Echocardiography. 2025 Feb;42(2):e70098. doi: 10.1111/echo.70098.

ABSTRACT

Artificial intelligence (AI) has transformed medical imaging by detecting insights and patterns often imperceptible to the human eye, enhancing diagnostic accuracy and efficiency. In cardiovascular imaging, numerous AI models have been developed for cardiac computed tomography (CCT), a primary tool for assessing coronary artery disease (CAD). CCT provides comprehensive, non-invasive assessment, including plaque burden, stenosis severity, and functional assessments such as CT-derived fractional flow reserve (FFRct). Its prognostic value in predicting major adverse cardiovascular events (MACE) has increased the demand for CCT, consequently adding to radiologists' workloads. This review aims to examine AI's role in CCT for ischemic heart disease, highlighting its potential to streamline workflows and improve the efficiency of cardiac care through machine learning and deep learning applications.

PMID:39927866 | DOI:10.1111/echo.70098

Brief Bioinform. 2024 Nov 22;26(1):bbaf046. doi: 10.1093/bib/bbaf046.

ABSTRACT

The interactions between long noncoding RNA (lncRNA) and microRNA (miRNA) play critical roles in life processes, highlighting the necessity to enhance the performance of state-of-the-art models. Here, we introduced TEC-LncMir, a novel approach for predicting lncRNA-miRNA interaction using Transformer Encoder and convolutional neural networks (CNNs). TEC-LncMir treats lncRNA and miRNA sequences as natural languages, encodes them using the Transformer Encoder, and combines representations of a pair of microRNA and lncRNA into a contact tensor (a three-dimensional array). Afterward, TEC-LncMir treats the contact tensor as a multi-channel image, utilizes a four-layer CNN to extract the contact tensor's features, and then uses these features to predict the interaction between the pair of lncRNA and miRNA. We applied a series of comparative experiments to demonstrate that TEC-LncMir significantly improves lncRNA-miRNA interaction prediction, compared with existing state-of-the-art models. We also trained TEC-LncMir utilizing a large training dataset, and as expected, TEC-LncMir achieves unprecedented performance. Moreover, we integrated miRanda into TEC-LncMir to show the secondary structures of high-confidence interactions. Finally, we utilized TEC-LncMir to identify microRNAs interacting with lncRNA NEAT1, where NEAT1 performs as a competitive endogenous RNA of the microRNAs' targets (mRNAs) in brain cells. We also demonstrated the regulatory mechanism of NEAT1 in Alzheimer's disease via transcriptome analysis and sequence alignment analysis. Overall, our results demonstrate the effectivity of TEC-LncMir, suggest a potential regulation of miRNAs by NEAT1 in Alzheimer's disease, and take a significant step forward in lncRNA-miRNA interaction prediction.

PMID:39927859 | DOI:10.1093/bib/bbaf046

J Chem Inf Model. 2025 Feb 10. doi: 10.1021/acs.jcim.4c01877. Online ahead of print.

ABSTRACT

Nanobodies have emerged as a versatile class of biologics with promising therapeutic applications, driving the need for robust tools to predict their epitopes, a critical step for in silico affinity maturation and epitope-targeted design. While molecular docking has long been employed for epitope identification, it requires substantial expertise. With the advent of AI driven tools, epitope identification has become more accessible to a broader community increasing the risk of models' misinterpretation. In this study, we critically evaluate the nanobody epitope prediction performance of two leading models: AlphaFold3 and AlphaFold2-Multimer (v.2.3.2), highlighting their strengths and limitations. Our analysis revealed that the overall success rate remains below 50% for both tools, with AlphaFold3 achieving a modest overall improvement. Interestingly, a significant improvement in AlphaFold3's performance was observed within a specific nanobody class. To address this discrepancy, we explored factors influencing epitope identification, demonstrating that accuracy heavily depends on CDR3 characteristics, such as its 3D spatial conformation and length, which drive binding interactions with the antigen. Additionally, we assessed the robustness of AlphaFold3's confidence metrics, highlighting their potential for broader applications. Finally, we evaluated different strategies aimed at improving the prediction success rate. This study can be extended to assess the accuracy of emerging deep learning models adopting an approach similar to that of AlphaFold3.

