Validation Set Research Articles

Differential diagnosis of congenital ventricular septal defect and atrial septal defect in children using deep learning–based analysis of chest radiographs

BackgroundChildren with atrial septal defect (ASD) and ventricular septal defect (VSD) are frequently examined for respiratory symptoms, even when the underlying disease is not found. Chest radiographs often serve as the primary imaging modality. It is crucial to differentiate between ASD and VSD due to their distinct treatment.PurposeTo assess whether deep learning analysis of chest radiographs can more effectively differentiate between ASD and VSD in children.MethodsIn this retrospective study, chest radiographs and corresponding radiology reports from 1,194 patients were analyzed. The cases were categorized into a training set and a validation set, comprising 480 cases of ASD and 480 cases of VSD, and a test set with 115 cases of ASD and 119 cases of VSD. Four deep learning network models—ResNet-CBAM, InceptionV3, EfficientNet, and ViT—were developed for training, and a fivefold cross-validation method was employed to optimize the models. Receiver operating characteristic (ROC) curve analyses were conducted to assess the performance of each model. The most effective algorithm was compared with the interpretations provided by two radiologists on 234 images from the test group.ResultsThe average accuracy, sensitivity, and specificity of the four deep learning models in the differential diagnosis of VSD and ASD were higher than 70%. The AUC values of ResNet-CBAM, IncepetionV3, EfficientNet, and ViT were 0.87, 0.91, 0.90, and 0.66, respectively. Statistical analysis showed that the differential diagnosis efficiency of InceptionV3 was the highest, reaching 87% classification accuracy. The accuracy of InceptionV3 in the differential diagnosis of VSD and ASD was higher than that of the radiologists.ConclusionsDeep learning methods such as IncepetionV3 based on chest radiographs in the study showed good performance for differential diagnosis of congenital VSD and ASD, which may be able to assist radiologists in diagnosis, education, and training, and reduce missed diagnosis and misdiagnosis.

Calculation of virtual 3D subtraction angiographies using conditional generative adversarial networks (cGANs)

ObjectiveSubtraction angiographies are calculated using a native and a contrast-enhanced 3D angiography images. This minimizes both bone and metal artifacts and results in a pure image of the vessels. However, carrying out the examination twice means double the radiation dose for the patient. With the help of generative AI, it could be possible to simulate subtraction angiographies from contrast-enhanced 3D angiographies and thus reduce the need for another dose of radiation without a cutback in quality. We implemented this concept by using conditional generative adversarial networks.MethodsWe selected all 3D subtraction angiographies from our PACS system, which had performed between 01/01/2018 and 12/31/2022 and randomly divided them into training, validation, and test sets (66%:17%:17%). We adapted the pix2pix framework to work on 3D data and trained a conditional generative adversarial network with 621 data sets. Additionally, we used 158 data sets for validation and 164 for testing. We evaluated two test sets with (n = 72) and without artifacts (n = 92). Five (blinded) neuroradiologists compared these datasets with the original subtraction dataset. They assessed similarity, subjective image quality, and severity of artifacts.ResultsImage quality and subjective diagnostic accuracy of the virtual subtraction angiographies revealed no significant differences compared to the original 3D angiographies. While bone and movement artifact level were reduced, artifact level caused by metal implants differed from case to case between both angiographies without one group being significant superior to the other.ConclusionConditional generative adversarial networks can be used to simulate subtraction angiographies in clinical practice, however, new artifacts can also appear as a result of this technology.

Utilizing Artificial Intelligence to Reconcile Indeterminate Lupus Anticoagulant Panel Results

