PLoS One. 2025 Apr 24;20(4):e0320077. doi: 10.1371/journal.pone.0320077. eCollection 2025.

ABSTRACT

Pseudouridine is an important modification site, which is widely present in a variety of non-coding RNAs and is involved in a variety of important biological processes. Studies have shown that pseudouridine is important in many biological functions such as gene expression, RNA structural stability, and various diseases. Therefore, accurate identification of pseudouridine sites can effectively explain the functional mechanism of this modification site. Due to the rapid increase of genomics data, traditional biological experimental methods to identify RNA modification sites can no longer meet the practical needs, and it is necessary to accurately identify pseudouridine sites from high-throughput RNA sequence data by computational methods. In this study, we propose a deep learning-based computational method, Definer, to accurately identify RNA pseudouridine loci in three species, Homo sapiens, Saccharomyces cerevisiae and Mus musculus. The method incorporates two sequence coding schemes, including NCP and One-hot, and then feeds the extracted RNA sequence features into a deep learning model constructed from CNN, GRU and Attention. The benchmark dataset contains data from three species, H. sapiens, S. cerevisiae and M. musculus, and the results using 10-fold cross-validation show that Definer significantly outperforms other existing methods. Meanwhile, the data sets of two species, H. sapiens and S. cerevisiae, were tested independently to further demonstrate the predictive ability of the model. In summary, our method, Definer, can accurately identify pseudouridine modification sites in RNA.

PMID:40273178 | DOI:10.1371/journal.pone.0320077

PLoS One. 2025 Apr 24;20(4):e0320979. doi: 10.1371/journal.pone.0320979. eCollection 2025.

ABSTRACT

The agricultural economy heavily relies on poultry production, making accurate forecasting of poultry data crucial for optimizing revenue, streamlining resource utilization, and maximizing productivity. This research introduces a novel application of the N-BEATS architecture for multi-dimensional poultry data forecasting with enhanced interpretability through an integrated Explainable AI (XAI) framework. Leveraging its advanced capabilities in time series modeling, N-BEATS is applied to predict multiple facets of poultry disease diagnostics using a multivariate dataset comprising key environmental parameters. The methodology empowers decision-making in poultry farm management by providing transparent and interpretable forecasts. Experimental results demonstrate that N-BEATS outperforms conventional deep learning models, including LSTM, GRU, RNN, and CNN, across various error metrics, achieving MAE of 0.172, RMSE of 0.313, MSLE of 0.042, R-squared of 0.034, and RMSLE of 0.204. The positive R-squared value indicates the model's robustness against underfitting and overfitting, surpassing the performance of other models with negative R-squared values. This study establishes N-BEATS as a superior and interpretable solution for complex, multi-dimensional forecasting challenges in poultry production, with significant implications for enhancing predictive analytics in agriculture.

PMID:40273069 | DOI:10.1371/journal.pone.0320979

PLOS Glob Public Health. 2025 Apr 24;5(4):e0004018. doi: 10.1371/journal.pgph.0004018. eCollection 2025.

ABSTRACT

Nomadic pastoralists are systematically underrepresented in the planning of health services and frequently missed by health campaigns due to their mobility. Previous studies have developed novel geospatial methods to address these challenges but rely on manual techniques that are too time and resource-intensive to scale on a national or regional level. To address this gap, we developed a computer vision-based approach to automatically locate active nomadic pastoralist settlements from satellite imagery. We curated labeled datasets of satellite images capturing approximately 1,000 historically active settlements in the Omo Valley of Ethiopia and the Samburu County of Kenya to train and evaluate deep learning models, studying their robustness to low spatial resolutions and limits in labeled training data. Using a novel training strategy that leveraged public road and water infrastructure data, we closed performance gaps introduced by shortages in labeled settlement data. We deployed our best model on a region spanning 5,400 square kilometers in the Omo Valley of Ethiopia, resulting in the identification of historical settlements with a 270-fold reduction in manual review volume. Our work serves as a promising framework for automating the localization of nomadic pastoralist settlements at a national scale for health campaigns and demographic surveillance.

