Sci Rep. 2025 Jul 8;15(1):24533. doi: 10.1038/s41598-025-09938-4.

ABSTRACT

Detecting skin melanoma in the early stage using dermoscopic images presents a complex challenge due to the inherent variability in images. Utilizing dermatology datasets, the study aimed to develop Automated Diagnostic Systems for early skin cancer detection. Existing methods often struggle with diverse skin types, cancer stages, and imaging conditions, highlighting a critical gap in reliability and explainability. The novel approach proposed through this research addresses this gap by utilizing a proposed model with advanced layers, including Global Average Pooling, Batch Normalization, Dropout, and dense layers with ReLU and Swish activations to improve model performance. The proposed model achieved accuracies of 95.23% and 96.48% for the two different datasets, demonstrating its robustness, reliability, and strong performance across other performance metrics. Explainable AI techniques such as Gradient-weighted Class Activation Mapping and Saliency Maps offered insights into the model's decision- making process. These advancements enhance skin cancer diagnostics, provide medical experts with resources for early detection, improve clinical outcomes, and increase acceptance of Deep Learning-based diagnostics in healthcare.

PMID:40629062 | DOI:10.1038/s41598-025-09938-4

Sci Rep. 2025 Jul 8;15(1):24568. doi: 10.1038/s41598-025-09423-y.

ABSTRACT

The precise diagnosis of heart disease represents a significant obstacle within the medical field, demanding the implementation of advanced diagnostic instruments and methodologies. This article conducts an extensive examination of the efficacy of different machine learning (ML) and deep learning (DL) models in forecasting heart disease using tabular dataset, with a particular focus on a binary classification task. An extensive array of preprocessing techniques is thoroughly examined in order to optimize the predictive models' quality and performance. Our study employs a wide range of ML algorithms, such as Logistic Regression (LR), Naive Bayes (NB), Support Vector Machine (SVM), Decision Tree (DT), Random Forest (RF), K-Nearest Neibors (KNN), AdaBoost (AB), Gradient Boosting Machine (GBM), Light Gradient Boosting Machine (LGBM), CatBoost (CB), Linear Discriminant Analysis (LDA), and Artificial Neural Network (ANN) to assess the predictive performance of these algorithms in the context of heart disease detection. By subjecting the ML models to exhaustive experimentation, this study evaluates the effects of different feature scaling, namely standardization, minmax scaling, and normalization technique on their performance. The assessment takes into account various parameters including accuracy (Acc), precision (Pre), recall (Rec), F1 score (F1), Area Under Curve (AUC), Cohen's Kappa (CK)and Logloss. The results of this research not only illuminate the optimal scaling methods and ML models for forecasting heart disease, but also offer valuable perspectives on the pragmatic ramifications of implementing these models within a healthcare environment. The research endeavors to make a scholarly contribution to the field of cardiology by utilizing predictive analytics to pave the way for improved early detection and diagnosis of heart disease. This is critical information for coordinating treatment and ensuring opportune intervention.

PMID:40629019 | DOI:10.1038/s41598-025-09423-y

Sci Rep. 2025 Jul 8;15(1):24444. doi: 10.1038/s41598-025-10773-w.

ABSTRACT

Cardiovascular diseases (CVDs) are the leading cause of mortality worldwide, emphasizing the need for accurate and early diagnosis. Electrocardiograms (ECG) provide a non-invasive means of diagnosing various cardiac conditions. However, traditional methods of interpreting ECG signals require substantial expertise and time, motivating the development of automated deep learning models to enhance diagnostic precision. This study proposes a novel approach that leverages masked autoencoders (MAE) to pretrain a model on ECG scalogram images, thereby enhancing the diagnostic accuracy for seven CVDs. Through extensive experimentation, we demonstrated that pretraining with an 85% masking ratio over 500 epochs yields optimal results. The pretrained ViT-S(MAE-scalo) network demonstrated remarkable performance in detecting CVDs, achieving an AUC of 0.986 and 92.43% accuracy in Lead II. Furthermore, the ensemble learning approach applied across 12 ECG leads enhanced the model's diagnostic capabilities, resulting in an AUC of 0.994 and 92.72% accuracy. The MAE-based models outperformed traditional models such as ResNet-34 and ViT-S pretrained on ImageNet or random weights, as well as other SSL models such as MoCo-v2 and BYOL. Notably, the MAE-based models demonstrated superior performance even with a significantly smaller dataset, using only 1/12th the size of the ImageNet dataset. These findings suggest that this efficient pretraining approach for deep learning models holds great potential for clinical application, particularly in resource-limited environments where labeled data is scarce. This method provides a scalable and cost-effective solution for improving CVD diagnosis.