Abstract Lupus anticoagulant (LA) is one laboratory criterion for diagnosing antiphospholipid antibody syndrome (APS), a condition associated with hypercoagulability, pregnancy complications, and death. LA diagnosis requires a complex panel, often performed at a specialized laboratory. Ruling out false positive or transient LAs requires ≥ two positive tests ≥ 12 weeks apart. Most LA tests are negative, with small subsets resulting positive or indeterminate. Indeterminates leave clinicians with limited understanding of appropriate clinical follow-up. Thus, the International Society on Thrombosis and Hemostasis discourages weak resulting nomenclature such as “indeterminate.” Our study used artificial intelligence to predict LA indeterminate results as positive or negative, identifying patients requiring further testing. This IRB approved study reviewed nearly 4000 LA tests performed from 2019-2023. Parameters included CBC, thromboelastography, renal/hepatic function assays, coagulation tests, antiphospholipid antibodies and LA tests. LA test components included screening tests (APTT, Dilute Russel Viper Venom Test (DRVVT) screen), mixing studies (1:1 APTT mix, DRVVT mix), phospholipid-dependent confirmation (DRVVT, hexagonal phase, platelet neutralization), and factor VIII. A positive LA test fulfilled 4 criteria: at least one positive screening test, mixing study and phospholipid-dependent assay and factor VIII inhibitor exclusion. An LA panel meeting 2-3 criteria resulted indeterminate. Studies meeting <2 criteria were negative. This analysis was performed using Machine Intelligence Learning Optimizer (MILO) platform which displays the performance of multiple models. MILO was trained with two classifications: positive and negative cases. Data was split into training (100 training positives; 100 negatives) and testing (primary and secondary validation) sets (100 positives; 1388 negatives). LA panels affected by drug interactions (9.4%) or containing incomplete datasets (21%) were excluded. Sensitivity, specificity, negative predictive value (NPV) and positive predictive value (PPV) determined model efficacy. 2903 negative (84%), 318 positive (9.1%), and 246 indeterminate (7.1%) LA results were included from 3467 patients. Only 84 of the 246 indeterminate patients (34%) were retested. Of these, 28 remained indeterminate, 43 became negative and 13 resulted positive. The best performing MILO models were the K-nearest neighbor (KNN) and logistic regression. The ability of these models to determine which indeterminate cases would become positive or negative was evaluated. KNN displayed the highest specificity (0.88) and PPV (0.57). All models had a high NPV and specificity due to substantive exposure to negative cases for the training and testing. Exclusion of some parameters did not improve the performance of the models. However, even the best model (various KNN models) had low sensitivity and PPV due to a small pool of positive training/testing cases and a much smaller group of indeterminate LA with subsequent positive results. Creating models accurately predicting indeterminate cases that may become positive enables appropriate clinical follow-up. Thus, improving model accuracy will require a larger sample size of positive indeterminates with follow-up.

Intelligent Road Surface State Recognition Method based on Multi-Layer Attention Residual Network

Abstract Data-driven road surface state recognition enhances the efficiency and accuracy of road management, contributing to increased safety and reliability in road traffic. However, traditional machine learning and deep learning-based road surface state recognition typically rely on extensive data for model training, making it challenging to adapt to complex tasks in diverse scenarios. Therefore, this paper proposes a Multi-layer Attention Residual Network (MARN)-based intelligent road surface state recognition method. First, a Residual Convolutional Neural Network (ResNet) is constructed as the backbone model of MARN to mitigate the gradient vanishing problem, allowing the network to extract deeper features. Subsequently, an adaptive multi-layer attention mechanism is introduced in each convolutional layer, enabling adaptive weighting of each feature channel in the dataset to enhance the model's focus on different features for better feature extraction. Furthermore, a cosine annealing learning rate adjuster is designed to improve the accuracy, robustness, and convergence during the model training process. Finally, the proposed MARN is validated using an image dataset containing six different road surface states. Comparative studies are conducted on the recognition accuracy of the proposed MARN, original ResNet, Visual Geometry Group network (VGG16), and Convolutional Neural Network (CNN). The impact of different batch sizes on the convergence speed of road surface state recognition under MARN is also analyzed. Results demonstrate that MARN achieves a training set accuracy of over 95%, surpassing VGG16 and CNN with accuracies below 85%. Compared to ResNet, MARN exhibits a 1.3% higher training set accuracy and a 0.25 lower validation set loss, showcasing superior accuracy and robustness in road surface state recognition.