PMID:40273062 | DOI:10.1371/journal.pgph.0004018

PLoS One. 2025 Apr 24;20(4):e0320566. doi: 10.1371/journal.pone.0320566. eCollection 2025.

ABSTRACT

This study proposes an advanced elevator fault precursor prediction method integrating Variational Mode Decomposition (VMD), Bidirectional Long Short-Term Memory (BILSTM), and an Autoencoder with an Attention Mechanism (AEAM), collectively referred to as the VMD-BILSTM-AEAM algorithm. This model addresses the challenges of feature redundancy and noise interference in elevator operation data, improving the stability and accuracy of fault predictions. Using a dataset of elevator operation parameters, including current, voltage, and running speed, the model utilizes the Attribute Correlation Density Ranking (ACDR) method for feature selection and the TSO-optimized VMD for denoising, enhancing data quality. Cross-validation and statistical analyses, including confidence interval calculations, were employed to validate the robustness of the model. The results demonstrate that the VMD-BILSTM-AEAM algorithm achieves a mean True Positive Rate (TPR) of 0.919 with a 95% confidence interval of 0.915 to 0.924, a mean False Positive Rate (FPR) of 0.090 with a 95% confidence interval of 0.087 to 0.092, and a mean Area Under the Curve (AUC) of 0.919 with a 95% confidence interval of 0.915 to 0.923. These performance metrics indicate a significant improvement over traditional and other deep learning models, confirming the model's superiority in predictive maintenance of elevators. The robust capability of the VMD-BILSTM-AEAM algorithm to accurately process and analyze time-series data, even in the presence of noise, highlights its potential for broader applications in predictive maintenance and fault detection across various domains.

PMID:40273057 | DOI:10.1371/journal.pone.0320566

Dis Colon Rectum. 2025 Mar 4. doi: 10.1097/DCR.0000000000003677. Online ahead of print.

ABSTRACT

BACKGROUND: Obesity is a risk factor for diverticulitis. However, it remains unclear whether visceral fat area, a more precise measurement of abdominal fat, is associated with the risk of diverticulitis.

OBJECTIVE: To estimate the risk of incident and recurrent diverticulitis according to visceral fat area.

DESIGN: A retrospective cohort study.

SETTINGS: The Mass General Brigham Biobank.

PATIENTS: 6,654 patients who underwent abdominal CT for clinical indications and had no diagnosis of diverticulitis, inflammatory bowel disease, or cancer before the scan.

MAIN OUTCOME MEASURES: Visceral fat area, subcutaneous fat area, and skeletal muscle area were quantified using a deep-learning model applied to abdominal CT. The main exposures were z-scores of body composition metrics, normalized by age, sex, and race. Diverticulitis cases were defined with the ICD codes for the primary or admitting diagnosis from the electronic health records. The risks of incident diverticulitis, complicated diverticulitis, and recurrent diverticulitis requiring hospitalization according to quartiles of body composition metrics z-scores were estimated.

RESULTS: A higher visceral fat area z-score was associated with an increased risk of incident diverticulitis (multivariable HR comparing the highest versus lowest quartile, 2.09; 95% CI, 1.48-2.95; P for trend <.0001), complicated diverticulitis (HR, 2.56; 95% CI, 1.10-5.99; P for trend = .02), and recurrence requiring hospitalization (HR, 2.76; 95% CI, 1.15-6.62; P for trend = .03). The association between visceral fat area and diverticulitis was not materially different among different strata of body mass index. Subcutaneous fat area and skeletal muscle area were not significantly associated with diverticulitis.

LIMITATIONS: The study population was limited to individuals who underwent CT scans for medical indication.

CONCLUSION: Higher visceral fat area derived from CT was associated with incident and recurrent diverticulitis. Our findings provide insight into the underlying pathophysiology of diverticulitis and may have implications for preventive strategies. See Video Abstract.

PMID:40272787 | DOI:10.1097/DCR.0000000000003677

Int J Comput Assist Radiol Surg. 2025 Apr 24. doi: 10.1007/s11548-025-03359-4. Online ahead of print.