PMID:40628987 | DOI:10.1038/s41598-025-10773-w

Sci Rep. 2025 Jul 8;15(1):24358. doi: 10.1038/s41598-025-09643-2.

ABSTRACT

The American Sign Language Recognition Dataset is a pivotal resource for research in visual-gestural languages for American Sign Language and Sign-Language MNIST Dataset. The dataset contains over 64,000 images meticulously labeled with the corresponding ASL gesture or letter to recognize and classify ASL signs. Recent computer vision and deep learning advances have enabled various ASL recognition techniques. However, further improvements in accuracy and robustness are still needed. This study comprehensively evaluates different ASL recognition methods and introduces a novel architecture called Sign Nevestro Densenet Attention (SNDA). All methods are evaluated on the ASL Recognition Dataset to ensure a representative evaluation. SNDA employs the Nadam optimizer for faster convergence during training. Accurate ASL classification has practical implications for improving communication accessibility. SNDA achieves state-of-the-art performance with 99.76% accuracy, perfect sensitivity, and high specificity and precision. These results validate the effectiveness of SNDA for ASL gesture recognition and highlight its potential to promote inclusivity for deaf and hard-of-hearing communities. The fused attention mechanism demonstrates how deep learning models can be enhanced for specific application domains.

PMID:40628976 | DOI:10.1038/s41598-025-09643-2

ACS Nano. 2025 Jul 8. doi: 10.1021/acsnano.5c04309. Online ahead of print.

ABSTRACT

The frequent global outbreaks of viral infectious diseases have significantly heightened the urgent demand for molecular testing at home. However, the labor-intensive sample preparation and nucleic acid amplification steps, along with the complexity and bulkiness of detection equipment, have limited the large-scale application of molecular testing at home. Here, we propose artificial intelligence-enhanced hand-driven microfluidic system (MACRO) based on RPA and CRISPR technologies for home diagnosis of multiple types of infectious diseases. Leveraging a multidimensional space hourglass structure design, precise spatiotemporal control of fluids can be achieved simply by flipping the chip. Through dual chemical reactions, the system eliminates the need for nucleic acid extraction and purification, simplifying sample preparation and obviating the reliance on heating equipment. The MACRO achieves attomolar sensitivity within 60 min from sample input to result, and 100% specificity for 27 HPV subtypes. Clinical validation using 140 cervical swab specimens demonstrated 98.57% accuracy with 100% specificity. Further, we validated MACRO through multiplex detection of three clinically critical respiratory pathogens (SARS-CoV-2, Influenza A, and Influenza B) in 70 samples, achieving 100% diagnostic concordance. To circumvent subjective errors and enable real-time data collection, we further developed a mobile health platform based on the YoLov8 image recognition algorithm to ensure rapid and precise result output. With the performance of cost-effectiveness ($1.34 per target), and independence from instrument support, MACRO provides a comprehensive solution for molecular testing at home, offering significant implications for enhancing early warning systems for major epidemics and improving public health emergency response capabilities.

PMID:40627810 | DOI:10.1021/acsnano.5c04309

J Bone Joint Surg Am. 2025 Jul 8. doi: 10.2106/JBJS.24.01429. Online ahead of print.

ABSTRACT

BACKGROUND: Privacy-preserving large language models (PP-LLMs) hold potential for assisting clinicians with documentation. We evaluated a PP-LLM to improve the clinical information on radiology request forms for musculoskeletal magnetic resonance imaging (MRI) and to automate protocoling, which ensures that the most appropriate imaging is performed.

METHODS: The present retrospective study included musculoskeletal MRI radiology request forms that had been randomly collected from June to December 2023. Studies without electronic medical record (EMR) entries were excluded. An institutional PP-LLM (Claude Sonnet 3.5) augmented the original radiology request forms by mining EMRs, and, in combination with rule-based processing of the LLM outputs, suggested appropriate protocols using institutional guidelines. Clinical information on the original and PP-LLM radiology request forms were compared with use of the RI-RADS (Reason for exam Imaging Reporting and Data System) grading by 2 musculoskeletal (MSK) radiologists independently (MSK1, with 13 years of experience, and MSK2, with 11 years of experience). These radiologists established a consensus reference standard for protocoling, against which the PP-LLM and of 2 second-year board-certified radiologists (RAD1 and RAD2) were compared. Inter-rater reliability was assessed with use of the Gwet AC1, and the percentage agreement with the reference standard was calculated.

