Kidney fibrosis is a key pathological feature in the progression of chronic kidney disease (CKD), traditionally diagnosed through invasive kidney biopsy. This study aimed to develop and validate a noninvasive, multi-center predictive model incorporating machine learning (ML) for assessing kidney fibrosis severity using biochemical markers.

This multi-center retrospective study included 598 patients with kidney fibrosis from four hospitals. A training cohort of 360 patients from Shanghai Tongji Hospital was used to develop a predictive nomogram and ML model, with fibrosis severity classified as mild or moderate-to-severe based on Banff scores. Logistic regression identified key predictors, which were incorporated into a nomogram and ML model. An external validation cohort of 238 patients from three additional hospitals was used for model evaluation.

Serum creatinine (Scr), estimated glomerular filtration rate (eGFR), parathyroid hormone (PTH), brain natriuretic peptide (BNP), and sex were identified as independent predictors of kidney fibrosis severity. The nomogram demonstrated superior discriminative ability in the training cohort (AUC: 0.89, 95% CI: 0.85-0.92) compared to eGFR (AUC: 0.83, 95% CI: 0.78-0.87) and Scr (AUC: 0.87, 95% CI: 0.83-0.91). Among ML models, the Random Forest (RF) model achieved the highest AUC (0.98). In external validation, the nomogram and RF models maintained robust performance with AUCs of 0.86 and 0.79, respectively.

This study presents a validated, noninvasive, multi-center Scr-based machine learning model for assessing kidney fibrosis severity in CKD. The integration of a clinical nomogram and ML approach offers a novel, practical alternative to biopsy for dynamic fibrosis evaluation.

Using machine learning to construct a prediction model for the risk of diabetes kidney disease (DKD) in the American diabetes population and evaluate its effect.

First, a dataset of five cycles from 2009 to 2018 was obtained from the National Health and Nutrition Examination Survey (NHANES) database, weighted and then standardized (with the study population in the United States), and the data were processed and randomly grouped using R software. Next, variable selection for DKD patients was conducted using Lasso regression, two-way stepwise iterative regression, and random forest methods. A nomogram model was constructed for the risk prediction of DKD. Finally, the predictive performance, predictive value, calibration, and clinical effectiveness of the model were evaluated through the receipt of ROC curves, Brier score values, calibration curves (CC), and decision curves (DCA). In addition, we will visualize it.

A total of 4371 participants were selected and included in this study. Patients were randomly divided into a training set (n = 3066 people) and a validation set (n = 1305 people) in a 7:3 ratio. Using machine learning algorithms and drawing Venn diagrams, five variables significantly correlated with DKD risk were identified, namely Age, Hba1c, ALB, Scr, and TP. The area under the ROC curve (AUC) of the training set evaluation index for this model is 0.735, the net benefit rate of DCA is 2%-90%, and the Brier score is 0.172. The area under the ROC curve of the validation set (AUC) is 0.717, and the DCA curve shows a good net benefit rate. The Brier score is 0.177, and the calibration curve results of the validation set and training set are almost consistent.

The DKD risk nomogram model constructed in this study has good predictive performance, which helps to evaluate the risk of DKD as early as possible in clinical practice and formulate relevant intervention and treatment measures. The visual result can be used by doctors or individuals to estimate the probability of DKD risk, as a reference to help make better treatment decisions.

Prostate cancer (PCa) is a leading cause of cancer-related mortality in men, with Gleason grading critical for prognosis and treatment decisions. Machine learning (ML) models offer potential for automated grading but are limited by dataset biases, staining variability, and data scarcity, reducing their generalizability. This study employs generative adversarial networks (GANs) to generate high-quality synthetic histopathological images to address these challenges. A conditional GAN (dcGAN) was developed and validated using expert pathologist review and Spatial Heterogeneous Recurrence Quantification Analysis (SHRQA), achieving 80% diagnostic quality approval. A convolutional neural network (EfficientNet) was trained on original and synthetic images and validated across TCGA, PANDA Challenge, and MAST trial datasets. Integrating synthetic images improved classification accuracy for Gleason 3 (26%, p = 0.0010), Gleason 4 (15%, p = 0.0274), and Gleason 5 (32%, p < 0.0001), with sensitivity and specificity reaching 81% and 92%, respectively. This study demonstrates that synthetic data significantly enhances ML-based Gleason grading accuracy and improves reproducibility, providing a scalable AI-driven solution for precision oncology.

Selecting the most suitable Automated Machine Learning (AutoML) tool is pivotal for achieving optimal performance in diverse classification tasks, including binary, multiclass, and multilabel scenarios. The wide range of frameworks with distinct features and capabilities complicates this decision, necessitating a systematic evaluation. This study benchmarks sixteen AutoML tools, including AutoGluon, AutoSklearn, TPOT, PyCaret, and Lightwood, across all three classification types using 21 real-world datasets. Unlike prior studies focusing on a subset of classification tasks or a limited number of tools, we provide a unified evaluation of sixteen frameworks, incorporating feature-based comparisons, time-constrained experiments, and multi-tier statistical validation. We also compared our findings with four representative prior benchmarks to contextualize our results within the existing literature. A key contribution of our study is the in-depth assessment of multilabel classification, exploring both native and label-powerset representations and revealing that several tools lack robust multilabel capabilities. Our findings demonstrate that AutoSklearn excels in predictive performance for binary and multiclass settings, albeit at longer training times, while Lightwood and AutoKeras offer faster training at the cost of predictive performance on complex datasets. AutoGluon emerges as the best overall solution, balancing predictive accuracy with computational efficiency. Our statistical analysis-at per-dataset, across-datasets, and all-datasets levels-confirms significant performance differences among tools, highlighting accuracy-speed trade-offs in AutoML. These insights underscore the importance of aligning tool selection with specific problem characteristics and resource constraints. The open-source code and reproducible experimental protocols further ensure the study's value as a robust resource for researchers and practitioners.

In the age of big data, privacy, particularly medical data privacy, is becoming increasingly important. Differential privacy (DP) has emerged as a key method for safeguarding privacy during data analysis and publishing. Cancer identification and classification play a vital role in early detection and treatment. This paper introduces a novel algorithm, DPFCM_GK, which combines differential privacy with fuzzy c-means (FCM) clustering using a Gaussian kernel function. The algorithm enhances cancer detection while ensuring data privacy. Three publicly available lung cancer datasets, along with a dataset from our hospital, are used to test and demonstrate the effectiveness of DPFCM_GK. The experimental results show that DPFCM_GK achieves high clustering accuracy and enhanced privacy as the privacy budget (ε) increases. For the UCIML, NLST, and NSCLC datasets, it reaches optimal results at lower ε (1.52, 1.24, and 2.32) compared to DPFCM. In the lung cancer dataset, DPFCM_GK outperforms DPFCM within, 0.05 ≤ ε ≤ 2.5, with significant differences (χ2 = 4.54 ∼ 29.12; P < 0.05), and both methods converge to an accuracy of 94.5% as ε increases. Although differential privacy initially increases iteration counts, DPFCM_GK demonstrates faster convergence and fewer iterations compared to DPFCM, with significant reductions (T= 23.08, 43.47, and 48.93; P<0.05). For the UCIML dataset, DPFCM_GK significantly reduces runtime compared to other models (DPFCM, LDP-SGD, LDP-Fed, LDP-FedSGD, MGM-DPL, LDP-FL) under the same privacy budget. The runtime reduction is statistically significant with T-values of (T = 21.08, 316.24, 102.35, 222.37, 162.23, 159.25; P < 0.05). DPFCM_GK still maintains excellent time efficiency when applied to the NLST and NSCLC datasets(P < 0.05). For the LLCS dataset, For the LLCS dataset, the DPFCM_GK demonstrates significant improvement as the privacy budget increases, especially in low-budget scenarios, where the performance gap is most pronounced (T=4.20, 8.44, 10.92, 3.95, 7.16, 8.51, P < 0.05). These results confirm DPFCM_GK as a practical solution for medical data analysis, balancing accuracy, privacy, and efficiency.

