Front Pharmacol. 2025 Jan 7;15:1522787. doi: 10.3389/fphar.2024.1522787. eCollection 2024.

ABSTRACT

INTRODUCTION: The Prunella vulgaris L. (PVL) and Taraxacum mongolicum Hand.-Mazz. (TH) herb pair, which is commonly used in traditional Chinese medicine (TCM), has been applied for the treatment of breast cancer. Although its efficacy is validated, the synergistic anti-breast cancer compound combinations within this herb pair and their underlying mechanisms of action remain unclear.

METHODS: This study aimed to identify and validate synergistic anti-breast cancer compound combinations within the PVL-TH pair using large-scale biomedical data, artificial intelligence and experimental methods. The first step was to investigate the anti-breast cancer effects of various PVL and TH extracts using in vitro cellular assays to identify the most effective superior extracts. These superior extracts were subjected to liquid chromatography-mass spectrometry (LC-MS) analysis to identify their constituent compounds. A deep learning-based prediction model, DeepMDS, was applied to predict synergistic anti-breast cancer multi-compound combinations. These predicted combinations were experimentally validated for their anti-breast cancer effects at actual content ratios found in the extracts. Preliminary bioinformatics analyses were conducted to explore the mechanisms of action of these superior combinations. We also compared the anti-breast cancer effects of superior extracts from different geographical origins and analyzed the contents of compounds to assess their representation of the anti-tumor effect of the corresponding TCM.

RESULTS: The results revealed that LC-MS analysis identified 27 and 21 compounds in the superior extracts (50% ethanol extracts) of PVL and TH, respectively. Based on these compounds, DeepMDS model predicted synergistic anti-breast cancer compound combinations such as F973 (caffeic acid, rosmarinic acid, p-coumaric acid, and esculetin), T271 (chlorogenic acid, cichoric acid, and caffeic acid), and T1685 (chlorogenic acid, rosmarinic acid, and scopoletin) from single PVL, single TH and PVL-TH herb pair, respectively. These combinations, at their actual concentrations in extracts, demonstrated superior anti-breast cancer activity compared to the corresponding extracts. The bioinformatics analysis revealed that these compounds could regulate tumor-related pathways synergistically, inhibiting tumor cell growth, inducing cell apoptosis, and blocking cell cycle progression. Furthermore, the concentration ratio and total content of compounds in F973 and T271 were closely associated with their anti-breast cancer effects in extracts from various geographical origins. The compound combination T1685 could represent the synergistic anti-breast cancer effects of the PVL-TH pair.

DISCUSSION: This study provides insights into exploring the representative synergistic anti-breast cancer compound combinations within the complex TCM.

PMID:39840098 | PMC:PMC11747269 | DOI:10.3389/fphar.2024.1522787

Int J Comput Assist Radiol Surg. 2025 Jan 21. doi: 10.1007/s11548-025-03321-4. Online ahead of print.

ABSTRACT

PURPOSE: Identifying muscles linked to postoperative physical function can guide protocols to enhance early recovery following total hip arthroplasty (THA). This study aimed to evaluate the association of preoperative pelvic and thigh muscle volume and quality with early physical function after THA in patients with unilateral hip osteoarthritis (HOA).

METHODS: Preoperative Computed tomography (CT) images of 61 patients (eight males and 53 females) with HOA were analyzed. Six muscle groups were segmented from CT images, and muscle volume and quality were calculated on the healthy and affected sides. Muscle quality was quantified using the mean CT values (Hounsfield units [HU]). Early postoperative physical function was evaluated using the Timed Up & Go test (TUG) at three weeks after THA. The effect of preoperative muscle volume and quality of both sides on early postoperative physical function was assessed.

RESULTS: On the healthy and affected sides, mean muscle mass was 9.7 cm3/kg and 8.1 cm3/kg, and mean muscle HU values were 46.0 HU and 39.1 HU, respectively. Significant differences in muscle volume and quality were observed between the affected and healthy sides. On analyzing the function of various muscle groups, the TUG score showed a significant association with the gluteus maximum volume and the gluteus medius/minimus quality on the affected side.

CONCLUSION: Patients with HOA showed significant muscle atrophy and fatty degeneration in the affected pelvic and thigh regions. The gluteus maximum volume and gluteus medius/minimus quality were associated with early postoperative physical function. Preoperative rehabilitation targeting the gluteal muscles on the affected side could potentially enhance recovery of physical function in the early postoperative period.