RESULTS: Overall, 500 musculoskeletal MRI radiology request forms were analyzed for 407 patients (202 women and 205 men with a mean age [and standard deviation] of 50.3 ± 19.5 years) across a range of anatomical regions, including the spine/pelvis (143 MRI scans; 28.6%), upper extremity (169 scans; 33.8%) and lower extremity (188 scans; 37.6%). Two hundred and twenty-two (44.4%) of the 500 MRI scans required contrast. The clinical information provided in the PP-LLM-augmented radiology request forms was rated as superior to that in the original requests. Only 0.4% to 0.6% of PP-LLM radiology request forms were rated as limited/deficient, compared with 12.4% to 22.6% of the original requests (p < 0.001). Almost-perfect inter-rater reliability was observed for LLM-enhanced requests (AC1 = 0.99; 95% confidence interval [CI], 0.99 to 1.0), compared with substantial agreement for the original forms (AC1 = 0.62; 95% CI, 0.56 to 0.67). For protocoling, MSK1 and MSK2 showed almost-perfect agreement on the region/coverage (AC1 = 0.96; 95% CI, 0.95 to 0.98) and contrast requirement (AC1 = 0.98; 95% CI, 0.97 to 0.99). Compared with the consensus reference standard, protocoling accuracy for the PP-LLM was 95.8% (95% CI, 94.0% to 97.6%), which was significantly higher than that for both RAD1 (88.6%; 95% CI, 85.8% to 91.4%) and RAD2 (88.2%; 95% CI, 85.4% to 91.0%) (p < 0.001 for both).

CONCLUSIONS: Musculoskeletal MRI request form augmentation with an institutional LLM provided superior clinical information and improved protocoling accuracy compared with clinician requests and non-MSK-trained radiologists. Institutional adoption of such LLMs could enhance the appropriateness of MRI utilization and patient care.

LEVEL OF EVIDENCE: Diagnostic Level III. See Instructions for Authors for a complete description of levels of evidence.

PMID:40627696 | DOI:10.2106/JBJS.24.01429

PLOS Digit Health. 2025 Jul 8;4(7):e0000669. doi: 10.1371/journal.pdig.0000669. eCollection 2025 Jul.

ABSTRACT

Antibiotic resistance is a severe danger to human health, and combination therapy with several antibiotics has emerged as a viable treatment option for multi-resistant strains. CombiANT is a recently developed agar plate-based assay where three reservoirs on the bottom of the plate create a diffusion landscape of three antibiotics that allows testing of the efficiency of antibiotic combinations. This test, however, requires manually assigning nine reference points to each plate, which can be prone to errors, especially when plates need to be graded in large batches and by different users. In this study, an automated deep learning-based image processing method is presented that can accurately segment bacterial growth and measure distances between key points on the CombiANT assay at sub-millimeter precision. The software was tested on 100 plates using photos captured by three different users with their mobile phone cameras, comparing the automated analysis with the human scoring. The result indicates significant agreement between the users and the software ([Formula: see text] mm mean absolute error) and remains consistent when applied to different photos of the same assay despite varying photo qualities and lighting conditions. The speed and robustness of the automated analysis could streamline clinical workflows and make it easier to tailor treatment to specific infections. It could also aid large-scale antibiotic research by quickly processing hundreds of experiments in batch, obtaining better data, and ultimately supporting the development of better treatment strategies. The software can easily be integrated into a potential smartphone application, making it accessible in resource-limited environments. Integrating deep learning-based smartphone image analysis with simple agar-based tests like CombiANT could unlock powerful tools for combating antibiotic resistance.

PMID:40627666 | DOI:10.1371/journal.pdig.0000669

PLoS One. 2025 Jul 8;20(7):e0327186. doi: 10.1371/journal.pone.0327186. eCollection 2025.

