Adv Sci (Weinh). 2025 Feb 7:e2413805. doi: 10.1002/advs.202413805. Online ahead of print.

ABSTRACT

Artificial intelligence (AI) in science is a key area of modern research. However, many current machine learning methods lack interpretability, making it difficult to grasp the physical mechanisms behind various phenomena, which hampers progress in related fields. This study focuses on the Poisson's ratio of a hexagonal lattice elastic network as it varies with structural deformation. By employing the Kolmogorov-Arnold Network (KAN), the transition of the network's Poisson's ratio from positive to negative as the hexagonal structural element shifts from a convex polygon to a concave polygon was accurately predicted. The KAN provides a clear mathematical framework that describes this transition, revealing the connection between the Poisson's ratio and the geometric properties of the hexagonal element, and accurately identifying the geometric parameters at which the Poisson's ratio equals zero. This work demonstrates the significant potential of the KAN network to clarify the mathematical relationships that underpin physical responses and structural behaviors.

PMID:39921316 | DOI:10.1002/advs.202413805

Int J Med Robot. 2025 Feb;21(1):e70043. doi: 10.1002/rcs.70043.

ABSTRACT

BACKGROUND: This research aims to use deep learning to create automated systems for better breast cancer detection and categorisation in mammogram images, helping medical professionals overcome challenges such as time consumption, feature extraction issues and limited training models.

METHODS: This research introduced a Lightweight Multihead attention Gannet Convolutional Neural Network (LMGCNN) to classify mammogram images effectively. It used wiener filtering, unsharp masking, and adaptive histogram equalisation to enhance images and remove noise, followed by Grey-Level Co-occurrence Matrix (GLCM) for feature extraction. Ideal feature selection is done by a self-adaptive quantum equilibrium optimiser with artificial bee colony.

RESULTS: The research assessed on two datasets, CBIS-DDSM and MIAS, achieving impressive accuracy rates of 98.2% and 99.9%, respectively, which highlight the superior performance of the LMGCNN model while accurately detecting breast cancer compared to previous models.

CONCLUSION: This method illustrates potential in aiding initial and accurate breast cancer detection, possibly leading to improved patient outcomes.

PMID:39921233 | DOI:10.1002/rcs.70043

Mol Cell Proteomics. 2025 Feb 5:100924. doi: 10.1016/j.mcpro.2025.100924. Online ahead of print.

ABSTRACT

Citrullination is a critical yet understudied post-translational modification (PTM) implicated in various biological processes. Exploring its role in health and disease requires a comprehensive understanding of the prevalence of this PTM at a proteome-wide scale. Although mass spectrometry has enabled the identification of citrullination sites in complex biological samples, it faces significant challenges, including limited enrichment tools and a high rate of false positives due to the identical mass with deamidation (+0.9840 Da) and errors in monoisotopic ion selection. These issues often necessitate manual spectrum inspection, reducing throughput in large-scale studies. In this work, we present a novel data analysis pipeline that incorporates the deep learning model Prosit-Cit into the MS database search workflow to improve both the sensitivity and precision of citrullination site identification. Prosit-Cit, an extension of the existing Prosit model, has been trained on ∼53,000 spectra from ∼2,500 synthetic citrullinated peptides and provides precise predictions for chromatographic retention time and fragment ion intensities of both citrullinated and deamidated peptides. This enhances the accuracy of identification and reduces false positives. Our pipeline demonstrated high precision on the evaluation dataset, recovering the majority of known citrullination sites in human tissue proteomes and improving sensitivity by identifying up to 14 times more citrullinated sites. Sequence motif analysis revealed consistency with previously reported findings, validating the reliability of our approach. Furthermore, extending the pipeline to a tissue proteome dataset of the model plant Arabidopsis thaliana enabled the identification of ∼200 citrullination sites across 169 proteins from 30 tissues, representing the first large-scale citrullination mapping in plants. This pipeline can be seamlessly applied to existing proteomics datasets, offering a robust tool for advancing biological discoveries and deepening our understanding of protein citrullination across species.

PMID:39921205 | DOI:10.1016/j.mcpro.2025.100924

Int J Radiat Oncol Biol Phys. 2025 Feb 5:S0360-3016(25)00085-9. doi: 10.1016/j.ijrobp.2025.01.028. Online ahead of print.

