Invest Ophthalmol Vis Sci. 2024 Jul 1;65(8):50. doi: 10.1167/iovs.65.8.50.

ABSTRACT

PURPOSE: Retinal microvascular changes are associated with ischemic stroke, and optical coherence tomography angiography (OCTA) is a potential tool to reveal the retinal microvasculature. We investigated the feasibility of using the OCTA image to automatically identify ischemic stroke and its subtypes (i.e. lacunar and non-lacunar stroke), and exploited the association of retinal biomarkers with the subtypes of ischemic stroke.

METHODS: Two cohorts were included in this study and a total of 1730 eyes from 865 participants were studied. A deep learning model was developed to discriminate the subjects with ischemic stroke from healthy controls and to distinguish the subtypes of ischemic stroke. We also extracted geometric parameters of the retinal microvasculature at different retinal layers to investigate the correlations.

RESULTS: Superficial vascular plexus (SVP) yielded the highest areas under the receiver operating characteristic curve (AUCs) of 0.922 and 0.871 for the ischemic stroke detection and stroke subtypes classification, respectively. For external data validation, our model achieved an AUC of 0.822 and 0.766 for the ischemic stroke detection and stroke subtypes classification, respectively. When parameterizing the OCTA images, we showed individuals with ischemic strokes had increased SVP tortuosity (B = 0.085, 95% confidence interval [CI] = 0.005-0.166, P = 0.038) and reduced FAZ circularity (B = -0.212, 95% CI = -0.42 to -0.005, P = 0.045); non-lacunar stroke had reduced SVP FAZ circularity (P = 0.027) compared to lacunar stroke.

CONCLUSIONS: Our study demonstrates the applicability of artificial intelligence (AI)-enhanced OCTA image analysis for ischemic stroke detection and its subtypes classification. Biomarkers from retinal OCTA images can provide useful information for clinical decision-making and diagnosis of ischemic stroke and its subtypes.

PMID:39083310 | DOI:10.1167/iovs.65.8.50

JAMA Netw Open. 2024 Jul 1;7(7):e2422520. doi: 10.1001/jamanetworkopen.2024.22520.

ABSTRACT

IMPORTANCE: Assessing nontechnical skills in operating rooms (ORs) is crucial for enhancing surgical performance and patient safety. However, automated and real-time evaluation of these skills remains challenging.

OBJECTIVE: To explore the feasibility of using motion features extracted from surgical video recordings to automatically assess nontechnical skills during cardiac surgical procedures.

DESIGN, SETTING, AND PARTICIPANTS: This cross-sectional study used video recordings of cardiac surgical procedures at a tertiary academic US hospital collected from January 2021 through May 2022. The OpenPose library was used to analyze videos to extract body pose estimations of team members and compute various team motion features. The Non-Technical Skills for Surgeons (NOTSS) assessment tool was employed for rating the OR team's nontechnical skills by 3 expert raters.

MAIN OUTCOMES AND MEASURES: NOTSS overall score, with motion features extracted from surgical videos as measures.

RESULTS: A total of 30 complete cardiac surgery procedures were included: 26 (86.6%) were on-pump coronary artery bypass graft procedures and 4 (13.4%) were aortic valve replacement or repair procedures. All patients were male, and the mean (SD) age was 72 (6.3) years. All surgical teams were composed of 4 key roles (attending surgeon, attending anesthesiologist, primary perfusionist, and scrub nurse) with additional supporting roles. NOTSS scores correlated significantly with trajectory (r = 0.51, P = .005), acceleration (r = 0.48, P = .008), and entropy (r = -0.52, P = .004) of team displacement. Multiple linear regression, adjusted for patient factors, showed average team trajectory (adjusted R2 = 0.335; coefficient, 10.51 [95% CI, 8.81-12.21]; P = .004) and team displacement entropy (adjusted R2 = 0.304; coefficient, -12.64 [95% CI, -20.54 to -4.74]; P = .003) were associated with NOTSS scores.