ABSTRACT

Various plant attributes, such as growing environment, growth cycle, and ecological distribution, can provide support to fields like agricultural production and biodiversity. This information is widely dispersed in texts. Manual extraction of this information is highly inefficient due to a fact that it not only takes considerable time but also increases the likelihood of overlooking relevant details. To convert textual data into structured information, we extract relational triples in the form of (subject, relation, object), where the subject represents the names of plants, the object represents the plant attributes, and the relation represents the classification of plant attributes. To reduce complexity, we employ a joint extraction of entities and relations based on a tagging scheme. The task is broken down into three parts. Firstly, a matrix is used to simultaneously match plant entities and plant attributes. Then, the predefined categories of plant attributes are classified. Finally, the categories of plant attributes are matched with entity-attribute pairs. The tagging-based method typically utilizes parameter sharing to facilitate interaction between different tasks, but it can also lead to issues such as error amplification and instability in parameter updates. Thus, we adopt improved techniques at different stages to enhance the performance of our model. This includes adjustment to the word embedding layer of BERT and optimization in relation prediction. The modification of the word embedding layer is intended to better integrate contextual information during text representation and reduce the interference of erroneous information. The relation prediction part mainly involves multi-level information fusion of textual information, thereby making corrections and highlighting important information. We name the three-stage method as "Bwdgv". Compared to the currently advanced PRGC model, the F1-score of the proposed method has an improvement of 1.4%. With the help of extracted triples, we can construct knowledge graphs and other tasks to better apply various plant attributes.

PMID:40627606 | DOI:10.1371/journal.pone.0327186

J Med Internet Res. 2025 Jul 8;27:e75347. doi: 10.2196/75347.

ABSTRACT

BACKGROUND: Recent advancements in large language models (LLMs) have generated significant interest in their potential for assessing psychological constructs, particularly personality traits. While prior research has explored LLMs' capabilities in zero-shot or few-shot personality inference, few studies have systematically evaluated LLM embeddings within a psychometric validity framework or examined their correlations with linguistic and emotional markers. Additionally, the comparative efficacy of LLM embeddings against traditional feature engineering methods remains underexplored, leaving gaps in understanding their scalability and interpretability for computational personality assessment.

OBJECTIVE: This study evaluates LLM embeddings for personality trait prediction through four key analyses: (1) performance comparison with zero-shot methods on PANDORA Reddit data, (2) psychometric validation and correlation with LIWC (Linguistic Inquiry and Word Count) and emotion features, (3) benchmarking against traditional feature engineering approaches, and (4) assessment of model size effects (OpenAI vs BERT vs RoBERTa). We aim to establish LLM embeddings as a psychometrically valid and efficient alternative for personality assessment.

METHODS: We conducted a multistage analysis using 1 million Reddit posts from the PANDORA Big Five personality dataset. First, we generated text embeddings using 3 LLM architectures (RoBERTa, BERT, and OpenAI) and trained a custom bidirectional long short-term memory model for personality prediction. We compared this approach against zero-shot inference using prompt-based methods. Second, we extracted psycholinguistic features (LIWC categories and National Research Council emotions) and performed feature engineering to evaluate potential performance enhancements. Third, we assessed the psychometric validity of LLM embeddings: reliability validity using Cronbach α and convergent validity analysis by examining correlations between embeddings and established linguistic markers. Finally, we performed traditional feature engineering on static psycholinguistic features to assess performance under different settings.

RESULTS: LLM embeddings trained using simple deep learning techniques significantly outperform zero-shot approaches on average by 45% across all personality traits. Although psychometric validation tests indicate moderate reliability, with an average Cronbach α of 0.63, correlation analyses spark a strong association with key linguistic or emotional markers; openness correlates highly with social (r=0.53), conscientiousness with linguistic (r=0.46), extraversion with social (r=0.41), agreeableness with pronoun usage (r=0.40), and neuroticism with politics-related text (r=0.63). Despite adding advanced feature engineering on linguistic features, the performance did not improve, suggesting that LLM embeddings inherently capture key linguistic features. Furthermore, our analyses demonstrated efficacy on larger model size with a computational cost trade-off.

CONCLUSIONS: Our findings demonstrate that LLM embeddings offer a robust alternative to zero-shot methods in personality trait analysis, capturing key linguistic patterns without requiring extensive feature engineering. The correlation between established psycholinguistic markers and the performance trade-off with computational cost provides a hint for future computational linguistic work targeting LLM for personality assessment. Further research should explore fine-tuning strategies to enhance psychometric validity.

PMID:40627556 | DOI:10.2196/75347

IEEE Trans Med Imaging. 2025 Jul 8;PP. doi: 10.1109/TMI.2025.3587131. Online ahead of print.

