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Biological Trace Element Research Jul 2024This study aimed to examine the relationship between serum cholesterol levels and the ratio of zinc (Zn) and copper (Cu) in the blood serum and the incidence of...
This study aimed to examine the relationship between serum cholesterol levels and the ratio of zinc (Zn) and copper (Cu) in the blood serum and the incidence of cardiovascular disease (CVD). In Phase I of the study, 9704 individuals between the age of 35 and 65 years were recruited. Phase II of the cohort study comprised 7561 participants who completed the 10-year follow-up. The variables which were measured at the baseline of the study included gender, age, systolic blood pressure (SBP), diastolic blood pressure (DBP); biochemical parameters including serum Cu, Zn, copper-zinc ratio (Cu/Zn), zinc-copper ratio (Zn/Cu); fasted lipid profile consisting of triglycerides (TG), total cholesterol (TC), high-density lipoprotein (HDL), low-density lipoprotein (LDL) as well as fasting serum glucose, and triglycerides-glucose (TyG) index. Decision tree (DT) and logical regression (LR) models were applied to examine the relationship between the aforementioned factors and CVD. CVD was diagnosed in 837 individuals (378 males and 459 females) out of 7561 participants. According to the LR models, SBP, TC, HDL, age, Zn/Cu, and TyG index for males and SBP, age, TyG index, HDL, TC, Cu/Zn, and Cu for females had the highest correlation with CVD (p-value ≤ 0.033). Based on the DT algorithm, 88% of males with SPB < 129.66 mmHg, younger age (age < 53 years), TyG index < 9.53, 173 ≤ TC < 187 mg/dL, and HDL ≥ 32 mg/dL had the lowest risk of CVD. Also, 98% of females with SBP < 128 mmHg, TyG index < 9.68, age < 44, TC < 222 mg/dL, and HDL ≥ 63.7 mg/dL had the lowest risk of CVD. It can be concluded that the Zn/Cu for men and Cu/Zn for women, along with dyslipidemia and SBP, could significantly predict the risk of CVD in this cohort from northeastern Iran.
PubMed: 38956010
DOI: 10.1007/s12011-024-04288-0 -
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 -
Journal of Imaging Informatics in... Jul 2024Abnormalities in adrenal gland size may be associated with various diseases. Monitoring the volume of adrenal gland can provide a quantitative imaging indicator for such...
Abnormalities in adrenal gland size may be associated with various diseases. Monitoring the volume of adrenal gland can provide a quantitative imaging indicator for such conditions as adrenal hyperplasia, adrenal adenoma, and adrenal cortical adenocarcinoma. However, current adrenal gland segmentation models have notable limitations in sample selection and imaging parameters, particularly the need for more training on low-dose imaging parameters, which limits the generalization ability of the models, restricting their widespread application in routine clinical practice. We developed a fully automated adrenal gland volume quantification and visualization tool based on the no new U-Net (nnU-Net) for the automatic segmentation of deep learning models to address these issues. We established this tool by using a large dataset with multiple parameters, machine types, radiation doses, slice thicknesses, scanning modes, phases, and adrenal gland morphologies to achieve high accuracy and broad adaptability. The tool can meet clinical needs such as screening, monitoring, and preoperative visualization assistance for adrenal gland diseases. Experimental results demonstrate that our model achieves an overall dice coefficient of 0.88 on all images and 0.87 on low-dose CT scans. Compared to other deep learning models and nnU-Net model tools, our model exhibits higher accuracy and broader adaptability in adrenal gland segmentation.
PubMed: 38955963
DOI: 10.1007/s10278-024-01158-y -
Documenta Ophthalmologica. Advances in... Jul 2024Multiple sclerosis (MS) is a neuro-inflammatory disease affecting the central nervous system (CNS), where the immune system targets and damages the protective myelin...
PURPOSE
Multiple sclerosis (MS) is a neuro-inflammatory disease affecting the central nervous system (CNS), where the immune system targets and damages the protective myelin sheath surrounding nerve fibers, inhibiting axonal signal transmission. Demyelinating optic neuritis (ON), a common MS symptom, involves optic nerve damage. We've developed NeuroVEP, a portable, wireless diagnostic system that delivers visual stimuli through a smartphone in a headset and measures evoked potentials at the visual cortex from the scalp using custom electroencephalography electrodes.