ABSTRACT

PURPOSE: The integration of advanced imaging and artificial intelligence (AI) technologies in radiotherapy has revolutionized cancer treatment by enhancing precision and adaptability. This study introduces a novel Dual Energy CT (DECT)-Guided Intelligent Radiation Therapy (DEIT) platform designed to streamline and optimize the radiotherapy process. The DEIT system combines DECT, a newly designed dual-layer multi-leaf collimator, deep learning algorithms for auto-segmentation, automated planning and QA capabilities.

METHODS: The DEIT system integrates an 80-slice CT scanner with an 87 cm bore size, a linear accelerator delivering four photon and five electron energies, and a flat panel imager optimized for MV Cone Beam CT acquisition. A comprehensive evaluation of the system's accuracy was conducted using end-to-end tests. Virtual monoenergetic CT images and electron density images of the DECT were generated and compared on both phantom and patient. The system's auto-segmentation algorithms were tested on five cases for each of the 99 organs at risk, and the automated optimization and planning capabilities were evaluated on clinical cases.

RESULTS: The DEIT system demonstrated systematic errors of less than 1 mm for target localization. DECT reconstruction showed electron density mapping deviations ranging from -0.052 to 0.001, with stable HU consistency across monoenergetic levels above 60 keV, except for high-Z materials at lower energies. Auto-segmentation achieved dice similarity coefficients above 0.9 for most organs with inference time less than 2 seconds. Dose-volume histogram (DVH) comparisons showed improved dose conformity indices and reduced doses to critical structures in Auto-plans compared to Manual Plans across various clinical cases. Additionally, high gamma passing rates at 2%/2mm in both 2D (above 97%) and 3D (above 99%) in vivo analyses further validate the accuracy and reliability of treatment plans.

CONCLUSIONS: The DEIT platform represents a viable solution for radiation treatment. The DEIT system utilizes AI-driven automation, real-time adjustments, and CT imaging to enhance the radiotherapy process, improving both efficiency and flexibility.

PMID:39921109 | DOI:10.1016/j.ijrobp.2025.01.028

Sci Rep. 2025 Feb 7;15(1):4641. doi: 10.1038/s41598-024-84737-x.

ABSTRACT

Dental caries is a very common chronic disease that may lead to pain, infection, and tooth loss if its diagnosis at an early stage remains undetected. Traditional methods of tactile-visual examination and bitewing radiography, are subject to intrinsic variability due to factors such as examiner experience and image quality. This variability can result in inconsistent diagnoses. Thus, the present study aimed to develop a deep learning-based AI model using the YOLOv8 algorithm for improving interproximal caries detection in bitewing radiographs. In this retrospective study on 552 radiographs, a total of 1,506 images annotated at Tehran University of Medical Science were processed. The YOLOv8 model was trained and the results were evaluated in terms of precision, recall, and the F1 score, whereby it resulted in a precision of 96.03% for enamel caries and 80.06% for dentin caries, thus showing an overall precision of 84.83%, a recall of 79.77%, and an F1 score of 82.22%. This proves its reliability in reducing false negatives and improving diagnostic accuracy. YOLOv8 enhances interproximal caries detection, offering a reliable tool for dental professionals to improve diagnostic accuracy and clinical outcomes.

PMID:39920198 | DOI:10.1038/s41598-024-84737-x

Sci Rep. 2025 Feb 8;15(1):4693. doi: 10.1038/s41598-024-83721-9.

ABSTRACT

Complete and high-quality photodocumentation in esophagoduodenogastroscopy (EGD) is essential for accurately diagnosing upper gastrointestinal diseases by reducing blind spot rates. Automated Photodocumentation Task (APT), an artificial intelligence-based system for real-time photodocumentation during EGD, was developed to assist endoscopists in focusing more on the observation rather than repetitive capturing tasks. This study aimed to evaluate the completeness and quality of APT's photodocumentation compared to endoscopists. The dataset comprised 37 EGD videos recorded at Seoul National University Hospital between March and June 2023. Virtual endoscopy was conducted by seven endoscopists and APT, capturing 11 anatomical landmarks from the videos. The primary endpoints were the completeness of capturing landmarks and the quality of the images. APT achieved an average accuracy of 98.16% in capturing landmarks. Compared to that of endoscopists, APT demonstrated similar completeness in photodocumentation (87.72% vs. 85.75%, P = .0.258), and the combined photodocumentation of endoscopists and APT reached higher completeness (91.89% vs. 85.75%, P < .0.001). APT captured images with higher mean opinion scores than those of endoscopists (3.88 vs. 3.41, P < .0.001). In conclusion, APT provides clear, high-quality endoscopic images while minimizing blind spots during EGD in real-time.