A validated heart-specific model for splice-disrupting variants in childhood heart disease

BackgroundCongenital heart disease (CHD) is the most common congenital anomaly. Almost 90% of isolated cases have an unexplained genetic etiology after clinical testing. Non-canonical splice variants that disrupt mRNA splicing through the loss or creation of exon boundaries are not routinely captured and/or evaluated by standard clinical genetic tests. Recent computational algorithms such as SpliceAI have shown an ability to predict such variants, but are not specific to cardiac-expressed genes and transcriptional isoforms.MethodsWe used genome sequencing (GS) (n = 1101 CHD probands) and myocardial RNA-Sequencing (RNA-Seq) (n = 154 CHD and n = 43 cardiomyopathy probands) to identify and validate splice disrupting variants, and to develop a heart-specific model for canonical and non-canonical splice variants that can be applied to patients with CHD and cardiomyopathy. Two thousand five hundred seventy GS samples from the Medical Genome Reference Bank were analyzed as healthy controls.ResultsOf 8583 rare DNA splice-disrupting variants initially identified using SpliceAI, 100 were associated with altered splice junctions in the corresponding patient myocardium affecting 95 genes. Using strength of myocardial gene expression and genome-wide DNA variant features that were confirmed to affect splicing in myocardial RNA, we trained a machine learning model for predicting cardiac-specific splice-disrupting variants (AUC 0.86 on internal validation). In a validation set of 48 CHD probands, the cardiac-specific model outperformed a SpliceAI model alone (AUC 0.94 vs 0.67 respectively). Application of this model to an additional 947 CHD probands with only GS data identified 1% patients with canonical and 11% patients with non-canonical splice-disrupting variants in CHD genes. Forty-nine percent of predicted splice-disrupting variants were intronic and > 10 bp from existing splice junctions. The burden of high-confidence splice-disrupting variants in CHD genes was 1.28-fold higher in CHD cases compared with healthy controls.ConclusionsA new cardiac-specific in silico model was developed using complementary GS and RNA-Seq data that improved genetic yield by identifying a significant burden of non-canonical splice variants associated with CHD that would not be detectable through panel or exome sequencing.

Development of an MRI Radiomic Machine-Learning Model to Predict Triple-Negative Breast Cancer Based on Fibroglandular Tissue of the Contralateral Unaffected Breast in Breast Cancer Patients

Aim: The purpose of this study was to develop a radiomic-based machine-learning model to predict triple-negative breast cancer (TNBC) based on the contralateral unaffected breast’s fibroglandular tissue (FGT) in breast cancer patients. Materials and methods: This study retrospectively included 541 patients (mean age, 51 years; range, 26–82) who underwent a screening breast MRI between November 2016 and September 2018 and who were subsequently diagnosed with biopsy-confirmed, treatment-naïve breast cancer. Patients were divided into training (n = 250) and validation (n = 291) sets. In the training set, 132 radiomic features were extracted using the opensource CERR platform. Following feature selection, the final prediction model was created, based on a support vector machine with a polynomial kernel of order 2. Results: In the validation set, the final prediction model, which included four radiomic features, achieved an F1 score of 0.66, an area under the curve of 0.71, a sensitivity of 54% [47–60%], a specificity of 74% [65–84%], a positive predictive value of 84% [78–90%], and a negative predictive value of 39% [31–47%]. Conclusions: TNBC can be predicted based on radiomic features extracted from the FGT of the contralateral unaffected breast of patients, suggesting the potential for risk prediction specific to TNBC.

Comprehensive risk factor-based nomogram for predicting one-year mortality in patients with sepsis-associated encephalopathy

Sepsis-associated encephalopathy (SAE) is a frequent and severe complication in septic patients, characterized by diffuse brain dysfunction resulting from systemic inflammation. Accurate prediction of long-term mortality in these patients is critical for improving clinical outcomes and guiding treatment strategies. We conducted a retrospective cohort study using the MIMIC IV database to identify adult patients diagnosed with SAE. Patients were randomly divided into a training set (70%) and a validation set (30%). Least absolute shrinkage and selection operator regression and multivariate logistic regression were employed to identify significant predictors of 1-year mortality, which were then used to develop a prognostic nomogram. The model’s discrimination, calibration, and clinical utility were assessed using the area under the receiver operating characteristic curve (AUC), calibration plots, and decision curve analysis, respectively. A total of 3,882 SAE patients were included in the analysis. The nomogram demonstrated strong predictive performance with AUCs of 0.881 (95% CI: 0.865, 0.896) in the training set and 0.859 (95% CI: 0.830, 0.888) in the validation set. Calibration plots indicated good agreement between predicted and observed 1-year mortality rates. The decision curve analysis showed that the nomogram provided greater net benefit across a range of threshold probabilities compared to traditional scoring systems such as Glasgow Coma Scale and Sequential Organ Failure Assessment. Our study presents a robust and clinically applicable nomogram for predicting 1-year mortality in SAE patients. This tool offers superior predictive performance compared to existing severity scoring systems and has significant potential to enhance clinical decision-making and patient management in critical care settings.

