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Scientific Data Jul 2024Patients with congenital heart disease often have cardiac anatomy that deviates significantly from normal, frequently requiring multiple heart surgeries. Image...
Patients with congenital heart disease often have cardiac anatomy that deviates significantly from normal, frequently requiring multiple heart surgeries. Image segmentation from a preoperative cardiovascular magnetic resonance (CMR) scan would enable creation of patient-specific 3D surface models of the heart, which have potential to improve surgical planning, enable surgical simulation, and allow automatic computation of quantitative metrics of heart function. However, there is no publicly available CMR dataset for whole-heart segmentation in patients with congenital heart disease. Here, we release the HVSMR-2.0 dataset, comprising 60 CMR scans alongside manual segmentation masks of the 4 cardiac chambers and 4 great vessels. The images showcase a wide range of heart defects and prior surgical interventions. The dataset also includes masks of required and optional extents of the great vessels, enabling fairer comparisons across algorithms. Detailed diagnoses for each subject are also provided. By releasing HVSMR-2.0, we aim to encourage development of robust segmentation algorithms and clinically relevant tools for congenital heart disease.
Topics: Humans; Heart Defects, Congenital; Magnetic Resonance Imaging; Heart; Imaging, Three-Dimensional; Algorithms
PubMed: 38956063
DOI: 10.1038/s41597-024-03469-9 -
Cardiovascular Engineering and... Jul 2024Advanced material models and material characterization of soft biological tissues play an essential role in pre-surgical planning for vascular surgeries and... (Review)
Review
BACKGROUND AND OBJECTIVE
Advanced material models and material characterization of soft biological tissues play an essential role in pre-surgical planning for vascular surgeries and transcatheter interventions. Recent advances in heart valve engineering, medical device and patch design are built upon these models. Furthermore, understanding vascular growth and remodeling in native and tissue-engineered vascular biomaterials, as well as designing and testing drugs on soft tissue, are crucial aspects of predictive regenerative medicine. Traditional nonlinear optimization methods and finite element (FE) simulations have served as biomaterial characterization tools combined with soft tissue mechanics and tensile testing for decades. However, results obtained through nonlinear optimization methods are reliable only to a certain extent due to mathematical limitations, and FE simulations may require substantial computing time and resources, which might not be justified for patient-specific simulations. To a significant extent, machine learning (ML) techniques have gained increasing prominence in the field of soft tissue mechanics in recent years, offering notable advantages over conventional methods. This review article presents an in-depth examination of emerging ML algorithms utilized for estimating the mechanical characteristics of soft biological tissues and biomaterials. These algorithms are employed to analyze crucial properties such as stress-strain curves and pressure-volume loops. The focus of the review is on applications in cardiovascular engineering, and the fundamental mathematical basis of each approach is also discussed.
METHODS
The review effort employed two strategies. First, the recent studies of major research groups actively engaged in cardiovascular soft tissue mechanics are compiled, and research papers utilizing ML and deep learning (DL) techniques were included in our review. The second strategy involved a standard keyword search across major databases. This approach provided 11 relevant ML articles, meticulously selected from reputable sources including ScienceDirect, Springer, PubMed, and Google Scholar. The selection process involved using specific keywords such as "machine learning" or "deep learning" in conjunction with "soft biological tissues", "cardiovascular", "patient-specific," "strain energy", "vascular" or "biomaterials". Initially, a total of 25 articles were selected. However, 14 of these articles were excluded as they did not align with the criteria of focusing on biomaterials specifically employed for soft tissue repair and regeneration. As a result, the remaining 11 articles were categorized based on the ML techniques employed and the training data utilized.
RESULTS
ML techniques utilized for assessing the mechanical characteristics of soft biological tissues and biomaterials are broadly classified into two categories: standard ML algorithms and physics-informed ML algorithms. The standard ML models are then organized based on their tasks, being grouped into Regression and Classification subcategories. Within these categories, studies employ various supervised learning models, including support vector machines (SVMs), bagged decision trees (BDTs), artificial neural networks (ANNs) or deep neural networks (DNNs), and convolutional neural networks (CNNs). Additionally, the utilization of unsupervised learning approaches, such as autoencoders incorporating principal component analysis (PCA) and/or low-rank approximation (LRA), is based on the specific characteristics of the training data. The training data predominantly consists of three types: experimental mechanical data, including uniaxial or biaxial stress-strain data; synthetic mechanical data generated through non-linear fitting and/or FE simulations; and image data such as 3D second harmonic generation (SHG) images or computed tomography (CT) images. The evaluation of performance for physics-informed ML models primarily relies on the coefficient of determination . In contrast, various metrics and error measures are utilized to assess the performance of standard ML models. Furthermore, our review includes an extensive examination of prevalent biomaterial models that can serve as physical laws for physics-informed ML models.