CONCLUSIONS AND RELEVANCE: This study suggests a significant link between OR team movements and nontechnical skills ratings by NOTSS during cardiac surgical procedures, suggesting automated surgical video analysis could enhance nontechnical skills assessment. Further investigation across different hospitals and specialties is necessary to validate these findings.

PMID:39083274 | DOI:10.1001/jamanetworkopen.2024.22520

Brain Imaging Behav. 2024 Jul 31. doi: 10.1007/s11682-024-00902-w. Online ahead of print.

ABSTRACT

This systematic review examines the prevalence, underlying mechanisms, cohort characteristics, evaluation criteria, and cohort types in white matter hyperintensity (WMH) pipeline and implementation literature spanning the last two decades. Following Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) guidelines, we categorized WMH segmentation tools based on their methodologies from January 1, 2000, to November 18, 2022. Inclusion criteria involved articles using openly available techniques with detailed descriptions, focusing on WMH as a primary outcome. Our analysis identified 1007 visual rating scales, 118 pipeline development articles, and 509 implementation articles. These studies predominantly explored aging, dementia, psychiatric disorders, and small vessel disease, with aging and dementia being the most prevalent cohorts. Deep learning emerged as the most frequently developed segmentation technique, indicative of a heightened scrutiny in new technique development over the past two decades. We illustrate observed patterns and discrepancies between published and implemented WMH techniques. Despite increasingly sophisticated quantitative segmentation options, visual rating scales persist, with the SPM technique being the most utilized among quantitative methods and potentially serving as a reference standard for newer techniques. Our findings highlight the need for future standards in WMH segmentation, and we provide recommendations based on these observations.

PMID:39083144 | DOI:10.1007/s11682-024-00902-w

Neurosurg Rev. 2024 Jul 31;47(1):382. doi: 10.1007/s10143-024-02635-2.

ABSTRACT

Intracerebral hemorrhage (ICH) is a severe form of stroke with high morbidity and mortality, accounting for 10-15% of all strokes globally. Recent advancements in prognostic biomarkers and predictive models have shown promise in enhancing the prediction and management of ICH outcomes. Serum sestrin2, a stress-responsive protein, has been identified as a significant prognostic marker, correlating with severity indicators such as NIHSS scores and hematoma volume. Its levels predict early neurological deterioration and poor prognosis, offering predictive capabilities comparable to traditional measures. Furthermore, a deep learning-based AI model demonstrated superior performance in predicting early hematoma enlargement, with higher sensitivity and specificity than conventional methods. Additionally, long-term outcome prediction models using CT radiomics and machine learning have achieved high accuracy, particularly with the Random Forest algorithm. These advancements underscore the potential of integrating novel biomarkers and advanced computational techniques to improve prognostication and management of ICH, aiming to enhance patient care and survival rates. The incorporation of serum sestrin2, AI, and machine learning in predictive models represents a significant step forward in the clinical management of ICH, offering new avenues for research and clinical application.

PMID:39083096 | DOI:10.1007/s10143-024-02635-2

Eur Arch Otorhinolaryngol. 2024 Jul 31. doi: 10.1007/s00405-024-08870-z. Online ahead of print.

ABSTRACT

BACKGROUND: Medical imaging segmentation is the use of image processing techniques to expand specific structures or areas in medical images. This technique is used to separate and display different textures or shapes in an image. The aim of this study is to develop a deep learning-based method to perform maxillary sinus segmentation using cone beam computed tomography (CBCT) images. The proposed segmentation method aims to provide better image guidance to surgeons and specialists by determining the boundaries of the maxillary sinus cavities. In this way, more accurate diagnoses can be made and surgical interventions can be performed more successfully.

METHODS: In the study, axial CBCT images of 100 patients (200 maxillary sinuses) were used. These images were marked to identify the maxillary sinus walls. The marked regions are masked for use in the maxillary sinus segmentation model. U-Net, one of the deep learning methods, was used for segmentation. The training process was carried out for 10 epochs and 100 iterations per epoch. The epoch and iteration numbers in which the model showed maximum success were determined using the early stopping method.

