Opt Lett. 2024 Nov 15;49(22):6457-6460. doi: 10.1364/OL.533923.

ABSTRACT

The acquisition of the time of flight (ToF) of photons has found numerous applications in the biomedical field. Over the last decades, a few strategies have been proposed to deconvolve the temporal instrument response function (IRF) that distorts the experimental time-resolved data. However, these methods require burdensome computational strategies and regularization terms to mitigate noise contributions. Herein, we propose a deep learning model specifically to perform the deconvolution task in fluorescence lifetime imaging (FLI). The model is trained and validated with representative simulated FLI data with the goal of retrieving the true photon ToF distribution. Its performance and robustness are validated with well-controlled in vitro experiments using three time-resolved imaging modalities with markedly different temporal IRFs. The model aptitude is further established with in vivo preclinical investigation. Overall, these in vitro and in vivo validations demonstrate the flexibility and accuracy of deep learning model-based deconvolution in time-resolved FLI and diffuse optical imaging.

PMID:39546693 | DOI:10.1364/OL.533923

Med Phys. 2024 Nov 15. doi: 10.1002/mp.17516. Online ahead of print.

ABSTRACT

BACKGROUND: Pneumonia, a major infectious cause of morbidity and mortality among children worldwide, is typically diagnosed using low-dose pediatric chest X-ray [CXR (chest radiography)]. In pediatric CXR images, bone occlusion leads to a risk of missed diagnosis. Deep learning-based bone-suppression networks relying on training data have enabled considerable progress to be achieved in bone suppression in adult CXR images; however, these networks have poor generalizability to pediatric CXR images because of the lack of labeled pediatric CXR images (i.e., bone images vs. soft-tissue images). Dual-energy subtraction imaging approaches are capable of producing labeled adult CXR images; however, their application is limited because they require specialized equipment, and they are infrequently employed in pediatric settings. Traditional image processing-based models can be used to label pediatric CXR images, but they are semiautomatic and have suboptimal performance.

PURPOSE: We developed an efficient labeling approach for fine-tuning pediatric CXR bone-suppression networks capable of automatically suppressing bone structures in CXR images for pediatric patients without the need for specialized equipment and technologist training.

METHODS: Three steps were employed to label pediatric CXR images and fine-tune pediatric bone-suppression networks: distance transform-based bone-edge detection, traditional image processing-based bone suppression, and fully automated pediatric bone suppression. In distance transform-based bone-edge detection, bone edges were automatically detected by predicting bone-edge distance-transform images, which were then used as inputs in traditional image processing. In this processing, pediatric CXR images were labeled by obtaining bone images through a series of traditional image processing techniques. Finally, the pediatric bone-suppression network was fine-tuned using the labeled pediatric CXR images. This network was initially pretrained on a public adult dataset comprising 240 adult CXR images (A240) and then fine-tuned and validated on 40 pediatric CXR images (P260_40labeled) from our customized dataset (named P260) through five-fold cross-validation; finally, the network was tested on 220 pediatric CXR images (P260_220unlabeled dataset).

RESULTS: The distance transform-based bone-edge detection network achieved a mean boundary distance of 1.029. Moreover, the traditional image processing-based bone-suppression model obtained bone images exhibiting a relative Weber contrast of 93.0%. Finally, the fully automated pediatric bone-suppression network achieved a relative mean absolute error of 3.38%, a peak signal-to-noise ratio of 35.5 dB, a structural similarity index measure of 98.1%, and a bone-suppression ratio of 90.1% on P260_40labeled.

CONCLUSIONS: The proposed fully automated pediatric bone-suppression network, together with the proposed distance transform-based bone-edge detection network, can automatically acquire bone and soft-tissue images solely from CXR images for pediatric patients and has the potential to help diagnose pneumonia in children.

PMID:39546640 | DOI:10.1002/mp.17516

Nucleic Acids Res. 2024 Nov 15:gkae1117. doi: 10.1093/nar/gkae1117. Online ahead of print.