CONCLUSION
ML models offer an accurate, fast, and reliable approach for evaluating the mechanical characteristics of diseased soft tissue segments and selecting optimal biomaterials for time-critical soft tissue surgeries. Among the various ML models examined in this review, physics-informed neural network models exhibit the capability to forecast the mechanical response of soft biological tissues accurately, even with limited training samples. These models achieve high values ranging from 0.90 to 1.00. This is particularly significant considering the challenges associated with obtaining a large number of living tissue samples for experimental purposes, which can be time-consuming and impractical. Additionally, the review not only discusses the advantages identified in the current literature but also sheds light on the limitations and offers insights into future perspectives.
PubMed: 38956008
DOI: 10.1007/s13239-024-00737-y -
Current Problems in Cardiology Jun 2024Cardiac amyloidosis, characterized by amyloid fibril deposition in the myocardium, leads to restrictive cardiomyopathy and heart failure. This review explores recent... (Review)
Review
Cardiac amyloidosis, characterized by amyloid fibril deposition in the myocardium, leads to restrictive cardiomyopathy and heart failure. This review explores recent advancements in imaging techniques for diagnosing and managing cardiac amyloidosis, highlighting their clinical applications, strengths, and limitations. Echocardiography remains a primary, non-invasive imaging modality but lacks specificity. Cardiac MRI (CMR), with Late Gadolinium Enhancement (LGE) and T1 mapping, offers superior tissue characterization, though at higher costs and limited availability. Scintigraphy with Tc-99m-PYP reliably diagnoses transthyretin (TTR) amyloidosis but is less effective for light chain (AL) amyloidosis, necessitating complementary diagnostics. Amyloid-specific PET tracers, such as florbetapir and flutemetamol, provide precise imaging and quantitative assessment for both TTR and AL amyloidosis. Challenges include differentiating between TTR and AL amyloidosis, early disease detection, and standardizing imaging protocols. Future research should focus on developing novel tracers, integrating multimodality imaging, and leveraging AI to enhance diagnostic accuracy and personalized treatment. Advancements in imaging have improved cardiac amyloidosis management. A multimodal approach, incorporating echocardiography, CMR, scintigraphy, and PET tracers, offers comprehensive assessment. Continued innovation in tracers and AI applications promises further enhancements in diagnosis, early detection, and patient outcomes.
PubMed: 38955249
DOI: 10.1016/j.cpcardiol.2024.102733 -
Scientific Reports Jul 2024The Electrocardiogram (ECG) records are crucial for predicting heart diseases and evaluating patient's health conditions. ECG signals provide essential peak values that...
The Electrocardiogram (ECG) records are crucial for predicting heart diseases and evaluating patient's health conditions. ECG signals provide essential peak values that reflect reliable health information. Analyzing ECG signals is a fundamental technique for computerized prediction with advancements in Very Large-Scale Integration (VLSI) technology and significantly impacts in biomedical signal processing. VLSI advancements focus on high-speed circuit functionality while minimizing power consumption and area occupancy. In ECG signal denoising, digital filters like Infinite Impulse Response (IIR) and Finite Impulse Response (FIR) are commonly used. The FIR filters are preferred for their higher-order performance and stability over IIR filters, especially in real-time applications. The Modified FIR (MFIR) blocks were reconstructed using the optimized adder-multiplier block for better noise reduction performance. The MIT-BIT database is used as reference where the noises are filtered by the MFIR based on Optimized Kogge Stone Adder (OKSA). Features are extracted and analyzed using Discrete wavelet transform (DWT) and Cross Correlation (CC). At this modern era, Hybrid methods of Machine Learning (HMLM) methods are preferred because of their combined performance which is better than non-fused methods. The accuracy of the Hybrid Neural Network (HNN) model reached 92.3%, surpassing other models such as Generalized Sequential Neural Networks (GSNN), Artificial Neural Networks (ANN), Support Vector Machine with linear kernel (SVM linear), and Support Vector Machine with Radial Basis Function kernel (SVM RBF) by margins of 3.3%, 5.3%, 23.3%, and 24.3%, respectively. While the precision of the HNN is 91.1%, it was slightly lower than GSNN and ANN but higher than both SVM linear and SVM -RBF. The HNN with various features are incorporated to improve the ECG classification. The accuracy of the HNN is switched to 95.99% when the DWT and CC are combined. Also, it improvises other parameters such as precision 93.88%, recall is 0.94, F1 score is 0.88, Kappa is 0.89, kurtosis is 1.54, skewness is 1.52 and error rate 0.076. These parameters are higher than recently developed models whose algorithms and methods accuracy is more than 90%.
