Digit Health. 2024 Mar 5;10:20552076241234624. doi: 10.1177/20552076241234624. eCollection 2024 Jan-Dec.

ABSTRACT

OBJECTIVES: Cardiac arrhythmia is one of the most severe cardiovascular diseases that can be fatal. Therefore, its early detection is critical. However, detecting types of arrhythmia by physicians based on visual identification is time-consuming and subjective. Deep learning can develop effective approaches to classify arrhythmias accurately and quickly. This study proposed a deep learning approach developed based on a Chapman-Shaoxing electrocardiogram (ECG) dataset signal to detect seven types of arrhythmias.

METHOD: Our DNN model is a hybrid CNN-BILSTM-BiGRU algorithm assisted by a multi-head self-attention mechanism regarding the challenging problem of classifying various arrhythmias of ECG signals. Additionally, the synthetic minority oversampling technique (SMOTE)-Tomek technique was utilized to address the data imbalance problem to detect and classify cardiac arrhythmias.

RESULT: The proposed model, trained with a single lead, was tested using a dataset containing 10,466 participants. The performance of the algorithm was evaluated using a random split validation approach. The proposed algorithm achieved an accuracy of 98.57% by lead II and 98.34% by lead aVF for the classification of arrhythmias.

CONCLUSION: We conducted an analysis of single-lead ECG signals to evaluate the effectiveness of our proposed hybrid model in diagnosing and classifying different types of arrhythmias. We trained separate classification models using each individual signal lead. Additionally, we implemented the SMOTE-Tomek technique along with cross-entropy loss as a cost function to address the class imbalance problem. Furthermore, we utilized a multi-headed self-attention mechanism to adjust the network structure and classify the seven arrhythmia classes. Our model achieved high accuracy and demonstrated good generalization ability in detecting ECG arrhythmias. However, further testing of the model with diverse datasets is crucial to validate its performance.

PMID:38449680 | PMC:PMC10916475 | DOI:10.1177/20552076241234624

Heliyon. 2024 Feb 18;10(5):e26415. doi: 10.1016/j.heliyon.2024.e26415. eCollection 2024 Mar 15.

ABSTRACT

Skin cancer is a prevalent form of cancer that necessitates prompt and precise detection. However, current diagnostic methods for skin cancer are either invasive, time-consuming, or unreliable. Consequently, there is a demand for an innovative and efficient approach to diagnose skin cancer that utilizes non-invasive and automated techniques. In this study, a unique method has been proposed for diagnosing skin cancer by employing an Xception neural network that has been optimized using Boosted Dipper Throated Optimization (BDTO) algorithm. The Xception neural network is a deep learning model capable of extracting high-level features from skin dermoscopy images, while the BDTO algorithm is a bio-inspired optimization technique that can determine the optimal parameters and weights for the Xception neural network. To enhance the quality and diversity of the images, the ISIC dataset is utilized, a widely accepted benchmark system for skin cancer diagnosis, and various image preprocessing and data augmentation techniques were implemented. By comparing the method with several contemporary approaches, it has been demonstrated that the method outperforms others in detecting skin cancer. The method achieves an average precision of 94.936%, an average accuracy of 94.206%, and an average recall of 97.092% for skin cancer diagnosis, surpassing the performance of alternative methods. Additionally, the 5-fold ROC curve and error curve have been presented for the data validation to showcase the superiority and robustness of the method.

PMID:38449650 | PMC:PMC10915520 | DOI:10.1016/j.heliyon.2024.e26415

Eye Vis (Lond). 2024 Mar 7;11(1):10. doi: 10.1186/s40662-024-00376-3.