PMID:39836355 | DOI:10.1007/s11548-025-03321-4

J Chem Phys. 2025 Jan 21;162(3):034120. doi: 10.1063/5.0236919.

ABSTRACT

Variational ab initio methods in quantum chemistry stand out among other methods in providing direct access to the wave function. This allows, in principle, straightforward extraction of any other observable of interest, besides the energy, but, in practice, this extraction is often technically difficult and computationally impractical. Here, we consider the electron density as a central observable in quantum chemistry and introduce a novel method to obtain accurate densities from real-space many-electron wave functions by representing the density with a neural network that captures known asymptotic properties and is trained from the wave function by score matching and noise-contrastive estimation. We use variational quantum Monte Carlo with deep-learning Ansätze to obtain highly accurate wave functions free of basis set errors and from them, using our novel method, correspondingly accurate electron densities, which we demonstrate by calculating dipole moments, nuclear forces, contact densities, and other density-based properties.

PMID:39836106 | DOI:10.1063/5.0236919

Aging (Albany NY). 2025 Jan 16;17. doi: 10.18632/aging.206190. Online ahead of print.

ABSTRACT

With the global population aging at an unprecedented rate, there is a need to extend healthy productive life span. This review examines how Deep Learning (DL) and Generative Artificial Intelligence (GenAI) are used in biomarker discovery, deep aging clock development, geroprotector identification and generation of dual-purpose therapeutics targeting aging and disease. The paper explores the emergence of multimodal, multitasking research systems highlighting promising future directions for GenAI in human and animal aging research, as well as clinical application in healthy longevity medicine.

PMID:39836094 | DOI:10.18632/aging.206190

Radiology. 2025 Jan;314(1):e233029. doi: 10.1148/radiol.233029.

ABSTRACT

"Just Accepted" papers have undergone full peer review and have been accepted for publication in Radiology: Artificial Intelligence. This article will undergo copyediting, layout, and proof review before it is published in its final version. Please note that during production of the final copyedited article, errors may be discovered which could affect the content. Background Detection and segmentation of lung tumors on CT scans are critical for monitoring cancer progression, evaluating treatment responses, and planning radiation therapy; however, manual delineation is labor-intensive and subject to physician variability. Purpose To develop and evaluate an ensemble deep learning model for automating identification and segmentation of lung tumors on CT scans. Materials and Methods A retrospective study was conducted between July 2019 and November 2024 using a large dataset of CT simulation scans and clinical lung tumor segmentations from radiotherapy plans. This dataset was used to train a 3D U-Net-based, image-multiresolution ensemble model to detect and segment lung tumors on CT scans. Model performance was evaluated on internal and external test sets composed of CT simulation scans and lung tumor segmentations from two affiliated medical centers, including single primary and metastatic lung tumors. Performance metrics included sensitivity, specificity, false positive rate, and Dice similarity coefficient (DSC). Model-predicted tumor volumes were compared with physician-delineated volumes. Group comparisons were made with Wilcoxon signed-rank test or one-way ANOVA. P < 0.05 indicated statistical significance. Results The model, trained on 1,504 CT scans with clinical lung tumor segmentations, achieved 92% sensitivity (92/100) and 82% specificity (41/50) in detecting lung tumors on the combined 150-CT scan test set. For a subset of 100 CT scans with a single lung tumor each, the model achieved a median model-physician DSC of 0.77 (IQR: 0.65-0.83) and an interphysician DSC of 0.80 (IQR: 0.72-0.86). Segmentation time was shorter for the model than for physicians (mean 76.6 vs. 166.1-187.7 seconds; p<0.001). Conclusion Routinely collected radiotherapy data were useful for model training. The key strengths of the model include a 3D U-Net ensemble approach for balancing volumetric context with resolution, robust tumor detection and segmentation performance, and the ability to generalize to an external site.

PMID:39835976 | DOI:10.1148/radiol.233029

J Chem Inf Model. 2025 Jan 21. doi: 10.1021/acs.jcim.4c02062. Online ahead of print.

