medRxiv [Preprint]. 2024 May 4:2024.05.03.24306808. doi: 10.1101/2024.05.03.24306808.

ABSTRACT

PURPOSE: Lung tissue and lung excursion segmentation in thoracic dynamic magnetic resonance imaging (dMRI) is a critical step for quantitative analysis of thoracic structure and function in patients with respiratory disorders such as Thoracic Insufficiency Syndrome (TIS). However, the complex variability of intensity and shape of anatomical structures and the low contrast between the lung and surrounding tissue in MR images seriously hamper the accuracy and robustness of automatic segmentation methods. In this paper, we develop an interactive deep-learning based segmentation system to solve this problem.

MATERIAL & METHODS: Considering the significant difference in lung morphological characteristics between normal subjects and TIS subjects, we utilized two independent data sets of normal subjects and TIS subjects to train and test our model. 202 dMRI scans from 101 normal pediatric subjects and 92 dMRI scans from 46 TIS pediatric subjects were acquired for this study and were randomly divided into training, validation, and test sets by an approximate ratio of 5:1:4. First, we designed an interactive region of interest (ROI) strategy to detect the lung ROI in dMRI for accelerating the training speed and reducing the negative influence of tissue located far away from the lung on lung segmentation. Second, we utilized a modified 2D U-Net to segment the lung tissue in lung ROIs, in which the adjacent slices are utilized as the input data to take advantage of the spatial information of the lungs. Third, we extracted the lung shell from the lung segmentation results as the shape feature and inputted the lung ROIs with shape feature into another modified 2D U-Net to segment the lung excursion in dMRI. To evaluate the performance of our approach, we computed the Dice coefficient (DC) and max-mean Hausdorff distance (MM-HD) between manual and automatic segmentations. In addition, we utilized Coefficient of Variation (CV) to assess the variability of our method on repeated dMRI scans and the differences of lung tidal volumes computed from the manual and automatic segmentation results.

RESULTS: The proposed system yielded mean Dice coefficients of 0.96±0.02 and 0.89±0.05 for lung segmentation in dMRI of normal subjects and TIS subjects, respectively, demonstrating excellent agreement with manual delineation results. The Coefficient of Variation and p-values show that the estimated lung tidal volumes of our approach are statistically indistinguishable from those derived by manual segmentations.

CONCLUSIONS: The proposed approach can be applied to lung tissue and lung excursion segmentation from dynamic MR images with high accuracy and efficiency. The proposed approach has the potential to be utilized in the assessment of patients with TIS via dMRI routinely.

PMID:38746267 | PMC:PMC11092696 | DOI:10.1101/2024.05.03.24306808

bioRxiv [Preprint]. 2024 May 2:2024.04.30.591806. doi: 10.1101/2024.04.30.591806.

ABSTRACT

Enabled by the explosion of data and substantial increase in computational power, deep learning has transformed fields such as computer vision and natural language processing (NLP) and it has become a successful method to be applied to many transcriptomic analysis tasks. A core advantage of deep learning is its inherent capability to incorporate feature computation within the machine learning models. This results in a comprehensive and machine-readable representation of sequences, facilitating the downstream classification and clustering tasks. Compared to machine translation problems in NLP, feature embedding is particularly challenging for transcriptomic studies as the sequences are string of thousands of nucleotides in length, which make the long-term dependencies between features from different parts of the sequence even more difficult to capture. This highlights the need for nucleotide sequence embedding methods that are capable of learning input sequence features implicitly. Here we introduce ntEmbd, a deep learning embedding tool that captures dependencies between different features of the sequences and learns a latent representation for given nucleotide sequences. We further provide two sample use cases, describing how learned RNA features can be used in downstream analysis. The first use case demonstrates ntEmbd ' s utility in classifying coding and noncoding RNA benchmarked against existing tools, and the second one explores the utility of learned representations in identifying adapter sequences in nanopore RNA-seq reads. The tool as well as the trained models are freely available on GitHub at https://github.com/bcgsc/ntEmbd.

PMID:38746190 | PMC:PMC11092672 | DOI:10.1101/2024.04.30.591806

Res Sq [Preprint]. 2024 Apr 26:rs.3.rs-4215086. doi: 10.21203/rs.3.rs-4215086/v1.