ABSTRACT

Artificial intelligence (AI) is an emerging field which could make an intelligent healthcare model a reality and has been garnering traction in the field of medicine, with promising results. There have been recent developments in machine learning and/or deep learning algorithms for applications in ophthalmology-primarily for diabetic retinopathy, and age-related macular degeneration. However, AI research in the field of cornea diseases is relatively new. Algorithms have been described to assist clinicians in diagnosis or detection of cornea conditions such as keratoconus, infectious keratitis and dry eye disease. AI may also be used for segmentation and analysis of cornea imaging or tomography as an adjunctive tool. Despite the potential advantages that these new technologies offer, there are challenges that need to be addressed before they can be integrated into clinical practice. In this review, we aim to summarize current literature and provide an update regarding recent advances in AI technologies pertaining to corneal diseases, and its potential future application, in particular pertaining to image analysis.

PMID:38448961 | DOI:10.1186/s40662-024-00376-3

BMC Med Inform Decis Mak. 2024 Mar 6;24(1):67. doi: 10.1186/s12911-024-02464-9.

ABSTRACT

Deep learning has been increasingly utilized in the medical field and achieved many goals. Since the size of data dominates the performance of deep learning, several medical institutions are conducting joint research to obtain as much data as possible. However, sharing data is usually prohibited owing to the risk of privacy invasion. Federated learning is a reasonable idea to train distributed multicenter data without direct access; however, a central server to merge and distribute models is needed, which is expensive and hardly approved due to various legal regulations. This paper proposes a continual learning framework for a multicenter study, which does not require a central server and can prevent catastrophic forgetting of previously trained knowledge. The proposed framework contains the continual learning method selection process, assuming that a single method is not omnipotent for all involved datasets in a real-world setting and that there could be a proper method to be selected for specific data. We utilized the fake data based on a generative adversarial network to evaluate methods prospectively, not ex post facto. We used four independent electrocardiogram datasets for a multicenter study and trained the arrhythmia detection model. Our proposed framework was evaluated against supervised and federated learning methods, as well as finetuning approaches that do not include any regulation to preserve previous knowledge. Even without a central server and access to the past data, our framework achieved stable performance (AUROC 0.897) across all involved datasets, achieving comparable performance to federated learning (AUROC 0.901).

PMID:38448921 | DOI:10.1186/s12911-024-02464-9

Breast Cancer. 2024 Mar 7. doi: 10.1007/s12282-024-01549-7. Online ahead of print.

ABSTRACT

BACKGROUND: Developing a deep learning (DL) model for digital breast tomosynthesis (DBT) images to predict Ki-67 expression.

METHODS: The institutional review board approved this retrospective study and waived the requirement for informed consent from the patients. Initially, 499 patients (mean age: 50.5 years, range: 29-90 years) referred to our hospital for breast cancer were participated, 126 patients with pathologically confirmed breast cancer were selected and their Ki-67 expression measured. The Xception architecture was used in the DL model to predict Ki-67 expression levels. The high Ki-67 vs low Ki-67 expression diagnostic performance of our DL model was assessed by accuracy, sensitivity, specificity, areas under the receiver operating characteristic curve (AUC), and by using sub-datasets divided by the radiological characteristics of breast cancer.

RESULTS: The average accuracy, sensitivity, specificity, and AUC were 0.912, 0.629, 0.985, and 0.883, respectively. The AUC of the four subgroups separated by radiological findings for the mass, calcification, distortion, and focal asymmetric density sub-datasets were 0.890, 0.750, 0.870, and 0.660, respectively.

CONCLUSIONS: Our results suggest the potential application of our DL model to predict the expression of Ki-67 using DBT, which may be useful for preoperatively determining the treatment strategy for breast cancer.

PMID:38448777 | DOI:10.1007/s12282-024-01549-7

Environ Sci Pollut Res Int. 2024 Mar 6. doi: 10.1007/s11356-024-32791-3. Online ahead of print.

