J Med Syst. 2025 Feb 11;49(1):22. doi: 10.1007/s10916-025-02156-5.

ABSTRACT

This study aims to apply a multi-modal approach of the deep learning method for survival prediction in patients with non-small-cell lung cancer (NSCLC) using CT-based radiomics. We utilized two public data sets from the Cancer Imaging Archive (TCIA) comprising NSCLC patients, 420 patients and 516 patients for Lung 1 training and Lung 2 testing, respectively. A 3D convolutional neural network (CNN) survival was applied to extract 256 deep-radiomics features for each patient from a CT scan. Feature selection steps are used to choose the radiomics signatures highly associated with overall survival. Deep-radiomics and traditional-radiomics signatures, and clinical parameters were fed into the DeepSurv neural network. The C-index was used to evaluate the model's effectiveness. In the Lung 1 training set, the model combining traditional-radiomics and deep-radiomics performs better than the single parameter models, and models that combine all three markers (traditional-radiomics, deep-radiomics, and clinical) are most effective with C-index 0.641 for Cox proportional hazards (Cox-PH) and 0.733 for DeepSurv approach. In the Lung 2 testing set, the model combining traditional-radiomics, deep-radiomics, and clinical obtained a C-index of 0.746 for Cox-PH and 0.751 for DeepSurv approach. The DeepSurv method improves the model's prediction compared to the Cox-PH, and models that combine all three parameters with the DeepSurv have the highest efficiency in training and testing data sets (C-index: 0.733 and 0.751, respectively). DeepSurv CT-based deep-radiomics method outperformed Cox-PH in survival prediction of patients with NSCLC patients. Models' efficiency is increased when combining multi parameters.

PMID:39930275 | DOI:10.1007/s10916-025-02156-5

Commun Biol. 2025 Feb 10;8(1):209. doi: 10.1038/s42003-025-07479-0.

ABSTRACT

Quantifying emesis in Suncus murinus (S. murinus) has traditionally relied on direct observation or reviewing recorded behaviour, which are laborious, time-consuming processes that are susceptible to operator error. With rapid advancements in deep learning, automated animal behaviour quantification tools with high accuracy have emerged. In this study, we pioneere the use of both three-dimensional convolutional neural networks and self-attention mechanisms to develop the Automatic Emesis Detection (AED) tool for the quantification of emesis in S. murinus, achieving an overall accuracy of 98.92%. Specifically, we use motion-induced emesis videos as training datasets, with validation results demonstrating an accuracy of 99.42% for motion-induced emesis. In our model generalisation and application studies, we assess the AED tool using various emetics, including resiniferatoxin, nicotine, copper sulphate, naloxone, U46619, cyclophosphamide, exendin-4, and cisplatin. The prediction accuracies for these emetics are 97.10%, 100%, 100%, 97.10%, 98.97%, 96.93%, 98.91%, and 98.41%, respectively. In conclusion, employing deep learning-based automatic analysis improves efficiency and accuracy and mitigates human bias and errors. Our study provides valuable insights into the development of deep learning neural network models aimed at automating the analysis of various behaviours in S. murinus, with potential applications in preclinical research and drug development.

PMID:39930110 | DOI:10.1038/s42003-025-07479-0

Eur Spine J. 2025 Feb 10. doi: 10.1007/s00586-025-08708-0. Online ahead of print.

ABSTRACT

PURPOSE: Cervical spine injuries in the elderly (defined as individuals aged 65 years and older) are increasing, often resulting from falls and minor trauma. Prognosis varies widely, influenced by multiple factors. This study aimed to develop a deep-learning-based predictive model for post-injury outcomes.

METHODS: This study analyzed a nationwide dataset from the Japan Association of Spine Surgeons with Ambition, comprising 1512 elderly patients (aged 65 years and older) with cervical spine injuries from 2010 to 2020. Deep learning predictive models were constructed for residence, mobility, and the American Spinal Injury Association Impairment Scale (AIS). The model's performance was compared with that of a traditional statistical analysis.