PMID:39927847 | DOI:10.1021/acs.jcim.4c01877

Front Neurorobot. 2025 Jan 24;18:1513354. doi: 10.3389/fnbot.2024.1513354. eCollection 2024.

ABSTRACT

INTRODUCTION: In recent years, Unmanned Aerial Vehicles (UAVs) have increasingly been deployed in various applications such as autonomous navigation, surveillance, and object detection. Traditional methods for UAV navigation and object detection have often relied on either handcrafted features or unimodal deep learning approaches. While these methods have seen some success, they frequently encounter limitations in dynamic environments, where robustness and computational efficiency become critical for real-time performance. Additionally, these methods often fail to effectively integrate multimodal inputs, which restricts their adaptability and generalization capabilities when facing complex and diverse scenarios.

METHODS: To address these challenges, we introduce NavBLIP, a novel visual-language model specifically designed to enhance UAV navigation and object detection by utilizing multimodal data. NavBLIP incorporates transfer learning techniques along with a Nuisance-Invariant Multimodal Feature Extraction (NIMFE) module. The NIMFE module plays a key role in disentangling relevant features from intricate visual and environmental inputs, allowing UAVs to swiftly adapt to new environments and improve object detection accuracy. Furthermore, NavBLIP employs a multimodal control strategy that dynamically selects context-specific features to optimize real-time performance, ensuring efficiency in high-stakes operations.

RESULTS AND DISCUSSION: Extensive experiments on benchmark datasets such as RefCOCO, CC12M, and Openlmages reveal that NavBLIP outperforms existing state-of-the-art models in terms of accuracy, recall, and computational efficiency. Additionally, our ablation study emphasizes the significance of the NIMFE and transfer learning components in boosting the model's performance, underscoring NavBLIP's potential for real-time UAV applications where adaptability and computational efficiency are paramount.

PMID:39927288 | PMC:PMC11802496 | DOI:10.3389/fnbot.2024.1513354

Front Med (Lausanne). 2025 Jan 24;11:1506686. doi: 10.3389/fmed.2024.1506686. eCollection 2024.

ABSTRACT

INTRODUCTION: Bones are a fundamental component of human anatomy, enabling movement and support. Bone fractures are prevalent in the human body, and their accurate diagnosis is crucial in medical practice. In response to this challenge, researchers have turned to deep-learning (DL) algorithms. Recent advancements in sophisticated DL methodologies have helped overcome existing issues in fracture detection.

METHODS: Nevertheless, it is essential to develop an automated approach for identifying fractures using the multi-region X-ray dataset from Kaggle, which contains a comprehensive collection of 10,580 radiographic images. This study advocates for the use of DL techniques, including VGG16, ResNet152V2, and DenseNet201, for the detection and diagnosis of bone fractures.

RESULTS: The experimental findings demonstrate that the proposed approach accurately identifies and classifies various types of fractures. Our system, incorporating DenseNet201 and VGG16, achieved an accuracy rate of 97% during the validation phase. By addressing these challenges, we can further improve DL models for fracture detection. This article tackles the limitations of existing methods for fracture detection and diagnosis and proposes a system that improves accuracy.

CONCLUSION: The findings lay the foundation for future improvements to radiographic systems used in bone fracture diagnosis.

PMID:39927268 | PMC:PMC11803505 | DOI:10.3389/fmed.2024.1506686

Phys Imaging Radiat Oncol. 2025 Jan 20;33:100701. doi: 10.1016/j.phro.2025.100701. eCollection 2025 Jan.

ABSTRACT

BACKGROUND AND PURPOSE: Growing evidence suggests that spatial dose variations across the rectal surface influence toxicity risk after radiotherapy. Existing methodologies employ a fixed, arbitrary physical extent for rectal dose mapping, limiting their analysis. We developed a method to standardise rectum contours, unfold them into 2D cylindrical surface maps, and identify subregions where higher doses increase rectal toxicities.

MATERIALS AND METHODS: Data of 1,048 patients with prostate cancer from the REQUITE study were used. Deep learning based automatic segmentations were generated to ensure consistency. Rectum length was standardised using linear transformations superior and inferior to the prostate. The automatic contours were validated against the manual contours through contour variation assessment with cylindrical mapping. Voxel-based analysis of the dose surface maps for the manual and automatic contours against individual rectal toxicities was performed using Student's t permutation test and Cox Proportional Hazards Model (CPHM). Significance was defined by permutation testing.