ABSTRACT

Drinking water is vital for human health and life, but detecting multiple contaminants in it is challenging. Traditional testing methods are both time-consuming and labor-intensive, lacking the ability to capture abrupt changes in water quality over brief intervals. This paper proposes a direct analysis and rapid detection method of three indicators of arsenic, cadmium, and selenium in complex drinking water systems by combining a novel long-path spectral imager with machine learning models. Our technique can obtain multiple parameters in about 1 s. The experiment involved setting up samples from various drinking water backgrounds and mixed groups, totaling 9360 injections. A raw visible light source ranging from 380 to 780 nm was utilized, uniformly dispersing light into the sample cell through a filter. The residual beam was captured by a high-definition camera, forming a distinctive spectrum. Three deep learning models-ResNet-50, SqueezeNet V1.1, and GoogLeNet Inception V1-were employed. Datasets were divided into training, validation, and test sets in a 6:2:2 ratio, and prediction performance across different datasets was assessed using the coefficient of determination and root mean square error. The experimental results show that a well-trained machine learning model can extract a lot of feature image information and quickly predict multi-dimensional drinking water indicators with almost no preprocessing. The model's prediction performance is stable under different background drinking water systems. The method is accurate, efficient, and real-time and can be widely used in actual water supply systems. This study can improve the efficiency of water quality monitoring and treatment in water supply systems, and the method's potential for environmental monitoring, food safety, industrial testing, and other fields can be further explored in the future.

PMID:38448769 | DOI:10.1007/s11356-024-32791-3

J Imaging Inform Med. 2024 Mar 6. doi: 10.1007/s10278-024-01071-4. Online ahead of print.

ABSTRACT

This study aimed to investigate the effects of intravenous injection of iodine contrast agent on the tracheal diameter and lung volume. In this retrospective study, a total of 221 patients (71.1 ± 12.4 years, 174 males) who underwent vascular dynamic CT examination including chest were included. Unenhanced, arterial phase, and delayed-phase images were scanned. The tracheal luminal diameters at the level of the thoracic inlet and both lung volumes were evaluated by a radiologist using a commercial software, which allows automatic airway and lung segmentation. The tracheal diameter and both lung volumes were compared between the unenhanced vs. arterial and delayed phase using a paired t-test. The Bonferroni correction was performed for multiple group comparisons. The tracheal diameter in the arterial phase (18.6 ± 2.4 mm) was statistically significantly smaller than those in the unenhanced CT (19.1 ± 2.5 mm) (p < 0.001). No statistically significant difference was found in the tracheal diameter between the delayed phase (19.0 ± 2.4 mm) and unenhanced CT (p = 0.077). Both lung volumes in the arterial phase were 4131 ± 1051 mL which was significantly smaller than those in the unenhanced CT (4332 ± 1076 mL) (p < 0.001). No statistically significant difference was found in both lung volumes between the delayed phase (4284 ± 1054 mL) and unenhanced CT (p = 0.068). In conclusion, intravenous infusion of iodine contrast agent transiently decreased the tracheal diameter and both lung volumes.

PMID:38448759 | DOI:10.1007/s10278-024-01071-4

Clin Oral Investig. 2024 Mar 7;28(3):198. doi: 10.1007/s00784-024-05598-2.

ABSTRACT

OBJECTIVES: This study aimed to use all permanent teeth as the target and establish an automated dental age estimation method across all developmental stages of permanent teeth, accomplishing all the essential steps of tooth determination, tooth development staging, and dental age assessment.

METHODS: A three-step framework for automatically estimating dental age was developed for children aged 3 to 15. First, a YOLOv3 network was employed to complete the tasks of tooth localization and numbering on a digital orthopantomogram. Second, a novel network named SOS-Net was established for accurate tooth development staging based on a modified Demirjian method. Finally, the dental age assessment procedure was carried out through a single-group meta-analysis utilizing the statistical data derived from our reference dataset.

RESULTS: The performance tests showed that the one-stage YOLOv3 detection network attained an overall mean average precision 50 of 97.50 for tooth determination. The proposed SOS-Net method achieved an average tooth development staging accuracy of 82.97% for a full dentition. The dental age assessment validation test yielded an MAE of 0.72 years with a full dentition (excluding the third molars) as its input.