ABSTRACT

RNA molecules function in numerous biological processes by folding into intricate structures. Here we present RASP v2.0, an updated database for RNA structure probing data featuring a substantially expanded collection of datasets along with enhanced online structural analysis functionalities. Compared to the previous version, RASP v2.0 includes the following improvements: (i) the number of RNA structure datasets has increased from 156 to 438, comprising 216 transcriptome-wide RNA structure datasets, 141 target-specific RNA structure datasets, and 81 RNA-RNA interaction datasets, thereby broadening species coverage from 18 to 24, (ii) a deep learning-based model has been implemented to impute missing structural signals for 59 transcriptome-wide RNA structure datasets with low structure score coverage, significantly enhancing data quality, particularly for low-abundance RNAs, (iii) three new online analysis modules have been deployed to assist RNA structure studies, including missing structure score imputation, RNA secondary and tertiary structure prediction, and RNA binding protein (RBP) binding prediction. By providing a resource of much more comprehensive RNA structure data, RASP v2.0 is poised to facilitate the exploration of RNA structure-function relationships across diverse biological processes. RASP v2.0 is freely accessible at http://rasp2.zhanglab.net/.

PMID:39546630 | DOI:10.1093/nar/gkae1117

Sci Adv. 2024 Nov 15;10(46):eadq0856. doi: 10.1126/sciadv.adq0856. Epub 2024 Nov 15.

ABSTRACT

Artificial intelligence models have been increasingly used in the analysis of tumor histology to perform tasks ranging from routine classification to identification of molecular features. These approaches distill cancer histologic images into high-level features, which are used in predictions, but understanding the biologic meaning of such features remains challenging. We present and validate a custom generative adversarial network-HistoXGAN-capable of reconstructing representative histology using feature vectors produced by common feature extractors. We evaluate HistoXGAN across 29 cancer subtypes and demonstrate that reconstructed images retain information regarding tumor grade, histologic subtype, and gene expression patterns. We leverage HistoXGAN to illustrate the underlying histologic features for deep learning models for actionable mutations, identify model reliance on histologic batch effect in predictions, and demonstrate accurate reconstruction of tumor histology from radiographic imaging for a "virtual biopsy."

PMID:39546597 | DOI:10.1126/sciadv.adq0856

PLoS One. 2024 Nov 15;19(11):e0306673. doi: 10.1371/journal.pone.0306673. eCollection 2024.

ABSTRACT

Considering that the traditional deep learning event extraction method ignores the correlation between word features and sequence information, it cannot fully explore the hidden associations between events and events and between events and primary attributes. To solve these problems, we developed a new framework for event extraction called the masked attention-guided dynamic graph aggregation network. On the one hand, to obtain effective word representation and sequence representation, an interaction and complementary relationship are established between word vectors and character vectors. At the same time, a squeeze layer is introduced in the bidirectional independent recurrent unit to model the sentence sequence from both positive and negative directions while retaining the local spatial details to the maximum extent and establishing practical long-term dependencies and rich global context representations. On the other hand, the designed masked attention mechanism can effectively balance the word vector features and sequence semantics and refine these features. The designed dynamic graph aggregation module establishes effective connections between events and events, and between events and essential attributes, strengthens the interactivity and association between them, and realizes feature transfer and aggregation on graph nodes in the neighborhood through dynamic strategies to improve the performance of event extraction. We designed a reconstructed weighted loss function to supervise and adjust each module individually to ensure the optimal feature representation. Finally, the proposed MaskDGNets framework is evaluated on two baseline datasets, DuEE and CCKS2020. It demonstrates its robustness and event extraction performance, with F1 of 81.443% and 87.382%, respectively.

PMID:39546454 | DOI:10.1371/journal.pone.0306673

Insights Imaging. 2024 Nov 15;15(1):277. doi: 10.1186/s13244-024-01851-0.

ABSTRACT

OBJECTIVES: This study aimed to develop and validate a deep-learning radiomics model using CT, T2, and DWI images for predicting pathological complete response (pCR) in patients with esophageal squamous cell carcinoma (ESCC) undergoing neoadjuvant chemoradiotherapy (nCRT).

MATERIALS AND METHODS: Patients with ESCC undergoing nCRT followed by surgery were retrospectively enrolled from three institutions and divided into training and testing cohorts. Both traditional and deep-learning radiomics features were extracted from pre-treatment CT, T2, and DWI. Multiple radiomics models were developed, both single modality and integrated, using machine learning algorithms. The models' performance was assessed using receiver operating characteristic curve analysis, with the area under the curve (AUC) as a primary metric, alongside sensitivity and specificity from the cut-off analysis.