RESULTS: The deep-learning model predicted the residence and AIS outcomes with varying accuracies. The highest accuracy was observed in predicting residence one year post-injury. The model also identified that the AIS score at discharge was significantly predicted by upper extremity trauma, mobility, and elbow extension strength. The deep learning model highlighted factors, such as upper extremity trauma, that were not considered significant in the traditional statistical analysis.

CONCLUSION: Our deep learning-based model offers a novel method for predicting outcomes following cervical spine injuries in the elderly population. The model is highly accurate and provides additional insights into potential prognostic factors. Such models can improve patient care and individualize future interventions.

PMID:39930051 | DOI:10.1007/s00586-025-08708-0

Sci Rep. 2025 Feb 10;15(1):4938. doi: 10.1038/s41598-025-89230-7.

ABSTRACT

Skin cancer represents a significant global health concern, where early and precise diagnosis plays a pivotal role in improving treatment efficacy and patient survival rates. Nonetheless, the inherent visual similarities between benign and malignant lesions pose substantial challenges to accurate classification. To overcome these obstacles, this study proposes an innovative hybrid deep learning model that combines ConvNeXtV2 blocks and separable self-attention mechanisms, tailored to enhance feature extraction and optimize classification performance. The inclusion of ConvNeXtV2 blocks in the initial two stages is driven by their ability to effectively capture fine-grained local features and subtle patterns, which are critical for distinguishing between visually similar lesion types. Meanwhile, the adoption of separable self-attention in the later stages allows the model to selectively prioritize diagnostically relevant regions while minimizing computational complexity, addressing the inefficiencies often associated with traditional self-attention mechanisms. The model was comprehensively trained and validated on the ISIC 2019 dataset, which includes eight distinct skin lesion categories. Advanced methodologies such as data augmentation and transfer learning were employed to further enhance model robustness and reliability. The proposed architecture achieved exceptional performance metrics, with 93.48% accuracy, 93.24% precision, 90.70% recall, and a 91.82% F1-score, outperforming over ten Convolutional Neural Network (CNN) based and over ten Vision Transformer (ViT) based models tested under comparable conditions. Despite its robust performance, the model maintains a compact design with only 21.92 million parameters, making it highly efficient and suitable for model deployment. The Proposed Model demonstrates exceptional accuracy and generalizability across diverse skin lesion classes, establishing a reliable framework for early and accurate skin cancer diagnosis in clinical practice.

PMID:39930026 | DOI:10.1038/s41598-025-89230-7

Sci Rep. 2025 Feb 10;15(1):4940. doi: 10.1038/s41598-025-89544-6.

ABSTRACT

Accurate space object pose estimation is crucial for various space tasks, including 3D reconstruction, satellite navigation, rendezvous and docking maneuvers, and collision avoidance. Many previous studies, however, often presuppose the availability of the space object's computer-aided design model for keypoint matching and model training. This work proposes a generalized pose estimation pipeline that is independent of 3D models and applicable to both instance- and category-level scenarios. The proposed framework consists of three parts based on deep learning approaches to accurately estimate space objects pose. First, a keypoints extractor is proposed to extract sub-pixel-level keypoints from input images. Then a multichannel matching network with triple loss is designed to obtain the matching pairs of keypoints in the body reference system. Finally, a pose graph optimization algorithm with a dynamic keyframes pool is designed to estimate the target pose and reduce long-term drifting pose errors. A space object dataset including nine different types of non-cooperative targets with 11,565 samples is developed for model training and evaluation. Extensive experimental results indicate that the proposed method demonstrates robust performance across various challenging conditions, including different object types, diverse illumination scenarios, varying rotation rates, and different image resolutions. To verify the demonstrated approach, the model is compared with several state-of-the-art approaches and shows superior estimation results. The mAPE and mMS scores of the proposed approach reach 0.63° and 0.767, respectively.

PMID:39930024 | DOI:10.1038/s41598-025-89544-6

Sci Rep. 2025 Feb 10;15(1):4931. doi: 10.1038/s41598-025-88451-0.