CONCLUSIONS: The proposed automated framework enhances the dental age estimation process in a fast and standard manner, enabling the reference of any accessible population.

CLINICAL RELEVANCE: The tooth development staging network can facilitate the precise identification of permanent teeth with abnormal growth, improving the effectiveness and comprehensiveness of dental diagnoses using pediatric orthopantomograms.

PMID:38448657 | DOI:10.1007/s00784-024-05598-2

Sci Rep. 2024 Mar 6;14(1):5532. doi: 10.1038/s41598-024-54482-2.

ABSTRACT

In ophthalmology, intravitreal operative medication therapy (IVOM) is a widespread treatment for diseases related to the age-related macular degeneration (AMD), the diabetic macular edema, as well as the retinal vein occlusion. However, in real-world settings, patients often suffer from loss of vision on time scales of years despite therapy, whereas the prediction of the visual acuity (VA) and the earliest possible detection of deterioration under real-life conditions is challenging due to heterogeneous and incomplete data. In this contribution, we present a workflow for the development of a research-compatible data corpus fusing different IT systems of the department of ophthalmology of a German maximum care hospital. The extensive data corpus allows predictive statements of the expected progression of a patient and his or her VA in each of the three diseases. For the disease AMD, we found out a significant deterioration of the visual acuity over time. Within our proposed multistage system, we subsequently classify the VA progression into the three groups of therapy "winners", "stabilizers", and "losers" (WSL classification scheme). Our OCT biomarker classification using an ensemble of deep neural networks results in a classification accuracy (F1-score) of over 98%, enabling us to complete incomplete OCT documentations while allowing us to exploit them for a more precise VA modelling process. Our VA prediction requires at least four VA examinations and optionally OCT biomarkers from the same time period to predict the VA progression within a forecasted time frame, whereas our prediction is currently restricted to IVOM/no therapy. We achieve a final prediction accuracy of 69% in macro average F1-score, while being in the same range as the ophthalmologists with 57.8 and 50 ± 10.7 % F1-score.

PMID:38448469 | DOI:10.1038/s41598-024-54482-2

Nat Commun. 2024 Mar 6;15(1):2036. doi: 10.1038/s41467-024-46211-0.

ABSTRACT

Methicillin-resistant Staphylococcus aureus (MRSA) poses significant morbidity and mortality in hospitals. Rapid, accurate risk stratification of MRSA is crucial for optimizing antibiotic therapy. Our study introduced a deep learning model, PyTorch_EHR, which leverages electronic health record (EHR) time-series data, including wide-variety patient specific data, to predict MRSA culture positivity within two weeks. 8,164 MRSA and 22,393 non-MRSA patient events from Memorial Hermann Hospital System, Houston, Texas are used for model development. PyTorch_EHR outperforms logistic regression (LR) and light gradient boost machine (LGBM) models in accuracy (AUROCPyTorch_EHR = 0.911, AUROCLR = 0.857, AUROCLGBM = 0.892). External validation with 393,713 patient events from the Medical Information Mart for Intensive Care (MIMIC)-IV dataset in Boston confirms its superior accuracy (AUROCPyTorch_EHR = 0.859, AUROCLR = 0.816, AUROCLGBM = 0.838). Our model effectively stratifies patients into high-, medium-, and low-risk categories, potentially optimizing antimicrobial therapy and reducing unnecessary MRSA-specific antimicrobials. This highlights the advantage of deep learning models in predicting MRSA positive cultures, surpassing traditional machine learning models and supporting clinicians' judgments.

PMID:38448409 | DOI:10.1038/s41467-024-46211-0

Anal Chem. 2024 Mar 6. doi: 10.1021/acs.analchem.3c04421. Online ahead of print.