Topics: Electrocardiography; Humans; Neural Networks, Computer; Signal Processing, Computer-Assisted; Algorithms; Wavelet Analysis; Machine Learning
PubMed: 38956261
DOI: 10.1038/s41598-024-65849-w -
Pediatric Cardiology Jul 2024Secundum atrial septal defect (ASD2) detection is often delayed, with the potential for late diagnosis complications. Recent work demonstrated artificial...
Secundum atrial septal defect (ASD2) detection is often delayed, with the potential for late diagnosis complications. Recent work demonstrated artificial intelligence-enhanced ECG analysis shows promise to detect ASD2 in adults. However, its application to pediatric populations remains underexplored. In this study, we trained a convolutional neural network (AI-pECG) on paired ECG-echocardiograms (≤ 2 days apart) to detect ASD2 from patients ≤ 18 years old without major congenital heart disease. Model performance was evaluated on the first ECG-echocardiogram pair per patient for Boston Children's Hospital internal testing and emergency department cohorts using area under the receiver operating (AUROC) and precision-recall (AUPRC) curves. The training cohort comprised of 92,377 ECG-echocardiogram pairs (46,261 patients; median age 8.2 years) with an ASD2 prevalence of 6.7%. Test groups included internal testing (12,631 patients; median age 7.4 years; 6.9% prevalence) and emergency department (2,830 patients; median age 7.5 years; 4.9% prevalence) cohorts. Model performance was higher in the internal test (AUROC 0.84, AUPRC 0.46) cohort than the emergency department cohort (AUROC 0.80, AUPRC 0.30). In both cohorts, AI-pECG outperformed ECG findings of incomplete right bundle branch block. Model explainability analyses suggest high-risk limb lead features include greater amplitude P waves (suggestive of right atrial enlargement) and V1 RSR' (suggestive of RBBB). Our findings demonstrate the promise of AI-pECG to inexpensively screen and/or detect ASD2 in pediatric patients. Future multicenter validation and prospective trials to inform clinical decision making are warranted.
PubMed: 38953953
DOI: 10.1007/s00246-024-03540-7 -
Osteoporosis International : a Journal... Jul 2024Long-term glucocorticoids (GCs) treatment is associated with osteoporosis and fractures. We investigated whether low-dose GC treatment also increased the risk of...
UNLABELLED
Long-term glucocorticoids (GCs) treatment is associated with osteoporosis and fractures. We investigated whether low-dose GC treatment also increased the risk of osteoporotic fractures, and the results showed that even low-dose GC treatment increased the risk of osteoporotic fractures, especially spine fractures.
PURPOSE
The effect of low-dose glucocorticoid (GC) therapy on the fracture risk in postmenopausal women with low bone mass was investigated.
METHODS
119,790 66-year-old postmenopausal women with low bone mass based on bone mineral density (BMD) results were included. GC group consisted of patients who had been prescribed oral GCs within 6 months of BMD testing. In GC group, GCs dosage was calculated by a defined daily dose (DDD), and divided into five groups according to GC usage (Group 1[G1]; < 11.25 DDDs, G2; ≥ 11.25, < 22.5 DDDs, G3; ≥ 22.5, < 45 DDDs, G4; ≥ 45, < 90 DDDs, G5; ≥ 90 DDDs). The risk of major osteoporotic fractures (MOF) and non-MOF was analyzed and compared with that of the control group during the 1-year follow-up.
RESULTS
The risk of total fracture was higher in G3-G5 than in the control group (G3, hazard ratio (HR) 1.25, 95% confidence interval [CI] 1.07-1.46; G4, 1.37 [1.13-1.66]; G5 1.45 [1.08-1.94]). The risk of MOF was higher in all groups except G2 than in the control group (G1, 1.23 [1.05-1.45]; G3, 1.37 [1.11-1.68]; G4, 1.41 [1.09-1.83]; G5, 1.66 [1.14-2.42]). The risk of spine fracture was significantly higher in all GC groups except G2 than in the control group. The risk of non-MOF was higher only in G4 than in the control group (G4, 1.48 [1.13-1.94]).
