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BioRxiv : the Preprint Server For... Jun 2024Emerging studies in humans have established the modulatory effects of repetitive transcranial magnetic stimulation (rTMS) over primary somatosensory cortex (S1) on...
BACKGROUND
Emerging studies in humans have established the modulatory effects of repetitive transcranial magnetic stimulation (rTMS) over primary somatosensory cortex (S1) on somatosensory cortex activity and perception. However, to date, research in this area has primarily focused on the hand and fingers, leaving a gap in our understanding of the modulatory effects of rTMS on somatosensory perception of the orofacial system and speech articulators.
OBJECTIVE
The present study aimed to examine the effects of different types of theta-burst stimulation-continuous TBS (cTBS), intermittent TBS (iTBS), or sham-over the tongue representation of left S1 on tactile acuity of the tongue.
METHODS
In a repeated-measures design, fifteen volunteers participated in four separate sessions, where cTBS, iTBS, sham, or no stimulation was applied over the tongue representation of left S1. Effects of TBS were measured on both temporal and spatial perceptual acuity of tongue using a custom vibrotactile stimulator.
RESULTS
CTBS significantly impaired spatial amplitude threshold at the time window of 16-30 minutes after stimulation, while iTBS improved it at the same time window. The effect of iTBS, however, was smaller than cTBS. In contrast, neither cTBS nor iTBS had any effect on the temporal discrimination threshold.
CONCLUSIONS
The current study establishes the validity of using TBS to modulate somatosensory perception of the orofacial system. Directly modifying somatosensation in the orofacial system has the potential to benefit clinical populations with abnormal tactile acuity, improve our understanding of the role of sensory systems in speech production, and enhance speech motor learning and rehabilitation.
HIGHLIGHTS
Theta-burst stimulation (TBS) can modulate somatosensation in the orofacial systemcTBS over S1 impaired spatial acuity of tongueiTBS over S1 improved spatial acuity of tongue.
PubMed: 38948808
DOI: 10.1101/2024.06.17.599457 -
World Journal of Gastroenterology Jun 2024Colorectal cancer significantly impacts global health, with unplanned reoperations post-surgery being key determinants of patient outcomes. Existing predictive models...
BACKGROUND
Colorectal cancer significantly impacts global health, with unplanned reoperations post-surgery being key determinants of patient outcomes. Existing predictive models for these reoperations lack precision in integrating complex clinical data.
AIM
To develop and validate a machine learning model for predicting unplanned reoperation risk in colorectal cancer patients.
METHODS
Data of patients treated for colorectal cancer ( = 2044) at the First Affiliated Hospital of Wenzhou Medical University and Wenzhou Central Hospital from March 2020 to March 2022 were retrospectively collected. Patients were divided into an experimental group ( = 60) and a control group ( = 1984) according to unplanned reoperation occurrence. Patients were also divided into a training group and a validation group (7:3 ratio). We used three different machine learning methods to screen characteristic variables. A nomogram was created based on multifactor logistic regression, and the model performance was assessed using receiver operating characteristic curve, calibration curve, Hosmer-Lemeshow test, and decision curve analysis. The risk scores of the two groups were calculated and compared to validate the model.
RESULTS
More patients in the experimental group were ≥ 60 years old, male, and had a history of hypertension, laparotomy, and hypoproteinemia, compared to the control group. Multiple logistic regression analysis confirmed the following as independent risk factors for unplanned reoperation ( < 0.05): Prognostic Nutritional Index value, history of laparotomy, hypertension, or stroke, hypoproteinemia, age, tumor-node-metastasis staging, surgical time, gender, and American Society of Anesthesiologists classification. Receiver operating characteristic curve analysis showed that the model had good discrimination and clinical utility.
CONCLUSION
This study used a machine learning approach to build a model that accurately predicts the risk of postoperative unplanned reoperation in patients with colorectal cancer, which can improve treatment decisions and prognosis.