RESULTS: After the segmentation operations performed with the U-Net model trained using CBCT images, both visual and numerical results were obtained. In order to measure the performance of the U-Net model, IoU (Intersection over Union) and F1 Score metrics were used. As a result of the tests of the model, the IoU value was found to be 0.9275 and the F1 Score value was 0.9784.

CONCLUSION: The U-Net model has shown high success in maxillary sinus segmentation. In this way, fast and highly accurate evaluations are possible, saving time by reducing the workload of clinicians and eliminating subjective errors.

PMID:39083060 | DOI:10.1007/s00405-024-08870-z

Nanoscale. 2024 Jul 31. doi: 10.1039/d4nr01944j. Online ahead of print.

ABSTRACT

Considerable efforts are currently being devoted to characterizing the topography of membrane-embedded proteins using combinations of biophysical and numerical analytical approaches. In this work, we present an end-to-end (i.e., human intervention-independent) algorithm consisting of two concatenated binary Graph Neural Network (GNNs) classifiers with the aim of detecting and quantifying dynamic clustering of particles. As the algorithm only needs simulated data to train the GNNs, it is parameter-independent. The GNN-based algorithm is first tested on datasets based on simulated, albeit biologically realistic data, and validated on actual fluorescence microscopy experimental data. Application of the new GNN method is shown to be faster than other currently used approaches for high-dimensional SMLM datasets, with the additional advantage that it can be implemented on standard desktop computers. Furthermore, GNN models obtained via training procedures are reusable. To the best of our knowledge, this is the first application of GNN-based approaches to the analysis of particle aggregation, with potential applications to the study of nanoscopic particles like the nanoclusters of membrane-associated proteins in live cells.

PMID:39082742 | DOI:10.1039/d4nr01944j

J Acoust Soc Am. 2024 Jul 1;156(1):706-724. doi: 10.1121/10.0028007.

ABSTRACT

Understanding speech in noisy environments is a challenging task, especially in communication situations with several competing speakers. Despite their ongoing improvement, assistive listening devices and speech processing approaches still do not perform well enough in noisy multi-talker environments, as they may fail to restore the intelligibility of a speaker of interest among competing sound sources. In this study, a quasi-causal deep learning algorithm was developed that can extract the voice of a target speaker, as indicated by a short enrollment utterance, from a mixture of multiple concurrent speakers in background noise. Objective evaluation with computational metrics demonstrated that the speaker-informed algorithm successfully extracts the target speaker from noisy multi-talker mixtures. This was achieved using a single algorithm that generalized to unseen speakers, different numbers of speakers and relative speaker levels, and different speech corpora. Double-blind sentence recognition tests on mixtures of one, two, and three speakers in restaurant noise were conducted with listeners with normal hearing and listeners with hearing loss. Results indicated significant intelligibility improvements with the speaker-informed algorithm of 17% and 31% for people without and with hearing loss, respectively. In conclusion, it was demonstrated that deep learning-based speaker extraction can enhance speech intelligibility in noisy multi-talker environments where uninformed speech enhancement methods fail.

PMID:39082692 | DOI:10.1121/10.0028007

Cancer Res. 2024 Jul 2;84(13):2060-2072. doi: 10.1158/0008-5472.CAN-23-1349.

