Front Plant Sci. 2025 Jan 8;15:1435301. doi: 10.3389/fpls.2024.1435301. eCollection 2024.

ABSTRACT

Smart farming is a hot research area for experts globally to fulfill the soaring demand for food. Automated approaches, based on convolutional neural networks (CNN), for crop disease identification, weed classification, and monitoring have substantially helped increase crop yields. Plant diseases and pests are posing a significant danger to the health of plants, thus causing a reduction in crop production. The cotton crop, is a major cash crop in Asian and African countries and is affected by different types of weeds leading to reduced yield. Weeds infestation starts with the germination of the crop, due to which diseases also invade the field. Therefore, proper monitoring of the cotton crop throughout the entire phases of crop development from sewing to ripening and reaping is extremely significant to identify the harmful and undesired weeds timely and efficiently so that proper measures can be taken to eradicate them. Most of the weeds and pests attack cotton plants at different stages of growth. Therefore, timely identification and classification of such weeds on virtue of their symptoms, apparent similarities, and effects can reduce the risk of yield loss. Weeds and pest infestation can be controlled through advanced digital gadgets like sensors and cameras which can provide a bulk of data to work with. Yet efficient management of this extraordinarily bulging agriculture data is a cardinal challenge for deep learning techniques too. In the given study, an approach based on deep CNN-based architecture is presented. This work covers identifying and classifying the cotton weeds efficiently alongside a comparison of other already existing CNN models like VGG-16, ResNet, DenseNet, and Xception Model. Experimental results indicate the accuracy of VGG-16, ResNet-101, DenseNet-121, XceptionNet as 95.4%, 97.1%, 96.9% and 96.1%, respectively. The proposed model achieved an accuracy of 98.3% outperforming other models.

PMID:39840351 | PMC:PMC11750437 | DOI:10.3389/fpls.2024.1435301

Front Genet. 2025 Jan 7;15:1494474. doi: 10.3389/fgene.2024.1494474. eCollection 2024.

ABSTRACT

Recent advancements in deep learning, particularly large language models (LLMs), made a significant impact on how researchers study microbiome and metagenomics data. Microbial protein and genomic sequences, like natural languages, form a language of life, enabling the adoption of LLMs to extract useful insights from complex microbial ecologies. In this paper, we review applications of deep learning and language models in analyzing microbiome and metagenomics data. We focus on problem formulations, necessary datasets, and the integration of language modeling techniques. We provide an extensive overview of protein/genomic language modeling and their contributions to microbiome studies. We also discuss applications such as novel viromics language modeling, biosynthetic gene cluster prediction, and knowledge integration for metagenomics studies.

PMID:39840283 | PMC:PMC11747409 | DOI:10.3389/fgene.2024.1494474

Front Neurorobot. 2025 Jan 7;18:1460255. doi: 10.3389/fnbot.2024.1460255. eCollection 2024.

ABSTRACT

INTRODUCTION: Tracking the hidden states of dynamic systems is a fundamental task in signal processing. Recursive Kalman Filters (KF) are widely regarded as an efficient solution for linear and Gaussian systems, offering low computational complexity. However, real-world applications often involve non-linear dynamics, making it challenging for traditional Kalman Filters to achieve accurate state estimation. Additionally, the accurate modeling of system dynamics and noise in practical scenarios is often difficult. To address these limitations, we propose the KalmanFormer, a hybrid model-driven and data-driven state estimator. By leveraging data, the KalmanFormer promotes the performance of state estimation under non-linear conditions and partial information scenarios.

METHODS: The proposed KalmanFormer integrates classical Kalman Filter with a Transformer framework. Specifically, it utilizes the Transformer to learn the Kalman Gain directly from data without requiring prior knowledge of noise parameters. The learned Kalman Gain is then incorporated into the standard Kalman Filter workflow, enabling the system to better handle non-linearities and model mismatches. The hybrid approach combines the strengths of data-driven learning and model-driven methodologies to achieve robust state estimation.

RESULTS AND DISCUSSION: To evaluate the effectiveness of KalmanFormer, we conducted numerical experiments in both synthetic and real-world dataset. The results demonstrate that KalmanFormer outperforms the classical Extended Kalman Filter (EKF) in the same settings. It achieves superior accuracy in tracking hidden states, demonstrating resilience to non-linearities and imprecise system models.

PMID:39840232 | PMC:PMC11747084 | DOI:10.3389/fnbot.2024.1460255

Data Brief. 2024 Dec 19;58:111243. doi: 10.1016/j.dib.2024.111243. eCollection 2025 Feb.