ABSTRACT

The majority of proteins must form higher-order assemblies to perform their biological functions. Despite the importance of protein quaternary structure, there are few machine learning models that can accurately and rapidly predict the symmetry of assemblies involving multiple copies of the same protein chain. Here, we address this gap by training several classes of protein foundation models, including ESM-MSA, ESM2, and RoseTTAFold2, to predict homo-oligomer symmetry. Our best model named Seq2Symm, which utilizes ESM2, outperforms existing template-based and deep learning methods. It achieves an average PR-AUC of 0.48 and 0.44 across homo-oligomer symmetries on two different held-out test sets compared to 0.32 and 0.23 for the template-based method. Because Seq2Symm can rapidly predict homo-oligomer symmetries using a single sequence as input (~ 80,000 proteins/hour), we have applied it to 5 entire proteomes and ~ 3.5 million unlabeled protein sequences to identify patterns in protein assembly complexity across biological kingdoms and species.

PMID:38746169 | PMC:PMC11092833 | DOI:10.21203/rs.3.rs-4215086/v1

Skin Res Technol. 2024 May;30(5):e13607. doi: 10.1111/srt.13607.

ABSTRACT

BACKGROUND: Timely diagnosis plays a critical role in determining melanoma prognosis, prompting the development of deep learning models to aid clinicians. Questions persist regarding the efficacy of clinical images alone or in conjunction with dermoscopy images for model training. This study aims to compare the classification performance for melanoma of three types of CNN models: those trained on clinical images, dermoscopy images, and a combination of paired clinical and dermoscopy images from the same lesion.

MATERIALS AND METHODS: We divided 914 image pairs into training, validation, and test sets. Models were built using pre-trained Inception-ResNetV2 convolutional layers for feature extraction, followed by binary classification. Training comprised 20 models per CNN type using sets of random hyperparameters. Best models were chosen based on validation AUC-ROC.

RESULTS: Significant AUC-ROC differences were found between clinical versus dermoscopy models (0.661 vs. 0.869, p < 0.001) and clinical versus clinical + dermoscopy models (0.661 vs. 0.822, p = 0.001). Significant sensitivity differences were found between clinical and dermoscopy models (0.513 vs. 0.799, p = 0.01), dermoscopy versus clinical + dermoscopy models (0.799 vs. 1.000, p = 0.02), and clinical versus clinical + dermoscopy models (0.513 vs. 1.000, p < 0.001). Significant specificity differences were found between dermoscopy versus clinical + dermoscopy models (0.800 vs. 0.288, p < 0.001) and clinical versus clinical + dermoscopy models (0.650 vs. 0.288, p < 0.001).

CONCLUSION: CNN models trained on dermoscopy images outperformed those relying solely on clinical images under our study conditions. The potential advantages of incorporating paired clinical and dermoscopy images for CNN-based melanoma classification appear less clear based on our findings.

PMID:38742379 | DOI:10.1111/srt.13607

Ergonomics. 2024 May 14:1-14. doi: 10.1080/00140139.2024.2349757. Online ahead of print.

ABSTRACT

This study proposes an integrated ergonomic evaluation designed to identify unsafe postures, whereby postural risks during industrial work are assessed in the context of virtual reality-based smart manufacturing. Unsafe postures were recognised by identifying the displacements of the centre of mass (COM) of body keypoints using a computer vision-based deep learning (DL) convolutional neural network approach. The risk levels for the identified unsafe postures were calculated using ergonomic risk assessment tools rapid upper limb assessment and rapid whole-body assessment. An analysis of variance was conducted to determine significant differences between the vertical and horizontal directions of postural movements associated with the most unsafe postures. The findings assess the ergonomic risk levels and identify the most unsafe postures during industrial work in smart manufacturing using DL method. The identified postural risks can help industry managers and researchers acquire a better understanding of unsafe postures.

PMID:38742363 | DOI:10.1080/00140139.2024.2349757

Front Cardiovasc Med. 2024 Apr 29;11:1332925. doi: 10.3389/fcvm.2024.1332925. eCollection 2024.