ABSTRACT

Patient-derived xenografts (PDX) model human intra- and intertumoral heterogeneity in the context of the intact tissue of immunocompromised mice. Histologic imaging via hematoxylin and eosin (H&E) staining is routinely performed on PDX samples, which could be harnessed for computational analysis. Prior studies of large clinical H&E image repositories have shown that deep learning analysis can identify intercellular and morphologic signals correlated with disease phenotype and therapeutic response. In this study, we developed an extensive, pan-cancer repository of >1,000 PDX and paired parental tumor H&E images. These images, curated from the PDX Development and Trial Centers Research Network Consortium, had a range of associated genomic and transcriptomic data, clinical metadata, pathologic assessments of cell composition, and, in several cases, detailed pathologic annotations of neoplastic, stromal, and necrotic regions. The amenability of these images to deep learning was highlighted through three applications: (i) development of a classifier for neoplastic, stromal, and necrotic regions; (ii) development of a predictor of xenograft-transplant lymphoproliferative disorder; and (iii) application of a published predictor of microsatellite instability. Together, this PDX Development and Trial Centers Research Network image repository provides a valuable resource for controlled digital pathology analysis, both for the evaluation of technical issues and for the development of computational image-based methods that make clinical predictions based on PDX treatment studies. Significance: A pan-cancer repository of >1,000 patient-derived xenograft hematoxylin and eosin-stained images will facilitate cancer biology investigations through histopathologic analysis and contributes important model system data that expand existing human histology repositories.

PMID:39082680 | DOI:10.1158/0008-5472.CAN-23-1349

Brief Bioinform. 2024 Jul 25;25(5):bbae374. doi: 10.1093/bib/bbae374.

ABSTRACT

Metabolic processes can transform a drug into metabolites with different properties that may affect its efficacy and safety. Therefore, investigation of the metabolic fate of a drug candidate is of great significance for drug discovery. Computational methods have been developed to predict drug metabolites, but most of them suffer from two main obstacles: the lack of model generalization due to restrictions on metabolic transformation rules or specific enzyme families, and high rate of false-positive predictions. Here, we presented MetaPredictor, a rule-free, end-to-end and prompt-based method to predict possible human metabolites of small molecules including drugs as a sequence translation problem. We innovatively introduced prompt engineering into deep language models to enrich domain knowledge and guide decision-making. The results showed that using prompts that specify the sites of metabolism (SoMs) can steer the model to propose more accurate metabolite predictions, achieving a 30.4% increase in recall and a 16.8% reduction in false positives over the baseline model. The transfer learning strategy was also utilized to tackle the limited availability of metabolic data. For the adaptation to automatic or non-expert prediction, MetaPredictor was designed as a two-stage schema consisting of automatic identification of SoMs followed by metabolite prediction. Compared to four available drug metabolite prediction tools, our method showed comparable performance on the major enzyme families and better generalization that could additionally identify metabolites catalyzed by less common enzymes. The results indicated that MetaPredictor could provide a more comprehensive and accurate prediction of drug metabolism through the effective combination of transfer learning and prompt-based learning strategies.

PMID:39082648 | DOI:10.1093/bib/bbae374

J Integr Neurosci. 2024 Jul 17;23(7):135. doi: 10.31083/j.jin2307135.

ABSTRACT

BACKGROUND: Autism spectrum disorder (ASD) is a neurodevelopmental disorder exhibiting heterogeneous characteristics in patients, including variability in developmental progression and distinct neuroanatomical features influenced by sex and age. Recent advances in deep learning models based on functional connectivity (FC) graphs have produced promising results, but they have focused on generalized global activation patterns and failed to capture specialized regional characteristics and accurately assess disease indications.

METHODS: To overcome these limitations, we propose a novel deep learning method that models FC with multi-head attention, which enables simultaneous modeling of the intricate and variable patterns of brain connectivity associated with ASD, effectively extracting abnormal patterns of brain connectivity. The proposed method not only identifies region-specific correlations but also emphasizes connections at specific, transient time points from diverse perspectives. The extracted FC is transformed into a graph, assigning weighted labels to the edges to reflect the degree of correlation, which is then processed using a graph neural network capable of handling edge labels.

RESULTS: Experiments on the autism brain imaging data exchange (ABIDE) I and II datasets, which include a heterogeneous cohort, showed superior performance over the state-of-the-art methods, improving accuracy by up to 3.7%p. The incorporation of multi-head attention in FC analysis markedly improved the distinction between typical brains and those affected by ASD. Additionally, the ablation study validated diverse brain characteristics in ASD patients across different ages and sexes, offering insightful interpretations.