ABSTRACT

Skin corrosion assessment is an essential toxicity end point that addresses safety concerns for topical dosage forms and cosmetic products. Previously, skin corrosion assessments required animal testing; however, differences in skin architecture and ethical concerns regarding animal models have fostered the advancement of alternative methods such as in silico and in vitro models. This study aimed to develop deep learning (DL) models based on recurrent neural networks (RNNs) for classifying skin corrosion of chemical compounds based on chemical language notation, molecular substructure, physicochemical properties, and a combination of these three properties called conjoint fingerprints. Simple RNN, long short-term memory, bidirectional long short-term memory (BiLSTM), gated recurrent units, and bidirectional gated recurrent units models, along with 11 molecular features, were employed to generate 55 RNN-based models. Applicability domain and permutation importance analysis were exploited for additional trustable prediction and explanation ability of the models, respectively. Our findings indicate that BiLSTM with conjoint features of MACCS keys and physicochemical descriptors is the most effective model with 84.3% accuracy, 89.8% area under the curve, and 57.6% Matthews correlation coefficient for the external test performance. Furthermore, our model accurately predicted the skin corrosion toxicity of all new and unseen compounds beyond our test set, highlighting prominent classification performance compared to existing skin corrosion models. This finding will contribute to the utilization of DL and conjoint characteristics of molecular structure to enhance the model's predictive capability for skin toxicity assessment.

PMID:39835935 | DOI:10.1021/acs.jcim.4c02062

Proc Natl Acad Sci U S A. 2025 Jan 28;122(4):e2415501122. doi: 10.1073/pnas.2415501122. Epub 2025 Jan 21.

ABSTRACT

Tropical cyclones (TCs), particularly those that rapidly intensify (RI), pose a significant threat due to the uncertainty in forecasting them. RI TC periods, which intensify by at least 13 m/s within 24 h, remain challenging to forecast accurately. Existing models achieve a probability of detection (POD) of 82.6% and a false alarm rate (FARate) of 27.2%. To address this, we developed a contrastive-based RI TC forecasting (RITCF-contrastive) model, utilizing satellite infrared imagery alongside atmospheric and oceanic data. The RITCF-contrastive model was tested on 1,149 TC periods in the Northwest Pacific from 2020 to 2021, achieving a POD of 92.3% and a FARate of 8.9%. RITCF-contrastive improves on previous models by addressing sample imbalance and incorporating TC structural features, leading to a 11.7% improvement in POD and a 3 times reduction in FARate compared to existing deep learning methods. The RITCF-contrastive model not only enhances RI TC forecasting but also offers a unique approach to forecasting these dangerous weather events.

PMID:39835899 | DOI:10.1073/pnas.2415501122

Electromagn Biol Med. 2025 Jan 21:1-18. doi: 10.1080/15368378.2024.2401540. Online ahead of print.

ABSTRACT

Brain tumors can cause difficulties in normal brain function and are capable of developing in various regions of the brain. Malignant tumours can develop quickly, pass through neighboring tissues, and extend to further brain regions or the central nervous system. In contrast, healthy tumors typically develop slowly and do not invade surrounding tissues. Individuals frequently struggle with sensory abnormalities, motor deficiencies affecting coordination, and cognitive impairments affecting memory and focus. In this research, Utilizing Phase-aware Composite Deep Neural Network Optimized with Coati Optimized Algorithm for Brain Tumor Identification Based on Magnetic resonance imaging (PACDNN-COA-BTI-MRI) is proposed. First, input images are taken from the brain tumour Dataset. To execute this, the input image is pre-processed using Multivariate Fast Iterative Filtering (MFIF) and it reduces the occurrence of over-fitting from the collected dataset; then feature extraction using Self-Supervised Nonlinear Transform (SSNT) to extract essential features like model, shape, and intensity. Then, the proposed PACDNN-COA-BTI-MRI is implemented in Matlab and the performance metrics Recall, Accuracy, F1-Score, Precision Specificity and ROC are analysed. Performance of the PACDNN-COA-BTI-MRI approach attains 16.7%, 20.6% and 30.5% higher accuracy; 19.9%, 22.2% and 30.1% higher recall and 16.7%, 21.9% and 30.8% higher precision when analysed through existing techniques brain tumor identification using MRI-Based Deep Learning Approach for Efficient Classification of Brain Tumor (MRI-DLA-ECBT), MRI-Based Brain Tumor Detection using Convolutional Deep Learning Methods and Chosen Machine Learning Techniques (MRI-BTD-CDMLT) and MRI-Based Brain Tumor Image Detection using CNN-Based Deep Learning Method (MRI-BTID-CNN) methods, respectively.