Topics: Humans; Male; Colorectal Neoplasms; Female; Machine Learning; Middle Aged; Reoperation; Retrospective Studies; Risk Factors; Risk Assessment; Aged; Postoperative Complications; Nomograms; ROC Curve; China; Adult
PubMed: 38946868
DOI: 10.3748/wjg.v30.i23.2991 -
Multiple Sclerosis and Related Disorders Jun 2024Optical coherence tomography (OCT) investigations have revealed that the thickness of inner retinal layers becomes decreased in multiple sclerosis (MS) patients,...
OBJECTIVE
Optical coherence tomography (OCT) investigations have revealed that the thickness of inner retinal layers becomes decreased in multiple sclerosis (MS) patients, compared to healthy control (HC) individuals. To date, a number of studies have applied machine learning to OCT thickness measurements, aiming to enable accurate and automated diagnosis of the disease. However, there have much less emphasis on other less common retinal imaging modalities, like infrared scanning laser ophthalmoscopy (IR-SLO), for classifying MS. IR-SLO uses laser light to capture high-resolution fundus images, often performed in conjunction with OCT to lock B-scans at a fixed position.
METHODS
We incorporated two independent datasets of IR-SLO images from the Isfahan and Johns Hopkins centers, consisting of 164 MS and 150 HC images. A subject-wise data splitting approach was employed to ensure that there was no leakage between training and test datasets. Several state-of-the-art convolutional neural networks (CNNs), including VGG-16, VGG-19, ResNet-50, and InceptionV3, and a CNN with a custom architecture were employed. In the next step, we designed a convolutional autoencoder (CAE) to extract semantic features subsequently given as inputs to four conventional ML classifiers, including support vector machine (SVM), k-nearest neighbor (K-NN), random forest (RF), and multi-layer perceptron (MLP).
RESULTS
The custom CNN (85 % accuracy, 85 % sensitivity, 87 % specificity, 93 % area under the receiver operating characteristics [AUROC], and 94 % area under the precision-recall curve [AUPRC]) outperformed state-of-the-art models (84 % accuracy, 83 % sensitivity, 87 % specificity, 92 % AUROC, and 94 % AUPRC); however, utilizing a combination of the CAE and MLP yields even superior results (88 % accuracy, 86 % sensitivity, 91 % specificity, 94 % AUROC, and 95 % AUPRC).
CONCLUSIONS
We utilized IR-SLO images to differentiate between MS and HC eyes, with promising results achieved using a combination of CAE and MLP. Future multi-center studies involving more heterogenous data are necessary to assess the feasibility of integrating IR-SLO images into routine clinical practice.
PubMed: 38945032
DOI: 10.1016/j.msard.2024.105743 -
Journal of X-ray Science and Technology Jun 2024Radiography plays an important role in medical care, and accurate positioning is essential for providing optimal quality images. Radiographs with insufficient diagnostic...
BACKGROUND
Radiography plays an important role in medical care, and accurate positioning is essential for providing optimal quality images. Radiographs with insufficient diagnostic value are rejected, and retakes are required. However, determining the suitability of retaking radiographs is a qualitative evaluation.
OBJECTIVE
To evaluate skull radiograph accuracy automatically using an unsupervised learning-based autoencoder (AE) and a variational autoencoder (VAE). In this study, we eliminated visual qualitative evaluation and used unsupervised learning to identify skull radiography retakes from the quantitative evaluation.
METHODS
Five skull phantoms were imaged on radiographs, and 1,680 images were acquired. These images correspond to two categories: normal images captured at appropriate positions and images captured at inappropriate positions. This study verified the discriminatory ability of skull radiographs using anomaly detection methods.
RESULTS
The areas under the curves for AE and VAE were 0.7060 and 0.6707, respectively, in receiver operating characteristic analysis. Our proposed method showed a higher discrimination ability than those of previous studies which had an accuracy of 52%.
CONCLUSIONS
Our findings suggest that the proposed method has high classification accuracy in determining the suitability of retaking skull radiographs. Automation of optimal image consideration, whether or not to retake radiographs, contributes to improving operational efficiency in busy X-ray imaging operations.