ABSTRACT

PURPOSE: Cerebral digital subtraction angiography (DSA) is a standard imaging technique in image-guided interventions for visualizing cerebral blood flow and therapeutic guidance thanks to its high spatio-temporal resolution. To date, cerebral perfusion characteristics in DSA are primarily assessed visually by interventionists, which is time-consuming, error-prone, and subjective. To facilitate fast and reproducible assessment of cerebral perfusion, this work aims to develop and validate a fully automatic and quantitative framework for perfusion DSA.

METHODS: We put forward a framework, perfDSA, that automatically generates deconvolution-based perfusion parametric images from cerebral DSA. It automatically extracts the arterial input function from the supraclinoid internal carotid artery (ICA) and computes deconvolution-based perfusion parametric images including cerebral blood volume (CBV), cerebral blood flow (CBF), mean transit time (MTT), and Tmax.

RESULTS: On a DSA dataset with 1006 patients from the multicenter MR CLEAN registry, the proposed perfDSA achieves a Dice of 0.73(±0.21) in segmenting the supraclinoid ICA, resulting in high accuracy of arterial input function (AIF) curves similar to manual extraction. Moreover, some extracted perfusion images show statistically significant associations (P=2.62e - 5) with favorable functional outcomes in stroke patients.

CONCLUSION: The proposed perfDSA framework promises to aid therapeutic decision-making in cerebrovascular interventions and facilitate discoveries of novel quantitative biomarkers in clinical practice. The code is available at https://github.com/RuishengSu/perfDSA .

PMID:40272658 | DOI:10.1007/s11548-025-03359-4

Immunol Res. 2025 Apr 24;73(1):76. doi: 10.1007/s12026-025-09632-7.

ABSTRACT

Artificial intelligence (AI) has revolutionized various biomedical fields, particularly immunology, by enhancing vaccine development, immunotherapies, and allergy treatments. AI helps identify potential vaccine candidates and predict how the body reacts to different antigens based on a vast number of genomic sequences and protein structures. AI can help cancer patients by analyzing their data and offering personalized immunotherapies. AI has also advanced the field of allergy by identifying potential allergens and predicting allergic reactions based on patient genetic and environmental factors. AI could also help diagnose multiple immunological diseases, including autoimmune diseases and immunodeficiencies, by analyzing patient history and laboratory results. AI has deepened our understanding of the human genome by providing numerous amounts of data from DNA sequences previously believed to be nonfunctional. Through machine learning and deep learning, many laborious research tasks, such as screening for DNA mutations, can be efficiently performed in a short amount of time. AI and machine learning are significantly advancing biomedical science in significant areas, including research and industry. This review discusses the latest AI-based tools that can be utilized in the field of immunology. AI tools significantly advance the field of medical research and healthcare by enabling new scientific discoveries and facilitating rapid clinical diagnosis.

PMID:40272607 | DOI:10.1007/s12026-025-09632-7

Invest Ophthalmol Vis Sci. 2025 Apr 1;66(4):72. doi: 10.1167/iovs.66.4.72.

ABSTRACT

PURPOSE: To develop a deep learning method for vessel segmentation in fundus images, measure retinal vessels, and study the connection between retinal vascular features and systemic indicators in diabetic patients.

METHODS: We conducted a study on patients with diabetes mellitus (DM) at various stages of diabetic retinopathy (DR) using data from the Joint Asia Diabetes Evaluation (JADE) Register. All participants underwent comprehensive clinical assessments, including anthropometric measurements, laboratory tests, and fundus photography, during each follow-up visit (2.81 average follow-up visits). A custom U-Net deep learning model utilizing a variety of open-source datasets was developed for the segmentation and measurement of retinal vessels. We investigated the relationship between systemic indicators and the severity of DR, analyzing the correlation coefficients between systemic indicators and retinal vascular characteristics.