ABSTRACT

Impedance flow cytometry (IFC) has been demonstrated to be an efficient tool for label-free bacterial investigation to obtain the electrical properties in real time. However, the accurate differentiation of different species of bacteria by IFC technology remains a challenge owing to the insignificant differences in data. Here, we developed a convolutional neural networks (ConvNet) deep learning approach to enhance the accuracy and efficiency of the IFC toward distinguishing various species of bacteria. First, more than 1 million sets of impedance data (comprising 42 characteristic features for each set) of various groups of bacteria were trained by the ConvNet model. To improve the efficiency for data analysis, the Spearman correlation coefficient and the mean decrease accuracy of the random forest algorithm were introduced to eliminate feature interaction and extract the opacity of impedance related to the bacterial wall and membrane structure as the predominant features in bacterial differentiation. Moreover, the 25 optimized features were selected with differentiation accuracies of >96% for three groups of bacteria (bacilli, cocci, and vibrio) and >95% for two species of bacilli (Escherichia coli and Salmonella enteritidis), compared to machine learning algorithms (complex tree, linear discriminant, and K-nearest neighbor algorithms) with a maximum accuracy of 76.4%. Furthermore, bacterial differentiation was achieved on spiked samples of different species with different mixing ratios. The proposed ConvNet deep learning-assisted data analysis method of IFC exhibits advantages in analyzing a huge number of data sets with capacity for extracting predominant features within multicomponent information and will bring about progress and advances in the fields of both biosensing and data analysis.

PMID:38448396 | DOI:10.1021/acs.analchem.3c04421

Br J Anaesth. 2024 Mar 5:S0007-0912(24)00059-X. doi: 10.1016/j.bja.2024.01.036. Online ahead of print.

ABSTRACT

BACKGROUND: Artificial intelligence (AI) for ultrasound scanning in regional anaesthesia is a rapidly developing interdisciplinary field. There is a risk that work could be undertaken in parallel by different elements of the community but with a lack of knowledge transfer between disciplines, leading to repetition and diverging methodologies. This scoping review aimed to identify and map the available literature on the accuracy and utility of AI systems for ultrasound scanning in regional anaesthesia.

METHODS: A literature search was conducted using Medline, Embase, CINAHL, IEEE Xplore, and ACM Digital Library. Clinical trial registries, a registry of doctoral theses, regulatory authority databases, and websites of learned societies in the field were searched. Online commercial sources were also reviewed.

RESULTS: In total, 13,014 sources were identified; 116 were included for full-text review. A marked change in AI techniques was noted in 2016-17, from which point on the predominant technique used was deep learning. Methods of evaluating accuracy are variable, meaning it is impossible to compare the performance of one model with another. Evaluations of utility are more comparable, but predominantly gained from the simulation setting with limited clinical data on efficacy or safety. Study methodology and reporting lack standardisation.

CONCLUSIONS: There is a lack of structure to the evaluation of accuracy and utility of AI for ultrasound scanning in regional anaesthesia, which hinders rigorous appraisal and clinical uptake. A framework for consistent evaluation is needed to inform model evaluation, allow comparison between approaches/models, and facilitate appropriate clinical adoption.

PMID:38448269 | DOI:10.1016/j.bja.2024.01.036

Adv Protein Chem Struct Biol. 2024;139:89-139. doi: 10.1016/bs.apcsb.2023.11.008. Epub 2024 Feb 21.