RESULTS: The study involved 151 patients, among whom 63 achieved pCR. The training cohort consisted of 89 patients from Institution 1 (median age 62, 73 males) and the testing cohort included 52 patients from Institution 2 (median age 62, 41 males), and 10 in a clinical trial from Institution 3 (median age 69, 9 males). The integrated model, combining traditional and deep learning radiomics features from CT, T2, and DWI, demonstrated the best performance with an AUC of 0.868 (95% CI: 0.766-0.959), sensitivity of 88% (95% CI: 73.9-100), and specificity of 78.4% (95% CI: 63.6-90.2) in the testing cohort. This model outperformed single-modality models and the clinical model.

CONCLUSION: A multimodality deep learning radiomics model, utilizing CT, T2, and DWI images, was developed and validated for accurately predicting pCR of ESCC following nCRT.

CRITICAL RELEVANCE STATEMENT: Our research demonstrates the satisfactory predictive value of multimodality deep learning radiomics for the response of nCRT in ESCC and provides a potentially helpful tool for personalized treatment including organ preservation strategy.

KEY POINTS: After neoadjuvant chemoradiotherapy, patients with ESCC have pCR rates of about 40%. The multimodality deep learning radiomics model, could predict pCR after nCRT with high accuracy. The multimodality radiomics can be helpful in personalized treatment of esophageal cancer.

PMID:39546168 | DOI:10.1186/s13244-024-01851-0

Insights Imaging. 2024 Nov 15;15(1):275. doi: 10.1186/s13244-024-01854-x.

ABSTRACT

OBJECTIVES: This study developed a deep learning radiomics (DLR) model utilizing baseline computed tomography enterography (CTE) to non-invasively predict stratified healing in Crohn's disease (CD) patients following infliximab (IFX) treatment.

METHODS: The study included 246 CD patients diagnosed at three hospitals. From the first two hospitals, 202 patients were randomly divided into a training cohort (n = 141) and a testing cohort (n = 61) in a 7:3 ratio. The remaining 44 patients from the third hospital served as the validation cohort. Radiomics and deep learning features were extracted from both the active lesion wall and mesenteric adipose tissue. The most valuable features were selected using univariate analysis and least absolute shrinkage and selection operator (LASSO) regression. Multivariate logistic regression was then employed to construct the radiomics, deep learning, and DLR models. Model performance was evaluated using receiver operating characteristic (ROC) curves.

RESULTS: The DLR model achieved an area under the ROC curve (AUC) of 0.948 (95% CI: 0.916-0.980), 0.889 (95% CI: 0.803-0.975), and 0.938 (95% CI: 0.868-1.000) in the training, testing, and validation cohorts, respectively in predicting mucosal healing (MH). Furthermore, the diagnostic performance of DLR model in predicting transmural healing (TH) was 0.856 (95% CI: 0.776-0.935).

CONCLUSIONS: We have developed a DLR model based on the radiomics and deep learning features of baseline CTE to predict stratified healing (MH and TH) in CD patients following IFX treatment with high accuracies in both testing and external cohorts.

CRITICAL RELEVANCE STATEMENT: The deep learning radiomics model developed in our study, along with the nomogram, can intuitively, accurately, and non-invasively predict stratified healing at baseline CT enterography.

KEY POINTS: Early prediction of mucosal and transmural healing in Crohn's Disease patients is beneficial for treatment planning. This model demonstrated excellent performance in predicting mucosal healing and had a diagnostic performance in predicting transmural healing of 0.856. CT enterography images of active lesion walls and mesenteric adipose tissue exhibit an association with stratified healing in Crohn's disease patients.

PMID:39546153 | DOI:10.1186/s13244-024-01854-x

Front Immunol. 2024 Oct 31;15:1416297. doi: 10.3389/fimmu.2024.1416297. eCollection 2024.

ABSTRACT

BACKGROUND: Preeclampsia (PE) poses significant diagnostic and therapeutic challenges. This study aims to identify novel genes for potential diagnostic and therapeutic targets, illuminating the immune mechanisms involved.

METHODS: Three GEO datasets were analyzed, merging two for training set, and using the third for external validation. Intersection analysis of differentially expressed genes (DEGs) and WGCNA highlighted candidate genes. These were further refined through LASSO, SVM-RFE, and RF algorithms to identify diagnostic hub genes. Diagnostic efficacy was assessed using ROC curves. A predictive nomogram and fully Connected Neural Network (FCNN) were developed for PE prediction. ssGSEA and correlation analysis were employed to investigate the immune landscape. Further validation was provided by qRT-PCR on human placental samples.