PubMed: 38943422
DOI: 10.3233/XST-230431 -
Aggressive Behavior Jun 2024Aggressive behaviors have been related to approach/avoidance tendencies. In our current study, we investigated whether approach/avoidance tendencies for angry versus...
Aggressive behaviors have been related to approach/avoidance tendencies. In our current study, we investigated whether approach/avoidance tendencies for angry versus fearful emotional expressions were differentially predictive of children's reactive and proactive aggression. A total of 116 children (58 girls, M = 10.90, standard deviation SD = 0.98) completed an approach/avoidance task (AAT) and a stimulus-response compatibility task (SRCT), both measuring the extent to which they tended to approach or avoid angry and fearful facial expressions relative to neutral facial expressions. Children also completed a self-report scale of reactive and proactive aggression. Although none of the approach/avoidance tendency scores correlated significantly with either of the aggression scores, stronger approach tendencies for angry faces and stronger avoidance tendencies for fearful faces in the AAT predicted more reactive aggression. Similar yet nonsignificant results were found for proactive aggression, but no effects were replicated in the SRCT. Our results thus invite the conclusion that reactive aggression is characterized by a tendency to approach angry faces and a tendency to avoid fearful faces. However, the poor discrimination between both types of aggression as well as the lack of convergence between the results of our two measures of approach/avoidance tendencies indicates that further research is needed to establish the role of approach/avoidance tendencies for emotional faces as markers for childhood aggression.
Topics: Humans; Aggression; Female; Male; Child; Facial Expression; Anger; Fear; Avoidance Learning; Child Behavior
PubMed: 38940213
DOI: 10.1002/ab.22162 -
Bioinformatics (Oxford, England) Jun 2024World Health Organization estimates that there were over 10 million cases of tuberculosis (TB) worldwide in 2019, resulting in over 1.4 million deaths, with a worrisome...
MOTIVATION
World Health Organization estimates that there were over 10 million cases of tuberculosis (TB) worldwide in 2019, resulting in over 1.4 million deaths, with a worrisome increasing trend yearly. The disease is caused by Mycobacterium tuberculosis (MTB) through airborne transmission. Treatment of TB is estimated to be 85% successful, however, this drops to 57% if MTB exhibits multiple antimicrobial resistance (AMR), for which fewer treatment options are available.
RESULTS
We develop a robust machine-learning classifier using both linear and nonlinear models (i.e. LASSO logistic regression (LR) and random forests (RF)) to predict the phenotypic resistance of Mycobacterium tuberculosis (MTB) for a broad range of antibiotic drugs. We use data from the CRyPTIC consortium to train our classifier, which consists of whole genome sequencing and antibiotic susceptibility testing (AST) phenotypic data for 13 different antibiotics. To train our model, we assemble the sequence data into genomic contigs, identify all unique 31-mers in the set of contigs, and build a feature matrix M, where M[i, j] is equal to the number of times the ith 31-mer occurs in the jth genome. Due to the size of this feature matrix (over 350 million unique 31-mers), we build and use a sparse matrix representation. Our method, which we refer to as MTB++, leverages compact data structures and iterative methods to allow for the screening of all the 31-mers in the development of both LASSO LR and RF. MTB++ is able to achieve high discrimination (F-1 >80%) for the first-line antibiotics. Moreover, MTB++ had the highest F-1 score in all but three classes and was the most comprehensive since it had an F-1 score >75% in all but four (rare) antibiotic drugs. We use our feature selection to contextualize the 31-mers that are used for the prediction of phenotypic resistance, leading to some insights about sequence similarity to genes in MEGARes. Lastly, we give an estimate of the amount of data that is needed in order to provide accurate predictions.
AVAILABILITY
The models and source code are publicly available on Github at https://github.com/M-Serajian/MTB-Pipeline.