ABSTRACT

In the case of neonates, especially low birth weight preterm and high-risk infants, portable X-rays are frequently used. However, the image quality of portable X-rays is significantly lower compared to standard adult or pediatric X-rays, leading to considerable challenges in identifying abnormalities. Although attempts have been made to introduce deep learning to address these image quality issues, the poor quality of the images themselves hinders the training of deep learning models, further emphasizing the need for image enhancement. Additionally, since neonates have a high cell division rate and are highly sensitive to radiation, increasing radiation exposure to improve image quality is not a viable solution. Therefore, it is crucial to enhance image quality through preprocessing before training deep learning models. While various image enhancement methods have been proposed, Contrast Limited Adaptive Histogram Equalization (CLAHE) has been recognized as an effective technique for contrast-based image improvement. However, despite extensive research, the process of setting CLAHE's hyperparameters still relies on a brute force, manual approach, making it inefficient. To address this issue, we propose a method called Bayesian Optimization CLAHE(BO-CLAHE), which leverages Bayesian optimization to automatically select the optimal hyperparameters for X-ray images used in diagnosing lung diseases in preterm and high-risk neonates. The images enhanced by BO-CLAHE demonstrated superior performance across several classification models, with particularly notable improvements in diagnosing Transient Tachypnea of the Newborn (TTN). This approach not only reduces radiation exposure but also contributes to the development of AI-based diagnostic tools, playing a crucial role in the early diagnosis and treatment of preterm and high-risk neonates.

PMID:39929905 | DOI:10.1038/s41598-025-88451-0

Comput Biol Med. 2025 Feb 9;187:109785. doi: 10.1016/j.compbiomed.2025.109785. Online ahead of print.

ABSTRACT

Knee Osteoarthritis (KOA), a leading cause of disability worldwide, is challenging to detect early due to subtle radiographic indicators. Diverse, extensive datasets are needed but are challenging to compile because of privacy, data collection limitations, and the progressive nature of KOA. However, a model capable of projecting genuine radiographs into different OA stages could augment data pools, enhance algorithm training, and offer pre-emptive prognostic insights. In this study, we developed a Cycle-Consistent Adversarial Network (CycleGAN) to generate synthetic past and future stages of KOA on any genuine radiograph. The model's effectiveness was validated through its impact on a KOA specialized Convolutional Neural Network (CNN). Transformations towards synthetic future disease states resulted in 83.76% of none-to-doubtful stage images being classified as moderate-to-severe stages, while retroactive transformations led to 75.61% of severe-stage images being classified as none-to-doubtful stages. Similarly, transformations from mild stages achieved 76.00% correct classification towards future stages and 69.00% for past stages. The CycleGAN demonstrated an exceptional ability to expand the knee joint space and eliminate bone-outgrowths (osteophytes), key radiographic indicators of disease progression. These results signify a promising potential for enhancing diagnostic models, data augmentation, and educational and prognostic uses. Nevertheless, further refinement, validation, and a broader evaluation process encompassing both CNN-based assessments and expert medical feedback are emphasized for future research and development.

PMID:39929004 | DOI:10.1016/j.compbiomed.2025.109785

J Med Internet Res. 2025 Feb 10;27:e60888. doi: 10.2196/60888.

ABSTRACT

BACKGROUND: Although catheter ablation (CA) is currently the most effective clinical treatment for atrial fibrillation, its variable therapeutic effects among different patients present numerous problems. Machine learning (ML) shows promising potential in optimizing the management and clinical outcomes of patients undergoing atrial fibrillation CA (AFCA).

OBJECTIVE: This scoping review aimed to evaluate the current scientific evidence on the application of ML for managing patients undergoing AFCA, compare the performance of various models across specific clinical tasks within AFCA, and summarize the strengths and limitations of ML in this field.

METHODS: Adhering to the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) guidelines, relevant studies published up to October 7, 2023, were searched from PubMed, Web of Science, Embase, the Cochrane Library, and ScienceDirect. The final included studies were confirmed based on inclusion and exclusion criteria and manual review. The PROBAST (Prediction model Risk Of Bias Assessment Tool) and QUADAS-2 (Quality Assessment of Diagnostic Accuracy Studies-2) methodological quality assessment tools were used to review the included studies, and narrative data synthesis was performed on the modeled results provided by these studies.