RESULT: Five biomarkers were identified with validation AUCs: CGB5 (0.663, 95% CI: 0.577-0.750), LEP (0.850, 95% CI: 0.792-0.908), LRRC1 (0.797, 95% CI: 0.728-0.867), PAPPA2 (0.839, 95% CI: 0.775-0.902), and SLC20A1 (0.811, 95% CI: 0.742-0.880), all of which are involved in key biological processes. The nomogram showed strong predictive power (C-index 0.873), while FCNN achieved an optimal AUC of 0.911 (95% CI: 0.732-1.000) in five-fold cross-validation. Immune infiltration analysis revealed the importance of T cell subsets, neutrophils, and NK cells in PE, linking these genes to immune mechanisms underlying PE pathogenesis.

CONCLUSION: CGB5, LEP, LRRC1, PAPPA2, and SLC20A1 are validated as key diagnostic biomarkers for PE. Nomogram and FCNN could credibly predict PE. Their association with immune infiltration underscores the crucial role of immune responses in PE pathogenesis.

PMID:39544937 | PMC:PMC11560445 | DOI:10.3389/fimmu.2024.1416297

J Eye Mov Res. 2024 Oct 10;17(2). doi: 10.16910/jemr.17.2.1. eCollection 2024.

ABSTRACT

The evaluation of design results plays a crucial role in the development of design. This study presents a design work evaluation system for design education that assists design instructors in conducting objective evaluations. An automatic design evaluation model based on convolutional neural networks has been established, which enables intelligent evaluation of student design works. During the evaluation process, the CAM is obtained. Simultaneously, an eye-tracking experiment was designed to collect gaze data and generate eye-tracking heat maps. By comparing the heat maps with CAM, an attempt was made to explore the correlation between the focus of the evaluation's attention on human design evaluation and the CNN intelligent evaluation. The experimental results indicate that there is some certain correlation between humans and CNN in terms of the key points they focus on when conducting an evaluation. However, there are significant differences in background observation. The research results demonstrate that the intelligent evaluation model of CNN can automatically evaluate product design works and effectively classify and predict design product images. The comparison shows a correlation between artificial intelligence and the subjective evaluation of human eyes in evaluation strategy. Introducing artificial intelligence into the field of design evaluation for education has a strong potential to promote the development of design education.

PMID:39544878 | PMC:PMC11561857 | DOI:10.16910/jemr.17.2.1

Front Neurorobot. 2024 Oct 31;18:1457843. doi: 10.3389/fnbot.2024.1457843. eCollection 2024.

ABSTRACT

Over the past few years, a growing number of researchers have dedicated their efforts to focusing on temporal modeling. The advent of transformer-based methods has notably advanced the field of 2D image-based vision tasks. However, with respect to 3D video tasks such as action recognition, applying temporal transformations directly to video data significantly increases both computational and memory demands. This surge in resource consumption is due to the multiplication of data patches and the added complexity of self-aware computations. Accordingly, building efficient and precise 3D self-attentive models for video content represents as a major challenge for transformers. In our research, we introduce an Long and Short-term Temporal Difference Vision Transformer (LS-VIT). This method incorporates short-term motion details into images by weighting the difference across several consecutive frames, thereby equipping the original image with the ability to model short-term motions. Concurrently, we integrate a module designed to understand long-term motion details. This module enhances the model's capacity for long-term motion modeling by directly integrating temporal differences from various segments via motion excitation. Our thorough analysis confirms that the LS-VIT achieves high recognition accuracy across multiple benchmarks (e.g., UCF101, HMDB51, Kinetics-400). These research results indicate that LS-VIT has the potential for further optimization, which can improve real-time performance and action prediction capabilities.

PMID:39544849 | PMC:PMC11560894 | DOI:10.3389/fnbot.2024.1457843

Front Plant Sci. 2024 Oct 31;15:1398277. doi: 10.3389/fpls.2024.1398277. eCollection 2024.