Association between body roundness index and risk of osteoarthritis: a cross-sectional study

BackgroundThe link between body roundness index (BRI) and osteoarthritis (OA) has yet to be validated. Our aim was to explore this connection between BRI and OA risk.MethodsThis cross-sectional study utilized the 1999–2018 National Health and Nutrition Examination Survey retrieved data. To assess the association between BRI and OA risk, we performed weighted multivariable regression analysis (MVRA), with smooth curve fitting for potential nonlinear association and subgroup analysis and interaction tests for relationships in specific subgroups. A 7:3 ratio was adopted for the random division of the acquired data into training and validation sets. Subsequently, least absolute shrinkage and selection operator regression, along with MVRA, were conducted for the training set to isolate variables for a prediction model. This model was visualized using the nomogram and was followed by evaluation. Finally, the validation set was utilized to validate the model.ResultsThis study enrolled 12,946 individuals. Following the adjustment for all covariables, OA risk increased by 18% with every unit rise in BRI (odd ratio [OR] = 1.18; 95% confidence interval [CI]: 1.13–1.23; P < 0.0001). Upon regarding BRI as a categorical variable, it was divided into quartiles for subsequent analysis. In comparison to quartile 1, the risk of OA was increased in quartile 2 (OR = 1.58; 95% CI: 1.22–2.03; P = 0.0006), quartile 3 (OR = 1.83; 95% CI: 1.40–2.40; P < 0.0001) and quartile 4 (OR = 2.70; 95% CI: 1.99–3.66; P < 0.0001). Smooth curve fitting revealed no non-linear relationships. None of the subgroups showed a statistically significant interaction (all P > 0.05). After selecting the variables, a prediction model was developed. The prediction model exhibited favorable discriminatory power, high accuracy, and potential clinical benefits in training and validation sets.ConclusionsThe BRI was positively associated with OA risk. Our predictive model demonstrated that combining BRI with other easily accessible factors was helpful in assessing and managing high-risk OA groups.

Lymph node metastasis determined miRNAs in esophageal squamous cell carcinoma.

There is no golden noninvasive and effective technique to diagnose lymph node metastasis (LNM) for esophageal squamous cell carcinoma (ESCC) patients. Here, a classifier was proposed consisting of miRNAs to screen ESCC patients with LNM from the ones without LNM. miRNA expression and clinical data files of 93 ESCC samples were downloaded from TCGA as the discovery set and 119 ESCC samples with similar dataset GSE43732 as the validation set. Differentially expressed miRNAs (DE-miRNAs) were analyzed between patients with LNM and without LNM. LASSO regression was performed for selecting the DE-miRNA pair to consist the classifier. To validate the accuracy and reliability of the classifier, the SVM and AdaBoost algorithms were applied. The CCK-8 and wound healing assay were used to evaluate the role of the miRNA in ESCC cells. There were 43 DE miRNAs between the LNM+ group and LNM- group. Among them, miR-224-5p, miR-99a-5p, miR-100-5p, miR-34c-5p, miR-503-5p, and miR-452-5p were identified by LASSO to establish the classifier. SVM and AdaBoost showed that the model could classify the ESCC patients with LNM from the ones without LNM precisely and reliably in 2 data sets. miR-224-5p in the classifier as the top contributor to discriminate the two groups of patients based on AdaBoost, promoted ESCC cell proliferation and migration in vitro. The classifier based on these 6 miRNAs could classify the ESCC patients with LNM from the ones without LNM successfully.