CONCLUSION: These results emphasize the effectiveness of the method in enhancing diagnostic accuracy and its potential in advancing neurological research for ASD diagnosis.

PMID:39082298 | DOI:10.31083/j.jin2307135

Proc Biol Sci. 2024 Jul;291(2027):20240423. doi: 10.1098/rspb.2024.0423. Epub 2024 Jul 31.

ABSTRACT

In ecology and evolutionary biology, the synthesis and modelling of data from published literature are commonly used to generate insights and test theories across systems. However, the tasks of searching, screening, and extracting data from literature are often arduous. Researchers may manually process hundreds to thousands of articles for systematic reviews, meta-analyses, and compiling synthetic datasets. As relevant articles expand to tens or hundreds of thousands, computer-based approaches can increase the efficiency, transparency and reproducibility of literature-based research. Methods available for text mining are rapidly changing owing to developments in machine learning-based language models. We review the growing landscape of approaches, mapping them onto three broad paradigms (frequency-based approaches, traditional Natural Language Processing and deep learning-based language models). This serves as an entry point to learn foundational and cutting-edge concepts, vocabularies, and methods to foster integration of these tools into ecological and evolutionary research. We cover approaches for modelling ecological texts, generating training data, developing custom models and interacting with large language models and discuss challenges and possible solutions to implementing these methods in ecology and evolution.

PMID:39082244 | DOI:10.1098/rspb.2024.0423

ACS Sens. 2024 Jul 31. doi: 10.1021/acssensors.4c01139. Online ahead of print.

ABSTRACT

There is an increasing need for simple-to-use, noninvasive, and rapid tools to identify and separate various cell types or subtypes at the single-cell level with sufficient throughput. Often, the selection of cells based on their direct biological activity would be advantageous. These steps are critical in immune therapy, regenerative medicine, cancer diagnostics, and effective treatment. Today, live cell selection procedures incorporate some kind of biomolecular labeling or other invasive measures, which may impact cellular functionality or cause damage to the cells. In this study, we first introduce a highly accurate single-cell segmentation methodology by combining the high spatial resolution of a phase-contrast microscope with the adhesion kinetic recording capability of a resonant waveguide grating (RWG) biosensor. We present a classification workflow that incorporates the semiautomatic separation and classification of single cells from the measurement data captured by an RWG-based biosensor for adhesion kinetics data and a phase-contrast microscope for highly accurate spatial resolution. The methodology was tested with one healthy and six cancer cell types recorded with two functionalized coatings. The data set contains over 5000 single-cell samples for each surface and over 12,000 samples in total. We compare and evaluate the classification using these two types of surfaces (fibronectin and noncoated) with different segmentation strategies and measurement timespans applied to our classifiers. The overall classification performance reached nearly 95% with the best models showing that our proof-of-concept methodology could be adapted for real-life automatic diagnostics use cases. The label-free measurement technique has no impact on cellular functionality, directly measures cellular activity, and can be easily tuned to a specific application by varying the sensor coating. These features make it suitable for applications requiring further processing of selected cells.

PMID:39082162 | DOI:10.1021/acssensors.4c01139

J Comput Biol. 2024 Jul 31. doi: 10.1089/cmb.2023.0452. Online ahead of print.

ABSTRACT

The development of new drugs is a vital effort that has the potential to improve human health, well-being and life expectancy. Molecular property prediction is a crucial step in drug discovery, as it helps to identify potential therapeutic compounds. However, experimental methods for drug development can often be time-consuming and resource-intensive, with a low probability of success. To address such limitations, deep learning (DL) methods have emerged as a viable alternative due to their ability to identify high-discriminating patterns in molecular data. In particular, graph neural networks (GNNs) operate on graph-structured data to identify promising drug candidates with desirable molecular properties. These methods represent molecules as a set of node (atoms) and edge (chemical bonds) features to aggregate local information for molecular graph representation learning. Despite the availability of several GNN frameworks, each approach has its own shortcomings. Although, some GNNs may excel in certain tasks, they may not perform as well in others. In this work, we propose a hybrid approach that incorporates different graph-based methods to combine their strengths and mitigate their limitations to accurately predict molecular properties. The proposed approach consists in a multi-layered hybrid GNN architecture that integrates multiple GNN frameworks to compute graph embeddings for molecular property prediction. Furthermore, we conduct extensive experiments on multiple benchmark datasets to demonstrate that our hybrid approach significantly outperforms the state-of-the-art graph-based models. The data and code scripts to reproduce the results are available in the repository, https://github.com/pedro-quesado/HybridGNN.