RESULTS: The analysis of 23 included studies showcased the contributions of ML in identifying potential ablation targets, improving ablation strategies, and predicting patient prognosis. The patient data used in these studies comprised demographics, clinical characteristics, various types of imaging (9/23, 39%), and electrophysiological signals (7/23, 30%). In terms of model type, deep learning, represented by convolutional neural networks, was most frequently applied (14/23, 61%). Compared with traditional clinical scoring models or human clinicians, the model performance reported in the included studies was generally satisfactory, but most models (14/23, 61%) showed a high risk of bias due to lack of external validation.

CONCLUSIONS: Our evidence-based findings suggest that ML is a promising tool for improving the effectiveness and efficiency of managing patients undergoing AFCA. While guiding data preparation and model selection for future studies, this review highlights the need to address prevalent limitations, including lack of external validation, and to further explore model generalization and interpretability.

PMID:39928932 | DOI:10.2196/60888

PLoS One. 2025 Feb 10;20(2):e0317304. doi: 10.1371/journal.pone.0317304. eCollection 2025.

ABSTRACT

The World Natural Heritage is a rare and irreplaceable natural landscape recognized by all mankind, with outstanding significance and universal value. Among them, the World Heritage Karst sites(WHKs) holds an important position due to its special natural beauty and aesthetic value. In the field of landscape evaluation, interdisciplinary and interdisciplinary cooperation using different methods has always been a research focus. However, there is still a gap in the evaluation of natural landscape aesthetic value based on UGC(User Generated Content) data and deep learning models. This article is based on a public perspective, using social media UGC data, crawling images and texts as data sources, and combining SegFormer deep learning models, ArcGIS spatial analysis, natural Language Processing Technology (NLP) and other methods to conduct quantitative research on aesthetic value. Research has found that: (1) Huangguoshu Scenic Area has an excellent natural environment, and landscape elements with high naturalness (vegetation, water) are more attractive to tourists, with diverse landscape combinations; (2) There is no complete positive correlation between tourist sentiment bias, landscape diversity, and vegetation coverage. Emphasis is placed on the aesthetic perception path from bottom to top, from the surface to the inside. The comprehensive emotional value is 14.35, and the emotional values are all positively distributed. The distribution density and extreme value of positive emotions are greater than those of negative emotions; (3) The emotional bias of tourists is directly related to visual sensitivity, showing a synchronous trend of change. The visual sensitivity of the Great Waterfall and Dishuitan areas is relatively high, mostly at I-II level sensitivity. This method enhances the data source channel, which is conducive to obtaining the correct tourist evaluation orientation. In traditional subjective landscape evaluation, rational parameter indicators are added to reduce the probability of error, provide data support for its natural beauty description, break through the time and space limitations of aesthetic evaluation, and provide scientific reference for quantifying the aesthetic value of other heritage sites.

PMID:39928674 | DOI:10.1371/journal.pone.0317304

PLoS Comput Biol. 2025 Feb 10;21(2):e1012286. doi: 10.1371/journal.pcbi.1012286. Online ahead of print.

ABSTRACT

There is an important challenge in systematically interpreting the internal representations of deep neural networks (DNNs). Existing techniques are often less effective for non-tabular tasks, or they primarily focus on qualitative, ad-hoc interpretations of models. In response, this study introduces a cognitive science-inspired, multi-dimensional quantification and visualization approach that captures two temporal dimensions of model learning: the "information-processing trajectory" and the "developmental trajectory." The former represents the influence of incoming signals on an agent's decision-making, while the latter conceptualizes the gradual improvement in an agent's performance throughout its lifespan. Tracking the learning curves of DNNs enables researchers to explicitly identify the model appropriateness of a given task, examine the properties of the underlying input signals, and assess the model's alignment (or lack thereof) with human learning experiences. To illustrate this method, we conducted 750 runs of simulations on two temporal tasks: gesture detection and sentence classification, showcasing its applicability across different types of deep learning tasks. Using four descriptive metrics to quantify the mapped learning curves-start, end - start, max, tmax-, we identified significant differences in learning patterns based on data sources and class distinctions (all p's < .0001), the prominent role of spatial semantics in gesture learning, and larger information gains in language learning. We highlight three key insights gained from mapping learning curves: non-monotonic progress, pairwise comparisons, and domain distinctions. We reflect on the theoretical implications of this method for cognitive processing, language models and representations from multiple modalities.