Topics: Mycobacterium tuberculosis; Machine Learning; Drug Resistance, Bacterial; Microbial Sensitivity Tests; Anti-Bacterial Agents; Whole Genome Sequencing; Genome, Bacterial; Humans
PubMed: 38940175
DOI: 10.1093/bioinformatics/btae243 -
Plant Phenomics (Washington, D.C.) 2024The pod and seed counts are important yield-related traits in soybean. High-precision soybean breeders face the major challenge of accurately phenotyping the number of...
The pod and seed counts are important yield-related traits in soybean. High-precision soybean breeders face the major challenge of accurately phenotyping the number of pods and seeds in a high-throughput manner. Recent advances in artificial intelligence, especially deep learning (DL) models, have provided new avenues for high-throughput phenotyping of crop traits with increased precision. However, the available DL models are less effective for phenotyping pods that are densely packed and overlap in in situ soybean plants; thus, accurate phenotyping of the number of pods and seeds in soybean plant is an important challenge. To address this challenge, the present study proposed a bottom-up model, DEKR-SPrior (disentangled keypoint regression with structural prior), for in situ soybean pod phenotyping, which considers soybean pods and seeds analogous to human people and joints, respectively. In particular, we designed a novel structural prior (SPrior) module that utilizes cosine similarity to improve feature discrimination, which is important for differentiating closely located seeds from highly similar seeds. To further enhance the accuracy of pod location, we cropped full-sized images into smaller and high-resolution subimages for analysis. The results on our image datasets revealed that DEKR-SPrior outperformed multiple bottom-up models, viz., Lightweight-OpenPose, OpenPose, HigherHRNet, and DEKR, reducing the mean absolute error from 25.81 (in the original DEKR) to 21.11 (in the DEKR-SPrior) in pod phenotyping. This paper demonstrated the great potential of DEKR-SPrior for plant phenotyping, and we hope that DEKR-SPrior will help future plant phenotyping.
PubMed: 38939747
DOI: 10.34133/plantphenomics.0198 -
Current Research in Food Science 2024Discriminant analysis of similar food samples is an important aspect of achieving food quality control. The effective combination of Raman spectroscopy and machine...
Discriminant analysis of similar food samples is an important aspect of achieving food quality control. The effective combination of Raman spectroscopy and machine learning algorithms has become an extremely attractive approach to develop intelligent discrimination techniques. Feature spectral analysis can help researchers gain a deeper understanding of the data patterns in food quality discrimination. Herein, this work takes the discrimination of three brands of dairy products as an example to investigate the Raman spectral feature based on the support vector machines (SVM), extreme learning machines (ELM) and convolutional neural network (CNN) algorithms. The results show that there are certain differences in the optimal spectral feature interval corresponding to different machine learning algorithms. Selecting the appropriate spectral feature interval can maintain high recognition accuracy and improve the computational efficiency of the algorithm. For example, the SVM algorithm has a recognition accuracy of 100% in the 890-980 cm, 1410-1500 cm fusion spectral range, which takes about 200 s. The ELM algorithm also has a recognition accuracy of 100% in the 890-980 cm, 1410-1500 cm fusion spectral range, which takes less than 0.3 s. The CNN algorithm has a recognition accuracy of 100% in the 890-980 cm, 1050-1180 cm, 1410-1500 cm fusion spectral range, which takes about 80 s. In addition, by analyzing the distribution of spectral feature intervals based on Euclidean distance, the distribution of experimental samples based on feature spectra is visually displayed. Through the spectral feature analysis process of similar samples, a set of analysis strategies is provided to deeply reveal the data foundation of classification algorithms, which can provide reference for the analysis of relevant discriminative research patterns.
PubMed: 38939610
DOI: 10.1016/j.crfs.2024.100782 -
JACC. Advances Aug 2023Detection of heart failure with preserved ejection fraction (HFpEF) involves integration of multiple imaging and clinical features which are often discordant or...
BACKGROUND
Detection of heart failure with preserved ejection fraction (HFpEF) involves integration of multiple imaging and clinical features which are often discordant or indeterminate.