To study the differences in the urine metabolome between pediatric patients with severe Mycoplasma pneumoniae pneumonia (SMPP) and those with general Mycoplasma pneumoniae pneumonia (GMPP) via non-targeted metabolomics method, and potential biomarkers were explored through machine learning (ML) algorithms. The urine metabonomics data of 48 children with SMPP and 85 children with GMPP were collected via high performance liquid chromatography‒mass spectrometry (HPLC-MS/MS). The differential metabolites between the two groups were obtained via principal component analysis (PCA) and partial least squares-discriminant analysis (PLS-DA), and the significant metabolic pathways were screened via enrichment analysis. Potential biomarkers were identified using the random forest algorithm, and their relationships with clinical indicators were subsequently analyzed. A total of 136 significantly differential metabolites were identified in the urine samples from SMPP and GMPP. Of these, 68 metabolites were upregulated, and 68 were downregulated, predominantly belonging to the amino acid group. A total of 6 differential metabolic pathways were enriched, including Galactose metabolism, Pantothenate and CoA biosynthesis, Cysteine and methionine metabolism, Biotin metabolism, Glycine, serine and threonine metabolism, Arginine biosynthesis. Three significant potential biomarkers were identified through machine learning: 3-Hydroxyanthranilic acid (3-HAA), L-Kynurenine, and 16(R)-HETE. The area under the receiver operating characteristic curve (AUC) for this three-metabolite panel was 0.9142. There are great differences in the urine metabolome between SMPP and GMPP children, with multiple metabolic pathways being abnormally expressed. Three metabolites have been identified as potential biomarkers for the early detection of SMPP.

Acute respiratory distress syndrome (ARDS) is a critical condition commonly encountered in the intensive care unit (ICU), characterized by a high incidence and substantial mortality rate. Early detection and accurate prediction of ARDS can significantly improve patient outcomes. While machine learning (ML) models are increasingly being used for ARDS prediction, there is a lack of consensus on the most effective model or methodology. This study is the first to systematically evaluate the performance of ARDS prediction models based on multiple quantitative data sources. We compare the effectiveness of ML models via a meta-analysis, revealing factors affecting performance and suggesting strategies to enhance generalization and prediction accuracy.

This study aims to evaluate the performance of existing ARDS prediction models through a systematic review and meta-analysis, using metrics such as area under the receiver operating characteristic curve, sensitivity, specificity, and other relevant indicators. The findings will provide evidence-based insights to support the development of more accurate and effective ARDS prediction tools.

We performed a search across 6 electronic databases for studies developing ML predictive models for ARDS, with a cutoff date of December 29, 2024. The risk of bias in these models was evaluated using the Prediction model Risk of Bias Assessment Tool. Meta-analyses and investigations into heterogeneity were carried out using Meta-DiSc software (version 1.4), developed by the Ramón y Cajal Hospital's Clinical Biostatistics team in Madrid, Spain. Furthermore, sensitivity, subgroup, and meta-regression analyses were used to explore the sources of heterogeneity more comprehensively.

ML models achieved a pooled area under the receiver operating characteristic curve of 0.7407 for ARDS. The additional metrics were as follows: sensitivity was 0.67 (95% CI 0.66-0.67; P<.001; I²=97.1%), specificity was 0.68 (95% CI 0.67-0.68; P<.001; I²=98.5%), the diagnostic odds ratio was 6.26 (95% CI 4.93-7.94; P<.001; I²=95.3%), the positive likelihood ratio was 2.80 (95% CI 2.46-3.19; P<.001; I²=97.3%), and the negative likelihood ratio was 0.51 (95% CI 0.46-0.57; P<.001; I²=93.6%).

This study evaluates prediction models constructed using various ML algorithms, with results showing that ML demonstrates high performance in ARDS prediction. However, many of the existing models still have limitations. During model development, it is essential to focus on model quality, including reducing bias risk, designing appropriate sample sizes, conducting external validation, and ensuring model interpretability. Additionally, challenges such as physician trust and the need for prospective validation must also be addressed. Future research should standardize model development, optimize model performance, and explore how to better integrate predictive models into clinical practice to improve ARDS diagnosis and risk stratification.

PROSPERO CRD42024529403; https://www.crd.york.ac.uk/PROSPERO/view/CRD42024529403.

Heart failure (HF) after acute myocardial infarction (AMI) is a leading cause of mortality and morbidity worldwide. Accurate prediction and early identification of HF severity are crucial for initiating preventive measures and optimizing treatment strategies. This study aimed to develop an interpretable artificial intelligence (AI) model for HF severity prediction using multidimensional clinical data.

This study included data from 1574 AMI patients, including medical history, clinical features, physiological parameters, laboratory test, coronary angiography and echocardiography results. Both deep learning (TabNet, Multi-Layer Perceptron) and machine learning (Random Forest, XGboost) models were employed in constructing model. Additionally, the Shapley Additive Explanation (SHAP) method was used to elucidate clinical factors importance and enhance model interpretability. A web platform ( https://prediction-killip-gby.streamlit.app/ ) was also developed to facilitate clinical application.

Among the models, TabNet demonstrated the best performance, achieving an AUROC of 0.827 for KILLIP four-class classification and 0.831 for KILLIP binary classification. Key clinical factors such as GRACE score, NT-pro BNP, and TIMI score were highly correlated with KILLIP classification, aligning with established clinical knowledge.

By leveraging easily accessible multidimensional data, this model enables accurate early prediction and personalized diagnosis of HF risk and severity following AMI. It supports early clinical intervention and improves patient outcomes, offering significant clinical application value.

Not applicable.

Hyperspectral imaging (HSI) has shown significant diagnostic potential for both intra- and postoperative perfusion assessment. The purpose of this study was to combine machine learning and neural networks with HSI to develop a method for detecting flap malperfusion after microsurgical tissue reconstruction. Data records were analysed to assess the occurrence of flap loss after microsurgical procedures. A total of 59 free flaps were recorded, ten of which demonstrated postoperative malperfusion, leading to necrosis. Several supervised classification algorithms were evaluated to differentiate impaired perfusion from healthy tissue via HSI recordings. The best flap classification performance was observed using a convolutional neural network using HSI based perfusion parameters within 72 h after surgery, with an area under the curve of 0.82 ± 0.05, a sensitivity of 70% ± 33%, a specificity of 76% ± 26%, and an F1 score of 68% ± 28%. HSI combined with artificial intelligence approaches in diagnostic tools could significantly improve the detection of postoperative malperfusion and potentially increase flap salvage rates.