PMID:39928655 | DOI:10.1371/journal.pcbi.1012286

PLoS One. 2025 Feb 10;20(2):e0311250. doi: 10.1371/journal.pone.0311250. eCollection 2025.

ABSTRACT

The healthcare industry is generating a massive volume of data, promising a potential goldmine of information that can be extracted through machine learning (ML) techniques. The Intensive Care Unit (ICU) stands out as a focal point within hospitals and provides a rich source of data for informative analyses. This study examines the cardiac surgery ICU, where the vital topic of patient ventilation takes center stage. In other words, ventilator-supported breathing is a fundamental need within the ICU, and the limited availability of ventilators in hospitals has become a significant issue. A crucial consideration for healthcare professionals in the ICU is prioritizing patients who require ventilators immediately. To address this issue, we developed a prediction model using four ML and deep learning (DL) models-LDA, CatBoost, Artificial Neural Networks (ANN), and XGBoost-that are combined in an ensemble model. We utilized Simulated Annealing (SA) and Genetic Algorithm (GA) to tune the hyperparameters of the ML models constructing the ensemble. The results showed that our approach enhanced the sensitivity of the tuned ensemble model to 85.84%, which are better than the results of the ensemble model without hyperparameter tuning and those achieved using AutoML model. This significant improvement in model performance underscores the effectiveness of our hybrid approach in prioritizing the need for ventilators among ICU patients.

PMID:39928609 | DOI:10.1371/journal.pone.0311250

PLoS One. 2025 Feb 10;20(2):e0317396. doi: 10.1371/journal.pone.0317396. eCollection 2025.

ABSTRACT

In recent years, the challenge of imbalanced data has become increasingly prominent in machine learning, affecting the performance of classification algorithms. This study proposes a novel data-level oversampling method called Cluster-Based Reduced Noise SMOTE (CRN-SMOTE) to address this issue. CRN-SMOTE combines SMOTE for oversampling minority classes with a novel cluster-based noise reduction technique. In this cluster-based noise reduction approach, it is crucial that samples from each category form one or two clusters, a feature that conventional noise reduction methods do not achieve. The proposed method is evaluated on four imbalanced datasets (ILPD, QSAR, Blood, and Maternal Health Risk) using five metrics: Cohen's kappa, Matthew's correlation coefficient (MCC), F1-score, precision, and recall. Results demonstrate that CRN-SMOTE consistently outperformed the state-of-the-art Reduced Noise SMOTE (RN-SMOTE), SMOTE-Tomek Link, and SMOTE-ENN methods across all datasets, with particularly notable improvements observed in the QSAR and Maternal Health Risk datasets, indicating its effectiveness in enhancing imbalanced classification performance. Overall, the experimental findings indicate that CRN-SMOTE outperformed RN-SMOTE in 100% of the cases, achieving average improvements of 6.6% in Kappa, 4.01% in MCC, 1.87% in F1-score, 1.7% in precision, and 2.05% in recall, with setting SMOTE's neighbors' number to 5.

PMID:39928607 | DOI:10.1371/journal.pone.0317396

Crit Care Med. 2025 Feb 7. doi: 10.1097/CCM.0000000000006588. Online ahead of print.

ABSTRACT

OBJECTIVES: Length of stay (LOS) models support evaluating ICU care; however, current benchmarking models fail to consider differences in LOS between surviving and nonsurviving patients, which can lead to biased predictions toward the surviving population. We aim to develop a model addressing this as well as documentation bias to improve ICU benchmarking.

DESIGN: The Critical Care Outcomes Prediction Model (CCOPM) LOS uses patient characteristics, vitals, and laboratories during the first 24 hours of ICU admission to predict LOS in the hospital and ICU using a deep learning framework for modeling time to events with competing risk. Data was randomly divided into training, validation, and test (hold out) sets in a 2:1:1 ratio.

SETTING: Electronic ICU Research Institute database from participating tele-critical care programs.