ABSTRACT

BACKGROUND: The diagnostic performance and clinical validity of automatic intracoronary imaging (ICI) tools for atherosclerotic plaque assessment have not been systematically investigated so far.

METHODS: We performed a scoping review including studies on automatic tools for automatic plaque components assessment by means of optical coherence tomography (OCT) or intravascular imaging (IVUS). We summarized study characteristics and reported the specifics and diagnostic performance of developed tools.

RESULTS: Overall, 42 OCT and 26 IVUS studies fulfilling the eligibility criteria were found, with the majority published in the last 5 years (86% of the OCT and 73% of the IVUS studies). A convolutional neural network deep-learning method was applied in 71% of OCT- and 34% of IVUS-studies. Calcium was the most frequent plaque feature analyzed (26/42 of OCT and 12/26 of IVUS studies), and both modalities showed high discriminatory performance in testing sets [range of area under the curve (AUC): 0.91-0.99 for OCT and 0.89-0.98 for IVUS]. Lipid component was investigated only in OCT studies (n = 26, AUC: 0.82-0.86). Fibrous cap thickness or thin-cap fibroatheroma were mainly investigated in OCT studies (n = 8, AUC: 0.82-0.94). Plaque burden was mainly assessed in IVUS studies (n = 15, testing set AUC reported in one study: 0.70).

CONCLUSION: A limited number of automatic machine learning-derived tools for ICI analysis is currently available. The majority have been developed for calcium detection for either OCT or IVUS images. The reporting of the development and validation process of automated intracoronary imaging analyses is heterogeneous and lacks critical information.

SYSTEMATIC REVIEW REGISTRATION: Open Science Framework (OSF), https://osf.io/nps2b/.Graphical AbstractCentral Illustration.

PMID:38742173 | PMC:PMC11090039 | DOI:10.3389/fcvm.2024.1332925

Proc SPIE Int Soc Opt Eng. 2024 Feb;12926:129261N. doi: 10.1117/12.3001752. Epub 2024 Apr 2.

ABSTRACT

Glioblastoma (GBM) is most aggressive and common adult brain tumor. The standard treatments typically include maximal surgical resection, followed adjuvant radiotherapy and chemotherapy. However, the efficacy of these treatment is often limited, as tumor often infiltrate into the surrounding brain tissue, often extending beyond the radiologically defined margins. This infiltration contributes to the high recurrence rate and poor prognosis associated with GBM patients, necessitating advanced methods for early and accurate detection of tumor infiltration. Despite the great promise traditional supervised machine learning shows in predicting tumor infiltration beyond resectable margins, these methods are heavily reliant on expert-drawn Regions of Interest (ROIs), which are used to construct multi-variate models of different Magnetic Resonance (MR) signal characteristics associated with tumor infiltration. This process is both time consuming and resource intensive. Addressing this limitation, our study proposes a novel integration of fully automatic methods for generating ROIs with deep learning algorithms to create predictive maps of tumor infiltration. This approach uses pre-operative multi-parametric MRI (mpMRI) scans, encompassing T1, T1Gd, T2, T2-FLAIR, and ADC sequences, to fully leverage the knowledge from previously drawn ROIs. Subsequently, a patch based Convolutional Neural Network (CNN) model is trained on these automatically generated ROIs to predict areas of potential tumor infiltration. The performance of this model was evaluated using a leave-one-out cross-validation approach. Generated predictive maps binarized for comparison against post-recurrence mpMRI scans. The model demonstrates robust predictive capability, evidenced by the average cross-validated accuracy of 0.87, specificity of 0.88, and sensitivity of 0.90. Notably, the odds ratio of 8.62 indicates that regions identified as high-risk on the predictive map were significantly more likely to exhibit tumor recurrence than low-risk regions. The proposed method demonstrates that a fully automatic mpMRI analysis using deep learning can successfully predict tumor infiltration in peritumoral region for GBM patients while bypassing the intensive requirement for expert-drawn ROIs.

PMID:38742150 | PMC:PMC11089715 | DOI:10.1117/12.3001752

Heliyon. 2024 May 3;10(9):e30486. doi: 10.1016/j.heliyon.2024.e30486. eCollection 2024 May 15.

