Intelligent medicine最新文献

Objective

The incidence of cardiovascular diseases (CVD) is rising rapidly worldwide. Some forms of CVD, such as stroke and heart attack, are more common among patients with certain conditions. Atherosclerosis development is a major factor underlying cardiovascular events, such as heart attack and stroke, and its early detection may prevent such events. Ultrasound imaging of carotid arteries is a useful method for diagnosis of atherosclerotic plaques; however, an automated method to classify atherosclerotic plaques for evaluation of early-stage CVD is needed. Here, we propose an automated method for classification of high-risk atherosclerotic plaque ultrasound images.

Methods

Five deep learning (DL) models (VGG16, ResNet-50, GoogLeNet, XceptionNet, and SqueezeNet) were used for automated classification and the results compared with those of a machine learning (ML)-based technique, involving extraction of 23 texture features from ultrasound images and classification using a Support Vector Machine classifier. To enhance model interpretability, output gradient-weighted convolutional activation maps (GradCAMs) were generated and overlayed on original images.

Results

A series of indices, including accuracy, sensitivity, specificity, F1-score, Cohen-kappa index, and area under the curve values, were calculated to evaluate model performance. GradCAM output images allowed visualization of the most significant ultrasound image regions. The GoogLeNet model yielded the highest accuracy (98.20%).

Conclusion

ML models may be also suitable for applications requiring low computational resource. Further, DL models could be more completely automated than ML models.

Background

Brain stroke is a serious health issue that requires timely and accurate prediction for effective treatment and prevention. This study described a hybrid system that used the best feature selection method and classifier to predict brain stroke.

Methods

The Stroke Prediction Dataset from Kaggle was used for this study. Synthetic minority over-sampling technique (SMOTE) analysis was used to accomplish class balancing. Accuracy, sensitivity, specificity, precision, and the F-Measure were the main performance parameters considered for investigation. To determine the best combination for predicting brain stroke, the performance of five classifiers, Naïve Bayes (NB), support vector machine (SVM), random forest (RF), adaptive boosting (Adaboost), and extreme gradient boosting (XGBoost), was compared along with three feature selection techniques, mutual information (MI), Pearson correlation (PC), and feature importance (FI). The performance parameters were assessed using k-fold cross-validation.

Results

The hybrid system proposed in this study identified a reduced set of features that were able to effectively predict brain stroke. FI provided a feature reduction ratio of 36.3%. The most successful hybrid system for predicting brain stroke used FI as the feature selection technique and RF as the classifier, achieving an accuracy of 97.17%.

Conclusion

The proposed system predicted brain stroke with high accuracy. These findings could be used to inform the early detection and prevention of brain stroke, allowing healthcare professionals to provide timely and targeted care to at-risk patients.

Objective

Accurate infant brain parcellation is crucial for understanding early brain development; however, it is challenging due to the inherent low tissue contrast, high noise, and severe partial volume effects in infant magnetic resonance images (MRIs). The aim of this study was to develop an end-to-end pipeline that enabled accurate parcellation of infant brain MRIs.

Methods

We proposed an end-to-end pipeline that employs a two-stage global-to-local approach for accurate parcellation of infant brain MRIs. Specifically, in the global regions of interest (ROIs) localization stage, a combination of transformer and convolution operations was employed to capture both global spatial features and fine texture features, enabling an approximate localization of the ROIs across the whole brain. In the local ROIs refinement stage, leveraging the position priors from the first stage along with the raw MRIs, the boundaries of the ROIs are refined for a more accurate parcellation.

Results

We utilized the Dice ratio to evaluate the accuracy of parcellation results. Results on 263 subjects from National Database for Autism Research (NDAR), Baby Connectome Project (BCP) and Cross-site datasets demonstrated the better accuracy and robustness of our method than other competing methods.

Conclusion

Our end-to-end pipeline may be capable of accurately parcellating 6-month-old infant brain MRIs.

Background With the gradual increase of infertility in the world, among which male sperm problems are the main factor for infertility, more and more couples are using computer-assisted sperm analysis (CASA) to assist in the analysis and treatment of infertility. Meanwhile, the rapid development of deep learning (DL) has led to strong results in image classification tasks. However, the classification of sperm images has not been well studied in current deep learning methods, and the sperm images are often affected by noise in practical CASA applications. The purpose of this article is to investigate the anti-noise robustness of deep learning classification methods applied on sperm images.

Methods The SVIA dataset is a publicly available large-scale sperm dataset containing three subsets. In this work, we used subset-C, which provides more than 125,000 independent images of sperms and impurities, including 121,401 sperm images and 4,479 impurity images. To investigate the anti-noise robustness of deep learning classification methods applied on sperm images, we conducted a comprehensive comparative study of sperm images using many convolutional neural network (CNN) and visual transformer (VT) deep learning methods to find the deep learning model with the most stable anti-noise robustness.

Results This study proved that VT had strong robustness for the classification of tiny object (sperm and impurity) image datasets under some types of conventional noise and some adversarial attacks. In particular, under the influence of Poisson noise, accuracy changed from 91.45% to 91.08%, impurity precison changed from 92.7% to 91.3%, impurity recall changed from 88.8% to 89.5%, and impurity F1-score changed 90.7% to 90.4%. Meanwhile, sperm precision changed from 90.9% to 90.5%, sperm recall changed from 92.5% to 93.8%, and sperm F1-score changed from 92.1% to 90.4%.

Conclusion Sperm image classification may be strongly affected by noise in current deep learning methods; the robustness with regard to noise of VT methods based on global information is greater than that of CNN methods based on local information, indicating that the robustness with regard to noise is reflected mainly in global information.