PATIENTS: Six hundred sixty-nine thousand eight hundred seventy-six ICU admissions pertaining to 628,815 patients from 329 ICUs in 194 U.S. hospitals, from 2017 to 2019.

INTERVENTIONS: None.

MEASUREMENTS AND MAIN RESULTS: Model performance was assessed using the coefficient of determination (R2), concordance index, mean absolute error, and calibration. For individual stays in the test set, the ICU LOS model presented R2 = 0.29 and 0.23 for surviving and nonsurviving populations, respectively, at the individual level and R2 = 0.48 and 0.23 at the ICU level. Conversely, hospital LOS model presented R2 = 0.46 and 0.52 at the individual level and R2 = 0.71 and 0.64 at the ICU level. In the subset of the test set containing predictions from Acute Physiology and Chronic Health Evaluation (APACHE) IVb, R2 of ICU LOS for surviving and nonsurviving populations was, respectively, 0.30 and 0.23 for the CCOPM and 0.16 and zero for APACHE IVb. For hospital LOS, the values were R2 = 0.39 and 0.40 for the CCOPM and 0.27 and zero for APACHE IVb.

CONCLUSIONS: This novel LOS model represents a step forward in achieving more equitable benchmarking across diverse ICU settings with varying risk profiles.

PMID:39928543 | DOI:10.1097/CCM.0000000000006588

Med Biol Eng Comput. 2025 Feb 10. doi: 10.1007/s11517-025-03319-9. Online ahead of print.

ABSTRACT

Deformable registration between brain tumor images and brain atlas has been an important tool to facilitate pathological analysis. However, registration of images with tumors is challenging due to absent correspondences induced by the tumor. Furthermore, the tumor growth may displace the tissue, causing larger deformations than what is observed in healthy brains. Therefore, we propose a new reconstruction-driven cascade feature warping (RCFW) network for brain tumor images. We first introduce the symmetric-constrained feature reasoning (SFR) module which reconstructs the missed normal appearance within tumor regions, allowing a dense spatial correspondence between the reconstructed quasi-normal appearance and the atlas. The dilated multi-receptive feature fusion module is further introduced, which collects long-range features from different dimensions to facilitate tumor region reconstruction, especially for large tumor cases. Then, the reconstructed tumor images and atlas are jointly fed into the multi-stage feature warping module (MFW) to progressively predict spatial transformations. The method was performed on the Multimodal Brain Tumor Segmentation (BraTS) 2021 challenge database and compared with six existing methods. Experimental results showed that the proposed method effectively handles the problem of brain tumor image registration, which can maintain the smooth deformation of the tumor region while maximizing the image similarity of normal regions.

PMID:39928283 | DOI:10.1007/s11517-025-03319-9

Environ Monit Assess. 2025 Feb 10;197(3):258. doi: 10.1007/s10661-025-13722-2.

ABSTRACT

The threat of snakebites to public health, particularly in tropical and subtropical regions, requires effective mitigation strategies to avoid human-snake interactions. With the development of an IoT-based smart snake-trapping device, an innovative non-invasive solution for preventing snakebites is presented, autonomously capturing and identifying snakes. Using artificial intelligence (AI) and Internet of Things (IoT) technologies, the entire system is designed to improve the safety and efficiency of snake capture, both in rural and urban areas. A camera and sensors are installed in the device to detect heat and vibration signatures, mimicking the natural prey of snakes using tungsten wire and vibration motors to attract them into the trap. A real-time classification algorithm based on deep learning determines whether a snake is venomous or non-venomous as soon as the device detects it. This algorithm utilizes a transfer learning approach using a convolutional neural network (CNN) and has been trained using snake images, achieving an accuracy of 91.3%. As a result of this identification process, appropriate actions are taken, such as alerting authorities or releasing non-venomous snakes into the environment in a safe manner. Through the integration of IoT technology, users can receive real-time notifications and data regarding the trap via a smartphone application. The system's connectivity allows for timely intervention in case of venomous species, reducing snakebite risks. Additionally, the system provides information regarding snake movement patterns and species distribution, contributing to the study of broader ecological issues. An automated and efficient method of managing snakes could be implemented in snakebite-prone regions with the smart trapping device.