PMID:39920187 | DOI:10.1038/s41598-024-83721-9

Sci Rep. 2025 Feb 7;15(1):4633. doi: 10.1038/s41598-024-82241-w.

ABSTRACT

Accurately and early diagnosis of melanoma is one of the challenging tasks due to its unique characteristics and different shapes of skin lesions. So, in order to solve this issue, the current study examines various deep learning-based approaches and provide an effective approach for classifying dermoscopic images into two categories of skin lesions. This research focus on skin cancer images and provides solution using deep learning approaches. This research investigates three approaches for classifying skin cancer images. (1) Utilizing three fine-tuned pre-trained networks (VGG19, ResNet18, and MobileNet_V2) as classifiers. (2) Employing three pre-trained networks (ResNet-18, VGG19, and MobileNet v2) as feature extractors in conjunction with four machine learning classifiers (SVM, DT, Naïve Bayes, and KNN). (3) Utilizing a combination of the aforementioned pre-trained networks as feature extractors in conjunction with same machine learning classifiers. All these algorithms are trained using segmented images which are achieved by using the active contour approach. Prior to segmentation, preprocessing step is performed which involves scaling, denoising, and enhancing the image. Experimental performance is measured on the ISIC 2018 dataset which contains 3300 images of skin disease including benign and malignant type cancer images. 80% of the images from the ISIC 2018 dataset are allocated for training, while the remaining 20% are designated for testing. All approaches are trained using different parameters like epoch, batch size, and learning rate. The results indicate that combining ResNet-18 and MobileNet pre-trained networks using concatenation with an SVM classifier achieved the maximum accuracy of 92.87%.

PMID:39920179 | DOI:10.1038/s41598-024-82241-w

Sci Rep. 2025 Feb 7;15(1):4576. doi: 10.1038/s41598-025-88477-4.

ABSTRACT

Autoimmune Diseases are a complex group of diseases caused by the immune system mistakenly attacking body tissues. Their etiology involves multiple factors such as genetics, environmental factors, and abnormalities in immune cells, making prediction and treatment challenging. T cells, as a core component of the immune system, play a critical role in the human immune system and have a significant impact on the pathogenesis of autoimmune diseases. Several studies have demonstrated that T-cell receptors (TCRs) may be involved in the pathogenesis of various autoimmune diseases, which provides strong theoretical support and new therapeutic targets for the prediction and treatment of autoimmune diseases. This study focuses on the prediction of several autoimmune diseases mediated by T cells, and proposes two models: one is the AutoY model based on convolutional neural networks, and the other is the LSTMY model, a bidirectional LSTM network model that integrates the attention mechanism. Experimental results show that both models exhibit good performance in the prediction of the four autoimmune diseases, with the AutoY model performing slightly better in comparison. In particular, the average area under the ROC curve (AUC) of the AutoY model exceeded 0.93 in the prediction of all the diseases, and the AUC value reached 0.99 in two diseases, type 1 diabetes and multiple sclerosis. These results demonstrate the high accuracy, stability, and good generalization ability of the two models, which makes them promising tools in the field of autoimmune disease prediction and provides support for the use of the TCR bank for the noninvasive detection of autoimmune disease non-invasive detection is supported.

PMID:39920178 | DOI:10.1038/s41598-025-88477-4

Sci Rep. 2025 Feb 7;15(1):4662. doi: 10.1038/s41598-025-86727-z.