RESULTS: We enrolled a total of 637 patients diagnosed with DM and collected 3575 series of photographs for analysis. Some of the systemic indicators and retinal vascular metrics, including central retinal arteriolar equivalent, central retinal venular equivalent, arteriole-to-venule ratio, and fractal dimension, were significantly correlated with the severity of diabetic retinopathy (P < 0.05). Some physical characteristics, hematological parameters, renal function parameters, metabolism-related parameters, biochemical markers such as folic acid and fasting insulin, liver enzymes, and macrovascular indicators were significantly correlated with certain retinal vascular metrics (P < 0.05).

CONCLUSIONS: Multiple systemic indicators were identified as significantly associated with the advancement of diabetic retinopathy and retinal vascular metrics. Utilizing deep learning techniques for vessel segmentation and measurement on color fundus photographs can help elucidate the connections between retinal vascular characteristics and systemic indicators.

PMID:40272369 | DOI:10.1167/iovs.66.4.72

Int J Comput Dent. 2025 Apr 24;0(0):0. doi: 10.3290/j.ijcd.b6173229. Online ahead of print.

ABSTRACT

AIM: Panoramic radiography is a frequently utilized imaging technique in standard dental examinations and provides many advantages. In this context, studies have been conducted to develop tools to assist physicians in clinical practice by using deep learning models to interpret panoramic radiography images. However, studies in the existing literature have generally addressed these conditions separately and studies that develop a multiclass diagnostic charting model that can detect and segment all these conditions are very limited. Therefore, the aim of this study to develop a deep learning model that can accurately evaluate and segment various dental issues and anatomical structures in panoramic radiographs obtained from different radiography devices and settings.

MATERIALS AND METHODS: Panoramic radiographs were labelled for 33 different conditions in the categories of dental problems, dental restorations, dental implants, anatomical landmarks, periodontal conditions, jaw pathologies and periapical lesions. A YOLO-v8 model was employed to develop an artificial intelligence model for each labelling. A confusion matrix was utilised to successfully evaluate the developed models.

RESULTS: The algorithm achieved a precision value of 0.99-1 in accurately detecting various dental features, such as adult tooth numbering, filling, dental implants, dental pulp, root canal filling, mandibular canal, mandibular condyle, mandible, and pharyngeal airway. With respect to sensitivity, the adult tooth numbering, dental implants, mandibular canal, maxillary sinus, mandibular condyle, angulus mandible, nasal septum, mandible, and hard palate showed the highest values of 0.99-1. The F1-score reached the highest value of 0.99-1 for the root canal filling, adult tooth numbering, dental implants, mandibular canal, mandibular condyle, angulus mandible, mandible, and pharyngeal airway.

CONCLUSION: Artificial intelligence based on convolutional neural networks has a remarkable ability to detect different conditions observed in regular clinical evaluations in panoramic radiographs, displaying excellent performance. Based on these findings, it can be confidently stated that deep learning-based models has great potential to improve routine clinical practices for physicians.

PMID:40272192 | DOI:10.3290/j.ijcd.b6173229

J Dent Res. 2025 Apr 24:220345251322508. doi: 10.1177/00220345251322508. Online ahead of print.

ABSTRACT

Lymph node (LN) metastasis is a prevalent cause of recurrence in oral squamous cell carcinoma (OSCC). However, accurately identifying metastatic LNs (LNs+) remains challenging. This prospective clinical study aims to test the effectiveness of our convolutional neural network (CNN) model for identifying OSCC cervical LN+ in contrast-enhanced computed tomography (CECT) in clinical practice. A CNN model was developed and trained using a dataset of 8,380 CECT images from previous OSCC patients. It was then prospectively validated on 17,777 preoperative CECT images from 354 OSCC patients between October 17, 2023, and August 31, 2024. The model's predicted LN results were provided to the surgical team without influencing surgical or treatment plans. During surgery, the predicted LN+ were identified and sent for separate pathological examination. The accuracy of the model's predictions was compared with those of human experts and verified against pathology reports. The capacity of the model to assist radiologists in LN+ diagnosis was also assessed. The CNN model was trained over 40 epochs and successfully validated after each. Compared with human experts (2 radiologists, 2 surgeons, and 2 students), the CNN model achieved higher sensitivity (81.89% vs. 81.48%, 46.91%, 50.62%), specificity (99.31% vs. 99.15%, 98.36%, 96.27%), LN+ accuracy (76.19% vs. 75.43%, P = 0.854; 40.64%, P < 0.001; 37.44%, P < 0.001), and clinical accuracy (86.16% vs. 83%, 61%, 56%). With the model's assistance, the radiologists surpassed both the previous predictive results without the model's support and the model's performance alone. The CNN model demonstrated an accuracy comparable to that of radiologists in identifying, locating, and predicting cervical LN+ in OSCC patients. Furthermore, the model has the potential to assist radiologists in making more accurate diagnoses.