To summarise the currently developed risk prediction models for medication adherence in patients with chronic diseases and evaluate their performance and applicability.

Ensuring medication adherence is crucial in effectively managing chronic diseases. Although numerous studies have endeavoured to construct risk prediction models for predicting medication adherence in patients with chronic illnesses, the reliability and practicality of these models remain uncertain.

Systematic review.

We conducted searches on PubMed, Web of Science, Cochrane, CINAHL, Embase and Medline from inception until 16 July 2023. Two authors independently screened risk prediction models for medication adherence that met the predefined inclusion criteria. The Prediction Model Risk of Bias Assessment Tool (PROBAST) was employed to evaluate both the risk of bias and clinical applicability of the included studies. This systematic review adhered to the 2020 PRISMA checklist.

The study included a total of 11 risk prediction models from 11 studies. Medication regimen and age were the most common predictors. The use of PROBAST revealed that some essential methodological details were not thoroughly reported in these models. Due to limitations in methodology, all models were rated as having a high-risk for bias.

According to PROBAST, the current models for predicting medication adherence in patients with chronic diseases exhibit a high risk of bias. Future research should prioritise enhancing the methodological quality of model development and conducting external validations on existing models.

Based on the review findings, recommendations have been provided to refine the construction methodology of prediction models with an aim of identifying high-risk individuals and key factors associated with low medication adherence in chronic diseases.

This systematic review was conducted without patient or public participation.

Chronic obstructive pulmonary disease (COPD) and lung cancer, frequently comorbid conditions intricately linked through smoking, represent significant global health challenges. COPD is a common comorbidity in nonsmall cell lung cancer (NSCLC) patients and has been shown to negatively impact prognosis. However, the molecular mechanisms underlying the interplay between COPD and lung cancer remain unclear. This study aims to identify differentially expressed genes (DEGs) associated with COPD-related lung cancer and, using various machine learning (ML) algorithms, uncover potential biomarkers for prognosis. We analyzed RNA sequencing data from 41 lung cancer patients (with and without COPD) and identified 61 DEGs, all of which were upregulated in solitary lung cancer compared to COPD-associated cases. Functional enrichment analysis revealed that these genes are involved in biological processes such as granulocyte chemotaxis and smooth muscle contraction and molecular functions including neuropeptide receptor binding. Three ML methods-support vector machine recursive feature elimination (SVM-RFE), least absolute shrinkage and selection operator (LASSO), and random forest-were applied to prioritize key biomarkers. Three genes, GAP43, NMU, and TEX29, were consistently selected across all methods. Further analysis demonstrated significant correlations between these genes and immune cell infiltration, with notable differences in immune cell composition observed in COPD-associated lung cancer. High expression levels of GAP43, NMU, and TEX29 were associated with poor survival outcomes in lung cancer patients, as validated through survival analysis of TCGA database data. Our findings suggest that these genes may serve as diagnostic and prognostic biomarkers for COPD-related lung cancer, thereby providing insights into potential therapeutic targets. Further studies with larger cohorts are required to validate these results and elucidate the underlying molecular mechanisms.

Lung adenocarcinoma (LUAD) is the most frequently diagnosed subtype of non-small cell lung cancer (NSCLC). Notably, prognosis can vary significantly among LUAD patients with different tumor subtypes. The advent of radiomics and machine learning (ML) technologies enables the development of non-invasive pathology predictive models. We attempted to develop computed tomography (CT) radiomics-based diagnostic models, enhanced by ML, to predict LUAD malignancy grade and guide surgical strategies.

In this retrospective analysis, a total of 168 surgical patients with histology-confirmed LUAD were divided into low-risk group (n=93) and intermediate-to-high-risk group (n=75) based on postoperative pathology. The region of interest (ROI) was delineated on the preoperative CT images for all patients, followed by the extraction of radiomic features. Patients were randomly allocated to a training set (n=117) and a testing set (n=51) in a 7:3 ratio. Within the training set, clinical-radiological model (CM) and radiomics model (RM) were developed utilizing patients' clinical characteristics, radiological semantic features, and radiomic features, along with the calculation of Rad scores. After the Rad scores were combined with independent risk factors among clinical-radiological features, logistic regression (LR), decision tree (DT), random forest (RF), extreme gradient boosting (XGBoost), support vector machine (SVM), K-nearest neighbors (KNN), and naïve Bayes model (NBM) were employed to create different comprehensive models (COMs). The optimal model was identified based on the receiver operating characteristic (ROC) curves and the DeLong test. Finally, Shapley additive explanations (SHAP) were utilized to visualize the predictive processes of the models.

Among the 168 patients enrolled, there were 50 males (29.76%) aged 56 (49.25, 67.00) years and 118 females (70.24%) aged 56.5 (42.00, 64.00) years; Diameter (P<0.001), and consolidation-to-tumor ratio (CTR) ≥0.5 (P=0.002) were identified as independent risk factors for the malignancy degree of LUAD during CM creation. The CM had an area under the ROC curve (AUC) of 0.909 [95% confidence interval (CI): 0.856-0.962] in the training set and 0.920 (95% CI: 0.846-0.994) in the testing set. The RM, comprising seven radiomic features, achieved an AUC of 0.961 (95% CI: 0.926-0.996) in the training set and 0.957 (95% CI: 0.905-1.000) in the testing set. Among models created using various ML algorithms, the XGBoost model was identified as the optimal model. SHAP visualization revealed the model prediction processes and the values of different features.

We constructed and validated a robust, integrative model leveraging ML and CT radiomics, which amalgamates radiomics, clinical, and radiological attributes to precisely identify LUADs with elevated postoperative pathological grades. This enables doctors to formulate different surgical plans according to the pathology of the patients' tumors before the operation.

Deep vein thrombosis (DVT) is a prevalent peripheral vascular disease. The intricate and multifaceted nature of the associated mechanisms hinders a comprehensive understanding of disease-relevant targets. This study aimed to identify and examine the most distinctive genes linked to DVT.

In this study, the bulk RNA sequencing (bulk RNA-seq) analysis was conducted on whole blood samples from 11 DVT patients and six control groups. Topology analysis was performed using seven protein-protein interaction (PPI) network algorithms. The combination of weighted correlation network analysis (WGCNA) and clinical prediction models was employed to validate hub DEGs. Furthermore, single-cell RNA sequencing (scRNA-seq) was performed on peripheral blood samples from 3 DVT patients and three control groups to probe the cellular localization of target genes. Based on the same methodology as the internal test set, 12 DVT patients and six control groups were collected to construct an external test set and validated using machine learning (ML) algorithms and immunofluorescence (IF). Concurrently, the examination of the pathways in disparate cell populations was conducted on the basis of the CellChat pathway.