METHODS
Subject vision is evaluated using a short 2.5-min full-field visual evoked potentials (ffVEP) test, followed by a 12.5-min multifocal VEP (mfVEP) test. The ffVEP evaluates the integrity of the visual pathway by analyzing the P100 component from each eye, while the mfVEP evaluates 36 individual regions of the visual field for abnormalities. Extensive signal processing, feature extraction methods, and machine learning algorithms were explored for analyzing the mfVEPs. Key metrics from patients' ffVEP results were statistically evaluated against data collected from a group of subjects with normal vision. Custom visual stimuli with simulated defects were used to validate the mfVEP results which yielded 91% accuracy of classification.
RESULTS
20 subjects, 10 controls and 10 with MS and/or ON were tested with the NeuroVEP device and a standard-of-care (SOC) VEP testing device which delivers only ffVEP stimuli. In 91% of the cases, the ffVEP results agreed between NeuroVEP and SOC device. Where available, the NeuroVEP mfVEP results were in good agreement with Humphrey Automated Perimetry visual field analysis. The lesion locations deduced from the mfVEP data were consistent with Magnetic Resonance Imaging and Optical Coherence Tomography findings.
CONCLUSION
This pilot study indicates that NeuroVEP has the potential to be a reliable, portable, and objective diagnostic device for electrophysiology and visual field analysis for neuro-visual disorders.
PubMed: 38955958
DOI: 10.1007/s10633-024-09980-z -
Bridging Health Disparities in the Data-Driven World of Artificial Intelligence: A Narrative Review.Journal of Racial and Ethnic Health... Jul 2024Artificial intelligence (AI) holds exciting potential to revolutionize healthcare delivery in the United States. However, there are concerns about its potential to... (Review)
Review
BACKGROUND
Artificial intelligence (AI) holds exciting potential to revolutionize healthcare delivery in the United States. However, there are concerns about its potential to perpetuate disparities among historically marginalized populations.
OBJECTIVE
Following the guidelines of Preferred Reporting Items for Systematic Reviews and Meta-Analyses, we conducted a narrative review of current literature on AI and health disparities in the United States. We aimed to answer the question, Does AI have the potential to reduce or eliminate health disparities, or will its use further exacerbate these disparities?
METHODS
We searched the Ovid MEDLINE electronic database to identify and retrieve publications discussing AI and its impact on racial/ethnic health disparities. Articles were included if they discussed AI as a tool to mitigate racial health disparities with or without bias in developing and using AI.
RESULTS
This review included 65 articles. We identified six themes of limitations in AI that impact health equity: (1) biases in AI can perpetuate and exacerbate racial and ethnic inequities; (2) equity in algorithms should be a priority; (3) lack of diversity in the field of AI is concerning; (4) the need for regulation and testing algorithms for accuracy; (5) ethical standards for AI in health care are needed; and (6) the importance of promoting transparency and accountability.
CONCLUSIONS
While AI promises to enhance healthcare outcomes and address equity concerns, risks and challenges are associated with its implementation. To maximize the use of AI, it must be approached with an equity lens during all phases of development.
PubMed: 38955956
DOI: 10.1007/s40615-024-02057-2 -
Zhonghua Yu Fang Yi Xue Za Zhi [Chinese... Jun 2024To evaluate the modification of allergic dermatitis on the association between PM exposure and allergic rhinitis in preschool children. This cross-sectional study was...