RESULTS: Our method enabled the analysis of 1,048 patients using automatic segmentation. Student's t-test showed significance (p < 0.05) in the lower posterior for clinical-reported proctitis and patient-reported bowel urgency. Univariable CPHM identified a 3 % increased risk per Gy for clinician-reported proctitis and a 2 % increased risk per Gy for patient-reported bowel urgency in the lower posterior. No other endpoints were significant.

CONCLUSION: We developed a methodology that unfolds the rectum to a 2D surface map. The lower posterior was significant for clinician-reported proctitis and patient-reported bowel urgency, suggesting that reducing the dose in the region could decrease toxicity risk.

PMID:39927213 | PMC:PMC11803856 | DOI:10.1016/j.phro.2025.100701

Nanophotonics. 2025 Jan 27;14(2):121-151. doi: 10.1515/nanoph-2024-0536. eCollection 2025 Feb.

ABSTRACT

Nanophotonics, which explores significant light-matter interactions at the nanoscale, has facilitated significant advancements across numerous research fields. A key objective in this area is the design of ultra-compact, high-performance nanophotonic devices to pave the way for next-generation photonics. While conventional brute-force, intuition-based forward design methods have produced successful nanophotonic solutions over the past several decades, recent developments in optimization methods and artificial intelligence offer new potential to expand these capabilities. In this review, we delve into the latest progress in the inverse design of nanophotonic devices, where AI and optimization methods are leveraged to automate and enhance the design process. We discuss representative methods commonly employed in nanophotonic design, including various meta-heuristic algorithms such as trajectory-based, evolutionary, and swarm-based approaches, in addition to adjoint-based optimization. Furthermore, we explore state-of-the-art deep learning techniques, involving discriminative models, generative models, and reinforcement learning. We also introduce and categorize several notable inverse-designed nanophotonic devices and their respective design methodologies. Additionally, we summarize the open-source inverse design tools and commercial foundries. Finally, we provide our perspectives on the current challenges of inverse design, while offering insights into future directions that could further advance this rapidly evolving field.

PMID:39927200 | PMC:PMC11806510 | DOI:10.1515/nanoph-2024-0536

Open Med (Wars). 2025 Feb 4;20(1):20241110. doi: 10.1515/med-2024-1110. eCollection 2025.

ABSTRACT

BACKGROUND: The highly infectious coronavirus disease 2019 (COVID-19) is caused by severe acute respiratory syndrome coronavirus 2, the seventh coronavirus. It is the longest pandemic in recorded history worldwide. Many countries are still reporting COVID-19 cases even in the fifth year of its emergence.

OBJECTIVE: The performance of various machine learning (ML) and deep learning (DL) models was studied for image-based classification of the lungs infected with COVID-19, pneumonia (viral and bacterial), and normal cases from the chest X-rays (CXRs).

METHODS: The K-nearest neighbour and logistics regression as the two ML models, and Visual Geometry Group-19, Vision transformer, and ConvMixer as the three DL models were included in the investigation to compare the brevity of the detection and classification of the cases.

RESULTS: Among the investigated models, ConvMixer returned the best result in terms of accuracy, recall, precision, F1-score and area under the curve for both binary as well as multiclass classification. The pre-trained ConvMixer model outperformed the other four models in classifying. As per the performance observations, there was 97.1% accuracy for normal and COVID-19 + pneumonia-infected lungs, 98% accuracy for normal and COVID-19 infected lungs, 82% accuracy for normal + bacterial + viral infected lungs, and 98% accuracy for normal + pneumonia infected lungs. The DL models performed better than the ML models for binary and multiclass classification. The performance of these studied models was tried on other CXR image databases.

CONCLUSION: The suggested network effectively detected COVID-19 and different types of pneumonia by using CXR imagery. This could help medical sciences for timely and accurate diagnoses of the cases through bioimaging technology and the use of high-end bioinformatics tools.

PMID:39927166 | PMC:PMC11806240 | DOI:10.1515/med-2024-1110

J Pharm Bioallied Sci. 2024 Dec;16(Suppl 4):S3080-S3082. doi: 10.4103/jpbs.jpbs_1106_24. Epub 2024 Dec 10.