ABSTRACT

The objective was to use convolutional neural networks (CNNs) for automatic segmentation of hip cartilage and labrum based on 3D MRI. In this retrospective single-center study, CNNs with a U-Net architecture were used to develop a fully automated segmentation model for hip cartilage and labrum from MRI. Direct hip MR arthrographies (01/2020-10/2021) were selected from 100 symptomatic patients. Institutional routine protocol included a 3D T1 mapping sequence, which was used for manual segmentation of hip cartilage and labrum. 80 hips were used for training and the remaining 20 for testing. Model performance was assessed with six evaluation metrics including Dice similarity coefficient (DSC). In addition, model performance was tested on an external dataset (40 patients) with a 3D T2-weighted sequence from a different institution. Inter-rater agreement of manual segmentation served as benchmark for automatic segmentation performance. 100 patients were included (mean age 30 ± 10 years, 64% female patients). Mean DSC for cartilage was 0.92 ± 0.02 (95% confidence interval [CI] 0.92-0.93) and 0.83 ± 0.04 (0.81-0.85) for labrum and comparable (p = 0.232 and 0.297, respectively) to inter-rater agreement of manual segmentation: DSC cartilage 0.93 ± 0.04 (0.92-0.95); DSC labrum 0.82 ± 0.05 (0.80-0.85). When tested on the external dataset, the DSC was 0.89 ± 0.02 (0.88-0.90) and 0.71 ± 0.04 (0.69-0.73) for cartilage and labrum, respectively.The presented deep learning approach accurately segments hip cartilage and labrum from 3D MRI sequences and can potentially be used in clinical practice to provide rapid and accurate 3D MRI models.

PMID:39920175 | DOI:10.1038/s41598-025-86727-z

Int J Retina Vitreous. 2025 Feb 7;11(1):14. doi: 10.1186/s40942-025-00634-z.

ABSTRACT

BACKGROUND: To illustrate the treatment effect of Pegcetacoplan for atrophy secondary to age-related macular degeneration (AMD), on an individualized topographic progression prediction basis, using a deep learning model.

METHODS: Patients (N = 99) with atrophy secondary to AMD with longitudinal optical coherence tomography (OCT) data were retrospectively analyzed. We used a previously published deep-learning-based atrophy progression prediction algorithm to predict the 2-year atrophy progression, including the topographic likelihood of future retinal pigment epithelial and outer retinal atrophy (RORA), according to the baseline OCT input. The algorithm output was a step-less individualized topographic modeling of the RORA growth, allowing for illustrating the progression line corresponding to an 80% growth compared to the natural course of 100% growth.

RESULTS: The treatment effect of Pegcetacoplan was illustrated as the line when 80% of the growth is reached in this continuous model. Besides the well-known variability of atrophy growth rate, our results showed unequal growth according to the fundus location. It became evident that this difference is of potential functional interest for patient outcomes.

CONCLUSIONS: This model based on an 80% growth of RORA after two years illustrates the variable effect of treatment with Pegcetacoplan according to the individual situation, supporting personalized medical care.

PMID:39920843 | DOI:10.1186/s40942-025-00634-z

J Neuroinflammation. 2025 Feb 7;22(1):33. doi: 10.1186/s12974-025-03356-z.

ABSTRACT

Infection with enterohemorrhagic E. coli (EHEC) causes severe changes in the brain leading to angiopathy, encephalopathy and microglial activation. In this study, we investigated the role of tumour necrosis factor alpha (TNF-α) for microglial activation and brain pathology using a preclinical mouse model of EHEC infection. LC-MS/MS proteomics of mice injected with a combination of Shiga toxin (Stx) and lipopolysaccharide (LPS) revealed extensive alterations of the brain proteome, in particular enrichment of pathways involved in complement activation and coagulation cascades. Inhibition of TNF-α by the drug Etanercept strongly mitigated these changes, particularly within the complement pathway, suggesting TNF-α-dependent vasodilation and endothelial injury. Analysis of microglial populations using a novel human-in-the-loop deep learning algorithm for the segmentation of microscopic imaging data indicated specific morphological changes, which were reduced to healthy condition after inhibition of TNF-α. Moreover, the Stx/LPS-mediated angiopathy was significantly attenuated by inhibition of TNF-α. Overall, our findings elucidate the critical role of TNF-α in EHEC-induced brain pathology and highlight a potential therapeutic target for mitigating neuroinflammation, microglial activation and injury associated with EHEC infection.

PMID:39920757 | DOI:10.1186/s12974-025-03356-z

Neurocrit Care. 2025 Feb 7. doi: 10.1007/s12028-025-02214-3. Online ahead of print.