To evaluate the modification of allergic dermatitis on the association between PM exposure and allergic rhinitis in preschool children. This cross-sectional study was based on a questionnaire conducted between June 2019 and June 2020 to caregivers of children aged 3 to 6 years in the kindergartens of 7 Chinese cities to collect information on allergic rhinitis and allergic dermatitis. A mature machine learning-based space-time extremely randomized trees model was applied to estimate early-life, prenatal, and first-year exposure of PM, PM and PM at 1 km×1 km resolution. A combination of multilevel logistic regression and restricted cubic spline functions was used to quantitatively assess whether allergic dermatitis modifies the associations between size-specific PM exposure and the risk of childhood allergic rhinitis. The results showed that out of 28 408 children, 14 803 (52.1%) were boys and 13 605 (47.9%) were girls; the age of children ranged from 3.1 to 6.8 years, with a mean age of (4.9±0.9) years, of which 3 586 (12.6%) were diagnosed with allergic rhinitis. Among all children, 17 832 (62.8%) were breastfed for more than 6 months and 769 (2.7%) had parental history of atopy. A total of 21 548 children (75.9%) had a mother with an educational level of university or above and 7 338 (29.6%) had passive household cigarette smoke exposure. The adjusted s for childhood allergic rhinitis among the children with allergic dermatitis as per interquartile range (IQR) increase in early-life PM(9.8 μg/m), PM (14.9 μg/m) and PM (37.7 μg/m) were significantly higher than the corresponding s among the children without allergic dermatitis [: 1.45, 95% (1.26, 1.66) 1.33, 95% (1.20, 1.47), for PM; : 1.38, 95% (1.23, 1.56) 1.32, 95% (1.21, 1.45), for PM; : 1.56, 95% (1.31, 1.86) 1.46, 95% (1.28, 1.67), for PM]. The interactions between allergic dermatitis and size-specific PM exposure on childhood allergic rhinitis were statistically significant ( value=19.4, all for interaction<0.001). The similar patterns were observed for both prenatal and first-year size-specific PM exposure and the results of the dose-response relationship were consistent with those of the logistic regression. In conclusion, allergic dermatitis, as an important part of the allergic disease progression, may modify the association between ambient PM exposure and the risk of childhood allergic rhinitis. Children with allergic dermatitis should pay more attention to minimize outdoor air pollutants exposure to prevent the further progression of allergic diseases.
Topics: Humans; Particulate Matter; Child, Preschool; Rhinitis, Allergic; Female; Cross-Sectional Studies; Dermatitis, Atopic; China; Male; Environmental Exposure; Child; Air Pollutants; Particle Size; Air Pollution; Risk Factors; Logistic Models
PubMed: 38955730
DOI: 10.3760/cma.j.cn112150-20230915-00192 -
Clinical Radiology May 2024The objective of this study was to create and authenticate a prognostic model for lymph node metastasis (LNM) in colorectal cancer (CRC) that integrates clinical,...
Deep-learning features based on F18 fluorodeoxyglucose positron emission tomography/computed tomography (F-FDG PET/CT) to predict preoperative colorectal cancer lymph node metastasis.
AIM
The objective of this study was to create and authenticate a prognostic model for lymph node metastasis (LNM) in colorectal cancer (CRC) that integrates clinical, radiomics, and deep transfer learning features.
MATERIALS AND METHODS
In this study, we analyzed data from 119 CRC patients who underwent F18 fluorodeoxyglucose positron emission tomography/computed tomography (F-FDG PET/CT) scanning. The patient cohort was divided into training and validation subsets in an 8:2 ratio, with an additional 33 external data points for testing. Initially, we conducted univariate analysis to screen clinical parameters. Radiomics features were extracted from manually drawn images using pyradiomics, and deep-learning features, radiomics features, and clinical features were selected using Least Absolute Shrinkage and Selection Operator (LASSO) regression and Spearman correlation coefficient. We then constructed a model by training a support vector machine (SVM), and evaluated the performance of the prediction model by comparing the area under the curve (AUC), sensitivity, and specificity. Finally, we developed nomograms combining clinical and radiological features for interpretation and analysis.
RESULTS
The deep learning radiomics (DLR) nomogram model, which was developed by integrating deep learning, radiomics, and clinical features, exhibited excellent performance. The area under the curve was (AUC = 0.934, 95% confidence interval [CI]: 0.884-0.983) in the training cohort, (AUC = 0.902, 95% CI: 0.769-1.000) in the validation cohort, and (AUC = 0.836, 95% CI: 0.673-0.998) in the test cohort.
CONCLUSION
We developed a preoperative predictive machine-learning model using deep transfer learning, radiomics, and clinical features to differentiate LNM status in CRC, aiding in treatment decision-making for patients.