ABSTRACT

Metal dental implants may introduce metal artifacts (MA) during the CBCT imaging process, causing significant interference in subsequent diagnosis. In recent years, many deep learning methods for metal artifact reduction (MAR) have been proposed. Due to the huge difference between synthetic and clinical MA, supervised learning MAR methods may perform poorly in clinical settings. Many existing unsupervised MAR methods trained on clinical data often suffer from incorrect dental morphology. To alleviate the above problems, in this paper, we propose a new MAR method of Coupled Diffusion Models (CDM) for clinical dental CBCT images. Specifically, we separately train two diffusion models on clinical MA-degraded images and clinical clean images to obtain prior information, respectively. During the denoising process, the variances of noise levels are calculated from MA images and the prior of diffusion models. Then we develop a noise transformation module between the two diffusion models to transform the MA noise image into a new initial value for the denoising process. Our designs effectively exploit the inherent transformation between the misaligned MA-degraded images and clean images. Additionally, we introduce an MA-adaptive inference technique to better accommodate the MA degradation in different areas of an MA-degraded image. Experiments on our clinical dataset demonstrate that our CDM outperforms the comparison methods on both objective metrics and visual quality, especially for severe MA degradation. We will publicly release our code.

PMID:40627491 | DOI:10.1109/TMI.2025.3587131

IEEE Trans Med Imaging. 2025 Jul 8;PP. doi: 10.1109/TMI.2025.3584726. Online ahead of print.

ABSTRACT

Serial section electron microscopy (ssEM) is a pivotal technique for investigating neuronal connections and brain microstructures. However, imperfect sample preparation and image acquisition often lead to degradation, posing challenges for subsequent analysis. While previous deep learning methods, such as the interpolation model using spatially adaptive convolutions, have been proven to outperform conventional approaches, they struggle to recover high-frequency details, resulting in poor perceptual quality and segmentation performance. This study presents a novel approach leveraging diffusion models to restore missing slices of ssEM images. To accommodate the anisotropic characteristic of ssEM images, we enhance the backbone network with asymmetric and symmetric 3D convolutions. Additionally, we propose the Adaptive and Learnable Reconstruction (ALR) module with the First and Last slices Attention Block (FLAB) for effective feature extraction. A Multi-output Joint Strategy (MJS) is utilized for noise estimation, reducing training-testing discrepancies and achieving diffusion correction. Moreover, we also redesign the inference process to optimize the restoration of partially damaged slices, enabling restoration without additional artifact simulation or retraining. Experiment results demonstrate the effectiveness of our approach in generating more realistic slices and its superior performance in downstream tasks, surpassing previous methods.

PMID:40627490 | DOI:10.1109/TMI.2025.3584726

IEEE Trans Neural Netw Learn Syst. 2025 Jul 8;PP. doi: 10.1109/TNNLS.2025.3578664. Online ahead of print.

ABSTRACT

Model reuse techniques can reduce the resource requirements for training high-performance deep neural networks (DNNs) by leveraging existing models. However, unauthorized reuse and replication of DNNs can lead to copyright infringement and economic loss to the model owner. This underscores the need to analyze the reuse relation between DNNs and develop copyright protection techniques to safeguard intellectual property rights. Existing DNN copyright protection approaches suffer from several inherent limitations hindering their effectiveness in practical scenarios. For instance, existing white-box fingerprinting approaches cannot address the common heterogeneous reuse case where the model architecture is changed, and DNN fingerprinting approaches heavily rely on generating adversarial examples with good transferability, which is known to be challenging in the black-box setting. To bridge the gap, we propose a neuron functionality analysis-based reuse detector (NFARD), a neuron functionality (NF) analysis-based reuse detector, which only requires normal test samples to detect reuse relations by measuring the models' differences on a newly proposed model characterization, i.e., NF. A set of NF-based distance metrics is designed to make NFARD applicable to both white-box and black-box settings. Moreover, we devise a linear transformation method to handle heterogeneous reuse cases by constructing the optimal projection matrix for dimension consistency, significantly extending the application scope of NFARD. To the best of our knowledge, this is the first adversarial example-free method that exploits NF for DNN copyright protection. As a side contribution, we constructed a reuse detection benchmark named Reuse Zoo that covers various practical reuse techniques and popular datasets. Extensive evaluations on this comprehensive benchmark show that NFARD achieves $F1$ scores of 0.984 and 1.0 for detecting reuse relationships in black-box and white-box settings, respectively, while generating test suites $2{\sim } 99$ times faster than previous methods.