Epigenetic Age Estimation by Detecting DNA Methylation Status in Buccal Swabs.

The analysis of DNA methylation (DNAm) levels at specific CpG sites represents one of the most promising molecular techniques for estimating an individual's age. To date, a considerable number of studies have reported the development of age prediction models on the basis of DNAm in body fluids, with only a few utilizing buccal swabs. The objective of this study was to identify age-dependent methylation CpG sites in three different genes (HOXC4, TRIM59, and ELOVL2) in buccal swab samples from the Chinese Han population. A total of 461 buccal swabs, with an age range of 0.4-80.8 years, were divided into a training set (n=325) and a validation set (n=136). Samples were analyzed by pyrosequencing in order to identify age-related genes with correlation coefficient. A random forest regression model was ultimately proposed, including eight CpGs in three genes, with a mean absolute error (MAE) of 2.119 years. The model performs independent validation set with an MAE of 4.391 years. Our findings illustrate that buccal swabs present a suitable alternative to biological traces for age prediction based on DNAm pattern using pyrosequencing and random forest regression, offering the additional advantage of being collected noninvasively.

Artificial intelligence driven cyberattack detection system using integration of deep belief network with convolution neural network on industrial IoT

In Industry 4.0, information and communication technology (ICT) was employed in numerous significant infrastructures, like financial networks, smart factories, and power plants, to automate and certify industrial systems. In power control systems, ICT technologies such as IIoT have improved automated monitoring, but legacy methods, originally autonomous, now connect with external networks. This progress has presented safety vulnerabilities from legacy ICT systems. Hence, various cybersecurity approaches are developed and examined to deal with cyberattacks and vulnerabilities. Utilizing new cybersecurity models in power control systems poses risks due to their uncertified safety. Ensuring their stability and efficiency is significant for maintaining reliable power delivery and incorporating these technologies into power control systems. Therefore, this study designs a Next–Generation Cybersecurity Attack Detection using an ensemble deep learning model (NGCAD-EDLM) technique in the IIoT environment. The main cause of the NGCAD-EDLM technique is the automatic recognition of cyber-attacks. In the NGCAD-EDLM approach, the primary data normalization phase utilizing min-max normalization is performed. Next, the honey-badger algorithm (HBA) approach selects the feature subsets. Furthermore, an ensemble deep learning (DL) of two methods, namely convolutional neural networks (CNNs) and deep belief networks (DBNs) methods, are employed for classification. In addition, the DL techniques' hyperparameter selection is accomplished using the lotus effect optimization algorithm (LEOA) method. A complete set of simulation validation is performed to establish the experimental analysis of the NGCAD-EDLM method. The performance validation of the NGCAD-EDLM method exhibited a superior accuracy value of 99.21 % over other existing techniques.

Rethinking the Non-Maximum Suppression Step in 3D Object Detection from a Bird’s-Eye View

In camera-based bird’s-eye view (BEV) 3D object detection, non-maximum suppression (NMS) plays a crucial role. However, traditional NMS methods become ineffective in BEV scenarios where the predicted bounding boxes of small object instances often have no overlapping areas. To address this issue, this paper proposes a BEV intersection over union (IoU) computation method based on relative position and absolute spatial information, referred to as B-IoU. Additionally, a BEV circular search method, called B-Grouping, is introduced to handle prediction boxes of varying scales. Utilizing these two methods, a novel NMS strategy called BEV-NMS is developed to handle the complex prediction boxes in BEV perspectives. This BEV-NMS strategy is implemented in several existing algorithms. Based on the results from the nuScenes validation set, there was an average increase of 7.9% in mAP when compared to the strategy without NMS. The NDS also showed an average increase of 7.9% under the same comparison. Furthermore, compared to the Scale-NMS strategy, the mAP increased by an average of 3.4%, and the NDS saw an average improvement of 3.1%.

Identification of PANoptosis Subtypes to Assess the Prognosis and Immune Microenvironment of Lung Adenocarcinoma Patients: A Bioinformatics Combined Machine Learning Study.