CONCLUSION
Low-dose GC therapy can increase the risk of osteoporotic fractures, particularly spine fractures, in postmenopausal women with low bone mass.
PubMed: 38953946
DOI: 10.1007/s00198-024-07159-5 -
Annals of Laboratory Medicine Jul 2024Healthcare 4.0. refers to the integration of advanced technologies, such as artificial intelligence (AI) and big data analysis, into the healthcare sector. Recognizing...
BACKGROUND
Healthcare 4.0. refers to the integration of advanced technologies, such as artificial intelligence (AI) and big data analysis, into the healthcare sector. Recognizing the impact of Healthcare 4.0 technologies in laboratory medicine (LM), we seek to assess the overall awareness and implementation of Healthcare 4.0 among members of the Korean Society for Laboratory Medicine (KSLM).
METHODS
A web-based survey was conducted using an anonymous questionnaire. The survey comprised 36 questions covering demographic information (seven questions), big data (10 questions), and AI (19 questions).
RESULTS
In total, 182 (17.9%) of 1,017 KSLM members participated in the survey. Thirty-two percent of respondents considered AI to be the most important technology in LM in the era of Healthcare 4.0, closely followed by 31% who favored big data. Approximately 80% of respondents were familiar with big data but had not conducted research using it, and 71% were willing to participate in future big data research conducted by the KSLM. Respondents viewed AI as the most valuable tool in molecular genetics within various divisions. More than half of the respondents were open to the notion of using AI as assistance rather than a complete replacement for their roles.
CONCLUSIONS
This survey highlighted KSLM members' awareness of the potential applications and implications of big data and AI. We emphasize the complexity of AI integration in healthcare, citing technical and ethical challenges leading to diverse opinions on its impact on employment and training. This highlights the need for a holistic approach to adopting new technologies.
PubMed: 38953115
DOI: 10.3343/alm.2024.0111 -
Veterinary Medicine and Science Jul 2024Acute flaccid paralysis (AFP) is a complex clinical syndrome with various aetiologies. If untreated, AFP may lead to death due to failure of respiratory muscles. Tick...
BACKGROUND
Acute flaccid paralysis (AFP) is a complex clinical syndrome with various aetiologies. If untreated, AFP may lead to death due to failure of respiratory muscles. Tick paralysis, which is a noninfectious neurologic syndrome of AFP, occurs following tick attachment, engorgement, and injection of tick saliva toxins. There is no specific diagnostic test for tick paralysis, and mortality increases as definitive diagnosis is delayed. Although metabolomic investigation of tick saliva was conducted, there is a lack of research on metabolomic evaluation of hosts affected by tick paralysis.
OBJECTIVES
Thus, the aim of this study is to investigate metabolomic changes in serum samples of dogs with tick paralysis due to Rhipicephalus sanguineus using NMR-based metabolomics and to identify potential diagnostic/prognostic markers.
MATERIALS AND METHODS
Forty dogs infested with R. sanguineus, with clinical findings compatible with AFP and with a confirmed tick paralysis diagnosis ex juvantibus, constituted the Paralysis Group. Ten healthy dogs, which were admitted either for vaccination and/or check-up purposes, constituted the Control Group. After the confirmation tick paralysis, medical history, vaccination and nutritional status, body surface area and estimated tick numbers of all the dogs were noted. Physical examination included body temperature, heart and respiratory rate, capillary refill time evaluation and Modified Glasgow Coma Scale calculation. Serum samples were extracted from venous blood samples of all the dogs and were prepared for NMR analysis, and NMR-based metabolomics identification and quantification were performed.
RESULTS
NMR-based serum metabolomics of the present study revealed distinct up/down-regulated expressions, presenting a promising avenue. Moreover, it was observed that energy metabolism and especially liver functions were impaired in dogs with tick paralysis, and not only the respiratory system but also the kidneys were affected.
CONCLUSION
It was concluded that the present approach may help to better understand the pathological mechanisms developing in cases of AFP due to tick paralysis.
Topics: Animals; Dogs; Tick Paralysis; Dog Diseases; Magnetic Resonance Spectroscopy; Metabolomics; Female; Male; Rhipicephalus sanguineus; Metabolome; Paralysis
PubMed: 38952268
DOI: 10.1002/vms3.1528 -
Diabetes, Obesity & Metabolism Jul 2024To assess if early change in albuminuria was linked to an initial change in estimated glomerular filtration rate (eGFR) and long-term kidney outcomes in people with type...
Early reduction in albuminuria is associated with a steeper 'dip' in initial estimated glomerular filtration rate but favourable long-term kidney outcomes in people with diabetes receiving sodium-glucose cotransporter-2 inhibitors.