PMID:40271993 | DOI:10.1177/00220345251322508

Materials (Basel). 2025 Mar 29;18(7):1554. doi: 10.3390/ma18071554.

ABSTRACT

Optical sensing technologies play a crucial role in various fields such as biology, medicine, and food safety by measuring changes in material properties, such as the refractive index, light absorption, and scattering. Dielectric metasurfaces, with their subwavelength-scale geometric features and the ability to achieve high-quality-factor (Q-value) resonances through specific meta-atom designs, offer a new avenue for achieving faster and more sensitive material detection. The resonant wavelength, as one of the key indicators in meta-atom design, is usually determined using traditional solving methods such as electromagnetic simulations, which, although capable of providing high-precision prediction results, suffer from slow computational speed and long processing times. To address this issue, this paper proposes a forward prediction network for the amplitude spectrum of dielectric metasurfaces. Test results demonstrated that the mean square error of this network was consistently less than 10-3, and the neural network required less than 1 s, indicating its high-precision prediction capability. Furthermore, we employed transfer learning to apply this network to predict the near-infrared transmission spectra of high-Q-value resonant dielectric metasurfaces, achieving significant effectiveness. This method greatly enhanced the efficiency of metasurface design, and the designed network could serve as a universal backbone model for the forward prediction of spectral responses for other types of dielectric metasurfaces.

PMID:40271794 | DOI:10.3390/ma18071554

Materials (Basel). 2025 Mar 25;18(7):1448. doi: 10.3390/ma18071448.

ABSTRACT

Carbon fiber-reinforced polymer (CFRP) laminates are widely used in aerospace, automotive, and infrastructure industries due to their high strength-to-weight ratio. However, defect detection in CFRP remains challenging, particularly in low signal-to-noise ratio (SNR) conditions. Conventional segmentation methods often struggle with noise interference and signal variations, leading to reduced detection accuracy. In this study, we evaluate the impact of thermal image preprocessing on improving defect segmentation in CFRP laminates inspected via pulsed thermography. Polynomial approximations and first- and second-order derivatives were applied to refine thermographic signals, enhancing defect visibility and SNR. The U-Net architecture was used to assess segmentation performance on datasets with and without preprocessing. The results demonstrated that preprocessing significantly improved defect detection, achieving an Intersection over Union (IoU) of 95% and an F1-Score of 99%, outperforming approaches without preprocessing. These findings emphasize the importance of preprocessing in enhancing segmentation accuracy and reliability, highlighting its potential for advancing non-destructive testing techniques across various industries.

PMID:40271635 | DOI:10.3390/ma18071448

Mol Ecol. 2025 Apr 24:e17773. doi: 10.1111/mec.17773. Online ahead of print.

ABSTRACT

The process of domestication has altered many phenotypes. Selection on these phenotypes has long been hypothesised to indirectly select for increases in the genome-wide recombination rate. This hypothesis is potentially consistent with theory on the evolution of the recombination rate, but empirical support has been unclear. We review relevant theory, lab-based experiments, and data comparing recombination rates in wild progenitors and their domesticated counterparts. We utilise population sequencing data and a deep learning method to infer genome-wide recombination rates for new comparisons of chicken/red junglefowl, sheep/mouflon, and goat/bezoar. We find evidence of increased recombination in domesticated goats compared to bezoars but more mixed results in chicken and generally decreased recombination in domesticated sheep compared to mouflon. Our results add to a growing body of literature in plants and animals that finds no consistent evidence of an increase in genome-wide recombination with domestication.