PANoptosis, a novelty mechanism of cell death involving crosstalk between apoptosis, pyroptosis, and necroptosis, is strongly associated with tumor cell death and immunotherapy efficacy. However, its relevance in lung adenocarcinoma (LUAD) remains to be elucidated. In this study, we acquired 18 PANoptosis-related differentially expressed gene (PRDEG) of LUAD. Based on these genes, LUAD samples were identified with different sub-types by unsupervised clustering. Next, we compared the differences between the subtypes, including clinical features, immune microenvironment, and potentially sensitive drugs. Further-more, we used machine learning to identify hub prognostic PRDEGs, construct a risk score, and validate it on other external datasets. We incorporated the patient's clinical information and risk score into the proportional hazards model and lasso-cox models to find key prognostic features and constructed five prognostic models. The best model was identified via the area under the curve and validated on an external dataset. LUAD patients were divided into two clusters named C1 and C2, respectively. The C2 cluster exhibited shorter survival time, more advanced tumor stage, higher suppressive immune cell scores, such as dendritic cells, and higher expression of inhibitory immune checkpoints, such as LAG3 and CD86. TIMP1, CAV1, and CD69 were recognized as key prognostic factors, and risk scores predicted survival with significant differences in the external validation set. Risk score and N-stage were identified as critical prognostic features. The Coxph model outper-formed other machine learning clinical models. The 1-, 3-, and 5-year time-ROCs in the exter-nal validation set were 0.55, 0.59, and 0.60, respectively. We demonstrated the potential of PANoptosis-based molecular clustering and prognostic features in predicting the survival of patients with LUAD as well as the tumor mi-croenvironment.

Machine learning-based prediction of mortality in acute myocardial infarction with cardiogenic shock.

In the ICU, patients with acute myocardial infarction and cardiogenic shock (AMI-CS) often face high mortality rates, making timely and precise mortality risk prediction crucial for clinical decision-making. Despite existing models, machine learning algorithms hold the potential for improved predictive accuracy. In this study, a predictive model was developed using the MIMIC-IV database, with external validation performed on the eICU-CRD database. We included ICU patients diagnosed with AMI-CS. Feature selection was conducted using the Boruta algorithm, followed by the construction and comparison of four machine learning models: Logistic Regression (LR), eXtreme Gradient Boosting (XGBoost), Adaptive Boosting (AdaBoost), and Gaussian Naive Bayes (GNB). Model performance was evaluated based on metrics such as AUC (Area Under the Curve), accuracy, sensitivity, specificity, and so on. The SHAP method was employed to visualize and interpret the importance of model features. Finally, we constructed an online prediction model and conducted external validation in the eICU-CRD database. In this study, a total of 570 and 391 patients with AMI-CS were included from the MIMIC-IV and eICU-CRD databases, respectively. Among all machine learning algorithms evaluated, LR exhibited the best performance with a validation set AUC of 0.841(XGBoost: 0.835, AdaBoost: 0.839, GNB: 0.826). The model incorporated five variables: prothrombin time, blood urea nitrogen, age, beta-blockers and Angiotensin-Converting Enzyme Inhibitors or Angiotensin II Receptor Blockers. SHAP plots are employed to visualize the importance of model features and to interpret the results. An online prediction tool was developed, externally validated with the eICU-CRD database, achieving an AUC of 0.755. Employing the LR algorithm, we developed a predictive model for assessing the mortality risk among AMI-CS patients in the ICU setting. Through model predictions, this facilitates early detection of high-risk individuals, ensures judicious allocation of healthcare resources.

Study on risk factor analysis and model prediction of hyperuricemia in different populations.

The purpose of the present study was to explore the influencing factors of hyperuricemia (HUA) in different populations in Shandong Province based on clinical biochemical indicators. A prediction model for HUA was constructed to aid in the early prevention and screening of HUA. In total, 705 cases were collected from five hospitals, and the risk factors were analyzed by Pearson correlation analysis, binary logistic regression, and receiver operating characteristic (ROC) curve in the gender and age groups. All data were divided into a training set and test set (7:3). The training set included age, gender, total protein (TP), low-density lipoprotein cholesterol (LDL-C), and 15 other indicators. The random forest (RF) and support vector machine (SVM) methods were used to build the HUA model, and model performances were evaluated through 10-fold cross-validation to select the optimal method. Finally, features were extracted, and the ROC curve of the test set was generated. TP, LDL-C, and glucose (GLU) were risk factors for HUA, and the area under the curve (AUC) value of the SVM validation set was 0.875. The SVM model based on clinical biochemical indicators has good predictive ability for HUA, thus providing a reference for the diagnosis of HUA and the development of an HUA prediction model.