PMID:39082155 | DOI:10.1089/cmb.2023.0452

Eur Heart J Digit Health. 2024 May 14;5(4):444-453. doi: 10.1093/ehjdh/ztae038. eCollection 2024 Jul.

ABSTRACT

AIMS: The clinical feasibility of artificial intelligence (AI)-based electrocardiography (ECG) analysis for predicting obstructive coronary artery disease (CAD) has not been sufficiently validated in patients with stable angina, especially in large sample sizes.

METHODS AND RESULTS: A deep learning framework for the quantitative ECG (QCG) analysis was trained and internally tested to derive the risk scores (0-100) for obstructive CAD (QCGObstCAD) and extensive CAD (QCGExtCAD) using 50 756 ECG images from 21 866 patients who underwent coronary artery evaluation for chest pain (invasive coronary or computed tomography angiography). External validation was performed in 4517 patients with stable angina who underwent coronary imaging to identify obstructive CAD. The QCGObstCAD and QCGExtCAD scores were significantly increased in the presence of obstructive and extensive CAD (all P < 0.001) and with increasing degrees of stenosis and disease burden, respectively (all P trend < 0.001). In the internal and external tests, QCGObstCAD exhibited a good predictive ability for obstructive CAD [area under the curve (AUC), 0.781 and 0.731, respectively] and severe obstructive CAD (AUC, 0.780 and 0.786, respectively), and QCGExtCAD exhibited a good predictive ability for extensive CAD (AUC, 0.689 and 0.784). In the external test, the QCGObstCAD and QCGExtCAD scores demonstrated independent and incremental predictive values for obstructive and extensive CAD, respectively, over that with conventional clinical risk factors. The QCG scores demonstrated significant associations with lesion characteristics, such as the fractional flow reserve, coronary calcification score, and total plaque volume.

CONCLUSION: The AI-based QCG analysis for predicting obstructive CAD in patients with stable angina, including those with severe stenosis and multivessel disease, is feasible.

PMID:39081950 | PMC:PMC11284006 | DOI:10.1093/ehjdh/ztae038

Eur Heart J Digit Health. 2024 Apr 25;5(4):427-434. doi: 10.1093/ehjdh/ztae034. eCollection 2024 Jul.

ABSTRACT

AIMS: Deep learning methods have recently gained success in detecting left ventricular systolic dysfunction (LVSD) from electrocardiogram (ECG) waveforms. Despite their high level of accuracy, they are difficult to interpret and deploy broadly in the clinical setting. In this study, we set out to determine whether simpler models based on standard ECG measurements could detect LVSD with similar accuracy to that of deep learning models.

METHODS AND RESULTS: Using an observational data set of 40 994 matched 12-lead ECGs and transthoracic echocardiograms, we trained a range of models with increasing complexity to detect LVSD based on ECG waveforms and derived measurements. The training data were acquired from the Stanford University Medical Center. External validation data were acquired from the Columbia Medical Center and the UK Biobank. The Stanford data set consisted of 40 994 matched ECGs and echocardiograms, of which 9.72% had LVSD. A random forest model using 555 discrete, automated measurements achieved an area under the receiver operator characteristic curve (AUC) of 0.92 (0.91-0.93), similar to a deep learning waveform model with an AUC of 0.94 (0.93-0.94). A logistic regression model based on five measurements achieved high performance [AUC of 0.86 (0.85-0.87)], close to a deep learning model and better than N-terminal prohormone brain natriuretic peptide (NT-proBNP). Finally, we found that simpler models were more portable across sites, with experiments at two independent, external sites.