PMID:40271548 | DOI:10.1111/mec.17773

Front Plant Sci. 2025 Apr 9;16:1545216. doi: 10.3389/fpls.2025.1545216. eCollection 2025.

ABSTRACT

With the continuous advancement of modern agricultural technologies, the demand for precision fruit-picking techniques has been increasing. This study addresses the challenge of accurate recognition and harvesting of winter peaches by proposing a novel recognition model based on the residual network (ResNet) architecture-WinterPeachNet-aimed at enhancing the accuracy and efficiency of winter peach detection, even in resource-constrained environments. The WinterPeachNet model achieves a comprehensive improvement in network performance by integrating depthwise separable inverted bottleneck ResNet (DIBResNet), bidirectional feature pyramid network (BiFPN) structure, GhostConv module, and the YOLOv11 detection head (v11detect). The DIBResNet module, based on the ResNet architecture, introduces an inverted bottleneck structure and depthwise separable convolution technology, enhancing the depth and quality of feature extraction while effectively reducing the model's computational complexity. The GhostConv module further improves detection accuracy by reducing the number of convolution kernels. Additionally, the BiFPN structure strengthens the model's ability to detect objects of different sizes by fusing multi-scale feature information. The introduction of v11detect further optimizes object localization accuracy. The results show that the WinterPeachNet model achieves excellent performance in the winter peach detection task, with P = 0.996, R = 0.996, mAP50 = 0.995, and mAP50-95 = 0.964, demonstrating the model's efficiency and accuracy in the winter peach detection task. The high efficiency of the WinterPeachNet model makes it highly adaptable in resource-constrained environments, enabling effective object detection at a relatively low computational cost.

PMID:40271441 | PMC:PMC12014684 | DOI:10.3389/fpls.2025.1545216

Adv Mater. 2025 Apr 24:e2419383. doi: 10.1002/adma.202419383. Online ahead of print.

ABSTRACT

Snapshot spectral imaging is an emerging technology for fast data acquisition in dynamic environments, capturing high-volume spatial-spectral information in a single snapshot. However, it suffers from bulky cascading optics and cannot be directly used in space-restricted scenarios such as endoscope-assisted brain microsurgery and real-time cellular tissue imaging. In this work, an ultracompact strategy of parallelized metasurface computed tomography empowered by generative deep learning is proposed, which can effectively reduce the optics volume in snapshot spectral imaging from cm3 scale to sub-mm3 scale while retaining high resolution and speed of imaging so that the above-mentioned pain point problem is well addressed. The system comprises seven multifunctional sub-metasurfaces simultaneously acquiring multi-angle spectral projection and integration information of the target, uses the system-calibrated point spread functions as wavelength and spatial position distributions, and incorporates a generative adversarial deep neural network for fast reconstruction of spatial-spectral multiplexed images. Experimental results show that single snapshot imaging can be achieved in 38 ms with a spectral resolution of 10 nm in the spectral range of 450-650 nm. This technique paves the way for snapshot spectral imaging integration into various highly miniaturized microscopy and endoscopic imaging systems in applications such as advanced medical diagnosis.

PMID:40270309 | DOI:10.1002/adma.202419383

Adv Mater. 2025 Apr 24:e2419245. doi: 10.1002/adma.202419245. Online ahead of print.