ABSTRACT

BACKGROUND: Early prediction of hematoma expansion (HE) following nontraumatic intracerebral hemorrhage (ICH) may inform preemptive therapeutic interventions. We sought to identify how accurately machine learning (ML) radiomics models predict HE compared with expert clinicians using head computed tomography (HCT).

METHODS: We used data from 900 study participants with ICH enrolled in the Antihypertensive Treatment of Acute Cerebral Hemorrhage 2 Study. ML models were developed using baseline HCT images, as well as admission clinical data in a training cohort (n = 621), and their performance was evaluated in an independent test cohort (n = 279) to predict HE (defined as HE by 33% or > 6 mL at 24 h). We simultaneously surveyed expert clinicians and asked them to predict HE using the same initial HCT images and clinical data. Area under the receiver operating characteristic curve (AUC) were compared between clinician predictions, ML models using radiomic data only (a random forest classifier and a deep learning imaging model) and ML models using both radiomic and clinical data (three random forest classifier models using different feature combinations). Kappa values comparing interrater reliability among expert clinicians were calculated. The best performing model was compared with clinical predication.

RESULTS: The AUC for expert clinician prediction of HE was 0.591, with a kappa of 0.156 for interrater variability, compared with ML models using radiomic data only (a deep learning model using image input, AUC 0.680) and using both radiomic and clinical data (a random forest model, AUC 0.677). The intraclass correlation coefficient for clinical judgment and the best performing ML model was 0.47 (95% confidence interval 0.23-0.75).

CONCLUSIONS: We introduced supervised ML algorithms demonstrating that HE prediction may outperform practicing clinicians. Despite overall moderate AUCs, our results set a new relative benchmark for performance in these tasks that even expert clinicians find challenging. These results emphasize the need for continued improvements and further enhanced clinical decision support to optimally manage patients with ICH.

PMID:39920546 | DOI:10.1007/s12028-025-02214-3

EJNMMI Phys. 2025 Feb 8;12(1):15. doi: 10.1186/s40658-025-00716-9.

ABSTRACT

PURPOSE: Whole-brain segmentation via positron emission tomography (PET) imaging is crucial for advancing neuroscience research and clinical medicine, providing essential insights into biological metabolism and activity within different brain regions. However, the low resolution of PET images may have limited the segmentation accuracy of multiple brain structures. Therefore, we propose a generative multi-object segmentation model for brain PET images to achieve automatic and accurate segmentation.

METHODS: In this study, we propose a generative multi-object segmentation model for brain PET images with two learning protocols. First, we pretrained a latent mapping model to learn the mapping relationship between PET and MR images so that we could extract anatomical information of the brain. A 3D multi-object segmentation model was subsequently proposed to apply whole-brain segmentation to MR images generated from integrated latent mapping models. Moreover, a custom cross-attention module based on a cross-attention mechanism was constructed to effectively fuse the functional information and structural information. The proposed method was compared with various deep learning-based approaches in terms of the Dice similarity coefficient, Jaccard index, precision, and recall serving as evaluation metrics.

RESULTS: Experiments were conducted on real brain PET/MR images from 120 patients. Both visual and quantitative results indicate that our method outperforms the other comparison approaches, achieving 75.53% ± 4.26% Dice, 66.02% ± 4.55% Jaccard, 74.64% ± 4.15% recall and 81.40% ± 2.30% precision. Furthermore, the evaluation of the SUV distribution and correlation assessment in the regions of interest demonstrated consistency with the ground truth. Additionally, clinical tolerance rates, which are determined by the tumor background ratio, have confirmed the ability of the method to distinguish highly metabolic regions accurately from normal regions, reinforcing its clinical applicability.

CONCLUSION: For automatic and accurate whole-brain segmentation, we propose a novel 3D generative multi-object segmentation model for brain PET images, which achieves superior model performance compared with other deep learning methods. In the future, we will apply our whole-brain segmentation method to clinical practice and extend it to other multimodal tasks.

PMID:39920478 | DOI:10.1186/s40658-025-00716-9

Discov Ment Health. 2025 Feb 8;5(1):12. doi: 10.1007/s44192-025-00138-0.

