept-source OCT 6 × 6 mm scans on Plex Elite 9000 device were obtained. Automated segmentation of the choroidal layer was achieved using a deep-learning ResUNet model along with a volumetric smoothing approach. Phansalkar thresholding was employed to binarize the choroidal vasculature. The choroidal vessels were visualized in 3D maps, and divided into five sectors: nasal, temporal, superior, inferior, and central. Choroidal thickness (CT) and choroidal vascularity index (CVI) of the whole volumes were calculated using the automated software. The three vessels for each sector were measured, to obtain the mean choroidal vessel diameter (MChVD). The inter-vessel distance (IVD) was defined as the distance between the vessel and the nearest non-collateral vessel. The choroidal biomarkers obtained were compared between different age groups (18 to 34 years old, 35 to 59 years old, and ≥ 60) and sex. Linear mixed models and univariate analysis were used for statistical analysis. A total of 80 eyes of 53 patients were included in the analysis. The mean age of the patients was 44.7 ± 18.5 years, and 54.7% were females. Overall, 44 eyes of 29 females and 36 eyes of 24 males were included in the study. We observed that 33% of the eyes presented at least one choroidal vessel larger than 200 μm crossing the central 3000 μm of the macula. Also, we observed a significant decrease in mean CVI with advancing age (p < 0.05), whereas no significant changes in mean MChVD and IVD were observed (p > 0.05). Furthermore, CVI was increased in females compared to males in each sector, with a significant difference in the temporal sector (p < 0.05). MChVD and IVD did not show any changes with increasing age, whereas CVI decreased with increasing age. Also, CVI was increased in healthy females compared to males. The 3D assessment of choroidal vessels using a deep-learning approach represents an innovative, non-invasive technique for investigating choroidal vasculature, with potential applications in research and clinical practice.

PMID:39753934 | DOI:10.1038/s41598-025-85189-7

Surg Endosc. 2025 Jan 3. doi: 10.1007/s00464-024-11485-4. Online ahead of print.

ABSTRACT

BACKGROUND: Specific pelvic bone dimensions have been identified as predictors of total mesorectal excision (TME) difficulty and outcomes. However, manual measurement of these dimensions (pelvimetry) is labor intensive and thus, anatomic criteria are not included in the pre-operative difficulty assessment. In this work, we propose an automated workflow for pelvimetry based on pre-operative magnetic resonance imaging (MRI) volumes.

METHODS: We implement a deep learning-based framework to measure the predictive pelvic dimensions automatically. A 3D U-Net takes a sagittal T2-weighted MRI volume as input and determines five anatomic landmark locations: promontorium, S3-vertebrae, coccyx, dorsal, and cranial part of the os pubis. The landmarks are used to quantify the lengths of the pelvic inlet, outlet, depth, and the angle of the sacrum. For the development of the network, we used MRI volumes from 1707 patients acquired in eight TME centers. The automated landmark localization and pelvic dimensions measurements are assessed by comparison with manual annotation.

RESULTS: A center-stratified fivefold cross-validation showed a mean landmark localization error of 5.6 mm. The inter-observer variation for manual annotation was 3.7 ± 8.4 mm. The automated dimension measurements had a Spearman correlation coefficient ranging between 0.7 and 0.87.

CONCLUSION: To our knowledge, this is the first study to automate pelvimetry in MRI volumes using deep learning. Our framework can measure the pelvic dimensions with high accuracy, enabling the extraction of metrics that facilitate a pre-operative difficulty assessment of the TME.

PMID:39753930 | DOI:10.1007/s00464-024-11485-4

Sci Rep. 2025 Jan 2;15(1):404. doi: 10.1038/s41598-024-84577-9.

ABSTRACT

Most of toolpaths for machining is composed of series of short linear segments (G01 command), which limits the feedrate and machining quality. To generate a smooth machining path, a new optimization strategy is proposed to optimize the toolpath at the curvature level. First, the three essential components of optimization are introduced, and the local corner smoothness is converted into an optimization problem. The optimization challenge is then resolved by an intelligent optimization algorithm. Considering the influence of population size and computational resources on intelligent optimization algorithms, a deep learning algorithm (the Double-ResNet Local Smoothing (DRLS) algorithm) is proposed to further improve optimization efficiency. The First-Double-Local Smoothing (FDLS) algorithm is used to optimize the positions of NURBS (Non-Uniform Rational B-Spline) control points, and the Second-Double-Local Smoothing (SDLS) algorithm is employed to optimize the NURBS weights to generate a smoother toolpath, thus allowing the cutting tool to pass through each local corner at a higher feedrate during the machining process. In order to ensure machining quality, geometric constraints, drive condition constraints, and contour error constraints are taken into account during the feedrate planning process. Finally, three simulations are presented to verify the effectiveness of the proposed method.

PMID:39753859 | DOI:10.1038/s41598-024-84577-9

J Imaging Inform Med. 2025 Jan 3. doi: 10.1007/s10278-024-01385-3. Online ahead of print.