A total of 193 DEGs were identified in the internal test set. Additionally, a total of eight highly characteristic genes (including TLR1, TLR7, TLR8, CXCR4, DDX58, TNFSF10, FCGR1A and CD36) were identified by the PPI network algorithm. In accordance with the WGCNA model, the aforementioned genes were all situated within the blue core module, exhibiting a correlation coefficient of 0.84. The model demonstrated notable disparities in TLR8 (P = 0.018, AUC = 0.847), CXCR4 (P = 0.00088, AUC = 1.000), TNFSF10 (P = 0.00075, AUC = 0.958), and FCGR1A (P = 0.00022, AUC = 0.986). Furthermore, scRNA-seq demonstrated that B cells, T cells and monocytes play an active role in DVT. In the external validation set, CXCR4 was validated as a potential target by the ML algorithm and IF. In the context of the CellChat pathway, it indicated that MIF - (CD74 + CXCR4) plays a potential role.

The findings of this study indicate that CXCR4 may serve as a potential genetic marker for DVT, with MIF - (CD74 + CXCR4) potentially implicated in the regulatory mechanisms underlying DVT.

Hydrogen storage is a crucial technology for ensuring a sustainable energy transition. Underground Hydrogen Storage (UHS) in depleted hydrocarbon reservoirs, aquifers, and salt caverns provides a viable large-scale solution. However, hydrogen dispersion in cushion gases such as nitrogen (N2), methane (CH4), and carbon dioxide (CO2) lead to contamination, reduced purity, and increased purification costs. Existing experimental and numerical methods for predicting hydrogen dispersion coefficients (KL) are often limited by high costs, lengthy processing times, and insufficient accuracy in dynamic reservoir conditions. This study addresses these challenges by integrating experimental data with advanced machine learning (ML) techniques to model hydrogen dispersion. Various ML models-including Random Forest (RF), Least Squares Boosting (LSBoost), Bayesian Regression, Linear Regression (LR), Artificial Neural Networks (ANNs), and Support Vector Machines (SVMs)-were employed to quantify KL as a function of pressure (P) and displacement velocity (Um). Among these methods, RF outperformed the others, achieving an R2 of 0.9965 for test data and 0.9999 for training data, with RMSE values of 0.023 and 0.001, respectively. The findings highlight the potential of ML-driven approaches in optimizing UHS operations by enhancing predictive accuracy, reducing computational costs, and mitigating hydrogen contamination risks.

Bladder cancer is one of the top ten cancers globally, with its incidence steadily rising in China. Early detection and prognosis risk assessment play a crucial role in guiding subsequent treatment decisions for bladder cancer. However, traditional diagnostic methods such as bladder endoscopy, imaging, or pathology examinations heavily rely on the clinical expertise and experience of clinicians, exhibiting subjectivity and poor reproducibility.

With the rise of artificial intelligence, novel approaches, particularly those employing deep learning technology, have shown significant advancements in clinical tasks related to bladder cancer, including tumor detection, molecular subtyping identification, tumor staging and grading, prognosis prediction, and recurrence assessment.

Artificial intelligence, with its robust data mining capabilities, enhances diagnostic efficiency and reproducibility when assisting clinicians in decision-making, thereby reducing the risks of misdiagnosis and underdiagnosis. This not only helps alleviate the current challenges of talent shortages and uneven distribution of medical resources but also fosters the development of precision medicine.

This study provides a comprehensive review of the latest research advances and prospects of artificial intelligence technology in the precise diagnosis and treatment of bladder cancer.

Atherosclerosis is the major cause of cardiovascular diseases worldwide, and AIDS linked with chronic inflammation and immune activation, increases atherosclerosis risk. The application of bioinformatics and machine learning to identify hub genes for atherosclerosis and AIDS has yet to be reported. Thus, this study aims to identify the hub genes for atherosclerosis and AIDS. Gene expression profiles were downloaded from the Gene Expression Omnibus database. The Robust Multichip Average was performed for data preprocessing, and the limma package was used for screening differentially expressed genes. Enrichment analysis employed GO and KEGG, protein-protein interaction network was constructed. Hub genes were filtered using topological and machine learning algorithms and validated in external cohorts. Then immune infiltration and correlation analysis of hub genes were constructed. Nomogram, receiver operating curve, and single-sample gene set enrichment analysis were applied to evaluate hub genes. This study identified 48 intersecting genes. Enrichment analyses indicated that these genes are significantly enriched in viral response, inflammatory response, and cytokine signaling pathways. CCR5 and OAS1 were identified as common hub genes in atherosclerosis and AIDS for the first time, highlighting their roles in antiviral immunity, inflammation and immune infiltration. These findings contributed to understanding the shared pathogenesis of Atherosclerosis and AIDS and provided possible potential therapeutic targets for immunomodulatory therapy.

Compare the image quality of image reconstructed using deep learning-based image reconstruction (DLIR) and iterative reconstruction algorithms for head and neck dual-energy CT angiography (DECTA).

This prospective study comprised fifty-eight patients with head and neck DECTA. Images reconstructed by four algorithms (120-kVp-like with ASIR-V40%, 50 keV with ASIR-V40%, 50 keV with DLIR-M, 50 keV with DLIR-H) were compared. CT attenuation, image noise, signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) were all calculated. Edge rise distance (ERD) and edge-rise slope (ERS) were measured on the right common carotid artery to reflect spatial resolution. Quantitative data are summarized as the mean ± SD. The subjective image quality scores using a 5-point Likert scale were obtained for the following: overall image quality, edge sharpness of vessels, image noise, and artifacts.

The CT attenuation of all vessels in the 120kVp-like images were lower than the 3 sets of 50 keV images with significant difference (all P < 0.05). In the 50 keV images, both sternocleidomastoid muscle (SCM) and white matter (WM) had a minimum noise in DLIR-H group, and a maximum in ASIR-V40% group with significant difference (all P < 0.001). SNR and CNR in 50 keV images of all vessels had the same results: highest in DLIR-H group and lowest in ASIR-V40% group with significant differences (all P < 0.05). The mean value of ERD showed no significant difference among the four groups (P = 0.082). While the 120kVp-like images had the lowest ERS, which showed statistically significant difference with the other groups (all P < 0.001). In terms of overall image quality, sharpness, and artifacts, the scores of DLIR-M and DLIR-H at 50 keV were not statistically different (all P > 0.05), and were higher than ASIR-V40% at 50 keV images (all P < 0.05), and higher than ASIR-V40% at 120 kVp-like (all P < 0.05). The scores of DLIR-H at 50 keV were highest in terms of noise and average scores.

DLIR is a potential solution for DECTA reconstruction since it can greatly reduce image noise, improving image quality of head and neck DECTA at 50 keV It is worth considering adopting in routine head and neck CTA applications.

The landscape of diagnostic testing is undergoing a significant transformation, driven by the integration of artificial intelligence (AI) and machine learning (ML) into decentralized, rapid, and accessible sensor platforms for point-of-care testing (POCT). The COVID-19 pandemic has accelerated the shift from centralized laboratory testing but also catalyzed the development of next-generation POCT platforms that leverage ML to enhance the accuracy, sensitivity, and overall efficiency of point-of-care sensors. This Perspective explores how ML is being embedded into various POCT modalities, including lateral flow assays, vertical flow assays, nucleic acid amplification tests, and imaging-based sensors, illustrating their impact through different applications. We also discuss several challenges, such as regulatory hurdles, reliability, and privacy concerns, that must be overcome for the widespread adoption of ML-enhanced POCT in clinical settings and provide a comprehensive overview of the current state of ML-driven POCT technologies, highlighting their potential impact in the future of healthcare.