ABSTRACT

INTRODUCTION: In response to the current mainstream deep learning detection methods with a large number of learned parameters and the complexity of apple leaf disease scenarios, the paper proposes a lightweight method and names it LCGSC-YOLO. This method is based on the LCNet(A Lightweight CPU Convolutional Neural Network) and GSConv(Group Shuffle Convolution) module modified YOLO(You Only Look Once) framework.

METHODS: Firstly, the lightweight LCNet is utilized to reconstruct the backbone network, with the purpose of reducing the number of parameters and computations of the model. Secondly, the GSConv module and the VOVGSCSP (Slim-neck by GSConv) module are introduced in the neck network, which makes it possible to minimize the number of model parameters and computations while guaranteeing the fusion capability among the different feature layers. Finally, coordinate attention is embedded in the tail of the backbone and after each VOVGSCSP module to improve the problem of detection accuracy degradation issue caused by model lightweighting.

RESULTS: The experimental results show the LCGSC-YOLO can achieve an excellent detection performance with mean average precision of 95.5% and detection speed of 53 frames per second (FPS) on the mixed datasets of Plant Pathology 2021 (FGVC8) and AppleLeaf9.

DISCUSSION: The number of parameters and Floating Point Operations (FLOPs) of the LCGSC-YOLO are much less thanother related comparative experimental algorithms.

PMID:39544536 | PMC:PMC11560749 | DOI:10.3389/fpls.2024.1398277

Front Plant Sci. 2024 Oct 31;15:1460060. doi: 10.3389/fpls.2024.1460060. eCollection 2024.

ABSTRACT

With the advancement of computer vision technology, vision-based target perception has emerged as a predominant approach for harvesting robots to identify and locate fruits. However, little attention has been paid to the fact that fruits may be obscured by stems or other objects. In order to improve the vision detection ability of fruit harvesting robot, a fruit target selection and location approach considering obstacle perception was proposed. To enrich the dataset for tomato harvesting, synthetic data were generated by rendering a 3D simulated model of the tomato greenhouse environment, and automatically producing corresponding pixel-level semantic segmentation labels. An attention-based spatial-relationship feature extraction module (SFM) with lower computational complexity was designed to enhance the ability of semantic segmentation network DeepLab v3+ in accurately segmenting linear-structured obstructions such as stems and wires. An adaptive K-means clustering method was developed to distinguish individual instances of fruits. Furthermore, a barrier-free fruit selection algorithm that integrates information of obstacles and fruit instances was proposed to identify the closest and largest non-occluded fruit as the optimal picking target. The improved semantic segmentation network exhibited enhanced performance, achieving an accuracy of 96.75%. Notably, the Intersection-over-Union (IoU) of wire and stem classes was improved by 5.0% and 2.3%, respectively. Our target selection method demonstrated accurate identification of obstacle types (96.15%) and effectively excluding fruits obstructed by strongly resistant objects (86.67%). Compared to the fruit detection method without visual obstacle avoidance (Yolo v5), our approach exhibited an 18.9% increase in selection precision and a 1.3% reduction in location error. The improved semantic segmentation algorithm significantly increased the segmentation accuracy of linear-structured obstacles, and the obstacle perception algorithm effectively avoided occluded fruits. The proposed method demonstrated an appreciable ability in precisely selecting and locating barrier-free fruits within non-structural environments, especially avoiding fruits obscured by stems or wires. This approach provides a more reliable and practical solution for fruit selection and localization for harvesting robots, while also being applicable to other fruits and vegetables such as sweet peppers and kiwis.

PMID:39544532 | PMC:PMC11560766 | DOI:10.3389/fpls.2024.1460060

Health Inf Sci Syst. 2024 Nov 12;12(1):53. doi: 10.1007/s13755-024-00311-9. eCollection 2024 Dec.