ABSTRACT

This paper presents a comprehensive dataset of mid-infrared spectra for dried and roasted cocoa beans (Theobroma cacao L.), along with their corresponding theobromine and caffeine content. Infrared data were acquired using Attenuated Total Reflectance-Fourier Transform Infrared (ATR-FTIR) spectroscopy, while High-Performance Liquid Chromatography (HPLC) was employed to accurately quantify theobromine and caffeine in the dried cocoa beans. The theobromine/caffeine relationship served as a robust chemical marker for distinguishing between different cocoa varieties. This dataset provides a basis for further research, enabling the integration of mid-infrared spectral data with HPLC (as a standard) to fine-tune machine learning and deep learning models that could be used to simultaneously predict the theobromine and caffeine content, as well as cocoa variety in both dried and roasted cocoa samples using a non-destructive approach based on spectral data. The tools developed from this dataset could significantly advance automated processes in the cocoa industry and support decision-making on an industrial scale, facilitating real-time quality control of cocoa-based products, improving cocoa variety classification, and optimizing bean selection, blending strategies, and product formulation, while reducing the need for labor-intensive and costly quantification methods. The dataset is organized into Excel sheets and structured according to experimental conditions and replicates, providing a valuable framework for further analysis, model development, and calibration of multivariate statistical models.

PMID:39840227 | PMC:PMC11748727 | DOI:10.1016/j.dib.2024.111243

Cureus. 2024 Dec 22;16(12):e76185. doi: 10.7759/cureus.76185. eCollection 2024 Dec.

ABSTRACT

Several studies explored the application of artificial intelligence (AI) in magnetic resonance imaging (MRI)-based rectal cancer (RC) staging, but a comprehensive evaluation remains lacking. This systematic review aims to review the performance of AI models in MRI-based RC staging. PubMed and Embase were searched from the inception of the database till October 2024 without any language and year restrictions. The prospective or retrospective studies evaluating AI models (including machine learning (ML) and deep learning (DL)) for diagnostic performance in MRI-based RC staging compared with any comparator were included in this review. The performance metrics were considered as outcomes. Two independent reviewers were involved in the study selection and data extraction to limit bias; any disagreements were resolved through mutual consensus or discussion with a third reviewer. A total of 716 records were identified from the databases. Out of these, 14 studies (1.95%) were finally included in this review. These studies were published between 2019 and 2024. Various MRI technologies were adapted by the studies and multiple AI models were developed. DL was the most common. The MRI images including T1-weighted images (14.28%), T2-weighted images (85.71%), diffusion-weighted images (42.85%), or the combination of these from different landscapes and systems were used to develop the AI models. The models were built using various techniques, mainly DL such as conventional neural network (28.57%), DL reconstruction (14.28%), Weakly supervISed model DevelOpment fraMework (7.12%), deep neural network (7.12%), Faster region-based CNN (7.12%), ResNet, DL-based clinical-radiomics nomogram (7.12%), LASSO (7.12%), and random forest classifier (7.12%). All the models that used single-type images or combined imaging modalities showed a better performance than manual assessment in terms of higher accuracy, sensitivity, specificity, positive likelihood ratio, negative likelihood ratio, and area under the curve with a score of >0.75. This is considered to be a good performance. The current study indicates that MRI-based AI models for RC staging show great promise with a high performance.

PMID:39840208 | PMC:PMC11748814 | DOI:10.7759/cureus.76185

J Biomed Opt. 2025 Jan;30(1):016005. doi: 10.1117/1.JBO.30.1.016005. Epub 2025 Jan 21.

ABSTRACT

SIGNIFICANCE: Radiofrequency ablation to treat atrial fibrillation (AF) involves isolating the pulmonary vein from the left atria to prevent AF from occurring. However, creating ablation lesions within the pulmonary veins can cause adverse complications.

AIM: We propose automated classification algorithms to classify optical coherence tomography (OCT) volumes of human venoatrial junctions.

APPROACH: A dataset of comprehensive OCT volumes of 26 venoatrial junctions was used for this study. Texture, statistical, and optical features were extracted from OCT patches. Patches were classified as a left atrium or pulmonary vein using random forest (RF), logistic regression (LR), and convolutional neural networks (CNNs). The features were inputs into the RF and LR classifiers. The inputs to the CNNs included: (1) patches and (2) an ensemble of patches and patch-derived features.

RESULTS: Utilizing a sevenfold cross-validation, the patch-only CNN balances sensitivity and specificity best, with an area under the receiver operating characteristic (AUROC) curve of 0.84 ± 0.109 across the test sets. RF is more sensitive than LR, with an AUROC curve of 0.78 ± 0.102 .

CONCLUSIONS: Cardiac tissues can be identified in benchtop OCT images by automated analysis. Extending this analysis to data obtained in vivo is required to tune automated analysis further. Performing this classification in vivo could aid doctors in identifying substrates of interest and treating AF.

PMID:39840147 | PMC:PMC11747903 | DOI:10.1117/1.JBO.30.1.016005

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