OBJECTIVES
The authors applied artificial intelligence (AI) to analyze a single apical 4-chamber transthoracic echocardiogram video clip to detect HFpEF.
METHODS
A 3-dimensional convolutional neural network was developed and trained on apical 4-chamber video clips to classify patients with HFpEF (diagnosis of heart failure, ejection fraction ≥50%, and echocardiographic evidence of increased filling pressure; cases) vs without HFpEF (ejection fraction ≥50%, no diagnosis of heart failure, normal filling pressure; controls). Model outputs were classified as HFpEF, no HFpEF, or nondiagnostic (high uncertainty). Performance was assessed in an independent multisite data set and compared to previously validated clinical scores.
RESULTS
Training and validation included 2,971 cases and 3,785 controls (validation holdout, 16.8% patients), and demonstrated excellent discrimination (area under receiver-operating characteristic curve: 0.97 [95% CI: 0.96-0.97] and 0.95 [95% CI: 0.93-0.96] in training and validation, respectively). In independent testing (646 cases, 638 controls), 94 (7.3%) were nondiagnostic; sensitivity (87.8%; 95% CI: 84.5%-90.9%) and specificity (81.9%; 95% CI: 78.2%-85.6%) were maintained in clinically relevant subgroups, with high repeatability and reproducibility. Of 701 and 776 indeterminate outputs from the Heart Failure Association-Pretest Assessment, Echocardiographic and Natriuretic Peptide Score, Functional Testing (HFA-PEFF), and Final Etiology and Heavy, Hypertensive, Atrial Fibrillation, Pulmonary Hypertension, Elder, and Filling Pressure (H2FPEF) scores, the AI HFpEF model correctly reclassified 73.5% and 73.6%, respectively. During follow-up (median: 2.3 [IQR: 0.5-5.6] years), 444 (34.6%) patients died; mortality was higher in patients classified as HFpEF by AI (HR: 1.9 [95% CI: 1.5-2.4]).
CONCLUSIONS
An AI HFpEF model based on a single, routinely acquired echocardiographic video demonstrated excellent discrimination of patients with vs without HFpEF, more often than clinical scores, and identified patients with higher mortality.
PubMed: 38939447
DOI: 10.1016/j.jacadv.2023.100452 -
Frontiers in Oncology 2024To evaluate the effectiveness of MRI-based radiomics models in distinguishing between Warthin tumors (WT) and misdiagnosed or ambiguous pleomorphic adenoma (PA).
PURPOSE
To evaluate the effectiveness of MRI-based radiomics models in distinguishing between Warthin tumors (WT) and misdiagnosed or ambiguous pleomorphic adenoma (PA).
METHODS
Data of patients with PA and WT from two centers were collected. MR images were used to extract radiomic features. The optimal radiomics model was found by running nine machine learning algorithms after feature reduction and selection. To create a clinical model, univariate logistic regression (LR) analysis and multivariate LR were used. The independent clinical predictors and radiomics were combined to create a nomogram. Two integrated models were constructed by the ensemble and stacking algorithms respectively based on the clinical model and the optimal radiomics model. The models' performance was evaluated using the area under the curve (AUC).
RESULTS
There were 149 patients included in all. Gender, age, and smoking of patients were independent clinical predictors. With the greatest average AUC (0.896) and accuracy (0.839) in validation groups, the LR model was the optimal radiomics model. In the average validation group, the radiomics model based on LR did not have a higher AUC (0.795) than the clinical model (AUC = 0.909). The nomogram (AUC = 0.953) outperformed the radiomics model in terms of discrimination performance. The nomogram in the average validation group had a highest AUC than the stacking model (0.914) or ensemble model (0.798).
CONCLUSION
Misdiagnosed or ambiguous PA and WT can be non-invasively distinguished using MRI-based radiomics models. The nomogram exhibited excellent and stable diagnostic performance. In daily work, it is necessary to combine with clinical parameters for distinguishing between PA and WT.
PubMed: 38939335
DOI: 10.3389/fonc.2024.1392343