Development and validation of a nomogram-based model to predict combined esophageal injury post-esophagoscopy for esophageal foreign bodies.

The aim of this study is to create and validate a nomogram model for predicting complications of esophageal injury post-esophagoscopy in patients with esophageal foreign bodies (EFB). We examined 303 patients who underwent esophagoscopy from January 2019 to December 2022 at a leading hospital in Anhui, known for its expertise in otorhinolaryngology-head and neck surgery. The patients were split into a modeling group and a validation group in a 7:3 ratio. Logistic regression analysis was employed to determine the risk factors for esophageal injury after undergoing esophagoscopy in patients with EFB. Based on these factors, a nomogram risk prediction model was developed and assessed using a goodness of fit test. Logistic regression analysis revealed that the type of foreign body, failure of gastroscopic retrieval, duration of lodgment, lodgment site, and presence of combined cardiovascular and cerebrovascular diseases were significant (p < 0.05) independent risk factors for esophageal injury following esophagoscopy for EFB. The area under the ROC curve for the training set was 0.850, and the Hosmer-Lemeshow goodness of fit test resulted in a p value of 0.908. For the validation set, the area under the ROC curve was 0.848, and the Hosmer-Lemeshow test gave a p value of 0.665. The calibration curve showed a close alignment between the predicted and observed values. The type of foreign body, duration of lodgment, lodgment site, previous failure of gastroscopic retrieval, and history of combined cardiovascular and cerebrovascular diseases are significant risk factors for esophageal injury following EFB esophagoscopy. This model accurately quantifies the risk of esophageal injury after EFB esophagoscopy.

Development and Validation of a Prediction Model for Dysphagia in Community-Dwelling Older Adults.

Objectives. Dysphagia is a geriatric syndrome, which may lead to complications such as dehydration, malnutrition, aspiration, pneumonia, and a significant reduction in quality of life. The purpose of this study was to construct and validate a prediction model for dysphagia in community-dwelling older adults and provide an assessment tool for the prevention and control of dysphagia. Design. Cross-sectional study. Setting. The community-dwelling Chinese older adults. Participants. 3655 participants aged 65 years and older were involved, who were randomly divided into the training set and the validation set. Methods. Data were collected and analyzed from June 2022 to September 2022. Univariate and multivariate logistic regression analysis were used to identify independent risk factors for dysphagia. We applied R software to develop a nomogram model to predict dysphagia in community-dwelling older adults. The predictive value of the model was assessed by the area under the ROC curve (AUC), the calibration curve was used to evaluate the reliability of the nomogram model for predicting dysphagia in community-dwelling older adults. The model's clinical utility was further evaluated using a Decision Curve Analysis (DCA). Results. The incidence of dysphagia was 11.8% (320/3655). Maximum tongue pressure, number of molars, pneumonia, ADL, sarcopenia, age, neurological diseases, and rheumatic immune diseases were selected as risk predictors for dysphagia. The prediction model demonstrated fair discriminative ability with the AUC was 0.709 (95%CI: 0.679-0.739) in the training set and 0.693 (95%Cl: 0.640-0.747) in the validation set, the calibration is adequate, and the Hosmer and Lemeshow test showed p values of 0.163 and 0.415, respectively. The DCA curve of our model shows a positive clinical net benefit. Conclusions. The prediction model established in this study was of a certain predictive value for the risk of dysphagia in community-dwelling older adults. By estimating the likelihood of future outcomes or the onset of certain diseases, it can assist medical personnel in formulating preventive strategies, lessening the workload of nurses, and also diminishing the financial burden on patients, thereby enhancing their overall quality of life.

Convolutional Neural Network Incorporating Multiple Attention Mechanisms for MRI Classification of Lumbar Spinal Stenosis.