ABSTRACT

A novel automated medication verification system (AMVS) aims to address the limitation of manual medication verification among healthcare professionals with a high workload, thereby reducing medication errors in hospitals. Specifically, the manual medication verification process is time-consuming and prone to errors, especially in healthcare settings with high workloads. The proposed system strategy is to streamline and automate this process, enhancing efficiency and reducing medication errors. The system employs deep learning models to swiftly and accurately classify multiple medications within a single image without requiring manual labeling during model construction. It comprises edge detection and classification to verify medication types. Unlike previous studies conducted in open spaces, our study takes place in a closed space to minimize the impact of optical changes on image capture. During the experimental process, the system individually identifies each drug within the image by edge detection method and utilizes a classification model to determine each drug type. Our research has successfully developed a fully automated drug recognition system, achieving an accuracy of over 95 % in identifying drug types and conducting segmentation analyses. Specifically, the system demonstrates an accuracy rate of approximately 96 % for drug sets containing fewer than ten types and 93 % for those with ten types. This verification system builds an image classification model quickly. It holds promising potential in assisting nursing staff during AMVS, thereby reducing the likelihood of medication errors and alleviating the burden on nursing staff.

PMID:38742071 | PMC:PMC11089321 | DOI:10.1016/j.heliyon.2024.e30486

Front Neurosci. 2024 Apr 29;18:1369832. doi: 10.3389/fnins.2024.1369832. eCollection 2024.

ABSTRACT

INTRODUCTION: In urban traffic management, the timely detection of road faults plays a crucial role in improving traffic efficiency and safety. However, conventional methods often fail to fully leverage the information from road topology and traffic data.

METHODS: To address this issue, we propose an innovative detection system that combines Artificial Neural Networks (ANNs), specifically Graph Convolutional Networks (GCN), Bidirectional Gated Recurrent Units (BiGRU), and self-attention mechanisms. Our approach begins by representing the road topology as a graph and utilizing GCN to model it. This allows us to learn the relationships between roads and capture their structural dependencies. By doing so, we can effectively incorporate the spatial information provided by the road network. Next, we employ BiGRU to model the historical traffic data, enabling us to capture the temporal dynamics and patterns in the traffic flow. The BiGRU architecture allows for bidirectional processing, which aids in understanding the traffic conditions based on both past and future information. This temporal modeling enhances our system's ability to handle time-varying traffic patterns. To further enhance the feature representations, we leverage self-attention mechanisms. By combining the hidden states of the BiGRU with self-attention, we can assign importance weights to different temporal features, focusing on the most relevant information. This attention mechanism helps to extract salient features from the traffic data. Subsequently, we merge the features learned by GCN from the road topology and BiGRU from the traffic data. This fusion of spatial and temporal information provides a comprehensive representation of the road status.

RESULTS AND DISCUSSIONS: By employing a Multilayer Perceptron (MLP) as a classifier, we can effectively determine whether a road is experiencing a fault. The MLP model is trained using labeled road fault data through supervised learning, optimizing its performance for fault detection. Experimental evaluations of our system demonstrate excellent performance in road fault detection. Compared to traditional methods, our system achieves more accurate fault detection, thereby improving the efficiency of urban traffic management. This is of significant importance for city administrators, as they can promptly identify road faults and take appropriate measures for repair and traffic diversion.

PMID:38741790 | PMC:PMC11089108 | DOI:10.3389/fnins.2024.1369832

Front Med (Lausanne). 2024 Apr 29;11:1380405. doi: 10.3389/fmed.2024.1380405. eCollection 2024.

ABSTRACT

INTRODUCTION: Non-melanoma skin cancer comprising Basal cell carcinoma (BCC), Squamous cell carcinoma (SCC), and Intraepidermal carcinoma (IEC) has the highest incidence rate among skin cancers. Intelligent decision support systems may address the issue of the limited number of subject experts and help in mitigating the parity of health services between urban centers and remote areas.