PMID:40627481 | DOI:10.1109/TNNLS.2025.3578664

IEEE Trans Biomed Eng. 2025 Jul 8;PP. doi: 10.1109/TBME.2025.3587003. Online ahead of print.

ABSTRACT

Knee Osteoarthritis (KOA) is a prevalent musculoskeletal disorder that severely impacts mobility and quality of life, particularly among older adults. Its diagnosis often relies on subjective assessments using the Kellgren-Lawrence (KL) grading system, leading to variability in clinical evaluations. To address these challenges, we propose a confidence-driven deep learning framework for early KOA detection, focusing on distinguishing KL-0 and KL-2 stages. The Siamese-based framework integrates a novel multi-level feature extraction architecture with a hybrid loss strategy. Specifically, multi-level Global Average Pooling (GAP) layers are employed to extract features from varying network depths, ensuring comprehensive feature representation, while the hybrid loss strategy partitions training samples into high-, medium-, and low-confidence subsets. Tailored loss functions are applied to improve model robustness and effectively handle uncertainty in annotations. Experimental results on the Osteoarthritis Initiative (OAI) dataset demonstrate that the proposed framework achieves competitive accuracy, sensitivity, and specificity, comparable to those of expert radiologists. Cohen's kappa values ($\kappa$ $>$ 0.85)) confirm substantial agreement, while McNemar's test ($p$ $>$ 0.05) indicates no statistically significant differences between the model and radiologists. Additionally, Confidence distribution analysis reveals that the model emulates radiologists' decision-making patterns. These findings highlight the potential of the proposed approach to serve as an auxiliary diagnostic tool, enhancing early KOA detection and reducing clinical workload. Our code is available at https://github.com/ZWang78/Confidence.

PMID:40627470 | DOI:10.1109/TBME.2025.3587003

Proc Natl Acad Sci U S A. 2025 Jul 15;122(28):e2500004122. doi: 10.1073/pnas.2500004122. Epub 2025 Jul 8.

ABSTRACT

Glioma is the most common primary malignant brain tumor and preoperative genetic profiling is essential for the management of glioma patients. Our study focused on tumor regions segmentation and predicting the World Health Organization (WHO) grade, isocitrate dehydrogenase (IDH) mutation, and 1p/19q codeletion status using deep learning models on preoperative MRI. To achieve accurate tumor segmentation, we developed an optimal mass transport (OMT) approach to transform irregular MRI brain images into tensors. In addition, we proposed an algebraic preclassification (APC) model utilizing multimode OMT tensor singular value decomposition (SVD) to estimate preclassification probabilities. The fully automated deep learning model named OMT-APC was used for multitask classification. Our study incorporated preoperative brain MRI data from 3,565 glioma patients across 16 datasets spanning Asia, Europe, and America. Among these, 2,551 patients from 5 datasets were used for training and internal validation. In comparison, 1,014 patients from 11 datasets, including 242 patients from The Cancer Genome Atlas (TCGA), were used as independent external test. The OMT segmentation model achieved mean lesion-wise Dice scores of 0.880. The OMT-APC model was evaluated on the TCGA dataset, achieving accuracies of 0.855, 0.917, and 0.809, with AUC scores of 0.845, 0.908, and 0.769 for WHO grade, IDH mutation, and 1p/19q codeletion, respectively, which outperformed the four radiologists in all tasks. These results highlighted the effectiveness of our OMT and tensor SVD-based methods in brain tumor genetic profiling, suggesting promising applications for algebraic and geometric methods in medical image analysis.

PMID:40627394 | DOI:10.1073/pnas.2500004122

Dentomaxillofac Radiol. 2025 Jul 8:twaf054. doi: 10.1093/dmfr/twaf054. Online ahead of print.

ABSTRACT

OBJECTIVES: To reduce the problems arising from the inability to identify dental implant brands, this study aims to classify various dental implant brands using deep learning algorithms on panoramic radiographs.

METHODS: Images of four different dental implant systems (NucleOSS, Medentika, Nobel, and Implance) were used from a total of 5,375 cropped panoramic radiographs. To enhance image clarity and reduce blurriness, the Contrast Limited Adaptive Histogram Equalization (CLAHE) filter was applied. GoogleNet, ResNet-18, VGG16, and ShuffleNet deep learning algorithms were utilized to classify the four different dental implant systems. To evaluate the classification performance of the algorithms, ROC curves and confusion matrices were generated. Based on these confusion matrices, accuracy, precision, sensitivity, and F1 score were calculated. The Z-test was used to compare the performance metrics across different algorithms.