ABSTRACT

The combination of artificial neural networks (ANN) and spiking neural networks (SNN) holds great promise for advancing artificial general intelligence (AGI). However, the reported ANN and SNN computational architectures are independent and require a large number of auxiliary circuits and external algorithms for fusion training. Here, a novel vertical bulk heterojunction neuromorphic transistor (VHNT) capable of emulating both ANN and SNN computational functions is presented. TaOx-based electrochemical reactions and PDVT-10/N2200-based bulk heterojunctions are used to realize spike coding and voltage coding, respectively. Notably, the device exhibits remarkable efficiency, consuming a mere 0.84 nJ of energy consumption for a single multiply accumulate (MAC) operation with excellent linearity. Moreover, the device can be switched to spiking neuron and self-activation neuron by simply changing the programming without auxiliary circuits. Finally, the VHNT-based artificial spiking neural network (ASNN) fusion simulation architecture is demonstrated, achieving 95% accuracy for Canadian-Institute-For-Advanced-ResearchResearch-10 (CIFARResearch-10) dataset while significantly enhancing training speed and efficiency. This work proposes a novel device strategy for developing high-performance, low-power, and environmentally adaptive AGI.

PMID:40270224 | DOI:10.1002/adma.202419245

Perioper Med (Lond). 2025 Apr 23;14(1):47. doi: 10.1186/s13741-025-00531-x.

ABSTRACT

BACKGROUND: Interest in artificial intelligence (AI) research in anesthesia is growing rapidly. However, there is a lack of bibliometric analysis to measure and analyze global scientific publications in this field. The aim of this study was to identify the hotspots and trends in AI research in anesthesia through bibliometric analysis.

METHODS: English articles and reviews published from 2000 to 2023 were retrieved from the Web of Science Core Collection (WoSCC) database. The extracted data were summarized and analyzed using Microsoft Excel, and bibliometric analysis were conducted with VOSviewer software.

RESULTS: AI research literature in anesthesia has exhibited rapid growth in recent years. The United States leads in the number of publications and citations, with Stanford University as the most prolific institution. Hyung-Chul Lee is the author with the highest number of publications. The journal Anesthesiology is highly recognized and authoritative in this field. Recent keywords include "musculoskeletal pain", "precision medicine", "stratification", "images", "mean arterial pressure", " enhanced recovery after surgery", "frailty", "telehealth", "postoperative delirium" and "postoperative mortality" indicating hot topics in AI research in anesthesia.

CONCLUSIONS: Publications on AI research in the field of anesthesia have experienced rapid growth over the past two decades and are likely to continue increasing. Research areas such as depth of anesthesia (DOA) and drug infusion (including electroencephalography and deep learning), perioperative risk assessment and prediction (covering mean arterial pressure, frailty, postoperative delirium, and mortality), image classification and recognition (for applications such as ultrasound-guided nerve blocks, vascular access, and difficult airway assessment), and perioperative pain management (particularly musculoskeletal pain) have garnered significant attention. Additionally, topics such as precision medicine, enhanced recovery after surgery, and telehealth are emerging as new hotspots and future directions in this field.

PMID:40270031 | DOI:10.1186/s13741-025-00531-x

NMR Biomed. 2025 Jun;38(6):e70036. doi: 10.1002/nbm.70036.

ABSTRACT

In healthcare sector, magnetic resonance imaging (MRI) images are taken for multiple sclerosis (MS) assessment, classification, and management. However, interpreting an MRI scan requires an exceptional amount of skill because abnormalities on scans are frequently inconsistent with clinical symptoms, making it difficult to convert the findings into effective treatment strategies. Furthermore, MRI is an expensive process, and its frequent utilization to monitor an illness increases healthcare costs. To overcome these drawbacks, this research employs advanced technological approaches to develop a deep learning system for classifying types of MS through clinical brain MRI scans. The major innovation of this model is to influence the convolution network with attention concept and recurrent-based deep learning for classifying the disorder; this also proposes an optimization algorithm for tuning the parameter to enhance the performance. Initially, the total images as 3427 are collected from database, in which the collected samples are categorized for training and testing phase. Here, the segmentation is carried out by adaptive and attentive-based mask regional convolution neural network (AA-MRCNN). In this phase, the MRCNN's parameters are finely tuned with an enhanced pine cone optimization algorithm (EPCOA) to guarantee outstanding efficiency. Further, the segmented image is given to recurrent MobileNet with long short term memory (RM-LSTM) for getting the classification outcomes. Through experimental analysis, this deep learning model is acquired 95.4% for accuracy, 95.3% for sensitivity, and 95.4% for specificity. Hence, these results prove that it has high potential for appropriately classifying the sclerosis disorder.