Objective

Tuberculosis (TB) is among the most frequent causes of infectious-disease-related mortality. Despite being treatable by antibiotics, tuberculosis often goes misdiagnosed and untreated, especially in rural and low-resource areas. Chest X-rays are frequently used to aid diagnosis; however, this presents additional challenges because of the possibility of abnormal radiological appearance and a lack of radiologists in areas where the infection is most prevalent. Implementing deep-learning-based imaging techniques for computer-aided diagnosis has the potential to enable accurate diagnoses and lessen the burden on medical specialists. In the present work, we aimed to develop deep-learning-based segmentation and classification models for accurate and precise detection of tuberculosis in chest X-ray images, with visualization of infection using gradient-weighted class activation mapping (Grad-CAM) heatmaps.

Methods

First, we trained the UNet segmentation model using 704 chest X-ray radiographs taken from the Montgomery County and Shenzhen Hospital datasets. Next, we implemented the trained UNet model on 1,400 tuberculosis and control chest X-ray scans to segment the lung region. The images were taken from the National Institute of Allergy and Infectious Diseases (NIAID) TB portal program dataset. Then, we applied the deep learning Xception model to classify the segmented lung region into tuberculosis and normal classes. We further investigated the visualization capabilities of the model using Grad-CAM to view tuberculosis abnormalities in chest X-rays and discuss them from radiological perspectives.

Results

For segmentation by the UNet model, we achieved accuracy, Jaccard index, Dice coefficient, and area under the curve (AUC) values of 96.35%, 90.38%, 94.88%, and 0.99, respectively. For classification by the Xception model, we achieved classification accuracy, precision, recall, F1-score, and AUC values of 99.29%, 99.30%, 99.29%, 99.29%, and 0.999, respectively. The Grad-CAM heatmap images from the tuberculosis class showed similar heatmap patterns, where lesions were primarily present in the upper part of the lungs.

Conclusion

The findings may verify our system's efficacy and superiority to clinician precision in tuberculosis diagnosis using chest X-rays and raise the possibility of a valuable setup, particularly in environments with a scarcity of radiological expertise.

Millimeter waves are electromagnetic waves with wavelengths of 1–10 mm, which have characteristics of high frequency and short wavelength. They have gradually and widely been used in engineering and medical fields. We have identified studies related to millimeter waves in the biomedical field and summarized the biological effects of millimeter waves and their current status in medical applications. Finally, the shortcomings of existing studies and future developments were analyzed and discussed, with the aim of providing a reference for further research and development of millimeter waves in the medical field.

Background

Colorectal cancer (CRC) is the second leading cause of cancer fatalities and the third most common human disease. Identifying molecular subgroups of CRC and treating patients accordingly could result in better therapeutic success compared with treating all CRC patients similarly. Studies have highlighted the significance of CRC as a major cause of mortality worldwide and the potential benefits of identifying molecular subtypes to tailor treatment strategies and improve patient outcomes.

Methods

This study proposed an unsupervised learning approach using hierarchical clustering and feature selection to identify molecular subtypes and compares its performance with that of conventional methods. The proposed model contained gene expression data from CRC patients obtained from Kaggle and used dimension reduction techniques followed by Z-score-based outlier removal. Agglomerative hierarchy clustering was used to identify molecular subtypes, with a P-value-based approach for feature selection. The performance of the model was evaluated using various classifiers including multilayer perceptron (MLP).

Results

The proposed methodology outperformed conventional methods, with the MLP classifier achieving the highest accuracy of 89% after feature selection. The model successfully identified molecular subtypes of CRC and differentiated between different subtypes based on their gene expression profiles.

Conclusion

This method could aid in developing tailored therapeutic strategies for CRC patients, although there is a need for further validation and evaluation of its clinical significance.

With the continuous improvement and development of modern network information technology and the continuous improvement of people's demands for health care, the traditional health care model has evolved, giving birth to a new telemedicine health care model. Telemedicine refers to the comprehensive application of information technology for medical information transmission and long-distance communication between different places. It integrates medicine, computer technology, and communication technology for remote monitoring, diagnosis, consultation, case discussion, teaching, and surgery as well as a series of medical activities. With the continuous development of communication technology, telemedicine is also constantly changing. As a relatively novel technology, telemedicine is sought after by major hospitals. With the advancement of internet technology, digitization and informatization have been gradually applied in telemedicine, but due to various factors, telemedicine still has great limitations. This paper summarized the development status of telemedicine; discussed in detail the development of telemedicine at home and abroad; reviewed the application of telemedicine as well as the feasibility and limitations of its promotion and development; and put forward an outlook for the future development of telemedicine.

One concern about the application of medical artificial intelligence (AI) regards the “black box” feature which can only be viewed in terms of its inputs and outputs, with no way to understand the AI's algorithm. This is problematic because patients, physicians, and even designers, do not understand why or how a treatment recommendation is produced by AI technologies. One view claims that the worry about black-box medicine is unreasonable because AI systems outperform human doctors in identifying the disease. Furthermore, under the medical AI-physician-patient model, the physician can undertake the responsibility of interpreting the medical AI's diagnosis. In this study, we focus on the potential harm caused by the unexplainability feature of medical AI and try to show that such possible harm is underestimated. We will seek to contribute to the literature from three aspects. First, we appealed to a thought experiment to show that although the medical AI systems perform better on accuracy, the harm caused by medical AI's misdiagnoses may be more serious than that caused by human doctors’ misdiagnoses in some cases. Second, in patient-centered medicine, physicians were obligated to provide adequate information to their patients in medical decision-making. However, the unexplainability feature of medical AI systems would limit the patient's autonomy. Last, we tried to illustrate the psychological and financial burdens that may be caused by the unexplainablity feature of medical AI systems, which seems to be ignored by the previous ethical discussions.