As artificial intelligence (AI) tools become increasingly mainstream, they can potentially transform neurology clinical practice by improving patient care and reducing clinician workload. However, with these promises also come perils, and neurologists must understand AI as it becomes integrated into health care. This article presents a brief background on AI and explores some of the potential applications in health care and neurology clinical practice with a focus on improving diagnostic testing, documentation, and clinical workflows and highlighting opportunities to address long-standing human biases and challenges and potential mitigation strategies.

Coronary artery plaque rupture (PR) is closely associated with immune-inflammatory responses. The systemic inflammatory index (SII) and the systemic inflammatory response index (SIRI) have shown potential in predicting the occurrence of PR.

This study aims to establish a machine learning (ML) model that integrates baseline patient characteristics, SII, and SIRI to predict PR. The goal is to identify high-risk PR patients before intravascular imaging examinations.

We included 337 patients with acute coronary syndrome who underwent emergency percutaneous coronary intervention and coronary optical coherence tomography (OCT) at the Affiliated Hospital of Zunyi Medical University, China, from May 2023 to October 2023. PR was determined by OCT images. Through manual feature selection, nine features, including SII and SIRI, were included, and an ML model was built using the XGBoost algorithm. Model performance was evaluated using receiver operating characteristic curves and calibration curves. SHAP values were used to assess the contribution of each feature to the model.

The ML model demonstrated a higher area under the curve value (AUC = 0.81) compared to using SII or SIRI alone for prediction. The ML model also showed good calibration. SHAP values revealed that the top three features in the ML model were SII, LDL-C, and SIRI.

The immuno-inflammatory index, which integrates comprehensive clinical characteristics, can predict the occurrence of PR. However, large-scale, multicenter studies are needed to confirm the generalizability of the predictive model.

Predicting vancomycin exposure is essential for optimizing dosing regimens in sepsis patients. While population pharmacokinetic (PPK) models are commonly used, their performance is limited. Machine learning (ML) models offer advantages over PPK models, but it remains unclear which model-PPK, Bayesian, ML, or hybrid PPK-ML-is best for predicting vancomycin exposure across different clinical scenarios in sepsis patients. This study compares the performance of these models in predicting the 24 hour area under the blood concentration curve (AUC24) to support precision dosing in sepsis care. Data from sepsis patients treated with intravenous vancomycin were sourced from the MIMIC-IV database. The data set was split into training and testing sets, and four models-PPK, Bayesian, ML, and hybrid-were developed. In the testing set, AUC24 was predicted using all models, and performance was evaluated using mean absolute error, mean squared error, root mean squared error, mean absolute percentage error (MAPE), and R². A total of 4,059 patients were included. In the absence of vancomycin concentration data, the hybrid model outperformed both PPK and Bayesian models, with MAPE improvements of 58% and 17%, respectively. When vancomycin concentration data were available, the Bayesian model demonstrated the best performance (MAPE: 13.37% vs 68.17%, 34.17%, and 28.52% for PPK, Random Forest, and hybrid models). The hybrid model is recommended to predict AUC24 when concentration data were unavailable, while the Bayesian model should be used when concentrations were available, offering robust strategies for precise vancomycin dosing in sepsis patients.

This study evaluates and compares the performance of four models-PPK, Bayesian, ML, and hybrid PPK-ML-in predicting vancomycin exposure (AUC24) in sepsis patients using real-world data from the MIMIC-IV database. These results underscore the importance of selecting appropriate models based on the availability of concentration data, providing valuable guidance for precision dosing strategies in sepsis care. This work contributes to advancing personalized vancomycin therapy, optimizing dosing regimens, and improving clinical outcomes in sepsis patients.

A prediction model is hereby developed to identify poor treatment outcomes during the intensive phase in patients with initial treatment of pulmonary tuberculosis (TB).

The data of inpatients with pulmonary TB were collected from a tertiary hospital located in Southeastern China from July 2019 to December 2023. The included patients were divided into the modeling group and the validation group. The outcome indicator was based on a comparison of pulmonary CT findings before and after the two-month intensive phase of anti-TB treatment. In the modeling group, the independent risk factors of pulmonary TB patients were obtained through logistic regression analysis and then a prediction model was established. The discriminative ability (the area under the curve of the receiver operating characteristic, AUC), its calibration (GiViTI calibration chart), and its clinical applicability (decision curve analysis, DCA) were respectively evaluated. In addition, the prediction effectiveness was compared with that of the machine learning model.

A total of 1,625 patients were included in this study, and 343 patients had poor treatment outcomes in the intensive phase of anti-TB treatment. Logistic regression analysis identified several independent risk factors for poor treatment outcomes, including diabetes, cavities in the lungs, tracheobronchial TB, increased C-reactive protein, and decreased hemoglobin. The AUC values were 0.815 for the modeling group and 0.851 for the validation group. In the machine learning models, the AUC values of the random forest model and the integrated model were 0.821 and 0.835, respectively.

The prediction model established in this study presents good performance in predicting poor treatment outcomes during the intensive phase in patients with pulmonary TB.

Hydrocephalus, particularly congenital hydrocephalus in infants, remains underexplored in deep learning research. While deep learning has been widely applied to medical image analysis, few studies have specifically addressed the automated classification of hydrocephalus. This study proposes a convolutional neural network (CNN) model based on the VGG16 architecture to detect hydrocephalus in infant head CT images. The model integrates an automated method for ventricular volume extraction, applying windowing, histogram equalization, and thresholding techniques to segment the ventricles from surrounding brain structures. Morphological operations refine the segmentation and contours are extracted for visualization and volume measurement. The dataset consists of 105 head CT scans, each with 60 slices covering the ventricular volume, resulting in 6300 slices. Manual segmentation by three trained radiologists served as the reference standard. The automated method showed a high correlation with manual measurements, with R2 values ranging from 0.94 to 0.99. The mean absolute percentage error (MAPE) ranged 3.99 to 11.13%, while the root mean square error (RRMSE) from 4.56 to 13.74%. To improve model robustness, the dataset was preprocessed, normalized, and augmented with rotation, shifting, zooming, and flipping. The VGG16-based CNN used pre-trained convolutional layers with additional fully connected layers for classification, predicting hydrocephalus or normal labels. Performance evaluation using a multi-split strategy (15 independent splits) achieved a mean accuracy of 90.4% ± 1.2%. This study presents an automated approach for ventricular volume extraction and hydrocephalus detection, offering a promising tool for clinical and research applications with high accuracy and reduced observer bias.

Machine learning (ML), a major branch of artificial intelligence, has not only demonstrated the potential to significantly improve numerous sectors of healthcare but has also made significant contributions to the field of solid organ transplantation. ML provides revolutionary opportunities in areas such as donor-recipient matching, post-transplant monitoring, and patient care by automatically analyzing large amounts of data, identifying patterns, and forecasting outcomes.