CONCLUSION: Our study demonstrates the value of simple electrocardiographic models that perform nearly as well as deep learning models, while being much easier to implement and interpret.

PMID:39081946 | PMC:PMC11284011 | DOI:10.1093/ehjdh/ztae034

Eur Heart J Digit Health. 2024 Apr 17;5(4):401-408. doi: 10.1093/ehjdh/ztae027. eCollection 2024 Jul.

ABSTRACT

Coronary artery disease (CAD) is a leading health challenge worldwide. Exercise stress testing is a foundational non-invasive diagnostic tool. Nonetheless, its variable accuracy prompts the exploration of more reliable methods. Recent advancements in machine learning (ML), including deep learning and natural language processing, have shown potential in refining the interpretation of stress testing data. Adhering to Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines, we conducted a systematic review of ML applications in stress electrocardiogram (ECG) and stress echocardiography for CAD prognosis. Medical Literature Analysis and Retrieval System Online, Web of Science, and the Cochrane Library were used as databases. We analysed the ML models, outcomes, and performance metrics. Overall, seven relevant studies were identified. Machine-learning applications in stress ECGs resulted in sensitivity and specificity improvements. Some models achieved rates of above 96% in both metrics and reduced false positives by up to 21%. In stress echocardiography, ML models demonstrated an increase in diagnostic precision. Some models achieved specificity and sensitivity rates of up to 92.7 and 84.4%, respectively. Natural language processing applications enabled the categorization of stress echocardiography reports, with accuracy rates nearing 98%. Limitations include a small, retrospective study pool and the exclusion of nuclear stress testing, due to its well-documented status. This review indicates the potential of artificial intelligence applications in refining CAD stress testing assessment. Further development for real-world use is warranted.

PMID:39081945 | PMC:PMC11284008 | DOI:10.1093/ehjdh/ztae027

Eur Heart J Digit Health. 2024 May 8;5(4):435-443. doi: 10.1093/ehjdh/ztae035. eCollection 2024 Jul.

ABSTRACT

AIMS: Implantable loop recorders (ILRs) provide continuous single-lead ambulatory electrocardiogram (aECG) monitoring. Whether these aECGs could be used to identify worsening heart failure (HF) is unknown.

METHODS AND RESULTS: We linked ILR aECG from Medtronic device database to the left ventricular ejection fraction (LVEF) measurements in Optum® de-identified electronic health record dataset. We trained an artificial intelligence (AI) algorithm [aECG-convolutional neural network (CNN)] on a dataset of 35 741 aECGs from 2247 patients to identify LVEF ≤ 40% and assessed its performance using the area under the receiver operating characteristic curve. Ambulatory electrocardiogram-CNN was then used to identify patients with increasing risk of HF hospitalization in a real-world cohort of 909 patients with prior HF diagnosis. This dataset provided 12 467 follow-up monthly evaluations, with 201 HF hospitalizations. For every month, time-series features from these predictions were used to categorize patients into high- and low-risk groups and predict HF hospitalization in the next month. The risk of HF hospitalization in the next 30 days was significantly higher in the cohort that aECG-CNN identified as high risk [hazard ratio (HR) 1.89; 95% confidence interval (CI) 1.28-2.79; P = 0.001] compared with low risk, even after adjusting patient demographics (HR 1.88; 95% CI 1.27-2.79 P = 0.002).

CONCLUSION: An AI algorithm trained to detect LVEF ≤40% using ILR aECGs can also readily identify patients at increased risk of HF hospitalizations by monitoring changes in the probability of HF over 30 days.

PMID:39081943 | PMC:PMC11284004 | DOI:10.1093/ehjdh/ztae035

Eur Heart J Digit Health. 2024 May 12;5(4):454-460. doi: 10.1093/ehjdh/ztae039. eCollection 2024 Jul.