METHOD: In this research, we propose a transformer-based model for the segmentation of histopathology images not only into inflammation and cancers such as BCC, SCC, and IEC but also to identify skin tissues and boundaries that are important in decision-making. Accurate segmentation of these tissue types will eventually lead to accurate detection and classification of non-melanoma skin cancer. The segmentation according to tissue types and their visual representation before classification enhances the trust of pathologists and doctors being relatable to how most pathologists approach this problem. The visualization of the confidence of the model in its prediction through uncertainty maps is also what distinguishes this study from most deep learning methods.

RESULTS: The evaluation of proposed system is carried out using publicly available dataset. The application of our proposed segmentation system demonstrated good performance with an F1 score of 0.908, mean intersection over union (mIoU) of 0.653, and average accuracy of 83.1%, advocating that the system can be used as a decision support system successfully and has the potential of subsequently maturing into a fully automated system.

DISCUSSION: This study is an attempt to automate the segmentation of the most occurring non-melanoma skin cancer using a transformer-based deep learning technique applied to histopathology skin images. Highly accurate segmentation and visual representation of histopathology images according to tissue types by the proposed system implies that the system can be used for skin-related routine pathology tasks including cancer and other anomaly detection, their classification, and measurement of surgical margins in the case of cancer cases.

PMID:38741771 | PMC:PMC11089103 | DOI:10.3389/fmed.2024.1380405

Proc Mach Learn Res. 2024 May;238:946-954.

ABSTRACT

Voxel-based multiple testing is widely used in neuroimaging data analysis. Traditional false discovery rate (FDR) control methods often ignore the spatial dependence among the voxel-based tests and thus suffer from substantial loss of testing power. While recent spatial FDR control methods have emerged, their validity and optimality remain questionable when handling the complex spatial dependencies of the brain. Concurrently, deep learning methods have revolutionized image segmentation, a task closely related to voxel-based multiple testing. In this paper, we propose DeepFDR, a novel spatial FDR control method that leverages unsupervised deep learning-based image segmentation to address the voxel-based multiple testing problem. Numerical studies, including comprehensive simulations and Alzheimer's disease FDG-PET image analysis, demonstrate DeepFDR's superiority over existing methods. DeepFDR not only excels in FDR control and effectively diminishes the false nondiscovery rate, but also boasts exceptional computational efficiency highly suited for tackling large-scale neuroimaging data.

PMID:38741695 | PMC:PMC11090200

Int J Occup Saf Ergon. 2024 May 14:1-9. doi: 10.1080/10803548.2024.2345511. Online ahead of print.

ABSTRACT

Objectives. Currently, numerous studies have focused on testing or modeling to evaluate the safe service life of thermal protective clothing after thermal aging, reducing the risk to occupational personnel. However, testing will render the garment unsuitable for subsequent use and a series of input parameters for modeling are not readily available. In this study, a novel image recognition strategy was proposed to discriminate the mechanical strength of thermal protective fabric after thermal aging based on transfer learning. Methods. Data augmentation was used to overcome the shortcoming of insufficient training samples. Four pre-trained models were used to explore their performance in three sample classification modes. Results. The experimental results show that the VGG-19 model achieves the best performance in the three-classification mode (accuracy = 91%). The model was more accurate in identifying fabric samples in the early and late stages of strength decline. For fabric samples in the middle stage of strength decline, the three-classification mode was better than the four-classification and six-classification modes. Conclusions. The findings provide novel insights into the image-based mechanical strength evaluation of thermal protective fabrics after aging.

PMID:38741556 | DOI:10.1080/10803548.2024.2345511

Plant J. 2024 May 13. doi: 10.1111/tpj.16790. Online ahead of print.

ABSTRACT

As a promising model, genome-based plant breeding has greatly promoted the improvement of agronomic traits. Traditional methods typically adopt linear regression models with clear assumptions, neither obtaining the linkage between phenotype and genotype nor providing good ideas for modification. Nonlinear models are well characterized in capturing complex nonadditive effects, filling this gap under traditional methods. Taking populus as the research object, this paper constructs a deep learning method, DCNGP, which can effectively predict the traits including 65 phenotypes. The method was trained on three datasets, and compared with other four classic models-Bayesian ridge regression (BRR), Elastic Net, support vector regression, and dualCNN. The results show that DCNGP has five typical advantages in performance: strong prediction ability on multiple experimental datasets; the incorporation of batch normalization layers and Early-Stopping technology enhancing the generalization capabilities and prediction stability on test data; learning potent features from the data and thus circumventing the tedious steps of manual production; the introduction of a Gaussian Noise layer enhancing predictive capabilities in the case of inherent uncertainties or perturbations; fewer hyperparameters aiding to reduce tuning time across datasets and improve auto-search efficiency. In this way, DCNGP shows powerful predictive ability from genotype to phenotype, which provide an important theoretical reference for building more robust populus breeding programs.