ABSTRACT

This systematic review examines the diverse applications of AI in all areas of dentistry. The search was conducted using the terms "Artificial Intelligence," "Dentistry," "Machine learning," "Deep learning," and "Diagnostic System." Out of 607 publications analyzed from 2010 to 2024, only 13 were selected for inclusion based on their relevance and publication year. AI in dentistry offers both advantages and challenges. It enhances diagnosis, therapy, and patient outcomes through complex algorithms and massive datasets. However, issues such as data privacy, dental professional job displacement, and the necessity for thorough validation and regulation to ensure safety and efficacy remain significant concerns.

PMID:39926925 | PMC:PMC11804999 | DOI:10.4103/jpbs.jpbs_1106_24

Front Med (Lausanne). 2025 Jan 24;12:1510357. doi: 10.3389/fmed.2025.1510357. eCollection 2025.

ABSTRACT

PURPOSE: Acute Mesenteric Ischemia (AMI) is a critical condition marked by restricted blood flow to the intestine, which can lead to tissue necrosis and fatal outcomes. We aimed to develop a deep learning (DL) model based on CT angiography (CTA) imaging and clinical data to diagnose AMI.

METHODS: A retrospective study was conducted on 228 patients suspected of AMI, divided into training and test sets. Clinical data (medical history and laboratory indicators) was included in a multivariate logistic regression analysis to identify the independent factors associated with AMI and establish a clinical factors model. The arterial and venous CTA images were utilized to construct DL model. A Fusion Model was constructed by integrating clinical factors into the DL model. The performance of the models was assessed using receiver operating characteristic (ROC) curves and decision curve analysis (DCA).

RESULTS: Albumin and International Normalized Ratio (INR) were associated with AMI by univariate and multivariate logistic regression (P < 0.05). In the test set, the area under ROC curve (AUC) of the clinical factor model was 0.60 (sensitivity 0.47, specificity 0.86). The AUC of the DL model based on CTA images reached 0.90, which was significantly higher than the AUC values of the clinical factor model, as confirmed by the DeLong test (P < 0.05). The Fusion Model also showed exceptional performance in terms of AUC, accuracy, sensitivity, specificity, and precision, with values of 0.96, 0.94, 0.94, 0.95, and 0.98, respectively. DCA indicated that the Fusion Model provided a greater net benefit than those of models based solely on imaging and clinical information across the majority of the reasonable threshold probabilities.

CONCLUSION: The incorporation of CTA images and clinical information into the model markedly enhances the diagnostic accuracy and efficiency of AMI. This approach provides a reliable tool for the early diagnosis of AMI and the subsequent implementation of appropriate clinical intervention.

PMID:39926426 | PMC:PMC11802816 | DOI:10.3389/fmed.2025.1510357

Front Oncol. 2025 Jan 24;15:1516264. doi: 10.3389/fonc.2025.1516264. eCollection 2025.

ABSTRACT

The integrated application of artificial intelligence (AI) and digital pathology (DP) technology has opened new avenues for advancements in oncology and molecular pathology. Consequently, studies in renal cell carcinoma (RCC) have emerged, highlighting potential in histological subtype classification, molecular aberration identification, and outcome prediction by extracting high-throughput features. However, reviews of these studies are still rare. To address this gap, we conducted a thorough literature review on DP and AI applications in RCC through database searches. Notably, we found that AI models based on deep learning achieved area under the curve (AUC) of over 0.93 in subtype classification, 0.89-0.96 in grading of clear cell RCC, 0.70-0,89 in molecular prediction, and over 0.78 in survival prediction. This review finally discussed the current state of researches and potential future directions.

PMID:39926279 | PMC:PMC11802434 | DOI:10.3389/fonc.2025.1516264

RSC Adv. 2025 Feb 7;15(6):4262-4273. doi: 10.1039/d4ra07951e. eCollection 2025 Feb 6.