ABSTRACT

AIMS: Current deep learning algorithms for automatic ECG analysis have shown notable accuracy but are typically narrowly focused on singular diagnostic conditions. This exploratory study aims to investigate the capability of a single deep learning model to predict a diverse range of both cardiac and non-cardiac discharge diagnoses based on a single ECG collected in the emergency department.

METHODS AND RESULTS: In this study, we assess the performance of a model trained to predict a broad spectrum of diagnoses. We find that the model can reliably predict 253 ICD codes (81 cardiac and 172 non-cardiac) in the sense of exceeding an AUROC score of 0.8 in a statistically significant manner.

CONCLUSION: The model demonstrates proficiency in handling a wide array of cardiac and non-cardiac diagnostic scenarios, indicating its potential as a comprehensive screening tool for diverse medical encounters.

PMID:39081937 | PMC:PMC11284007 | DOI:10.1093/ehjdh/ztae039

Eur Heart J Digit Health. 2024 Apr 15;5(4):416-426. doi: 10.1093/ehjdh/ztae029. eCollection 2024 Jul.

ABSTRACT

AIMS: Recently, deep learning artificial intelligence (AI) models have been trained to detect cardiovascular conditions, including hypertrophic cardiomyopathy (HCM), from the 12-lead electrocardiogram (ECG). In this external validation study, we sought to assess the performance of an AI-ECG algorithm for detecting HCM in diverse international cohorts.

METHODS AND RESULTS: A convolutional neural network-based AI-ECG algorithm was developed previously in a single-centre North American HCM cohort (Mayo Clinic). This algorithm was applied to the raw 12-lead ECG data of patients with HCM and non-HCM controls from three external cohorts (Bern, Switzerland; Oxford, UK; and Seoul, South Korea). The algorithm's ability to distinguish HCM vs. non-HCM status from the ECG alone was examined. A total of 773 patients with HCM and 3867 non-HCM controls were included across three sites in the merged external validation cohort. The HCM study sample comprised 54.6% East Asian, 43.2% White, and 2.2% Black patients. Median AI-ECG probabilities of HCM were 85% for patients with HCM and 0.3% for controls (P < 0.001). Overall, the AI-ECG algorithm had an area under the receiver operating characteristic curve (AUC) of 0.922 [95% confidence interval (CI) 0.910-0.934], with diagnostic accuracy 86.9%, sensitivity 82.8%, and specificity 87.7% for HCM detection. In age- and sex-matched analysis (case-control ratio 1:2), the AUC was 0.921 (95% CI 0.909-0.934) with accuracy 88.5%, sensitivity 82.8%, and specificity 90.4%.

CONCLUSION: The AI-ECG algorithm determined HCM status from the 12-lead ECG with high accuracy in diverse international cohorts, providing evidence for external validity. The value of this algorithm in improving HCM detection in clinical practice and screening settings requires prospective evaluation.

PMID:39081936 | PMC:PMC11284003 | DOI:10.1093/ehjdh/ztae029

Patterns (N Y). 2024 May 15;5(7):100992. doi: 10.1016/j.patter.2024.100992. eCollection 2024 Jul 12.

ABSTRACT

Prostate cancer diagnosis and treatment relies on precise MRI lesion segmentation, a challenge notably for small (<15 mm) and intermediate (15-30 mm) lesions. Our study introduces ProLesA-Net, a multi-channel 3D deep-learning architecture with multi-scale squeeze and excitation and attention gate mechanisms. Tested against six models across two datasets, ProLesA-Net significantly outperformed in key metrics: Dice score increased by 2.2%, and Hausdorff distance and average surface distance improved by 0.5 mm, with recall and precision also undergoing enhancements. Specifically, for lesions under 15 mm, our model showed a notable increase in five key metrics. In summary, ProLesA-Net consistently ranked at the top, demonstrating enhanced performance and stability. This advancement addresses crucial challenges in prostate lesion segmentation, enhancing clinical decision making and expediting treatment processes.

PMID:39081575 | PMC:PMC11284496 | DOI:10.1016/j.patter.2024.100992