PMID:38741374 | DOI:10.1111/tpj.16790

Int J Comput Assist Radiol Surg. 2024 May 13. doi: 10.1007/s11548-024-03165-4. Online ahead of print.

ABSTRACT

PURPOSE: Lymph nodes (LNs) in the chest have a tendency to enlarge due to various pathologies, such as lung cancer or pneumonia. Clinicians routinely measure nodal size to monitor disease progression, confirm metastatic cancer, and assess treatment response. However, variations in their shapes and appearances make it cumbersome to identify LNs, which reside outside of most organs.

METHODS: We propose to segment LNs in the mediastinum by leveraging the anatomical priors of 28 different structures (e.g., lung, trachea etc.) generated by the public TotalSegmentator tool. The CT volumes from 89 patients available in the public NIH CT Lymph Node dataset were used to train three 3D off-the-shelf nnUNet models to segment LNs. The public St. Olavs dataset containing 15 patients (out-of-training-distribution) was used to evaluate the segmentation performance.

RESULTS: For LNs with short axis diameter ≥ 8 mm, the 3D cascade nnUNet model obtained the highest Dice score of 67.9 ± 23.4 and lowest Hausdorff distance error of 22.8 ± 20.2. For LNs of all sizes, the Dice score was 58.7 ± 21.3 and this represented a ≥ 10% improvement over a recently published approach evaluated on the same test dataset.

CONCLUSION: To our knowledge, we are the first to harness 28 distinct anatomical priors to segment mediastinal LNs, and our work can be extended to other nodal zones in the body. The proposed method has the potential for improved patient outcomes through the identification of enlarged nodes in initial staging CT scans.

PMID:38740719 | DOI:10.1007/s11548-024-03165-4

Acad Radiol. 2024 May 12:S1076-6332(24)00245-9. doi: 10.1016/j.acra.2024.04.036. Online ahead of print.

ABSTRACT

RATIONALE AND OBJECTIVES: To build a risk stratification by incorporating PET/CT-based deep learning features and whole-body metabolic tumor volume (MTVwb), which was to make predictions about overall survival (OS) and progression-free survival (PFS) for those with non-small cell lung cancer (NSCLC) as a complement to the TNM staging.

MATERIALS AND METHODS: The study enrolled 590 patients with NSCLC (413 for training and 177 for testing). Features were extracted by employing a convolutional neural network. The combined risk stratification (CRS) was constructed by the selected features and MTVwb, which were contrasted and integrated with TNM staging. In the testing set, those were verified.

RESULTS: Multivariate analysis revealed that CRS was an independent predictor of OS and PFS. C-indexes of the CRS demonstrated statistically significant increases in comparison to TNM staging, excepting predicting OS in the testing set (for OS, C-index=0.71 vs. 0.691 in the training set and 0.73 vs. 0.736 in the testing set; for PFS, C-index=0.702 vs. 0.686 in the training set and 0.732 vs. 0.71 in the testing set). The nomogram that combined CRS with TNM staging demonstrated the most superior model performance in the training and testing sets (C-index=0.741 and 0.771).

CONCLUSION: The addition of CRS improves TNM staging's predictive power and shows potential as a useful tool to support physicians in making treatment decisions.

PMID:38740530 | DOI:10.1016/j.acra.2024.04.036

Anal Chim Acta. 2024 Jun 15;1308:342575. doi: 10.1016/j.aca.2024.342575. Epub 2024 Apr 6.