PMID:39928180 | DOI:10.1007/s10661-025-13722-2

Anal Chem. 2025 Feb 10. doi: 10.1021/acs.analchem.4c06604. Online ahead of print.

ABSTRACT

Electronic nose (E-nose) has been applied many times for exhale biomarker detection for lung cancer, which is a leading cause of cancer-related mortality worldwide. These noninvasive breath testing techniques can be used for the early diagnosis of lung cancer patients and help improve their five year survival. However, there are still many key challenges to be addressed, including accurately identifying the kind of volatile organic compounds (VOCs) biomarkers in human-exhaled breath and the concentrations of these VOCs, which may vary at different stages of lung cancer. Recent research has mainly focused on E-nose based on a metal oxide semiconductor sensor array with proposed single gas qualitative and quantitative algorithms, but there are few breakthroughs in the detection of multielement gaseous mixtures. This work proposes two hybrid deep-learning models that combine the Transformer and CNN algorithms for the identification of VOC types and the quantification of their concentrations. The classification accuracy of the qualitative model reached 99.35%, precision reached 99.31%, recall was 99.00%, and kappa was 98.93%, which are all higher than those of the comparison algorithms, like AlexNet, MobileNetV3, etc. The quantitative model achieved an average R2 of 0.999 and an average RMSE of only 0.109 on the mixed gases. Otherwise, the parameter count and FLOPs of only 0.7 and 50.28 M, respectively, of the model proposed in this work were much lower than those of the comparison models. The detailed experiments demonstrated the potential of our proposed models for screening patients with early stage lung cancer.

PMID:39928114 | DOI:10.1021/acs.analchem.4c06604

Ann Med. 2025 Dec;57(1):2461679. doi: 10.1080/07853890.2025.2461679. Epub 2025 Feb 10.

ABSTRACT

Gastric cancer (GC) occupies the first few places in the world among tumors in terms of incidence and mortality, causing serious harm to human health, and at the same time, its treatment greatly consumes the health care resources of all countries in the world. The diagnosis of GC is usually based on histopathologic examination, and it is very important to be able to detect and identify cancerous lesions at an early stage, but some endoscopists' lack of diagnostic experience and fatigue at work lead to a certain rate of under diagnosis. The rapid and striking development of Artificial intelligence (AI) has helped to enhance the ability to extract abnormal information from endoscopic images to some extent, and more and more researchers are applying AI technology to the diagnosis of GC. This initiative has not only improved the detection rate of early gastric cancer (EGC), but also significantly improved the survival rate of patients after treatment. This article reviews the results of various AI-assisted diagnoses of EGC in recent years, including the identification of EGC, the determination of differentiation type and invasion depth, and the identification of borders. Although AI has a better application prospect in the early diagnosis of ECG, there are still major challenges, and the prospects and limitations of AI application need to be further discussed.

PMID:39928093 | DOI:10.1080/07853890.2025.2461679

Sleep Breath. 2025 Feb 10;29(1):95. doi: 10.1007/s11325-025-03247-w.

ABSTRACT

PURPOSE: This exploratory study introduces a portable, wearable device using a single accelerometer to monitor twelve sleep positions. Targeted for home use, the device aims to assist patients with mild conditions such as gastroesophageal reflux disease (GERD) by tracking sleep postures, promoting healthier habits, and improving both reflux symptoms and sleep quality without requiring hospital-based monitoring.

METHODS: The study developed AnpoNet, a lightweight deep learning model combining 1D-CNN and LSTM, optimized with BN and Dropout. The 1D-CNN captures short-term movement features, while the LSTM identifies long-term temporal dependencies. Experiments were conducted on data from 15 participants performing twelve sleep positions, with each position recorded for one minute at a sampling frequency of 50 Hz. The model was evaluated using 5-Fold cross-validation and unseen participant data to assess generalization.

RESULTS: AnpoNet achieved a classification accuracy of 94.67% ± 0.80% and an F1-score of 92.94% ± 1.35%, outperforming baseline models. Accuracy was computed as the mean of accuracies obtained for three participants in the test set, averaged over five independent random seeds. This evaluation approach ensures robustness by accounting for variability in both individual participant performance and model initialization, underscoring its potential for real-world, home-based applications.