ABSTRACT

Virtual screening (VS) methodologies have become key in the drug discovery process but are also applicable to other fields including catalysis, material design, and, more recently, insecticide solutions. Indeed, the search for effective pest control agents is a critical industrial objective, driven by the need to meet stringent regulations and address public health concerns. Cockroaches, known vectors of numerous diseases, represent a major challenge due to the toxicity of existing control measures to humans. In this article, we leverage an Artificial Intelligence (AI)-based screening of the Drug Bank (DB) database to identify novel acetylcholinesterase (AChE) inhibitors, a previously uncharacterized target in the American cockroach (Periplaneta americana). Our AI-based VS pipeline starts with the deep-learning-based AlphaFold to predict the previously unknown 3D structure of AChE based on its amino acid sequence. This first step enables the subsequent ligand-receptor VS of potential inhibitors, the development of which is performed using a consensus VS protocol based on two different tools: Glide, an industry-leading solution, and METADOCK 2, a metaheuristic-based tool that takes advantage of GPU acceleration. The proposed VS pipeline is further refined through rescoring to pinpoint the most promising biocide compounds against cockroaches. We show the search space explored by different metaheuristics generated by METADOCK 2 and how this search is more exhaustive, but complementary, than the one offered by Glide. Finally, we applied Molecular Mechanics Generalized Born Surface Area (MMGBSA) to list the most promising compounds to inhibit the AChE enzyme.

PMID:39926230 | PMC:PMC11804414 | DOI:10.1039/d4ra07951e

World J Gastroenterol. 2025 Feb 7;31(5):102692. doi: 10.3748/wjg.v31.i5.102692.

ABSTRACT

In this letter, we comment on a recent article published in the World Journal of Gastroenterology by Xiao et al, where the authors aimed to use a deep learning model to automatically detect gastrointestinal lesions during capsule endoscopy (CE). CE was first presented in 2000 and was approved by the Food and Drug Administration in 2001. The indications of CE overlap with those of regular diagnostic endoscopy. However, in clinical practice, CE is usually used to detect lesions in areas inaccessible to standard endoscopies or in cases of bleeding that might be missed during conventional endoscopy. Since the emergence of CE, many physiological and technical challenges have been faced and addressed. In this letter, we summarize the current challenges and briefly mention the proposed methods to overcome these challenges to answer a central question: Do we still need CE?

PMID:39926220 | PMC:PMC11718605 | DOI:10.3748/wjg.v31.i5.102692

Sci Rep. 2025 Feb 9;15(1):4815. doi: 10.1038/s41598-025-88753-3.

ABSTRACT

Skin cancer is a prevalent health concern, and accurate segmentation of skin lesions is crucial for early diagnosis. Existing methods for skin lesion segmentation often face trade-offs between efficiency and feature extraction capabilities. This paper proposes Dual Skin Segmentation (DuaSkinSeg), a deep-learning model, to address this gap by utilizing dual encoders for improved performance. DuaSkinSeg leverages a pre-trained MobileNetV2 for efficient local feature extraction. Subsequently, a Vision Transformer-Convolutional Neural Network (ViT-CNN) encoder-decoder architecture extracts higher-level features focusing on long-range dependencies. This approach aims to combine the efficiency of MobileNetV2 with the feature extraction capabilities of the ViT encoder for improved segmentation performance. To evaluate DuaSkinSeg's effectiveness, we conducted experiments on three publicly available benchmark datasets: ISIC 2016, ISIC 2017, and ISIC 2018. The results demonstrate that DuaSkinSeg achieves competitive performance compared to existing methods, highlighting the potential of the dual encoder architecture for accurate skin lesion segmentation.

PMID:39924555 | DOI:10.1038/s41598-025-88753-3

Sci Rep. 2025 Feb 9;15(1):4825. doi: 10.1038/s41598-025-88579-z.