PMID:39835842 | DOI:10.1080/15368378.2024.2401540

J Acoust Soc Am. 2025 Jan 1;157(1):382-395. doi: 10.1121/10.0034857.

ABSTRACT

Underwater acoustic propagation is a complex phenomenon in the ocean environment. Traditional methods for calculating acoustic propagation loss rely on solving complex partial differential equations. Deep learning methods, leveraging their robust nonlinear approximation capabilities, can model various physical phenomena effectively, significantly reducing computation time and cost. Despite considerable advancements in the study of various inverse underwater acoustic problems, research focused on forward physical modeling is still nascent. This study proposes an end-to-end architecture for predicting underwater acoustic transmission loss (TL). This architecture employs a data-driven approach capable of swiftly and accurately predicting the complete acoustic field. It employs a U-Net model integrated with an adaptive multi-scale dilated module, named MultiScale-DUNet, which effectively predicts by assimilating multi-scale acoustic field information. It is demonstrated that the MultiScale-DUNet is capable of predicting acoustic TL in complex two-dimensional ocean environments within the end-to-end framework. The results indicate that the MultiScale-DUNet can rapidly predict the acoustic TL while maintaining high accuracy under computationally inexpensive conditions. This end-to-end technology for predicting underwater acoustic TL holds broad application prospects in fields such as underwater exploration and real-time underwater monitoring.

PMID:39835828 | DOI:10.1121/10.0034857

Hum Brain Mapp. 2025 Feb 1;46(2):e70136. doi: 10.1002/hbm.70136.

ABSTRACT

Magnetic susceptibility source separation (χ-separation), an advanced quantitative susceptibility mapping (QSM) method, enables the separate estimation of paramagnetic and diamagnetic susceptibility source distributions in the brain. Similar to QSM, it requires solving the ill-conditioned problem of dipole inversion, suffering from so-called streaking artifacts. Additionally, the method utilizes reversible transverse relaxation ( R 2 ' = R 2 * - R 2 $$ {R}_2^{\prime }={R}_2^{\ast }-{R}_2 $$ ) to complement frequency shift information for estimating susceptibility source concentrations, requiring time-consuming data acquisition for R 2 $$ {R}_2 $$ (e.g., multi-echo spin-echo) in addition to multi-echo GRE data for R 2 * $$ {R}_2^{\ast } $$ . To address these challenges, we develop a new deep learning network, χ-sepnet, and propose two deep learning-based susceptibility source separation pipelines, χ-sepnet- R 2 ' $$ {R}_2^{\prime } $$ for inputs with multi-echo GRE and multi-echo spin-echo (or turbo spin-echo) and χ-sepnet- R 2 * $$ {R}_2^{\ast } $$ for input with multi-echo GRE only. The neural network is trained using multiple head orientation data that provide streaking artifact-free labels, generating high-quality χ-separation maps. The evaluation of the pipelines encompasses both qualitative and quantitative assessments in healthy subjects, and visual inspection of lesion characteristics in multiple sclerosis patients. The susceptibility source-separated maps of the proposed pipelines delineate detailed brain structures with substantially reduced artifacts compared to those from the conventional regularization-based reconstruction methods. In quantitative analysis, χ-sepnet- R 2 ' $$ {R}_2^{\prime } $$ achieves the best outcomes followed by χ-sepnet- R 2 * $$ {R}_2^{\ast } $$ , outperforming the conventional methods. When the lesions of multiple sclerosis patients are classified into subtypes, most lesions are identified as the same subtype in the maps from χ-sepnet- R 2 ' $$ {R}_2^{\prime } $$ and χ-sepnet- R 2 * $$ {R}_2^{\ast } $$ (paramagnetic susceptibility: 99.6% and diamagnetic susceptibility: 98.4%; both out of 250 lesions). The χ-sepnet- R 2 * $$ {R}_2^{\ast } $$ pipeline, which only requires multi-echo GRE data, has demonstrated its potential to offer broad clinical and scientific applications, although further evaluations for various diseases and pathological conditions are necessary.

PMID:39835664 | DOI:10.1002/hbm.70136

Front Public Health. 2025 Jan 6;12:1442114. doi: 10.3389/fpubh.2024.1442114. eCollection 2024.

ABSTRACT

INTRODUCTION: Diabetic retinopathy grading plays a vital role in the diagnosis and treatment of patients. In practice, this task mainly relies on manual inspection using human visual system. However, the human visual system-based screening process is labor-intensive, time-consuming, and error-prone. Therefore, plenty of automated screening technique have been developed to address this task.