To conduct a comprehensive bibliometric analysis of publications on the use of ML in transplantation to understand current research trends and their implications.

On July 18, a thorough search strategy was used with the Web of Science database. ML and transplantation-related keywords were utilized. With the aid of the VOS viewer application, the identified articles were subjected to bibliometric variable analysis in order to determine publication counts, citation counts, contributing countries, and institutions, among other factors.

Of the 529 articles that were first identified, 427 were deemed relevant for bibliometric analysis. A surge in publications was observed over the last four years, especially after 2018, signifying growing interest in this area. With 209 publications, the United States emerged as the top contributor. Notably, the "Journal of Heart and Lung Transplantation" and the "American Journal of Transplantation" emerged as the leading journals, publishing the highest number of relevant articles. Frequent keyword searches revealed that patient survival, mortality, outcomes, allocation, and risk assessment were significant themes of focus.

The growing body of pertinent publications highlights ML's growing presence in the field of solid organ transplantation. This bibliometric analysis highlights the growing importance of ML in transplant research and highlights its exciting potential to change medical practices and enhance patient outcomes. Encouraging collaboration between significant contributors can potentially fast-track advancements in this interdisciplinary domain.

Obstructive sleep apnea (OSA) is a prevalent sleep disorder with a high rate of undiagnosed patients, primarily due to the complexity of its diagnosis made by polysomnography (PSG). Considering the severe comorbidities associated with OSA, especially in the cardiovascular system, the development of early screening tools for this disease is imperative. Heart rate variability (HRV) is a simple and non-invasive approach used as a probe to evaluate cardiac autonomic modulation, with a variety of newly developed indices lacking studies with OSA patients.

We aimed to evaluate numerous HRV indices, derived from linear but mainly nonlinear indices, combined or not with oxygen saturation indices, for detecting the presence and severity of OSA using machine learning models.

ECG waveforms were collected from 291 PSG recordings to calculate 34 HRV indices. Minimum oxygen saturation value during sleep (SatMin), the percentage of total sleep time the patient spent with oxygen saturation below 90% (T90), and patient anthropometric data were also considered as inputs to the models. The Apnea-Hypopnea Index (AHI) was used to categorize into severity classes of OSA (normal, mild, moderate, severe) to train multiclass or binary (normal-to-mild and moderate-to-severe) classification models, using the Random Forest (RF) algorithm. Since the OSA severity groups were unbalanced, we used the Synthetic Minority Over-sampling Technique (SMOTE) to oversample the minority classes.

Multiclass models achieved a mean area under the ROC curve (AUROC) of 0.92 and 0.86 in classifying normal individuals and severe OSA patients, respectively, when using all attributes. When the groups were dichotomized into normal-to-mild OSA vs. moderate-to-severe OSA, an AUROC of 0.83 was obtained. As revealed by RF, the importance of features indicates that all feature modalities (HRV, SpO2, and anthropometric variables) contribute to the top 10 ranks.

The present study demonstrates the feasibility of using classification models to detect the presence and severity of OSA using these indices. Our findings have the potential to contribute to the development of rapid screening tools aimed at assisting individuals affected by this condition, to expedite diagnosis and initiate timely treatment.

Currently, there is a lack of effective early assessment tools for predicting the onset and development of cardiac arrest (CA). With the increasing attention of clinical researchers on machine learning (ML), some researchers have developed ML models for predicting the occurrence and prognosis of CA, with certain models appearing to outperform traditional scoring tools. However, these models still lack systematic evidence to substantiate their efficacy.

This systematic review and meta-analysis was conducted to evaluate the prediction value of ML in CA for occurrence, good neurological prognosis, mortality, and the return of spontaneous circulation (ROSC), thereby providing evidence-based support for the development and refinement of applicable clinical tools.

PubMed, Embase, the Cochrane Library, and Web of Science were systematically searched from their establishment until May 17, 2024. The risk of bias in all prediction models was assessed using the Prediction Model Risk of Bias Assessment Tool.

In total, 93 studies were selected, encompassing 5,729,721 in-hospital and out-of-hospital patients. The meta-analysis revealed that, for predicting CA, the pooled C-index, sensitivity, and specificity derived from the imbalanced validation dataset were 0.90 (95% CI 0.87-0.93), 0.83 (95% CI 0.79-0.87), and 0.93 (95% CI 0.88-0.96), respectively. On the basis of the balanced validation dataset, the pooled C-index, sensitivity, and specificity were 0.88 (95% CI 0.86-0.90), 0.72 (95% CI 0.49-0.95), and 0.79 (95% CI 0.68-0.91), respectively. For predicting the good cerebral performance category score 1 to 2, the pooled C-index, sensitivity, and specificity based on the validation dataset were 0.86 (95% CI 0.85-0.87), 0.72 (95% CI 0.61-0.81), and 0.79 (95% CI 0.66-0.88), respectively. For predicting CA mortality, the pooled C-index, sensitivity, and specificity based on the validation dataset were 0.85 (95% CI 0.82-0.87), 0.83 (95% CI 0.79-0.87), and 0.79 (95% CI 0.74-0.83), respectively. For predicting ROSC, the pooled C-index, sensitivity, and specificity based on the validation dataset were 0.77 (95% CI 0.74-0.80), 0.53 (95% CI 0.31-0.74), and 0.88 (95% CI 0.71-0.96), respectively. In predicting CA, the most significant modeling variables were respiratory rate, blood pressure, age, and temperature. In predicting a good cerebral performance category score 1 to 2, the most significant modeling variables in the in-hospital CA group were rhythm (shockable or nonshockable), age, medication use, and gender; the most significant modeling variables in the out-of-hospital CA group were age, rhythm (shockable or nonshockable), medication use, and ROSC.

ML represents a currently promising approach for predicting the occurrence and outcomes of CA. Therefore, in future research on CA, we may attempt to systematically update traditional scoring tools based on the superior performance of ML in specific outcomes, achieving artificial intelligence-driven enhancements.

PROSPERO International Prospective Register of Systematic Reviews CRD42024518949; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=518949.

One of the main goals of metagenomic studies is to describe the taxonomic diversity of microbial communities. A crucial step in metagenomic analysis is metagenomic binning, which involves the (supervised) classification or (unsupervised) clustering of metagenomic sequences. Various machine learning models have been applied to address this task. In this review, the contributions of artificial neural networks (ANN) in the context of metagenomic binning are detailed, addressing both supervised, unsupervised, and semi-supervised approaches. 34 ANN-based binning tools are systematically compared, detailing their architectures, input features, datasets, advantages, disadvantages, and other relevant aspects. The findings reveal that deep learning approaches, such as convolutional neural networks and autoencoders, achieve higher accuracy and scalability than traditional methods. Gaps in benchmarking practices are highlighted, and future directions are proposed, including standardized datasets and optimization of architectures, for third-generation sequencing. This review provides support to researchers in identifying trends and selecting suitable tools for the metagenomic binning problem.