PMID:40269999 | DOI:10.1002/nbm.70036

Breast Cancer Res. 2025 Apr 23;27(1):62. doi: 10.1186/s13058-025-02008-7.

ABSTRACT

BACKGROUND: Streamlining the clinical procedure of human epidermal growth factor receptor 2 (HER2) examination is challenging. Previous studies neglected the intra-class variability within both HER2-positive and -negative groups and lacked multi-cohort validation. To address this deficiency, this study collected data from multiple cohorts to develop a robust model for HER2 scoring utilizing only Hematoxylin&Eosin-stained whole slide images (WSIs).

METHODS: A total of 578 WSIs were collected from five cohorts, including three public and two private datasets. Each WSI underwent adaptive scale cropping. The transfer-learning-based probabilistic aggregation (TL-PA) model and multi-instance learning (MIL)-based models were compared, both of which were trained on Cohort A and validated on Cohorts B-D. The model demonstrating superior performance was further evaluated in the neoadjuvant therapy (NAT) cohort. Scoring performance was assessed using the area under the receiver operating characteristic curve (AUC). Correlation between the model scores and specific grades (HER2 levels, pathological complete response (pCR) status, residual cancer burden (RCB) grades) were evaluated using Spearman rank correlation and Dunn's test. Patch analysis was performed with manually defined features.

RESULTS: For HER2 scoring, the TL-PA significantly outperformed the MIL-based models, achieving robust AUCs in four validation cohorts (Cohort A: 0.75, Cohort B: 0.75, Cohort C: 0.77, Cohort D: 0.77). Correlation analysis confirmed a moderate association between model scores and manual reader-defined HER2-IHC status (Coefficient(Spearman) = 0.37, P(Spearman) = 0.001) as well as RCB grades (Coefficient(Spearman) = 0.45, P(Spearman) = 0.0006). In Cohort NAT, with the non-pCR as the positive control, the AUC was 0.77. Patch analysis revealed a core-to-peritumoral probability decrease pattern as malignancy spread outward from the lesion's core.

CONCLUSION: TL-PA shows robust generalization for HER2 scoring with minimal data; however, it still inadequately capture intra-class variability. This indicates that future deep-learning endeavors should incorporate more detailed annotations to better align the model's focus with the reasoning of pathologists.

PMID:40269991 | DOI:10.1186/s13058-025-02008-7

Mol Cancer. 2025 Apr 23;24(1):123. doi: 10.1186/s12943-025-02321-x.

ABSTRACT

Drug resistance is one of the main reasons for cancer treatment failure, leading to a rapid recurrence/disease progression of the cancer. Recently, artificial intelligence (AI) has empowered physicians to use its powerful data processing and pattern recognition capabilities to extract and mine valuable drug resistance information from large amounts of clinical or omics data, to study drug resistance mechanisms, to evaluate and predict drug resistance, and to develop innovative therapeutic strategies to reduce drug resistance. In this review, we proposed a feasible workflow for incorporating AI into tumor drug resistance research, highlighted current AI-driven tumor drug resistance applications, and discussed the opportunities and challenges encountered in the process. Based on a comprehensive literature analysis, we systematically summarized the role of AI in tumor drug resistance research, including drug development, resistance mechanism elucidation, drug sensitivity prediction, combination therapy optimization, resistance phenotype identification, and clinical biomarker discovery. With the continuous advancement of AI technology and rigorous validation of clinical data, AI models are expected to fuel the development of precision oncology by improving efficacy, guiding therapeutic decisions, and optimizing patient prognosis. In summary, by leveraging clinical and omics data, AI models are expected to pioneer new therapy strategies to mitigate tumor drug resistance, improve efficacy and patient survival, and provide novel perspectives and tools for oncology treatment.

PMID:40269930 | DOI:10.1186/s12943-025-02321-x