METHODS: Among these techniques, the deep learning models have demonstrated promising outcomes in various types of machine vision tasks. However, most of the medical image analysis-oriented deep learning approaches are built upon the convolutional operations, which might neglect the global dependencies between long-range pixels in the medical images. Therefore, the vision transformer models, which can unveil the associations between global pixels, have been gradually employed in medical image analysis. However, the quadratic computation complexity of attention mechanism has hindered the deployment of vision transformer in clinical practices. Bearing the analysis above in mind, this study introduces an integrated self-attention mechanism with both softmax and linear modules to guarantee efficiency and expressiveness, simultaneously. To be specific, a portion of query and key tokens, which are much less than the original query and key tokens, are adopted in the attention module by adding a set of proxy tokens. Note that the proxy tokens can fully utilize both the advantages of softmax and linear attention.

RESULTS: To evaluate the performance of the presented approach, the comparison experiments between state-of-the-art algorithms and the proposed approach are conducted. Experimental results demonstrate that the proposed approach achieves superior outcome over the state-of-the-art algorithms on the publicly available datasets.

DISCUSSION: Accordingly, the proposed approach can be taken as a potentially valuable instrument in clinical practices.

PMID:39835306 | PMC:PMC11743363 | DOI:10.3389/fpubh.2024.1442114

Front Med (Lausanne). 2025 Jan 6;11:1510792. doi: 10.3389/fmed.2024.1510792. eCollection 2024.

ABSTRACT

Sepsis remains a leading cause of morbidity and mortality worldwide due to its rapid progression and heterogeneous nature. This review explores the potential of Artificial Intelligence (AI) to transform sepsis management, from early detection to personalized treatment and real-time monitoring. AI, particularly through machine learning (ML) techniques such as random forest models and deep learning algorithms, has shown promise in analyzing electronic health record (EHR) data to identify patterns that enable early sepsis detection. For instance, random forest models have demonstrated high accuracy in predicting sepsis onset in intensive care unit (ICU) patients, while deep learning approaches have been applied to recognize complications such as sepsis-associated acute respiratory distress syndrome (ARDS). Personalized treatment plans developed through AI algorithms predict patient-specific responses to therapies, optimizing therapeutic efficacy and minimizing adverse effects. AI-driven continuous monitoring systems, including wearable devices, provide real-time predictions of sepsis-related complications, enabling timely interventions. Beyond these advancements, AI enhances diagnostic accuracy, predicts long-term outcomes, and supports dynamic risk assessment in clinical settings. However, ethical challenges, including data privacy concerns and algorithmic biases, must be addressed to ensure fair and effective implementation. The significance of this review lies in addressing the current limitations in sepsis management and highlighting how AI can overcome these hurdles. By leveraging AI, healthcare providers can significantly enhance diagnostic accuracy, optimize treatment protocols, and improve overall patient outcomes. Future research should focus on refining AI algorithms with diverse datasets, integrating emerging technologies, and fostering interdisciplinary collaboration to address these challenges and realize AI's transformative potential in sepsis care.

PMID:39835096 | PMC:PMC11743359 | DOI:10.3389/fmed.2024.1510792

Front Med (Lausanne). 2025 Jan 6;11:1478842. doi: 10.3389/fmed.2024.1478842. eCollection 2024.

ABSTRACT

BACKGROUND: The conventional treatment for locally advanced head and neck squamous cell carcinoma (LA-HNSCC) is surgery; however, the efficacy of definitive chemoradiotherapy (CRT) remains controversial.

OBJECTIVE: This study aimed to evaluate the ability of deep learning (DL) models to identify patients with LA-HNSCC who can achieve organ preservation through definitive CRT and provide individualized adjuvant treatment recommendations for patients who are better suited for surgery.

METHODS: Five models were developed for treatment recommendations. Their performance was assessed by comparing the difference in overall survival rates between patients whose actual treatments aligned with the model recommendations and those whose treatments did not. Inverse probability treatment weighting (IPTW) was employed to reduce bias. The effect of the characteristics on treatment plan selection was quantified through causal inference.