CONCLUSION: This study provides a foundation for a portable system enabling continuous, non-invasive sleep posture monitoring at home. By addressing the needs of GERD patients, the device holds promise for improving sleep quality and supporting positional therapy. Future research will focus on larger cohorts, extended monitoring durations, and user-friendly interfaces for broader adoption.

PMID:39928075 | DOI:10.1007/s11325-025-03247-w

Med Phys. 2025 Feb 10. doi: 10.1002/mp.17672. Online ahead of print.

ABSTRACT

BACKGROUND: Given the recent increased emphasis on multimodal neural networks to solve complex modeling tasks, the problem of outcome prediction for a course of treatment can be framed as fundamentally multimodal in nature. A patient's response to treatment will vary based on their specific anatomy and the proposed treatment plan-these factors are spatial and closely related. However, additional factors may also have importance, such as non-spatial descriptive clinical characteristics, which can be structured as tabular data. It is critical to provide models with as comprehensive of a patient representation as possible, but inputs with differing data structures are incompatible in raw form; traditional models that consider these inputs require feature engineering prior to modeling. In neural networks, feature engineering can be organically integrated into the model itself, under one governing optimization, rather than performed prescriptively beforehand. However, the native incompatibility of different data structures must be addressed. Methods to reconcile structural incompatibilities in multimodal model inputs are called data fusion. We present a novel joint early pre-spatial (JEPS) fusion technique and demonstrate that differences in fusion approach can produce significant model performance differences even when the data is identical.

PURPOSE: To present a novel pre-spatial fusion technique for volumetric neural networks and demonstrate its impact on model performance for pretreatment prediction of overall survival (OS).

METHODS: From a retrospective cohort of 531 head and neck patients treated at our clinic, we prepared an OS dataset of 222 data-complete cases at a 2-year post-treatment time threshold. Each patient's data included CT imaging, dose array, approved structure set, and a tabular summary of the patient's demographics and survey data. To establish single-modality baselines, we fit both a Cox Proportional Hazards model (CPH) and a dense neural network on only the tabular data, then we trained a 3D convolutional neural network (CNN) on only the volume data. Then, we trained five competing architectures for fusion of both modalities: two early fusion models, a late fusion model, a traditional joint fusion model, and the novel JEPS, where clinical data is merged into training upstream of most convolution operations. We used standardized 10-fold cross validation to directly compare the performance of all models on identical train/test splits of patients, using area under the receiver-operator curve (AUC) as the primary performance metric. We used a two-tailed Student t-test to assess the statistical significance (p-value threshold 0.05) of any observed performance differences.

RESULTS: The JEPS design scored the highest, achieving a mean AUC of 0.779 ± 0.080. The late fusion model and clinical-only CPH model scored second and third highest with 0.746 ± 0.066 and 0.720 ± 0.091 mean AUC, respectively. The performance differences between these three models were not statistically significant. All other comparison models scored significantly worse than the top performing JEPS model.

CONCLUSION: For our OS evaluation, our JEPS fusion architecture achieves better integration of inputs and significantly improves predictive performance over most common multimodal approaches. The JEPS fusion technique is easily applied to any volumetric CNN.

PMID:39928034 | DOI:10.1002/mp.17672

J Chem Inf Model. 2025 Feb 10. doi: 10.1021/acs.jcim.5c00006. Online ahead of print.

ABSTRACT

Antimicrobial peptides are a promising strategy to combat antimicrobial resistance. However, the experimental discovery of antimicrobial peptides is both time-consuming and laborious. In recent years, the development of computational technologies (especially deep learning) has provided new opportunities for antimicrobial peptide prediction. Various computational models have been proposed to predict antimicrobial peptide. In this review, we focus on deep learning models for antimicrobial peptide prediction. We first collected and summarized available data resources for antimicrobial peptides. Subsequently, we summarized existing deep learning models for antimicrobial peptides and discussed their limitations and challenges. This study aims to help computational biologists design better deep learning models for antimicrobial peptide prediction.

PMID:39927895 | DOI:10.1021/acs.jcim.5c00006