The blood brain barrier (BBB) is an endothelial-derived structure which restricts the movement of certain molecules between the general somatic circulatory system to the central nervous system (CNS). While the BBB maintains homeostasis by regulating the molecular environment induced by cerebrovascular perfusion, it also presents significant challenges in developing therapeutics intended to act on CNS targets. Many drug development practices rely partly on extensive cell and animal models to predict, to an extent, whether prospective therapeutic molecules can cross the BBB. In interest to reduce costs and improve prediction accuracy, many propose using advanced computational modeling of BBB permeability profiles leveraging empirical data. Given the scale of growth in machine learning and deep learning, we review the most recent machine learning approaches in predicting BBB permeability.

Heart failure is a complex and prevalent condition with significant implications for patient management and survival prediction. Traditional predictive models often fall short in accuracy due to their reliance on pre-specified predictors and assumptions of variable independence. This review aims to assess the role of machine learning (ML) algorithms in predicting heart failure survival, comparing their performance with traditional statistical methods and identifying key predictive features. We conducted a review of studies utilizing ML algorithms for heart failure survival prediction. Data were sourced from PubMed/MEDLINE, Google Scholar, ScienceDirect, Embase, DOAJ, and the Cochrane Library, covering studies published until July 2024. A total of 10 studies were reviewed, encompassing 468,171 patients with heart failure. ML algorithms, particularly random forests and gradient boosting methods, demonstrated superior performance compared to traditional statistical models. These algorithms effectively identified key risk factors and stratified patients into risk categories with high accuracy. Notably, extreme learning machine (ELM) and CatBoost models showed exceptional predictive capabilities, as indicated by metrics such as Harrell's concordance index (C-index) and area under the curve (AUC). Key predictive features included ejection fraction (EF), serum creatinine (S Cr), and blood urea nitrogen (BUN). ML algorithms offer significant advantages in predicting heart failure survival by uncovering complex patterns and improving risk stratification. Their integration into clinical practice could lead to more personalized treatment strategies and enhanced patient outcomes. However, challenges such as data quality, model interpretability, and integration into clinical workflows need to be addressed.

Adeno-associated virus (AAV) has emerged as a leading vector for gene therapy due to its broad host range, low pathogenicity, and ability to facilitate long-term gene expression. However, AAV vectors face limitations, including immunogenicity and insufficient targeting specificity. To enhance the efficacy of gene therapy, researchers have been modifying the AAV vector using various methods. Traditional experimental approaches for optimizing AAV vector are often time-consuming, resource-intensive, and difficult to replicate. The advancement of artificial intelligence (AI), particularly machine learning, offers significant potential to accelerate capsid optimization while reducing development time and manufacturing costs. This review compares traditional and AI-based methods of AAV vector engineering and highlights recent research in AAV engineering using AI algorithms.

Auscultation is one of the key medical skills in physical examination. The main problem with auscultation is the lack of objectivity of the findings and great dependence on the experience of the examiner. Auscultation using machine learning and neural networks promises great potential for solving these problems in clinical practice.A selective search for studies in PubMed was carried out, which revealed the possibilities of machine learning in medical diagnostics.In all the studies identified, significant differences were shown between the respective test groups in favour of artificial intelligence (AI). In addition to the positive study results, the limitations of AI could also be analysed and critically scrutinised.Medical research in the field of artificial intelligence is still in its infancy. The prospects and limitations of AI must be further investigated and require close attention in the collaboration between clinicians, scientists and AI experts. Publicly funded projects such as DigitaLung (a digital auscultation system for the differential diagnosis of lung diseases using machine learning), which aims to improve lung auscultation using AI, will help to unlock the diagnostic benefits of AI for patient care and could improve care in the future.

We aimed to evaluate the image quality and diagnostic performance of chronic lung allograft dysfunction (CLAD) with lung ventilation single-photon emission computed tomography (SPECT) images acquired briefly using a convolutional neural network (CNN) in patients after lung transplantation and to explore the feasibility of short acquisition times. We retrospectively identified 93 consecutive lung-transplant recipients who underwent ventilation SPECT/computed tomography (CT). We employed a CNN to distinguish the images acquired in full time from those acquired in a short time. The image quality was evaluated using the structural similarity index (SSIM) loss and normalized mean square error (NMSE). The correlation between functional volume/morphological volume (F/M) ratios of full-time SPECT images and predicted SPECT images was evaluated. Differences in the F/M ratio were evaluated using Bland-Altman plots, and the diagnostic performance was compared using the area under the curve (AUC). The learning curve, obtained using MSE, converged within 100 epochs. The NMSE was significantly lower (P < 0.001) and the SSIM was significantly higher (P < 0.001) for the CNN-predicted SPECT images compared to the short-time SPECT images. The F/M ratio of full-time SPECT images and predicted SPECT images showed a significant correlation (r = 0.955, P < 0.0001). The Bland-Altman plot revealed a bias of -7.90% in the F/M ratio. The AUC values were 0.942 for full-time SPECT images, 0.934 for predicted SPECT images and 0.872 for short-time SPECT images. Our findings suggest that a deep-learning-based approach can significantly curtail the acquisition time of ventilation SPECT, while preserving the image quality and diagnostic accuracy for CLAD.

We aimed to characterize the brain abnormalities that are associated with the cognitive and physical performance of patients with relapsing-remitting multiple sclerosis (RRMS) using a deep learning algorithm.

Three-dimensional (3D) nnU-Net was employed to calculate a novel spatial abnormality map by T1-weighted images and 281 RRMS patients (Dataset-1, male/female = 101/180, median age [range] = 35.0 [17.0, 65.0] years) were categorized into subtypes. Comparison of clinical and MRI features between RRMS subtypes was conducted by Kruskal-Wallis test. Kaplan-Meier analysis was conducted to investigate disability progression in RRMS subtypes. Additional validation using two other RRMS datasets (Dataset-2, n = 33 and Dataset-3, n = 56) was conducted.

Five RRMS subtypes were identified: (1) a Frontal-I subtype showing preserved cognitive performance and mild physical disability, and low risk of disability worsening; (2) a Frontal-II subtype showing low cognitive scores and severe physical disability with significant brain volume loss, and a high propensity for disability worsening; (3) a temporal-cerebellar subtype demonstrating lowest cognitive scores and severest physical disability among all subtypes but remaining relatively stable during follow-up; (4) an occipital subtype demonstrating similar clinical and imaging characteristics as the Frontal-II subtype, except a large number of relapses at baseline and preserved cognitive performance; and (5) a subcortical subtype showing preserved cognitive performance and low physical disability but a similar prognosis as the occipital and Frontal-II subtypes. Additional validation confirmed the above findings.

Spatial abnormality maps can explain heterogeneity in cognitive and physical performance in RRMS and may contribute to stratified management.

Question Can a deep learning algorithm characterize the brain abnormalities associated with the cognitive and physical performance of patients with RRMS? Findings Five RRMS subtypes were identified by the algorithm that demonstrated variable cognitive and physical performance. Clinical relevance The spatial abnormality maps derived RRMS subtypes had distinct cognitive and physical performances, which have a potential for individually tailored management.