ABSTRACT

We extend existing techniques by using generative adversarial network (GAN) models to reduce the appearance of cast shadows in radiographs across various age groups. We retrospectively collected 11,500 adult and paediatric wrist radiographs, evenly divided between those with and without casts. The test subset consisted of 750 radiographs with cast and 750 without cast. We extended the results from a previous study that employed CycleGAN by enhancing the model using a perceptual loss function and a self-attention layer. The CycleGAN model which incorporates a self-attention layer and perceptual loss function delivered a similar quantitative performance as the original model. This model was applied to images from 20 cases where the original reports recommended CT scanning or repeat radiographs without the cast, which were then evaluated by radiologists for qualitative assessment. The results demonstrated that the generated images could improve radiologists' diagnostic confidence, in some cases leading to more decisive reports. Where available, the reports from follow-up imaging were compared with those produced by radiologists reading AI-generated images. Every report, except two, provided identical diagnoses as those associated with follow-up imaging. The ability of radiologists to perform robust reporting with downsampled AI-enhanced images is clinically meaningful and warrants further investigation. Additionally, radiologists were unable to distinguish AI-enhanced from unenhanced images. These findings suggest the cast suppression technique could be integrated as a tool to augment clinical workflows, with the potential benefits of reducing patient doses, improving operational efficiencies, reducing delays in diagnoses, and reducing the number of patient visits.

PMID:39753829 | DOI:10.1007/s10278-024-01385-3

J Imaging Inform Med. 2025 Jan 3. doi: 10.1007/s10278-024-01352-y. Online ahead of print.

ABSTRACT

Gastrointestinal tract-related cancers pose a significant health burden, with high mortality rates. In order to detect the anomalies of the gastrointestinal tract that may progress to cancer, a video capsule endoscopy procedure is employed. The number of video capsule endoscopic ( VCE ) images produced per examination is enormous, which necessitates hours of analysis by clinicians. Therefore, there is a pressing need for automated computer-aided lesion classification techniques. Computer-aided systems utilize deep learning (DL) techniques, as they can potentially enhance anomaly detection rates. However, most of the DL techniques available in the literature utilizes the static frames for the classification purpose, which uses only the spatial information of the image. In addition, they only perform binary classification. Thus, the presented work proposes a framework to perform multi-class classification of VCE images by using the dynamic information of the images. The proposed algorithm is a combination of the fractional order variational model and the DL model. The fractional order variational model captures the dynamic information of VCE images by estimating optical flow color maps. Optical flow color maps are fed to the DL model for training. The DL model performs the multi-class classification task and localizes the region of interest with the maximum class score. DL model is inspired by the Faster RCNN approach, and its backbone architecture is EfficientNet B0. The proposed framework achieves the average AUC value of 0.98, mAP value of 0.93, and 0.878 as balanced accuracy value. Hence, the proposed model is efficient in VCE image classification and detection of region of interest.

PMID:39753827 | DOI:10.1007/s10278-024-01352-y

Curr Oncol Rep. 2025 Jan 3. doi: 10.1007/s11912-024-01627-1. Online ahead of print.

ABSTRACT

PURPOSE OF REVIEW: This review aims to evaluate the impact of artificial intelligence (AI) on cancer health equity, specifically investigating whether AI is addressing or widening disparities in cancer outcomes.

RECENT FINDINGS: Recent studies demonstrate significant advancements in AI, such as deep learning for cancer diagnosis and predictive analytics for personalized treatment, showing potential for improved precision in care. However, concerns persist about the performance of AI tools across diverse populations due to biased training data. Access to AI technologies also remains limited, particularly in low-income and rural settings. AI holds promise for advancing cancer care, but its current application risks exacerbating existing health disparities. To ensure AI benefits all populations, future research must prioritize inclusive datasets, integrate social determinants of health, and develop ethical frameworks. Addressing these challenges is crucial for AI to contribute positively to cancer health equity and guide future research and policy development.

PMID:39753817 | DOI:10.1007/s11912-024-01627-1

Sci Rep. 2025 Jan 3;15(1):690. doi: 10.1038/s41598-024-84692-7.

ABSTRACT

Detecting brain tumours (BT) early improves treatment possibilities and increases patient survival rates. Magnetic resonance imaging (MRI) scanning offers more comprehensive information, such as better contrast and clarity, than any alternative scanning process. Manually separating BTs from several MRI images gathered in medical practice for cancer analysis is challenging and time-consuming. Tumours and MRI scans of the brain are exposed utilizing methods and machine learning technologies, simplifying the process for doctors. MRI images can sometimes appear normal even when a patient has a tumour or malignancy. Deep learning approaches have recently depended on deep convolutional neural networks to analyze medical images with promising outcomes. It supports saving lives faster and rectifying some medical errors. With this motivation, this article presents a new explainable artificial intelligence with semantic segmentation and Bayesian machine learning for brain tumors (XAISS-BMLBT) technique. The presented XAISS-BMLBT technique mainly concentrates on the semantic segmentation and classification of BT in MRI images. The presented XAISS-BMLBT approach initially involves bilateral filtering-based image pre-processing to eliminate the noise. Next, the XAISS-BMLBT technique performs the MEDU-Net+ segmentation process to define the impacted brain regions. For the feature extraction process, the ResNet50 model is utilized. Furthermore, the Bayesian regularized artificial neural network (BRANN) model is used to identify the presence of BTs. Finally, an improved radial movement optimization model is employed for the hyperparameter tuning of the BRANN technique. To highlight the improved performance of the XAISS-BMLBT technique, a series of simulations were accomplished by utilizing a benchmark database. The experimental validation of the XAISS-BMLBT technique portrayed a superior accuracy value of 97.75% over existing models.

PMID:39753735 | DOI:10.1038/s41598-024-84692-7