ABSTRACT

The word 'cancer' encompasses a heterogenous group of distinct disease types characterized by a spectrum of pathological features, genetic alterations and response to therapies. According to the World Health Organization, cancer is the second leading cause of death worldwide, responsible for one in six deaths and hence imposes a significant burden on global healthcare systems. High-throughput omics technologies combined with advanced imaging tools, have revolutionized our ability to interrogate the molecular landscape of tumors and has provided unprecedented understanding of the disease. Yet, there is a gap between basic research discoveries and their translation into clinically meaningful therapies for improving patient care. To bridge this gap, there is a need to analyse the vast amounts of high dimensional datasets from multi-omics platforms. The integration of multi-omics data with clinical information like patient history, histological examination and imaging has led to the novel concept of clinicomics and may expedite the bench-to-bedside transition in cancer. The journey from omics to clinicomics has gained momentum with development of radiomics which involves extracting quantitative features from medical imaging data with the help of deep learning and artificial intelligence (AI) tools. These features capture detailed information about the tumor's shape, texture, intensity, and spatial distribution. Together, the related fields of multiomics, translational bioinformatics, radiomics and clinicomics may provide evidence-based recommendations tailored to the individual cancer patient's molecular profile and clinical characteristics. In this chapter, we summarize multiomics studies in solid cancers with a specific focus on breast cancer. We also review machine learning and AI based algorithms and their use in cancer diagnosis, subtyping, prognosis and predicting treatment resistance and relapse.

PMID:38448145 | DOI:10.1016/bs.apcsb.2023.11.008

Adv Protein Chem Struct Biol. 2024;139:263-288. doi: 10.1016/bs.apcsb.2023.11.015. Epub 2024 Feb 19.

ABSTRACT

In the past three decades, interest in using carbon-based nanomaterials (CBNs) in biomedical application has witnessed remarkable growth. Despite the rapid advancement, the translation of laboratory experimentation to clinical applications of nanomaterials is one of the major challenges. This might be attributed to poor understanding of bio-nano interface. Arguably, the most significant barrier is the complexity that arises by interplay of several factors like properties of nanomaterial (shape, size, surface chemistry), its interaction with suspending media (surface hydration and dehydration, surface reconstruction and release of free surface energy) and the interaction with biomolecules (conformational change in biomolecules, interaction with membrane and receptor). Tailoring a nanomaterial that minimally interacts with protein and lipids in the medium while effectively acts on target site in biological milieu has been very difficult. Computational methods and artificial intelligence techniques have displayed potential in effectively addressing this problem. Through predictive modelling and deep learning, computer-based methods have demonstrated the capability to create accurate models of interactions between nanoparticles and cell membranes, as well as the uptake of nanomaterials by cells. Computer-based simulations techniques enable these computational models to forecast how making particular alterations to a material's physical and chemical properties could enhance functional aspects, such as the retention of drugs, the process of cellular uptake and biocompatibility. We review the most recent progress regarding the bio-nano interface studies between the plasma proteins and CBNs with a special focus on computational simulations based on molecular dynamics and density functional theory.

PMID:38448137 | DOI:10.1016/bs.apcsb.2023.11.015

Adv Protein Chem Struct Biol. 2024;139:141-171. doi: 10.1016/bs.apcsb.2023.11.007. Epub 2023 Dec 20.

ABSTRACT

Advancements in genome sequencing have expanded the scope of investigating mutations in proteins across different diseases. Amino acid mutations in a protein alter its structure, stability and function and some of them lead to diseases. Identification of disease-causing mutations is a challenging task and it will be helpful for designing therapeutic strategies. Hence, mutation data available in the literature have been curated and stored in several databases, which have been effectively utilized for developing computational methods to identify deleterious mutations (drivers), using sequence and structure-based properties of proteins. In this chapter, we describe the contents of specific databases that have information on disease-causing and neutral mutations followed by sequence and structure-based properties. Further, characteristic features of disease-causing mutations will be discussed along with computational methods for identifying cancer hotspot residues and disease-causing mutations in proteins.

PMID:38448134 | DOI:10.1016/bs.apcsb.2023.11.007

Neuroimage. 2024 Mar 4:120559. doi: 10.1016/j.neuroimage.2024.120559. Online ahead of print.