RESULTS: A total of 7,376 patients with LA-HNSCC were enrolled. Balanced Individual Treatment Effect for Survival data (BITES) demonstrated superior performance in both the CRT recommendation (IPTW-adjusted hazard ratio (HR): 0.84, 95% confidence interval (CI), 0.72-0.98) and the adjuvant therapy recommendation (IPTW-adjusted HR: 0.77, 95% CI, 0.61-0.85), outperforming other models and the National Comprehensive Cancer Network guidelines (IPTW-adjusted HR: 0.87, 95% CI, 0.73-0.96).

CONCLUSION: BITES can identify the most suitable treatment option for an individual patient from the three most common treatment options. DL models facilitate the establishment of a valid and reliable treatment recommendation system supported by quantitative evidence.

PMID:39835092 | PMC:PMC11744519 | DOI:10.3389/fmed.2024.1478842

Cureus. 2024 Dec 20;16(12):e76095. doi: 10.7759/cureus.76095. eCollection 2024 Dec.

ABSTRACT

Preeclampsia is one of the leading causes of maternal and perinatal morbidity and mortality. Early prediction is the need of the hour so that interventions like aspirin prophylaxis can be started. Nowadays, machine learning (ML) is increasingly being used to predict the disease and its prognosis. This review explores the methodologies, predictors, and performance of ML models for preeclampsia prediction, emphasizing their comparative advantages, challenges, and clinical applicability. We conducted a systematic search of databases including PubMed, Cochrane, and Scopus for studies published in the last 10 years using terms such as "preeclampsia", "risk factors", "machine learning", "artificial intelligence", and "deep learning". Words and phrases were selected using MeSH, a controlled vocabulary. Appropriate articles were selected using Boolean operators "OR" and "AND". The database search yielded 325 records. After removing duplicates and non-English articles, and completing a title and abstract search 55 research articles were assessed for eligibility of which 11 were included in this review. The risk of bias was found to be high in three of the studies and low in the rest. Clinicodemographic characteristics, laboratory reports, Doppler ultrasound, and some innovative ones like genotypic data and fundal images were predictors used to train ML models. More than ten different ML models were used in the 11 studies from diverse countries like the United States, the United Kingdom, China, and Korea. The area under the curve varied from 0.76 to 0.97. ML algorithms such as extreme gradient boosting (XGBoost), random forest, and neural networks consistently demonstrated superior predictive accuracy Non-interpretable or black box ML models may not find clinical application on ethical grounds. The future of preeclampsia prediction using ML lies in balancing model performance with interpretability. Human oversight remains indispensable in implementing and interpreting these models to achieve better maternal outcomes. Further research and validation across diverse populations are critical to establishing the universal applicability of these promising ML-based approaches.

PMID:39834976 | PMC:PMC11743919 | DOI:10.7759/cureus.76095

Front Oncol. 2025 Jan 6;14:1428929. doi: 10.3389/fonc.2024.1428929. eCollection 2024.

ABSTRACT

OBJECTIVE: This meta-analysis aims to evaluate the diagnostic accuracy of magnetic resonance imaging (MRI) based radiomic features for predicting epidermal growth factor receptor (EGFR) mutation status in non-small cell lung cancer (NSCLC) patients with brain metastases.

METHODS: We systematically searched PubMed, Embase, Cochrane Library, Web of Science, Scopus, Wanfang, and China National Knowledge Infrastructure (CNKI) for studies published up to April 30, 2024. We included those studies that utilized MRI-based radiomic features to detect EGFR mutations in NSCLC patients with brain metastases. Sensitivity, specificity, positive and negative likelihood ratios (PLR, NLR), and area under the curve (AUC) were calculated to evaluate the accuracy. Quality assessment was performed using the quality assessment of prognostic accuracy studies 2 (QUADAS-2) tool. Meta-analysis was conducted using random-effects models.

RESULTS: A total of 13 studies involving 2,348 patients were included. The pooled sensitivity and specificity of MRI-based radiomic features for detecting EGFR mutations were 0.86 (95% CI: 0.74-0.93) and 0.83 (95% CI: 0.72-0.91), respectively. The PLR and NLR were calculated as 5.14 (3.09, 8.55) and 0.17 (0.10, 0.31), respectively. Substantial heterogeneity was observed, with I² values exceeding 50% for all parameters. The AUC for the receiver operating characteristic analysis was 0.91 (95% CI: 0.88-0.93). Subgroup analysis indicated that deep learning models and studies conducted in Asian showed higher diagnostic accuracy compared to their respective counterparts.