ABSTRACT

To accelerate the clinical adoption of quantitative magnetic resonance imaging (qMRI), frameworks are needed that not only allow for rapid acquisition, but also flexibility, cost efficiency, and high accuracy in parameter mapping. In this study, feed-forward deep neural network (DNN)- and iterative fitting-based frameworks are compared for multi-parametric (MP) relaxometry based on phase-cycled balanced steady-state free precession (pc-bSSFP) imaging. The performance of supervised DNNs (SVNN), self-supervised physics-informed DNNs (PINN), and an iterative fitting framework termed motion-insensitive rapid configuration relaxometry (MIRACLE) was evaluated in silico and in vivo in brain tissue of healthy subjects, including Monte Carlo sampling to simulate noise. DNNs were trained on three distinct in silico parameter distributions and at different signal-to-noise-ratios. The PINN framework, which incorporates physical knowledge into the training process, ensured more consistent inference and increased robustness to training data distribution compared to the SVNN. Furthermore, DNNs utilizing the full information of the underlying complex-valued MR data demonstrated ability to accelerate the data acquisition by a factor of 3. Whole-brain relaxometry using DNNs proved to be effective and adaptive, suggesting the potential for low-cost DNN retraining. This work emphasizes the advantages of in silico DNN MP-qMRI pipelines for rapid data generation and DNN training without extensive dictionary generation, long parameter inference times, or prolonged data acquisition, highlighting the flexible and rapid nature of lightweight machine learning applications for MP-qMRI.

PMID:39924554 | DOI:10.1038/s41598-025-88579-z

Sci Rep. 2025 Feb 9;15(1):4835. doi: 10.1038/s41598-025-85301-x.

ABSTRACT

Accurate segmentation of skin lesions is crucial for reliable clinical diagnosis and effective treatment planning. Automated techniques for skin lesion segmentation assist dermatologists in early detection and ongoing monitoring of various skin diseases, ultimately improving patient outcomes and reducing healthcare costs. To address limitations in existing approaches, we introduce a novel U-shaped segmentation architecture based on our Residual Space State Block. This efficient model, termed 'SSR-UNet,' leverages bidirectional scanning to capture both global and local features in image data, achieving strong performance with low computational complexity. Traditional CNNs struggle with long-range dependencies, while Transformers, though excellent at global feature extraction, are computationally intensive and require large amounts of data. Our SSR-UNet model overcomes these challenges by efficiently balancing computational load and feature extraction capabilities. Additionally, we introduce a spatially-constrained loss function that mitigates gradient stability issues by considering the distance between label and prediction boundaries. We rigorously evaluated SSR-UNet on the ISIC2017 and ISIC2018 skin lesion segmentation benchmarks. The results showed that the accuracy of Mean Intersection Over Union, Classification Accuracy and Specificity indexes in ISIC2017 datasets reached 80.98, 96.50 and 98.04, respectively, exceeding the best indexes of other models by 0.83, 0.99 and 0.38, respectively. The accuracy of Mean Intersection Over Union, Dice Coefficient, Classification Accuracy and Sensitivity on ISIC2018 datasets reached 82.17, 90.21, 95.34 and 88.49, respectively, exceeding the best indicators of other models by 1.71, 0.27, 0.65 and 0.04, respectively. It can be seen that SSR-UNet model has excellent performance in most aspects.

PMID:39924544 | DOI:10.1038/s41598-025-85301-x

Sci Rep. 2025 Feb 9;15(1):4826. doi: 10.1038/s41598-024-83597-9.

ABSTRACT

Mammography is the recommended imaging modality for breast cancer screening. Expressions of human epidermal growth factor receptor 2 (HER2), estrogen receptor (ER), and progesterone receptor (PR) are critical to the development of therapeutic strategies for breast cancer. In this study, a deep learning model (CBAM ResNet-18) was developed to predict the expression of these three receptors on mammography without manual segmentation of masses. Mammography of patients with pathologically proven breast cancer was obtained from two centers. A deep learning-based model (CBAM ResNet-18) for predicting HER2, ER, and PR expressions was trained and validated using five-fold cross-validation on a training dataset. The performance of the model was further tested using an external test dataset. Area under receiver operating characteristic curve (AUC), accuracy (ACC), and F1-score were calculated to assess the ability of the model to predict each receptor. For comparison we also developed original ResNet-18 without attention module and VGG-19 with and without attention module. The AUC (95% CI), ACC, and F1-score were 0.708 (0.609, 0.808), 0.651, 0.528, respectively, in the HER2 test dataset; 0.785 (0.673, 0.897), 0.845, 0.905, respectively, in the ER test dataset; and 0.706 (0.603, 0.809), 0.678, 0.773, respectively, in the PR test dataset. The proposed model demonstrates superior performance compared to the original ResNet-18 without attention module and VGG-19 with and without attention module. The model has the potential to predict HER2, PR, and especially ER expressions, and thus serve as an adjunctive diagnostic tool for breast cancer.