ABSTRACT

Depression is one of the most prevalent mental conditions which could impair people's productivity and lead to severe consequences. The diagnosis of this disease is complex as it often relies on a physician's subjective interview-based screening. The aim of our work is to propose deep learning models for automatic depression detection by using different data modalities, which could assist in the diagnosis of depression. Current works on automatic depression detection mostly are tested on a single dataset, which might lack robustness, flexibility and scalability. To alleviate this problem, we design a novel Graph Neural Network-enhanced Transformer model named DePressionDetect Net (DPD Net) that leverages textual, audio and visual features and can work under two different application settings: the clinical setting and the social media setting. The model consists of a unimodal encoder module for encoding single modality, a multimodal encoder module for integrating the multimodal information, and a detection module for producing the final prediction. We also propose a model named DePressionDetect-with-EEG Net (DPD-E Net) to incorporate Electroencephalography (EEG) signals and speech data for depression detection. Experiments across four benchmark datasets show that DPD Net and DPD-E Net can outperform the state-of-the-art models on three datasets (i.e., E-DAIC dataset, Twitter depression dataset and MODMA dataset), and achieve competitive performance on the fourth one (i.e., D-vlog dataset). Ablation studies demonstrate the advantages of the proposed modules and the effectiveness of combining diverse modalities for automatic depression detection.

PMID:39544256 | PMC:PMC11557813 | DOI:10.1007/s13755-024-00311-9

J Biol Methods. 2024 Aug 9;11(3):e99010017. doi: 10.14440/jbm.2024.0016. eCollection 2024.

ABSTRACT

Gene expression data are used to discover meaningful hidden information in gene datasets. Cancer and other disorders may be diagnosed based on differences in gene expression profiles, and this information can be gleaned by gene sequencing. Thanks to the tremendous power of artificial intelligence (AI), healthcare has become a significant user of deep learning (DL) for predicting cancer diseases and categorizing gene expression. Gene expression Microarrays have been proved effective in predicting cancer diseases and categorizing gene expression. Gene expression datasets contain only limited samples, but the features of cancer are diverse and complex. To overcome their dimensionality, gene expression datasets must be enhanced. By learning and analyzing features of input data, it is possible to extract features, as multidimensional arrays, from the data. Synthetic samples are needed to strengthen the range of information. DL strategies may be used when gene expression data are used to diagnose and classify cancer diseases.

PMID:39544183 | PMC:PMC11557296 | DOI:10.14440/jbm.2024.0016

Can Assoc Radiol J. 2024 Nov 15:8465371241296834. doi: 10.1177/08465371241296834. Online ahead of print.

ABSTRACT

Purpose: Pediatric low-grade gliomas (pLGG) are the most common brain tumour in children, and the molecular diagnosis of pLGG enables targeted treatment. We use MRI-based Convolutional Neural Networks (CNNs) for molecular subtype identification of pLGG and augment the models using tumour location probability maps. Materials and Methods: MRI FLAIR sequences of 214 patients (110 male, mean age of 8.54 years, 143 BRAF fused and 71 BRAF V600E mutated pLGG tumours) from January 2000 to December 2018 were included in this retrospective REB-approved study. Tumour segmentations (volumes of interest-VOIs) were provided by a pediatric neuroradiology fellow and verified by a pediatric neuroradiologist. Patients were randomly split into development and test sets with an 80/20 ratio. The 3D binary VOI masks for each class in the development set were combined to derive the probability density functions of tumour location. Three pipelines for molecular diagnosis of pLGG were developed: location-based, CNN-based, and hybrid. The experiment was repeated 100 times each with different model initializations and data splits, and the Areas Under the Receiver Operating Characteristic Curve (AUROC) was calculated, and Student's t-test was conducted. Results: The location-based classifier achieved an AUROC of 77.9, 95% confidence interval (CI) (76.8, 79.0). CNN-based classifiers achieved an AUROC of 86.1, 95% CI (85.0, 87.3), while the tumour-location-guided CNNs outperformed the other classifiers with an average AUROC of 88.64, 95% CI (87.6, 89.7), which was statistically significant (P-value .0018). Conclusion: Incorporating tumour location probability maps into CNN models led to significant improvements for molecular subtype identification of pLGG.

PMID:39544176 | DOI:10.1177/08465371241296834

Anal Methods. 2024 Nov 15. doi: 10.1039/d4ay01346h. Online ahead of print.

ABSTRACT

Agriculture has a substantial demand for classification, and each agricultural product exhibits a unique ion signal. This paper summarizes the classification techniques of ion-selective electrode arrays in agriculture. Initially, data sample collection methods based on ion-selective electrode arrays are summarized. The paper then discusses the current state of classification algorithms from the perspectives of machine learning, artificial neural networks, extreme learning machines, and deep learning, along with their existing research in ion-selective electrodes and related fields. Then, the potential applications in crop and livestock growth status classification, soil classification, agricultural product quality classification, and agricultural product type classification are discussed. Ultimately, the future challenges of ion-selective electrode research are discussed from the perspectives of the sensor itself and algorithms combined with sensor arrays, which also positively impact the promotion of their application in agriculture. This work will advance the application of classification techniques combined with ion-selective electrode arrays in agriculture.