ABSTRACT

As the field of computational cognitive neuroscience continues to expand and generate new theories, there is a growing need for more advanced methods to test the hypothesis of brain-behavior relationships. Recent progress in Bayesian cognitive modeling has enabled the combination of neural and behavioral models into a single unifying framework. However, these approaches require manual feature extraction, and lack the capability to discover previously unknown neural features in more complex data. Consequently, this would hinder the expressiveness of the models. To address these challenges, we propose a Neurocognitive Variational Autoencoder (NCVA) to conjoin high-dimensional EEG with a cognitive model in both generative and predictive modeling analyses. Importantly, our NCVA enables both the prediction of EEG signals given behavioral data and the estimation of cognitive model parameters from EEG signals. This novel approach can allow for a more comprehensive understanding of the triplet relationship between behavior, brain activity, and cognitive processes.

PMID:38447682 | DOI:10.1016/j.neuroimage.2024.120559

Am J Obstet Gynecol MFM. 2024 Mar 4:101337. doi: 10.1016/j.ajogmf.2024.101337. Online ahead of print.

ABSTRACT

BACKGROUND: This study used electrocardiogram (ECG) data in conjunction with artificial intelligence (AI) methods as a non-invasive tool for detecting peripartum cardiomyopathy (PPCM).

OBJECTIVE: The primary objective was to assess the efficacy of a heart failure detection model for detecting peripartum cardiomyopathy detection using an AI deep learning model called a 1-dimensional convolutional neural network.

STUDY DESIGN: We first built a deep learning model for heart failure detection using retrospective data at University of Tennessee Health Science Center (UTHSC). Cases were adult and non-pregnant females with a heart failure diagnosis; controls were adult non-pregnant females without heart failure. The model was then tested on an independent cohort of pregnant women at UTHSC who either did or did not have peripartum cardiomyopathy. We also tested the model in an external cohort of pregnant women at Atrium Health Wake Forest Baptist (AHWFB). Key outcomes were assessed using the area under the receiver operating characteristic curve (AUC). We also repeated our analysis using only lead I ECG as an input to assess feasibility of remote monitoring via wearables that can capture single-lead ECG data.

RESULTS: The UTHSC heart failure cohort comprised 346,339 ECGs from 142,601 patients. In this cohort, 60% were Black and 37% were white, with an average age (SD) of 53 (19). The heart failure detection model achieved an AUC of 0.92 on the holdout set. We then tested the ability of the heart failure model to detect peripartum cardiomyopathy in an independent cohort of pregnant women from UTHSC and an external cohort of pregnant women from AWFBH. The independent UTHSC cohort included 158 ECGs from 115 patients; our deep learning model achieved an AUC of 0.83 [0.77-0.89] for this dataset. The external AHWFB cohort involved 80 ECGs from 43 patients; our deep learning model achieved an AUC of 0.83 [0.77-0.89] AUC of 0.94 [0.91-0.98] for this dataset. For identifying peripartum cardiomyopathy diagnosed 10 or more days post-delivery, the model achieved an AUC of 0.88 [0.81-0.94] for the UTHSC cohort and an AUC of 0.96 [0.93-0.99] for the AHWFB cohort. When we repeated our analysis by building a heart failure detection model using only lead I ECGs, we obtained similarly high detection accuracies, with AUCs of 0.73 and 0.93 for the UTHSC and AHWFB cohorts, respectively.

CONCLUSIONS: AI can accurately detect peripartum cardiomyopathy from ECG alone. A simple ECG-AI-based peripartum screening could result in a more timely diagnosis. Since results with 1-lead ECG data were similar to those obtained using all 12 leads, future studies will focus on remote screening for peripartum cardiomyopathy using smartwatches that can capture single-lead ECG data.

PMID:38447673 | DOI:10.1016/j.ajogmf.2024.101337

J Biomed Inform. 2024 Mar 4:104621. doi: 10.1016/j.jbi.2024.104621. Online ahead of print.