ABSTRACT

BACKGROUND: Alzheimer's disease (AD) is a prevalent neurodegenerative disease with no effective treatment. Efficient and rapid detection plays a crucial role in mitigating and managing AD progression. Deep learning-assisted smartphone-based microfluidic paper analysis devices (μPADs) offer the advantages of low cost, good sensitivity, and rapid detection, providing a strategic pathway to address large-scale disease screening in resource-limited areas. However, existing smartphone-based detection platforms usually rely on large devices or cloud servers for data transfer and processing. Additionally, the implementation of automated colorimetric enzyme-linked immunoassay (c-ELISA) on μPADs can further facilitate the realization of smartphone μPADs platforms for efficient disease detection.

RESULTS: This paper introduces a new deep learning-assisted offline smartphone platform for early AD screening, offering rapid disease detection in low-resource areas. The proposed platform features a simple mechanical rotating structure controlled by a smartphone, enabling fully automated c-ELISA on μPADs. Our platform successfully applied sandwich c-ELISA for detecting the β-amyloid peptide 1-42 (Aβ 1-42, a crucial AD biomarker) and demonstrated its efficacy in 38 artificial plasma samples (healthy: 19, unhealthy: 19, N = 6). Moreover, we employed the YOLOv5 deep learning model and achieved an impressive 97 % accuracy on a dataset of 1824 images, which is 10.16 % higher than the traditional method of curve-fitting results. The trained YOLOv5 model was seamlessly integrated into the smartphone using the NCNN (Tencent's Neural Network Inference Framework), enabling deep learning-assisted offline detection. A user-friendly smartphone application was developed to control the entire process, realizing a streamlined "samples in, answers out" approach.

SIGNIFICANCE: This deep learning-assisted, low-cost, user-friendly, highly stable, and rapid-response automated offline smartphone-based detection platform represents a good advancement in point-of-care testing (POCT). Moreover, our platform provides a feasible approach for efficient AD detection by examining the level of Aβ 1-42, particularly in areas with low resources and limited communication infrastructure.

PMID:38740448 | DOI:10.1016/j.aca.2024.342575

Neurosciences (Riyadh). 2024 May;29(2):77-89. doi: 10.17712/nsj.2024.2.20230103.

ABSTRACT

OBJECTIVES: The brain and spinal cord, constituting the central nervous system (CNS), could be impacted by an inflammatory disease known as multiple sclerosis (MS). The convolutional neural networks (CNN), a machine learning method, can detect lesions early by learning patterns on brain magnetic resonance image (MRI). We performed this study to investigate the diagnostic performance of CNN based MRI in the identification, classification, and segmentation of MS lesions.

METHODS: PubMed, Web of Science, Embase, the Cochrane Library, CINAHL, and Google Scholar were used to retrieve papers reporting the use of CNN based MRI in MS diagnosis. The accuracy, the specificity, the sensitivity, and the Dice Similarity Coefficient (DSC) were evaluated in this study.

RESULTS: In total, 2174 studies were identified and only 15 articles met the inclusion criteria. The 2D-3D CNN presented a high accuracy (98.81, 95% CI: 98.50-99.13), sensitivity (98.76, 95% CI: 98.42-99.10), and specificity (98.67, 95% CI: 98.22-99.12) in the identification of MS lesions. Regarding classification, the overall accuracy rate was significantly high (91.38, 95% CI: 83.23-99.54). A DSC rate of 63.78 (95% CI: 58.29-69.27) showed that 2D-3D CNN-based MRI performed highly in the segmentation of MS lesions. Sensitivity analysis showed that the results are consistent, indicating that this study is robust.

CONCLUSION: This metanalysis revealed that 2D-3D CNN based MRI is an automated system that has high diagnostic performance and can promptly and effectively predict the disease.

PMID:38740399 | DOI:10.17712/nsj.2024.2.20230103

Sci Total Environ. 2024 May 11:172877. doi: 10.1016/j.scitotenv.2024.172877. Online ahead of print.