ABSTRACT

INTRODUCTION: Perinatal mental disorders are prevalent, affecting 10-20% of pregnant women, and can negatively impact both maternal and neonatal outcomes. Traditional screening tools, such as the Edinburgh Postnatal Depression Scale (EPDS), present limitations due to subjectivity and time constraints in clinical settings. Recent advances in voice analysis and machine learning have shown potential for providing more objective screening methods. This study aimed to develop a deep learning model that analyzes the voices of pregnant women to screen for mental disorders, thereby offering an alternative to the traditional tools.

METHODS: A cross-sectional study was conducted among 204 pregnant women, from whom voice samples were collected during their one-month postpartum checkup. The audio data were preprocessed into 5000 ms intervals, converted into mel-spectrograms, and augmented using TrivialAugment and context-rich minority oversampling. The EfficientFormer V2-L model, pretrained on ImageNet, was employed with transfer learning for classification. The hyperparameters were optimized using Optuna, and an ensemble learning approach was used for the final predictions. The model's performance was compared to that of the EPDS in terms of sensitivity, specificity, and other diagnostic metrics.

RESULTS: Of the 172 participants analyzed (149 without mental disorders and 23 with mental disorders), the voice-based model demonstrated a sensitivity of 1.00 and a recall of 0.82, outperforming the EPDS in these areas. However, the EPDS exhibited higher specificity (0.97) and precision (0.84). No significant difference was observed in the area under the receiver operating characteristic curve between the two methods (p = 0.759).

DISCUSSION: The voice-based model showed higher sensitivity and recall, suggesting that it may be more effective in identifying at-risk individuals than the EPDS. Machine learning and voice analysis are promising objective screening methods for mental disorders during pregnancy, potentially improving early detection.

CONCLUSION: We developed a lightweight machine learning model to analyze pregnant women's voices for screening various mental disorders, achieving high sensitivity and demonstrating the potential of voice analysis as an effective and objective tool in perinatal mental health care.

PMID:39920468 | DOI:10.1007/s44192-025-00138-0

Int J Comput Assist Radiol Surg. 2025 Feb 7. doi: 10.1007/s11548-025-03329-w. Online ahead of print.

ABSTRACT

PURPOSE: In recent years, the detection of the duodenal papilla and surgical cannula has become a critical task in computer-assisted endoscopic retrograde cholangiopancreatography (ERCP) cannulation operations. The complex surgical anatomy, coupled with the small size of the duodenal papillary orifice and its high similarity to the background, poses significant challenges to effective computer-assisted cannulation. To address these challenges, we present a deep learning-driven graphical user interface (GUI) to assist ERCP cannulation.

METHODS: Considering the characteristics of the ERCP scenario, we propose a deep learning method for duodenal papilla and surgical cannula detection, utilizing four swin transformer decoupled heads (4STDH). Four different prediction heads are employed to detect objects of different sizes. Subsequently, we integrate the swin transformer module to identify attention regions to explore prediction potential deeply. Moreover, we decouple the classification and regression networks, significantly improving the model's accuracy and robustness through the separation prediction. Simultaneously, we introduce a dataset on papilla and cannula (DPAC), consisting of 1840 annotated endoscopic images, which will be publicly available. We integrated 4STDH and several state-of-the-art methods into the GUI and compared them.

RESULTS: On the DPAC dataset, 4STDH outperforms state-of-the-art methods with an mAP of 93.2% and superior generalization performance. Additionally, the GUI provides real-time positions of the papilla and cannula, along with the planar distance and direction required for the cannula to reach the cannulation position.

CONCLUSION: We validate the GUI's performance in human gastrointestinal endoscopic videos, showing deep learning's potential to enhance the safety and efficiency of clinical ERCP cannulation.

PMID:39920403 | DOI:10.1007/s11548-025-03329-w

Eur Spine J. 2025 Feb 8. doi: 10.1007/s00586-025-08668-5. Online ahead of print.

ABSTRACT

BACKGROUND: For cases of multilevel lumbar disc herniation (LDH), selecting the surgical approach for Percutaneous Transforaminal Endoscopic Discectomy (PTED) presents significant challenges and heavily relies on the physician's judgment. This study aims to develop a deep learning (DL)-based multimodal model that provides objective and referenceable support by comprehensively analyzing imaging and clinical data to assist physicians.