ABSTRACT

OBJECTIVE: The primary objective of this review is to investigate the effectiveness of machine learning and deep learning methodologies in the context of extracting adverse drug events (ADEs) from clinical benchmark datasets. We conduct an in-depth analysis, aiming to compare the merits and drawbacks of both machine learning and deep learning techniques, particularly within the framework of named-entity recognition (NER) and relation classification (RC) tasks related to ADE extraction. Additionally, our focus extends to the examination of specific features and their impact on the overall performance of these methodologies. In a broader perspective, our research extends to ADE extraction from various sources, including biomedical literature, social media data, and drug labels, removing the limitation to exclusively machine learning or deep learning methods.

METHODS: We conducted an extensive literature review on PubMed using the query "(((machine learning [Medical Subject Headings (MeSH) Terms]) OR (deep learning [MeSH Terms])) AND (adverse drug event [MeSH Terms])) AND (extraction)", and supplemented this with a snowballing approach to review 275 references sourced from retrieved articles.

RESULTS: In our analysis, we included twelve articles for review. For the NER task, deep learning models outperformed machine learning models. In the RC task, gradient Boosting, multilayer perceptron and random forest models excelled. The Bidirectional Encoder Representations from Transformers (BERT) model consistently achieved the best performance in the end-to-end task. Future efforts in the end-to-end task should prioritize improving NER accuracy, especially for 'ADE' and 'Reason'.

CONCLUSION: These findings hold significant implications for advancing the field of ADE extraction and pharmacovigilance, ultimately contributing to improved drug safety monitoring and healthcare outcomes.

PMID:38447600 | DOI:10.1016/j.jbi.2024.104621

Structure. 2024 Mar 4:S0969-2126(24)00043-1. doi: 10.1016/j.str.2024.02.004. Online ahead of print.

ABSTRACT

Identifying binding compounds against a target protein is crucial for large-scale virtual screening in drug development. Recently, network-based methods have been developed for compound-protein interaction (CPI) prediction. However, they are difficult to be applied to unseen (i.e., never-seen-before) proteins and compounds. In this study, we propose SgCPI to incorporate local known interacting networks to predict CPI interactions. SgCPI randomly samples the local CPI network of the query compound-protein pair as a subgraph and applies a heterogeneous graph neural network (HGNN) to embed the active/inactive message of the subgraph. For unseen compounds and proteins, SgCPI-KD takes SgCPI as the teacher model to distillate its knowledge by estimating the potential neighbors. Experimental results indicate: (1) the sampled subgraphs of the CPI network introduce efficient knowledge for unseen molecular prediction with the HGNNs, and (2) the knowledge distillation strategy is beneficial to the double-blind interaction prediction by estimating molecular neighbors and distilling knowledge.

PMID:38447575 | DOI:10.1016/j.str.2024.02.004

Comput Biol Med. 2024 Feb 29;171:108229. doi: 10.1016/j.compbiomed.2024.108229. Online ahead of print.

ABSTRACT

Conventional COVID-19 testing methods have some flaws: they are expensive and time-consuming. Chest X-ray (CXR) diagnostic approaches can alleviate these flaws to some extent. However, there is no accurate and practical automatic diagnostic framework with good interpretability. The application of artificial intelligence (AI) technology to medical radiography can help to accurately detect the disease, reduce the burden on healthcare organizations, and provide good interpretability. Therefore, this study proposes a new deep neural network (CNN) based on CXR for COVID-19 diagnosis - CodeNet. This method uses contrastive learning to make full use of latent image data to enhance the model's ability to extract features and generalize across different data domains. On the evaluation dataset, the proposed method achieves an accuracy as high as 94.20%, outperforming several other existing methods used for comparison. Ablation studies validate the efficacy of the proposed method, while interpretability analysis shows that the method can effectively guide clinical professionals. This work demonstrates the superior detection performance of a CNN using contrastive learning techniques on CXR images, paving the way for computer vision and artificial intelligence technologies to leverage massive medical data for disease diagnosis.

PMID:38447500 | DOI:10.1016/j.compbiomed.2024.108229