RESULTS: The accuracy rates of the deep learning algorithms were obtained as 96.00% for GoogleNet, 84.40% for ResNet-18, 98.90% for VGG16, and 84.80% for ShuffleNet. A statistically significant difference was found between the accuracy rate of the VGG16 algorithm and those of GoogleNet, ShuffleNet, and ResNet-18 (p < 0.001, p < 0.001, and p < 0.001, respectively).

CONCLUSIONS: With the achievement of high accuracy rates, deep learning algorithms are considered a valuable and powerful method for identifying dental implant brands.

PMID:40627380 | DOI:10.1093/dmfr/twaf054

J Imaging Inform Med. 2025 Jul 8. doi: 10.1007/s10278-025-01590-8. Online ahead of print.

ABSTRACT

Biomedical image classification is of paramount importance in enhancing diagnostic precision and improving patient outcomes across diverse medical disciplines. In recent years, the advent of deep learning methodologies has significantly transformed this domain by facilitating notable advancements in image analysis and classification endeavors. This paper provides a thorough overview of the application of deep learning techniques in biomedical image classification, encompassing various types of healthcare data, including medical images derived from modalities such as mammography, histopathology, and radiology. A detailed discourse on deep learning architectures, including convolutional neural networks (CNNs), recurrent neural networks (RNNs), and advanced models such as generative adversarial networks (GANs), is presented. Additionally, we delineate the distinctions between supervised, unsupervised, and reinforcement learning approaches, along with their respective roles within the context of biomedical imaging. This study systematically investigates 50 deep learning methodologies employed in the healthcare sector, elucidating their effectiveness in various tasks, including disease detection, image segmentation, and classification. It particularly emphasizes models that have been trained on publicly available datasets, thereby highlighting the significant role of open-access data in fostering advancements in AI-driven healthcare innovations. Furthermore, this review accentuates the transformative potential of deep learning in the realm of biomedical image analysis and delineates potential avenues for future research within this rapidly evolving field.

PMID:40627296 | DOI:10.1007/s10278-025-01590-8

Radiol Med. 2025 Jul 8. doi: 10.1007/s11547-025-02047-2. Online ahead of print.

ABSTRACT

Reduced muscle mass and function are associated with increased morbidity, and mortality. Ultrasound, despite being cost-effective and portable, is still underutilized in muscle trophism assessment due to its reliance on operator expertise and measurement variability. This proof-of-concept study aimed to overcome these limitations by developing a deep learning model that predicts muscle density, as assessed by CT, using Ultrasound data, exploring the feasibility of a novel Ultrasound-based parameter for muscle trophism.A sample of adult participants undergoing CT examination in our institution's emergency department between May 2022 and March 2023 was enrolled in this single-center study. Ultrasound examinations were performed with a L11-3 MHz probe. The rectus abdominis muscles, selected as target muscles, were scanned in the transverse plane, recording an Ultrasound image per side. For each participant, the same operator calculated the average target muscle density in Hounsfield Units from an axial CT slice closely matching the Ultrasound scanning plane.The final dataset included 1090 Ultrasound images from 551 participants (mean age 67 ± 17, 323 males). A deep learning model was developed to classify Ultrasound images into three muscle-density classes based on CT values. The model achieved promising performance, with a categorical accuracy of 70% and AUC values of 0.89, 0.79, and 0.90 across the three classes.This observational study introduces an innovative approach to automated muscle trophism assessment using Ultrasound imaging. Future efforts should focus on external validation in diverse populations and clinical settings, as well as expanding its application to other muscles.

PMID:40627283 | DOI:10.1007/s11547-025-02047-2

Phys Eng Sci Med. 2025 Jul 8. doi: 10.1007/s13246-025-01564-8. Online ahead of print.