METHODS: This retrospective study collected imaging and clinical data from patients with multilevel LDH. Each segmental MR scan was concurrently fed into a multi-input ResNet 50 model to predict the target segment. The target segment scan was then input to a custom model to predict the PTED approach direction. Clinical data, including the patient's lower limb sensory and motor functions, were used as feature variables in a machine learning (ML) model for prediction. Bayesian optimization was employed to determine the optimal weights for the fusion of the two models.

RESULT: The predictive performance of the multimodal model significantly outperformed the DL and ML models. For PTED target segment prediction, the multimodal model achieved an accuracy of 93.8%, while the DL and ML models achieved accuracies of 87.7% and 87.0%, respectively. Regarding the PTED approach direction, the multimodal model had an accuracy of 89.3%, significantly higher than the DL model's 87.8% and the ML model's 87.6%.

CONCLUSION: The multimodal model demonstrated excellent performance in predicting PTED target segments and approach directions. Its predictive performance surpassed that of the individual DL and ML models.

PMID:39920320 | DOI:10.1007/s00586-025-08668-5

Eur Spine J. 2025 Feb 8. doi: 10.1007/s00586-025-08696-1. Online ahead of print.

ABSTRACT

PURPOSE: This study aimed to develop and validate a deep learning radiomics nomogram (DLRN) to differentiate between tuberculous spondylitis (TS) and pyogenic spondylitis (PS) using contrast-enhanced MRI (CE-MRI).

METHODS: A retrospective approach was employed, enrolling patients diagnosed with TS or PS based on pathological examination at two centers. Clinical features were evaluated to establish a clinical model. Radiomics and deep learning (DL) features were extracted from contrast-enhanced T1-weighted images and subsequently fused. Following feature selection, radiomics, DL, combined DL-radiomics (DLR), and a deep learning radiomics nomogram (DLRN) were developed to differentiate TS from PS. Performance was assessed using metrics including the area under the curve (AUC), calibration curves, and decision curve analysis (DCA).

RESULTS: A total of 147 patients met the study criteria. Center 1 comprised the training cohort with 102 patients (52 TS and 50 PS), while Center 2 served as the external test cohort with 45 patients (17 TS and 28 PS). The DLRN model exhibited the highest diagnostic accuracy, achieving an AUC of 0.994 (95% CI: 0.983-1.000) in the training cohort and 0.859 (95% CI: 0.744-0.975) in the external test cohort. Calibration curves indicated good agreement for DLRN, and decision curve analysis (DCA) demonstrated it provided the greatest clinical benefit.

CONCLUSION: The CE-MRI-based DLRN showed robust diagnostic capability for distinguishing between TS and PS in clinical practice.

PMID:39920318 | DOI:10.1007/s00586-025-08696-1

Sci Rep. 2025 Feb 7;15(1):4594. doi: 10.1038/s41598-025-88658-1.

ABSTRACT

The influence of deep learning in the medical and molecular biology sectors is swiftly growing and holds the potential to improve numerous crucial domains. Osteoporosis is a significant global health issue, and the current treatment options are highly restricted. Transplanting genetically engineered MSCs has been acknowledged as a highly promising therapy for osteoporosis. We utilized a random walk-based technique to discern genes associated with ossification. The osteogenic value of these genes was assessed on the basis of information found in published scientific literature. GO enrichment analysis of these genes was performed to determine if they were enriched in any certain function. Immunohistochemical and western blot techniques were used to identify and measure protein expression. The expression of genes involved in osteogenic differentiation was examined via qRT‒PCR. Lentiviral transfection was utilized to suppress the expression of the FOXA1 gene in hBMSCs. An in vivo mouse model of ovariectomy was created, and radiographic examination was conducted to confirm the impact of FOXA1 knockdown on osteoporosis. The osteogenic score of each gene was calculated by assessing its similarity to osteo-specific genes. The majority of the genes with the highest rankings were linked with osteogenic differentiation, indicating that our approach is useful for identifying genes associated with ossification. GO enrichment analysis revealed that these pathways are enriched primarily in bone-related processes. FOXA1 is a crucial transcription factor that controls the process of osteogenic differentiation, as indicated by similarity analysis. FOXA1 was significantly increased in those with osteoporosis. Downregulation of FOXA1 markedly augmented the expression of osteoblast-specific genes and proteins, activated the ERK1/2 signaling pathway, intensified ALP activity, and promoted mineral deposition. In addition, excessive expression of FOXA1 significantly reduced ALP activity and mineral deposits. Using a mouse model in which the ovaries were surgically removed, researchers reported that suppressing the FOXA1 gene in bone marrow stem cells (BMSCs) prevented the loss of bone density caused by ovariectomy. This finding was confirmed by analyzing the bone structure via micro-CT. Furthermore, our approach can distinguish genes that exhibit osteogenic differentiation characteristics. This ability can aid in the identification of novel genes associated with osteogenic differentiation, which can be utilized in the treatment of osteoporosis. Computational and laboratory evidence indicates that reducing the expression of FOXA1 enhances the process of bone formation in bone marrow-derived mesenchymal stem cells (BMSCs) and may serve as a promising approach to prevent osteoporosis.