PMID:39924532 | DOI:10.1038/s41598-024-83597-9

Sci Rep. 2025 Feb 10;15(1):4857. doi: 10.1038/s41598-025-88688-9.

ABSTRACT

Raindrops on the windscreen significantly impact a driver's visibility during driving, affecting safe driving. Maintaining a clear windscreen is crucial for drivers to mitigate accident risks in rainy conditions. A real-time rain detection system and an innovative wiper control method are introduced based on machine vision and deep learning. An all-weather raindrop detection model is constructed using a convolutional neural network (CNN) architecture, utilising an improved YOLOv8 model. The all-weather model achieved a precision rate of 0.89, a recall rate of 0.83, and a detection speed of 63 fps, meeting the system's real-time requirements. The raindrop area ratio is computed through target detection, which facilitates the assessment of rainfall begins and ends, as well as intensity variations. When the raindrop area ratio exceeds the wiper activation threshold, the wiper starts, and when the area ratio approaches zero, the wiper stops. The wiper control method can automatically adjust the detection frequency and the wiper operating speed according to changes in rainfall intensity. The wiper activation threshold can be adjusted to make the wiper operation more in line with the driver's habits.

PMID:39924520 | DOI:10.1038/s41598-025-88688-9

Biomed Eng Online. 2025 Feb 9;24(1):16. doi: 10.1186/s12938-025-01348-x.

ABSTRACT

PURPOSE: The aim of this study is to convert low-dose PET (L-PET) images to full-dose PET (F-PET) images based on our Diffused Multi-scale Generative Adversarial Network (DMGAN) to offer a potential balance between reducing radiation exposure and maintaining diagnostic performance.

METHODS: The proposed method includes two modules: the diffusion generator and the u-net discriminator. The goal of the first module is to get different information from different levels, enhancing the generalization ability of the generator to the image and improving the stability of the training. Generated images are inputted into the u-net discriminator, extracting details from both overall and specific perspectives to enhance the quality of the generated F-PET images. We conducted evaluations encompassing both qualitative assessments and quantitative measures. In terms of quantitative comparisons, we employed two metrics, structure similarity index measure (SSIM) and peak signal-to-noise ratio (PSNR) to evaluate the performance of diverse methods.

RESULTS: Our proposed method achieved the highest PSNR and SSIM scores among the compared methods, which improved PSNR by at least 6.2% compared to the other methods. Compared to other methods, the synthesized full-dose PET image generated by our method exhibits a more accurate voxel-wise metabolic intensity distribution, resulting in a clearer depiction of the epilepsy focus.

CONCLUSIONS: The proposed method demonstrates improved restoration of original details from low-dose PET images compared to other models trained on the same datasets. This method offers a potential balance between minimizing radiation exposure and preserving diagnostic performance.

PMID:39924498 | DOI:10.1186/s12938-025-01348-x

Light Sci Appl. 2025 Feb 10;14(1):75. doi: 10.1038/s41377-024-01713-w.

ABSTRACT

Free of noble-metal and high in unit internal quantum efficiency of electroluminescence, organic molecules with thermally activated delayed fluorescence (TADF) features pose the potential to substitute metal-based phosphorescence materials and serve as the new-generation emitters for the mass production of organic light emitting diodes (OLEDs) display. Predicting the function of TADF emitters beyond classic chemical synthesis and material characterization experiments remains a great challenge. The advances in deep learning (DL) based artificial intelligence (AI) offer an exciting opportunity for screening high-performance TADF materials through efficiency evaluation. However, data-driven material screening approaches with the capacity to access the excited state properties of TADF emitters remain extremely difficult and largely unaddressed. Inspired by the fundamental principle that the excited state properties of TADF molecules are strongly dependent on their D-A geometric and electronic structures, we developed the Electronic Structure-Infused Network (ESIN) for TADF emitter screening. Designed with capacities of accurate prediction of the photoluminescence quantum yields (PLQYs) of TADF molecules based on elemental molecular geometry and orbital information and integrated with frontier molecular orbitals (FMOs) weight-based representation and modeling features, ESIN is a promising interpretable tool for emission efficiency evaluation and molecular design of TADF emitters.

PMID:39924488 | DOI:10.1038/s41377-024-01713-w