ABSTRACT

Despite huge attention in other domains, deep learning is only very slowly beginning to be applied in biodiversity research. Mayflies (Ephemeroptera), stoneflies (Plecoptera) and caddisflies (Trichoptera), often abbreviated as EPT, are frequently used for freshwater biomonitoring due to their large numbers and sensitivity to environmental changes. However, the unambiguous morphological identification of EPT species is a challenging, but fundamental task. Morphological identification of these freshwater insects is therefore not only extremely time-consuming and costly, but also often leads to misjudgments or generates datasets with low taxonomic resolution. Here, we investigated the application of deep learning to increase the efficiency and taxonomic resolution of biomonitoring programs. Our database contains 90 EPT taxa (genus or species level), with the number of images per category ranging from 21 to 300 (16,650 in total). Upon completion of training, a CNN (Convolutional Neural Network) model was created, capable of automatically classifying these taxa into their appropriate taxonomic categories with an accuracy of 98.7 %. For the extensive set of 68 tested taxa, our model achieved a perfect classification rate of 100 %. We achieved noteworthy classification accuracy with morphologically closely related taxa within the training data (e.g., species of the genus Baetis, Hydropsyche, Perla). Gradient-weighted Class Activation Mapping (Grad-CAM) visualized the morphological features responsible for the classification of the treated species in the CNN models. In the Ephemeroptera, the head was the most important feature, while the thorax and abdomen were equally important for the classification of Plecoptera taxa. For the order Trichoptera, the head and thorax were almost equally important. Our database is recognized as the most extensive aquatic insect database, notably distinguished by its wealth of included categories (taxa). Our approach can help solve long-standing challenges in biodiversity research and address pressing issues in monitoring programs by saving time in sample and data processing.

PMID:38740196 | DOI:10.1016/j.scitotenv.2024.172877

Bone. 2024 May 11:117115. doi: 10.1016/j.bone.2024.117115. Online ahead of print.

ABSTRACT

Osteoporotic fractures, prevalent in the elderly, pose a significant health and economic burden. Current methods for predicting fracture risk, primarily relying on bone mineral density, provide only modest accuracy. If better spatial resolution of trabecular bone in a clinical scan were available, a more complete assessment of fracture risk would be obtained using microarchitectural measures of bone (i.e. trabecular thickness, trabecular spacing, bone volume fraction, etc.). However, increased resolution comes at the cost of increased radiation or can only be applied at small volumes of distal skeletal locations. This study explores super-resolution (SR) technology to enhance clinical CT scans of proximal femurs and better reveal the trabecular microarchitecture of bone. Using a deep-learning-based (i.e. subset of artificial intelligence) SR approach, low-resolution clinical CT images were upscaled to higher resolution and compared to corresponding MicroCT-derived images. SR-derived 2-dimensional microarchitectural measurements, such as degree of anisotropy, bone volume fraction, trabecular spacing, and trabecular thickness were within 16 % error compared to MicroCT data, whereas connectivity density exhibited larger error (as high as 1094 %). SR-derived 3-dimensional microarchitectural metrics exhibited errors <18 %. This work showcases the potential of SR technology to enhance clinical bone imaging and holds promise for improving fracture risk assessments and osteoporosis detection. Further research, including larger datasets and refined techniques, can advance SR's clinical utility, enabling comprehensive microstructural assessment across whole bones, thereby improving fracture risk predictions and patient-specific treatment strategies.

PMID:38740120 | DOI:10.1016/j.bone.2024.117115

Comput Biol Med. 2024 May 10;176:108577. doi: 10.1016/j.compbiomed.2024.108577. Online ahead of print.

ABSTRACT

The development of modern medical devices and information technology has led to a rapid growth in the amount of data available for health protection information, with the concept of medical big data emerging globally, along with significant advances in cancer care relying on data-driven approaches. However, outstanding issues such as fragmented data governance, low-quality data specification, and data lock-in still make sharing challenging. Big data technology provides solutions for managing massive heterogeneous data while combining artificial intelligence (AI) techniques such as machine learning (ML) and deep learning (DL) to better mine the intrinsic connections between data. This paper surveys and organizes recent articles on big data technology and its applications in cancer, dividing them into three different types to outline their primary content and summarize their critical role in assisting cancer care. It then examines the latest research directions in big data technology in cancer and evaluates the current state of development of each type of application. Finally, current challenges and opportunities are discussed, and recommendations are made for the further integration of big data technology into the medical industry in the future.

PMID:38739981 | DOI:10.1016/j.compbiomed.2024.108577