PMID:39920313 | DOI:10.1038/s41598-025-88658-1

Sci Rep. 2025 Feb 7;15(1):4620. doi: 10.1038/s41598-025-87830-x.

ABSTRACT

Symmetric coin cell cycling is an important tool for the analysis of battery materials, enabling the study of electrode/electrolyte systems under realistic operating conditions. In the case of metal lithium SEI growth and shape changes, cycling studies are especially important to assess the impact of the alternation of anodic-cathodic polarization with the relevant electrolyte geometry and mass-transport conditions. Notwithstanding notable progress in analysis of lithium/lithium symmetric coin cell cycling data, on the one hand, some aspects of the cell electrochemical response still warrant investigation, and, on the other hand, very limited quantitative use is made of large corpora of experimental data generated in electrochemical experiments. This study contributes to shedding light on this highly technologically relevant problem, thanks to the combination of quantitative data exploitation and Partial Differential Equation (PDE) modelling for metal anode cycling. Toward this goal, we propose the use of a Convolutional Neural Network-Long-Short Term Memory (CNN-LSTM) to identify relevant physico-chemical parameters in the PDE model and to describe the behaviour of simulated and experimental charge-discharge profiles. Specifically, we have carried out parameter identification tasks for experimental data regarding the cycling of symmetric coin cells with Li chips as electrodes and LP30 electrolyte. Representative selection of numerical results highlights the advantages of this new approach with respect to traditional Least Squares fitting.

PMID:39920238 | DOI:10.1038/s41598-025-87830-x

PLoS One. 2025 Feb 7;20(2):e0318657. doi: 10.1371/journal.pone.0318657. eCollection 2025.

ABSTRACT

BACKGROUND: Retinal problems are critical because they can cause severe vision loss if not treated. Traditional methods for diagnosing retinal disorders often rely heavily on manual interpretation of optical coherence tomography (OCT) images, which can be time-consuming and dependent on the expertise of ophthalmologists. This leads to challenges in early diagnosis, especially as retinal diseases like diabetic macular edema (DME), Drusen, and Choroidal neovascularization (CNV) become more prevalent. OCT helps ophthalmologists diagnose patients more accurately by allowing for early detection. This paper offers a hybrid SE (Squeeze-and-Excitation)-Enhanced Hybrid Model for detecting retinal disorders from OCT images, including DME, Drusen, and CNV, using artificial intelligence and deep learning.

METHODS: The model integrates SE blocks with EfficientNetB0 and Xception architectures, which provide high success in image classification tasks. EfficientNetB0 achieves high accuracy with fewer parameters through model scaling strategies, while Xception offers powerful feature extraction using deep separable convolutions. The combination of these architectures enhances both the efficiency and classification performance of the model, enabling more accurate detection of retinal disorders from OCT images. Additionally, SE blocks increase the representational ability of the network by adaptively recalibrating per-channel feature responses.

RESULTS: The combined features from EfficientNetB0 and Xception are processed via fully connected layers and categorized using the Softmax algorithm. The methodology was tested on UCSD and Duke's OCT datasets and produced excellent results. The proposed SE-Improved Hybrid Model outperformed the current best-known approaches, with accuracy rates of 99.58% on the UCSD dataset and 99.18% on the Duke dataset.

CONCLUSION: These findings emphasize the model's ability to effectively diagnose retinal disorders using OCT images and indicate substantial promise for the development of computer-aided diagnostic tools in the field of ophthalmology.

PMID:39919140 | DOI:10.1371/journal.pone.0318657