Lumbar spinal stenosis (LSS) is a common cause of low back pain, especially in the elderly, and accurate diagnosis is critical for effective treatment. However, manual diagnosis using MRI images is time consuming and subjective, leading to a need for automated methods. This study aims to develop a convolutional neural network (CNN)-based deep learning model integrated with multiple attention mechanisms to improve the accuracy and robustness of LSS classification via MRI images. The proposed model is trained on a standardized MRI dataset sourced from multiple institutions, encompassing various lumbar degenerative conditions. During preprocessing, techniques such as image normalization and data augmentation are employed to enhance the model's performance. The network incorporates a Multi-Headed Self-Attention Module, a Slot Attention Module, and a Channel and Spatial Attention Module, each contributing to better feature extraction and classification. The model achieved 95.2% classification accuracy, 94.7% precision, 94.3% recall, and 94.5% F1 score on the validation set. Ablation experiments confirmed the significant impact of the attention mechanisms in improving the model's classification capabilities. The integration of multiple attention mechanisms enhances the model's ability to accurately classify LSS in MRI images, demonstrating its potential as a tool for automated diagnosis. This study paves the way for future research in applying attention mechanisms to the automated diagnosis of lumbar spinal stenosis and other complex spinal conditions.

Classification of Co-Manipulation Modus with Human-Human Teams for Future Application to Human-Robot Systems

Despite the existence of robots that can physically lift heavy loads, robots that can collaborate with people to move heavy objects are not readily available. This article makes progress toward effective human-robot co-manipulation by studying 30 human-human dyads that collaboratively manipulated an object weighing \(27 \mathrm{kg}\) without being co-located (i.e., participants were at either end of the extended object). Participants maneuvered around different obstacles with the object while exhibiting one of four modi–the manner or objective with which a team moves an object together–at any given time. Using force and motion signals to classify modus or behavior was the primary objective of this work. Our results showed that two of the originally proposed modi were very similar, such that one could effectively be removed while still spanning the space of common behaviors during our co-manipulation tasks. The three modi used in classification were quickly , smoothly and avoiding obstacles . Using a deep convolutional neural network (CNN), we classified three modi with up to 89% accuracy from a validation set. The capability to detect or classify modus during co-manipulation has the potential to greatly improve human-robot performance by helping to define appropriate robot behavior or controller parameters depending on the objective or modus of the team.

Nomogram based on virtual hyperemic pullback pressure gradients for predicting the suboptimal post-PCI QFR outcome after stent implantation.

Growing evidence shows an association between higher post-PCI quantitative flow ratios (QFR) and improved clinical prognosis, however, no models are available to predict suboptimal QFRs (< 0.91) after angiographically successful PCI. This study aims to establish a prediction nomogram for this domain. This study included 450 vessels derived from 421 consecutive patients enrolled in the PIONEER IV trial, which were randomly assigned in a 1:1 ratio to a training (N = 225) and internal validation (N = 225) set, with external validation performed in 97 vessels from 95 consecutive patients enrolled in the ASET Japan trial. LASSO regression was used for optimal feature selection, and multivariate logistic regression was subsequently utilized to construct the nomogram. The performance of the nomograms was assessed and validated by area under the receiver operating characteristics curve (AUC), calibration curves, decision curve analysis, and clinical impact curves. The nomogram was constructed incorporating a novel metric, quantitative flow ratio derived pullback pressure gradient (QFR-PPG), alongside four conventional parameters: left anterior descending artery disease, pre-procedural QFR, reference vessel diameter, and percent diameter stenosis. AUCs of the nomogram were 0.866 (95%CI:0.818-0.914), 0.784 (95% CI:0.722-0.847), and 0.781 (95% CI:0.682-0.879) in the training, internal validation and external validation sets, respectively. Bias-corrected curves revealed a strong consistency between actual observations and prediction. The risk of a suboptimal post-PCI QFR in patients after angiographically successful PCI can be effectively predicted using a nomogram incorporating five variables available pre-PCI, with its performance and clinical predictive value confirming its utility in helping clinicians with decision-making and planning revascularization. Registered on clinicaltrial.gov (NCT04923191 and NCT05117866).