PubMed: 38955636
DOI: 10.1016/j.crad.2024.05.017 -
Academic Radiology Jul 2024Surgery in combination with chemo/radiotherapy is the standard treatment for locally advanced esophageal cancer. Even after the introduction of minimally invasive...
RATIONALE AND OBJECTIVES
Surgery in combination with chemo/radiotherapy is the standard treatment for locally advanced esophageal cancer. Even after the introduction of minimally invasive techniques, esophagectomy carries significant morbidity and mortality. One of the most common and feared complications of esophagectomy is anastomotic leakage (AL). Our work aimed to develop a multimodal machine-learning model combining CT-derived and clinical data for predicting AL following esophagectomy for esophageal cancer.
MATERIAL AND METHODS
A total of 471 patients were prospectively included (Jan 2010-Dec 2022). Preoperative computed tomography (CT) was used to evaluate celia trunk stenosis and vessel calcification. Clinical variables, including demographics, disease stage, operation details, postoperative CRP, and stage, were combined with CT data to build a model for AL prediction. Data was split into 80%:20% for training and testing, and an XGBoost model was developed with 10-fold cross-validation and early stopping. ROC curves and respective areas under the curve (AUC), sensitivity, specificity, PPV, NPV, and F1-scores were calculated.
RESULTS
A total of 117 patients (24.8%) exhibited post-operative AL. The XGboost model achieved an AUC of 79.2% (95%CI 69%-89.4%) with a specificity of 77.46%, a sensitivity of 65.22%, PPV of 48.39%, NPV of 87.3%, and F1-score of 56%. Shapley Additive exPlanation analysis showed the effect of individual variables on the result of the model. Decision curve analysis showed that the model was particularly beneficial for threshold probabilities between 15% and 48%.
CONCLUSION
A clinically relevant multimodal model can predict AL, which is especially valuable in cases with low clinical probability of AL.
PubMed: 38955594
DOI: 10.1016/j.acra.2024.06.026 -
Academic Radiology Jul 2024Stroke-associated pneumonia (SAP) often appears as a complication following intracerebral hemorrhage (ICH), leading to poor prognosis and increased mortality rates....
RATIONALE AND OBJECTIVE
Stroke-associated pneumonia (SAP) often appears as a complication following intracerebral hemorrhage (ICH), leading to poor prognosis and increased mortality rates. Previous studies have typically developed prediction models based on clinical data alone, without considering that ICH patients often undergo CT scans immediately upon admission. As a result, these models are subjective and lack real-time applicability, with low accuracy that does not meet clinical needs. Therefore, there is an urgent need for a quick and reliable model to timely predict SAP.
METHODS
In this retrospective study, we developed an image-based model (DeepSAP) using brain CT scans from 244 ICH patients to classify the presence and severity of SAP. First, DeepSAP employs MRI-template-based image registration technology to eliminate structural differences between samples, achieving statistical quantification and spatial standardization of cerebral hemorrhage. Subsequently, the processed images and filtered clinical data were simultaneously input into a deep-learning neural network for training and analysis. The model was tested on a test set to evaluate diagnostic performance, including accuracy, specificity, and sensitivity.
RESULTS
Brain CT scans from 244 ICH patients (mean age, 60.24; 66 female) were divided into a training set (n = 170) and a test set (n = 74). The cohort included 143 SAP patients, accounting for 58.6% of the total, with 66 cases classified as moderate or above, representing 27% of the total. Experimental results showed an AUC of 0.93, an accuracy of 0.84, a sensitivity of 0.79, and a precision of 0.95 for classifying the presence of SAP. In comparison, the model relying solely on clinical data showed an AUC of only 0.76, while the radiomics method had an AUC of 0.74. Additionally, DeepSAP achieved an optimal AUC of 0.84 for the SAP grading task.
CONCLUSION
DeepSAP's accuracy in predicting SAP stems from its spatial normalization and statistical quantification of the ICH region. DeepSAP is expected to be an effective tool for predicting and grading SAP in clinic.
PubMed: 38955592
DOI: 10.1016/j.acra.2024.06.025 -
European Journal of Surgical Oncology :... Jun 2024
PubMed: 38955583
DOI: 10.1016/j.ejso.2024.108493