PMID:39543972 | DOI:10.1039/d4ay01346h

J Clin Pediatr Dent. 2024 Nov;48(6):161-172. doi: 10.22514/jocpd.2024.136. Epub 2024 Nov 3.

ABSTRACT

Traditional tooth image analysis methods primarily focus on feature extraction from individual images, often overlooking critical tooth shape and position information. This paper presents a novel computer-aided diagnosis method, Collaborative learning with Mask Region-based Convolutional Neural Network (Co-Mask R-CNN), designed to enhance tooth image analysis by leveraging the integration of complementary information. First, image enhancement is employed to generate an edge-enhanced tooth edge image. Then, a collaborative learning strategy combined with Mask R-CNN is introduced, where the original and edge images are input simultaneously, and a two-stream encoder extracts feature maps from complementary images. By utilizing an attention mechanism, the output features from the two branches are dynamically fused, quantifying the relative importance of the two complementary images at different spatial positions. Finally, the fused feature map is utilized for tooth instance segmentation. Extensive experiments are conducted using a proprietary dataset to evaluate the effectiveness of Co-Mask R-CNN, and the results are compared against those of an alternative segmentation network. The results demonstrate that Co-Mask R-CNN outperforms the other networks in terms of both segmentation accuracy and robustness. Consequently, this method holds considerable promise for providing medical professionals with precise tooth segmentation results, establishing a reliable foundation for subsequent tooth disease diagnosis and treatment.

PMID:39543893 | DOI:10.22514/jocpd.2024.136

Nat Comput Sci. 2024 Nov 14. doi: 10.1038/s43588-024-00725-1. Online ahead of print.

NO ABSTRACT

PMID:39543392 | DOI:10.1038/s43588-024-00725-1

Sci Rep. 2024 Nov 14;14(1):28019. doi: 10.1038/s41598-024-79175-8.

ABSTRACT

Efficient prediction of blastocyst formation from early-stage human embryos is imperative for improving the success rates of assisted reproductive technology (ART). Clinics transfer embryos at the blastocyst stage on Day-5 but Day-3 embryo transfer offers the advantage of a shorter culture duration, which reduces exposure to laboratory conditions, potentially enhancing embryonic development within a more conducive uterine environment and improving the likelihood of successful pregnancies. In this paper, we present a novel ResNet-GRU deep-learning model to predict blastocyst formation at 72 HPI. The model considers the time-lapse images from the incubator from Day 0 to Day 3. The model predicts blastocyst formation with a validation accuracy of 93% from the cleavage stage. The sensitivity and specificity are 0.97 and 0.77 respectively. The deep learning model presented in this paper will assist the embryologist in identifying the best embryo to transfer at Day 3, leading to improved patient outcomes and pregnancy rates in ART.

PMID:39543360 | DOI:10.1038/s41598-024-79175-8

Nat Methods. 2024 Nov 14. doi: 10.1038/s41592-024-02511-3. Online ahead of print.

ABSTRACT

The rapid growth of single-cell transcriptomic technology has produced an increasing number of datasets for both embryonic development and in vitro pluripotent stem cell-derived models. This avalanche of data surrounding pluripotency and the process of lineage specification has meant it has become increasingly difficult to define specific cell types or states in vivo, and compare these with in vitro differentiation. Here we utilize a set of deep learning tools to integrate and classify multiple datasets. This allows the definition of both mouse and human embryo cell types, lineages and states, thereby maximizing the information one can garner from these precious experimental resources. Our approaches are built on recent initiatives for large-scale human organ atlases, but here we focus on material that is difficult to obtain and process, spanning early mouse and human development. Using publicly available data for these stages, we test different deep learning approaches and develop a model to classify cell types in an unbiased fashion at the same time as defining the set of genes used by the model to identify lineages, cell types and states. We used our models trained on in vivo development to classify pluripotent stem cell models for both mouse and human development, showcasing the importance of this resource as a dynamic reference for early embryogenesis.

PMID:39543284 | DOI:10.1038/s41592-024-02511-3