AIM
To assess if early change in albuminuria was linked to an initial change in estimated glomerular filtration rate (eGFR) and long-term kidney outcomes in people with type 2 diabetes (T2D) receiving sodium-glucose cotransporter-2 (SGLT2) inhibitors.
METHODS
Using a medical database from a multicentre healthcare institute in Taiwan, we retrospectively enrolled 8310 people receiving SGLT2 inhibitors from 1 June 2016 to 31 December 2021. We compared the risks of initial eGFR decline, major adverse renal events (MARE; >50% eGFR reduction or development of end-stage kidney disease), major adverse cardiovascular events (MACE), or hospitalization for heart failure (HHF) using a Cox proportional hazards model.
RESULTS
In all, 36.8% (n = 3062) experienced a >30% decrease, 21.0% (n = 1743) experienced a 0%-30% decrease, 14.4% (n = 1199) experienced a 0%-30% increase, and 27.7% (n = 2306) experienced a >30% increase in urine albumin-to-creatine ratio (UACR) after 3 months of SGLT2 inhibitor treatment. Greater acute eGFR decline at 3 months correlated with greater UACR reduction: -3.6 ± 10.9, -2.0 ± 9.5, -1.1 ± 8.6, and -0.3 ± 9.7 mL/min/1.73 m for the respective UACR change groups (p < 0.001). Over a median of 29.0 months, >30% UACR decline was associated with a higher risk of >30% initial eGFR decline (hazard ratio [HR] 2.68, 95% confidence interval [CI] 1.61-4.47]), a lower risk of MARE (HR 0.66, 95% CI 0.48-0.89), and a comparable risk of MACE or HHF after multivariate adjustment (p < 0.05). The nonlinear analysis showed early UACR decline was linked to a lower risk of MARE but a higher risk of initial steep eGFR decline of >30%.
CONCLUSION
Physicians should be vigilant for the potential adverse effects of abrupt eGFR dipping associated with a profound reduction in UACR, despite the favourable long-term kidney outcomes in the population with T2D receiving SGLT2 inhibitor treatment.
PubMed: 38951860
DOI: 10.1111/dom.15734 -
BMJ Open Jul 2024Spirometry is a point-of-care lung function test that helps support the diagnosis and monitoring of chronic lung disease. The quality and interpretation accuracy of... (Randomized Controlled Trial)
Randomized Controlled Trial
Comparing performance of primary care clinicians in the interpretation of SPIROmetry with or without Artificial Intelligence Decision support software (SPIRO-AID): a protocol for a randomised controlled trial.
INTRODUCTION
Spirometry is a point-of-care lung function test that helps support the diagnosis and monitoring of chronic lung disease. The quality and interpretation accuracy of spirometry is variable in primary care. This study aims to evaluate whether artificial intelligence (AI) decision support software improves the performance of primary care clinicians in the interpretation of spirometry, against reference standard (expert interpretation).
METHODS AND ANALYSIS
A parallel, two-group, statistician-blinded, randomised controlled trial of primary care clinicians in the UK, who refer for, or interpret, spirometry. People with specialist training in respiratory medicine to consultant level were excluded. A minimum target of 228 primary care clinician participants will be randomised with a 1:1 allocation to assess fifty de-identified, real-world patient spirometry sessions through an online platform either with (intervention group) or without (control group) AI decision support software report. Outcomes will cover primary care clinicians' spirometry interpretation performance including measures of technical quality assessment, spirometry pattern recognition and diagnostic prediction, compared with reference standard. Clinicians' self-rated confidence in spirometry interpretation will also be evaluated. The primary outcome is the proportion of the 50 spirometry sessions where the participant's preferred diagnosis matches the reference diagnosis. Unpaired t-tests and analysis of covariance will be used to estimate the difference in primary outcome between intervention and control groups.
ETHICS AND DISSEMINATION
This study has been reviewed and given favourable opinion by Health Research Authority Wales (reference: 22/HRA/5023). Results will be submitted for publication in peer-reviewed journals, presented at relevant national and international conferences, disseminated through social media, patient and public routes and directly shared with stakeholders.
TRIAL REGISTRATION NUMBER
NCT05933694.
Topics: Humans; Spirometry; Artificial Intelligence; Primary Health Care; Randomized Controlled Trials as Topic; Software; United Kingdom; Decision Support Systems, Clinical
PubMed: 38950987
DOI: 10.1136/bmjopen-2024-086736