CONCLUSIONS: MRI-based radiomic features demonstrate a high potential for accurately detecting EGFR mutations in NSCLC patients with brain metastases, particularly when advanced deep learning techniques were employed. However, the variability in diagnostic performance across different studies underscores the need for standardized radiomic protocols to enhance reproducibility and clinical utility.

SYSTEMATIC REVIEW REGISTRATION: https://www.crd.york.ac.uk/prospero/, identifier CRD42024544131.

PMID:39834943 | PMC:PMC11743156 | DOI:10.3389/fonc.2024.1428929

Front Oncol. 2025 Jan 6;14:1423549. doi: 10.3389/fonc.2024.1423549. eCollection 2024.

ABSTRACT

BACKGROUND: Improvements in the clinical diagnostic use of magnetic resonance imaging (MRI) for the identification of liver disorders have been made possible by gadolinium ethoxybenzyl diethylenetriamine pentaacetic acid (Gd-EOB-DTPA). Gd-EOB-DTPA-enhanced magnetic resonance imaging (MRI) technology is in high demand.

OBJECTIVES: The purpose of the study is to segment the liver using an enhanced multi-gradient deep convolution neural network (EMGDCNN) and to identify and categorize a localized liver lesion using a Gd-EOB-DTPA-enhanced MRI.

METHODS: We provided the classifier images of the liver in five states (unenhanced, arterial, portal venous, equilibrium, and hepatobiliary) and labeled them with localized liver diseases (hepatocellular carcinoma, metastasis, hemangiomas, cysts, and scarring). The Shanghai Public Health Clinical Center ethics committee recruited 132 participants between August 2021 and February 2022. Fisher's exact test analyses liver lesion Gd-EOB-DTPA-enhanced MRI data.

RESULTS: Our method could identify and classify liver lesions at the same time. On average, 25 false positives and 0.6 real positives were found in the test instances. The percentage of correct answers was 0.790. AUC, sensitivity, and specificity evaluate the procedure. Our technique outperforms others in extensive testing.

CONCLUSION: EMGDCNN may identify and categorize a localized hepatic lesion in Gd-EOB-DTPA-enhanced MRI. We found that one network can detect and classify. Radiologists need higher detection capability.

PMID:39834934 | PMC:PMC11743610 | DOI:10.3389/fonc.2024.1423549

Front Artif Intell. 2025 Jan 6;7:1501184. doi: 10.3389/frai.2024.1501184. eCollection 2024.

ABSTRACT

Traditional Chinese medicine (TCM) has long utilized tongue diagnosis as a crucial method for assessing internal visceral condition. This study aims to modernize this ancient practice by developing an automated system for analyzing tongue images in relation to the five organs, corresponding to the heart, liver, spleen, lung, and kidney-collectively known as the "five viscera" in TCM. We propose a novel tongue image partitioning algorithm that divides the tongue into four regions associated with these specific organs, according to TCM principles. These partitioned regions are then processed by our newly developed OrganNet, a specialized neural network designed to focus on organ-specific features. Our method simulates the TCM diagnostic process while leveraging modern machine learning techniques. To support this research, we have created a comprehensive tongue image dataset specifically tailored for these five visceral pattern assessment. Results demonstrate the effectiveness of our approach in accurately identifying correlations between tongue regions and visceral conditions. This study bridges TCM practices with contemporary technology, potentially enhancing diagnostic accuracy and efficiency in both TCM and modern medical contexts.

PMID:39834879 | PMC:PMC11743429 | DOI:10.3389/frai.2024.1501184

Front Cardiovasc Med. 2025 Jan 6;11:1473482. doi: 10.3389/fcvm.2024.1473482. eCollection 2024.

ABSTRACT

INTRODUCTION: The risk of mortality associated with cardiac arrhythmias is considerable, and their diagnosis presents significant challenges, often resulting in misdiagnosis. This situation highlights the necessity for an automated, efficient, and real-time detection method aimed at enhancing diagnostic accuracy and improving patient outcomes.

METHODS: The present study is centered on the development of a portable deep learning model for the detection of arrhythmias via electrocardiogram (ECG) signals, referred to as CardioAttentionNet (CANet). CANet integrates Bi-directional Long Short-Term Memory (BiLSTM) networks, Multi-head Attention mechanisms, and Depthwise Separable Convolution, thereby facilitating its application in portable devices for early diagnosis. The architecture of CANet allows for effective processing of extended ECG patterns and detailed feature extraction without a substantial increase in model size.