Dental biofilm is traditionally identified visually, which can be challenging and time-consuming due to its color similarity with the tooth. The aim of this study was to evaluate the performance of U-Net neural networks for the automatic detection of dental biofilm without disclosing agents on intraoral photographs of deciduous and permanent teeth using an ensemble strategy.

This retrospective exploratory study was conducted on two datasets of intraoral images obtained from deciduous and permanent dentitions. The first dataset was used to validate dental biofilm annotations by an expert with disclosing agents. The second dataset, without disclosing agents, was employed to train and evaluate the U-Net neural network in the identification of dental biofilms using an ensemble strategy.

The performance of the ensemble method was assessed using a cross-validation procedure, with six groups dedicated to training, one group for validation, and one group exclusively taken as a test set for the final evaluation of the ensemble. The performance of the neural network was evaluated using accuracy, F1 score, sensitivity, and specificity. The U-Net neural network achieved an accuracy of 93.1%, sensitivity of 65.1%, specificity of 95.9%, and an F1 score of 63.0%.

The U-Net neural network using the ensemble strategy was able to automatically identify visually detectable dental biofilms on intraoral photographs. The application of this new knowledge will soon be available in clinical practice.

The atherogenic index of plasma (AIP) is considered an important marker of atherosclerosis and cardiovascular risk. However, its potential role in predicting length of stay (LOS), especially in patients with atherosclerotic cardiovascular disease (ASCVD), remains to be explored. We investigated the effect of AIP on hospital LOS in critically ill ASCVD patients and explored the risk factors affecting LOS in conjunction with machine learning.

Using data from the Medical Information Mart for Intensive Care (MIMIC)-IV. AIP was calculated as the logarithmic ratio of TG to HDL-C, and patients were stratified into four groups based on AIP values. We investigated the association between AIP and two key clinical outcomes: ICU LOS and total hospital LOS. Multivariate logistic regression models were used to evaluate these associations, while restricted cubic spline (RCS) regressions assessed potential nonlinear relationships. Additionally, machine learning (ML) techniques, including logistic regression (LR), decision tree (DT), random forest (RF), extreme gradient boosting (XGB), and light gradient boosting machine (LGB), were applied, with the Shapley additive explanation (SHAP) method used to determine feature importance.

The study enrolled a total of 2423 patients with critically ill ASCVD, predominantly male (54.91%), and revealed that higher AIP values were independently associated with longer ICU and hospital stays. Specifically, for each unit increase in AIP, the odds of prolonged ICU and hospital stays were significantly higher, with adjusted odds ratios (OR) of 1.42 (95% CI, 1.11-1.81; P = 0.006) and 1.73 (95% CI, 1.34-2.24; P < 0.001), respectively. The RCS regression demonstrated a linear relationship between increasing AIP and both ICU LOS and hospital LOS. ML models, specifically LGB (ROC:0.740) and LR (ROC:0.832) demonstrated superior predictive accuracy for these endpoints, identifying AIP as a vital component of hospitalization duration.

AIP is a significant predictor of ICU and hospital LOS in patients with critically ill ASCVD. AIP could serve as an early prognostic tool for guiding clinical decision-making and managing patient outcomes.

Artificial intelligence (AI) is revolutionizing scientific research by facilitating a paradigm shift in data analysis and discovery. This transformation is characterized by a fundamental change in scientific methods and concepts due to AI's ability to process vast datasets with unprecedented speed and accuracy. In breast cancer research, AI aids in early detection, prognosis, and personalized treatment strategies. Liquid biopsy, a noninvasive tool for detecting circulating tumor traits, could ideally benefit from AI's analytical capabilities, enhancing the detection of minimal residual disease and improving treatment monitoring. Extracellular vesicles (EVs), which are key elements in cell communication and cancer progression, could be analyzed with AI to identify disease-specific biomarkers. AI combined with EV analysis promises an enhancement in diagnosis precision, aiding in early detection and treatment monitoring. Studies show that AI can differentiate cancer types and predict drug efficacy, exemplifying its potential in personalized medicine. Overall, the integration of AI in biomedical research and clinical practice promises significant changes and advancements in diagnostics, personalized medicine-based approaches, and our understanding of complex diseases like cancer.

This study aimed to develop a clinical early warning prediction model to evaluate the prognosis and response to chemoimmunotherapy in patients with extensive-stage small cell lung cancer (ES-SCLC), thereby guiding clinical decision-making.

A retrospective analysis was conducted on the clinical data and radiomics parameters of 309 patients with ES-SCLC hospitalized at Baotou Cancer Hospital from February 2020 to September 2024. Patients were divided into reactive and non-reactive groups based on their response to chemoimmunotherapy.Machine learning algorithms (including random forests, decision trees, artificial neural networks, and generalized linear regression) were used to predict the combined treatment response. The model's predictive ability was evaluated using the receiver operating characteristic (ROC) curve and clinical decision curve analysis(DCA). The prognostic evaluation of patients receiving combination therapy was based on the COX regression model, with predictive performance assessed through nomogram visualization and calibration curves.

Out of 309 patients with ES-SCLC, 248 (80.26%) responded to combination therapy. Logistic regression and Least absolute shrinkage and selection operator (LASSO) regression analyses identified Energy, sum of squares(SOS), mean sum(MES), sum variance(SUV), sum entropy(SUE), difference variance(DIV), and pathomics score as independent risk factors for treatment response. The area under the ROC curve for predicting treatment response using machine learning were 0.764 (95% confidence interval [CI]: 0.707~0.821) and 0.901 (95% CI: 0.846~0.956) in the training and validation sets. The C-index of the radiomics and pathomics prognostic nomogram model based on the COX prognostic model was 0.766 and 0.812 in those sets, respectively.

We developed prediction model based on multi-omics demonstrated satisfactory performance in predicting chemoimmunotherapy response in patients with ES-SCLC. The random forest prediction model, in particular, provides accurate response and prognostic risk assessments, thereby assisting clinical decision-making.

Machine Learning (ML) has become an essential tool for analyzing biomedical data, facilitating the prediction of treatment outcomes and patient survival. However, the effectiveness of ML models heavily relies on both the choice of algorithms and the quality of the input data. In this study, we aimed to develop a novel predictive model to estimate individual survival for patients diagnosed with glioblastoma (GBM), focusing on key variables such as O6-Methylguanine-DNA Methyltransferase (MGMT) methylation status, age, and sex.

To identify the optimal approach, we utilized retrospective data from 218 patients treated at our brain tumor center. The performance of the ML models was evaluated within repeated tenfold regression. The pipeline comprised five regression estimators, including both linear and non-linear algorithms. Permutation feature importance highlighted the feature with the most significant impact on the model. Statistical significance was assessed using a permutation test procedure.

The best machine learning algorithm achieved a mean absolute error (MAE) of 12.65 (SD = ± 2.18) and an explained variance (EV) of 7% (SD = ± 1.8%) with p < 0.001. Linear algorithms led to more accurate predictions than non-linear estimators. Feature importance testing indicated that age and positive MGMT-methylation influenced the predictions the most.

In summary, here we provide a novel approach allowing to predict GBM patient's survival in months solely based on key parameters such as age, sex and MGMT-methylation status and underscores MGMT-methylation status as key prognostic factor for GBM patients survival.