ABSTRACT

Medical imaging has become an essential tool in the diagnosis and treatment of various diseases, and provides critical insights through ultrasound, MRI, and X-ray modalities. Despite its importance, challenges remain in the accurate segmentation and classification of complex structures owing to factors such as low contrast, noise, and irregular anatomical shapes. This study addresses these challenges by proposing a novel hybrid deep learning model that integrates the strengths of Convolutional Autoencoders (CAE), UNet, and SegNet architectures. In the preprocessing phase, a Convolutional Autoencoder is used to effectively reduce noise while preserving essential image details, ensuring that the images used for segmentation and classification are of high quality. The ability of CAE to denoise images while retaining critical features enhances the accuracy of the subsequent analysis. The developed model employs UNet for multiscale feature extraction and SegNet for precise boundary reconstruction, with Dynamic Feature Fusion integrated at each skip connection to dynamically weight and combine the feature maps from the encoder and decoder. This ensures that both global and local features are effectively captured, while emphasizing the critical regions for segmentation. To further enhance the model's performance, the Hybrid Emperor Penguin Optimizer (HEPO) was employed for feature selection, while the Hybrid Vision Transformer with Convolutional Embedding (HyViT-CE) was used for the classification task. This hybrid approach allows the model to maintain high accuracy across different medical imaging tasks. The model was evaluated using three major datasets: brain tumor MRI, breast ultrasound, and chest X-rays. The results demonstrate exceptional performance, achieving an accuracy of 99.92% for brain tumor segmentation, 99.67% for breast cancer detection, and 99.93% for chest X-ray classification. These outcomes highlight the ability of the model to deliver reliable and accurate diagnostics across various medical contexts, underscoring its potential as a valuable tool in clinical settings. The findings of this study will contribute to advancing deep learning applications in medical imaging, addressing existing research gaps, and offering a robust solution for improved patient care.

PMID:40627277 | DOI:10.1007/s13246-025-01564-8

Discov Oncol. 2025 Jul 8;16(1):1282. doi: 10.1007/s12672-025-03102-0.

ABSTRACT

Cervical cancer stands out among various cancers due to its potential for prevention and eradication, mainly through vaccination and proactive screening measures. However, there are still a large number of women in low- and middle-income countries who need to undergo cervical cancer screening. Conventional cervical cancer screening approaches possess distinct benefits and drawbacks regarding sensitivity, specificity, promptness, and expense. In recent years, artificial intelligence (AI) has gained widespread use to help healthcare professionals in performing extensive cervical cancer screenings, focusing on machine learning (ML) and deep learning (DL) techniques. Traditional screening methods combined with AI technology have shown initial effectiveness in cervical cancer screening. But it is necessary to address various challenges such as limited technology and resources, difficulties in integrating clinical workflows, and ethical and legal risks in large-scale population cervical cancer screening. In this review, how AI helps simplify workflows, aids in cytological segmentation and diagnosis, enhances the triage and diagnosis processes for human papillomavirus (HPV) and colposcopy were described firstly. Then we summarized the existing clinical cases of AI applied to large-scale cervical cancer screening. Finally, we discussed the challenges and limitations of AI implementation in large population cervical cancer screening. These insights may possess the capacity to transform cervical cancer screening on a global scale by improving diagnostic precision, facilitating early intervention, and increasing the overall efficacy of cervical cancer screening initiatives worldwide.

PMID:40627254 | DOI:10.1007/s12672-025-03102-0

Front Pharmacol. 2025 Jun 23;16:1554650. doi: 10.3389/fphar.2025.1554650. eCollection 2025.

ABSTRACT

INTRODUCTION: Drug safety has increasingly become a serious public health problem that threatens health and damages social economy. The common detection methods have the problem of high false positive rate. This study aims to introduce deep learning models into the adverse drug reaction (ADR) signal detection and compare different methods.

METHODS: The data are based on adverse events collected by Center for ADR Monitoring of Guangdong. Traditional statistical methods were used for data preliminary screening. We transformed data into free text, extracted text information and made classification prediction by using the Long Short-Term Memory (LSTM) model. We compared it with the existing signal detection methods, including Logistic Regression, Random Forest, K-NearestNeighbor, and Multilayer Perceptron. The feature importance of the included variables was analyzed.

RESULTS: A total of 2,376 ADR signals were identified between January 2018 and December 2019, comprising 448 positive signals and 1,928 negative signals. The sensitivity of the LSTM model based on free text reached 95.16%, and the F1-score was 0.9706. The sensitivity of Logistic Regression model based on feature variables was 86.83%, and the F1-score was 0.9063. The classification results of our model demonstrate superior sensitivity and F1-score compared to traditional methods. Several important variables "Reasons for taking medication", "Serious ADR scenario 4", "Adverse reaction analysis 5", and "Dosage" had an important influence on the result.

CONCLUSION: The application of deep learning models shows potential to improve the detection performance in ADR monitoring.

PMID:40626311 | PMC:PMC12230008 | DOI:10.3389/fphar.2025.1554650