RESULTS: Empirical results indicate that CANet outperformed traditional models in terms of predictive performance and stability, as confirmed by comprehensive cross-validation. The model demonstrated exceptional capabilities in detecting cardiac arrhythmias, surpassing existing models in both cross-validation and external testing scenarios. Specifically, CANet achieved high accuracy in classifying various arrhythmic events, with the following accuracies reported for different categories: Normal (97.37 ± 0.30%), Supraventricular (98.09 ± 0.25%), Ventricular (92.92 ± 0.09%), Atrial Fibrillation (99.07 ± 0.13%), and Unclassified arrhythmias (99.68 ± 0.06%). In external evaluations, CANet attained an average accuracy of 94.41%, with the area under the curve (AUC) for each category exceeding 99%, thereby demonstrating its substantial clinical applicability and significant advancements over traditional models.

DISCUSSION: The deep learning model proposed in this study has the potential to enhance the accuracy of early diagnosis for various types of arrhythmias. Looking ahead, this technology is anticipated to provide improved medical services for patients with heart disease through continuous, non-invasive monitoring and timely intervention.

PMID:39834732 | PMC:PMC11744002 | DOI:10.3389/fcvm.2024.1473482

Proc IEEE Int Symp Biomed Imaging. 2024 May;2024. doi: 10.1109/isbi56570.2024.10635530. Epub 2024 Aug 22.

ABSTRACT

Real-time dynamic MRI is important for visualizing time-varying processes in several applications, including cardiac imaging, where it enables free-breathing images of the beating heart without ECG gating. However, current real-time MRI techniques commonly face challenges in achieving the required spatio-temporal resolutions due to limited acceleration rates. In this study, we propose a deep learning (DL) technique for improving the estimation of stationary outer-volume signal from shifted time-interleaved undersampling patterns. Our approach utilizes the pseudo-periodic nature of the ghosting artifacts arising from the moving organs. Subsequently, this estimated outer-volume signal is subtracted from individual timeframes of the real-time MR time series, and each timeframe is reconstructed individually using physics-driven DL methods. Results show improved image quality at high acceleration rates, where conventional methods fail.

PMID:39834646 | PMC:PMC11742269 | DOI:10.1109/isbi56570.2024.10635530

Front Surg. 2025 Jan 6;11:1424716. doi: 10.3389/fsurg.2024.1424716. eCollection 2024.

ABSTRACT

INTRODUCTION: To develop an intelligent system based on artificial intelligence (AI) deep learning algorithms using deep learning tools, aiming to assist in the diagnosis of lumbar degenerative diseases by identifying lumbar spine magnetic resonance images (MRI) and improve the clinical efficiency of physicians.

METHODS: The PP-YOLOv2 algorithm, a deep learning technique, was used to design a deep learning program capable of automatically identifying the spinal diseases (lumbar disc herniation or lumbar spondylolisthesis) based on the lumbar spine MR images. A retrospective analysis was conducted on lumbar spine MR images of patients who visited our hospital from January 2017 to January 2022. The collected images were divided into a training set and a testing set. The training set images were used to establish and validate the deep learning program's algorithm. The testing set images were annotated, and the experimental results were recorded by three spinal specialists. The training set images were also validated using the deep learning program, and the experimental results were recorded. Finally, a comparison of the accuracy of the deep learning algorithm and that of spinal surgeons was performed to determine the clinical usability of deep learning technology based on the PP-YOLOv2 algorithm. A total of 654 patients were included in the final study, with 604 cases in the training set and 50 cases in the testing set.

RESULTS: The mean average precision (mAP) value of the deep learning algorithm reached 90.08% based on the PP-YOLOv2 algorithm. Through classification of the testing set, the deep learning algorithm showed higher sensitivity, specificity, and accuracy in diagnosing lumbar spine MR images compared to manual identification. Additionally, the testing time of the deep learning program was significantly shorter than that of manual identification, and the differences were statistically significant (P < 0.05).

CONCLUSIONS: Deep learning technology based on the PP-YOLOv2 algorithm can be used to identify normal intervertebral discs, lumbar disc herniation, and lumbar spondylolisthesis from lumbar MRI images. Its diagnostic performance is significantly higher than that of most spinal surgeons and can be practically applied in clinical settings.

PMID:39834502 | PMC:PMC11743461 | DOI:10.3389/fsurg.2024.1424716