Application of artificial intelligence based on deep learning in breast cancer screening and imaging diagnosis

Statistical Analysis of Gastric Cancer Cells Response to Broadband Terahertz Radiation with and without Contrast Nanoparticles

The paper describes the statistical analysis of the response of gastric cancer cells and normal cells to broadband terahertz radiation up to 4 THz, both with and without the use of nanostructured contrast agents. The THz spectroscopy analysis was comparatively performed under the ATR procedure and transmission measurement procedure. The statistical analysis was conducted towards multiple pairwise comparisons, including a support medium (without cells) versus a support medium with nanoparticles, normal cells versus normal cells with nanoparticles, and, respectively, tumor cells versus tumor cells with nanoparticles. When generally comparing the ATR procedure and transmission measurement procedure for a broader frequency domain, the differentiation between normal and tumor cells in the presence of contrast agents is superior when using the ATR procedure. THz contrast enhancement by using contrast agents derived from MRI-related contrast agents leads to only limited benefits and only for narrow THz frequency ranges, a disadvantage for THz medical imaging.

State-of-the-Art of Breast Cancer Diagnosis in Medical Images via Convolutional Neural Networks (CNNs)

Early detection of breast cancer is crucial for a better prognosis. Various studies have been conducted where tumor lesions are detected and localized on images. This is a narrative review where the studies reviewed are related to five different image modalities: histopathological, mammogram, magnetic resonance imaging (MRI), ultrasound, and computed tomography (CT) images, making it different from other review studies where fewer image modalities are reviewed. The goal is to have the necessary information, such as pre-processing techniques and CNN-based diagnosis techniques for the five modalities, readily available in one place for future studies. Each modality has pros and cons, such as mammograms might give a high false positive rate for radiographically dense breasts, while ultrasounds with low soft tissue contrast result in early-stage false detection, and MRI provides a three-dimensional volumetric image, but it is expensive and cannot be used as a routine test. Various studies were manually reviewed using particular inclusion and exclusion criteria; as a result, 91 recent studies that classify and detect tumor lesions on breast cancer images from 2017 to 2022 related to the five image modalities were included. For histopathological images, the maximum accuracy achieved was around 99 % , and the maximum sensitivity achieved was 97.29 % by using DenseNet, ResNet34, and ResNet50 architecture. For mammogram images, the maximum accuracy achieved was 96.52 % using a customized CNN architecture. For MRI, the maximum accuracy achieved was 98.33 % using customized CNN architecture. For ultrasound, the maximum accuracy achieved was around 99 % by using DarkNet-53, ResNet-50, G-CNN, and VGG. For CT, the maximum sensitivity achieved was 96 % by using Xception architecture. Histopathological and ultrasound images achieved higher accuracy of around 99 % by using ResNet34, ResNet50, DarkNet-53, G-CNN, and VGG compared to other modalities for either of the following reasons: use of pre-trained architectures with pre-processing techniques, use of modified architectures with pre-processing techniques, use of two-stage CNN, and higher number of studies available for Artificial Intelligence (AI)/machine learning (ML) researchers to reference. One of the gaps we found is that only a single image modality is used for CNN-based diagnosis; in the future, a multiple image modality approach can be used to design a CNN architecture with higher accuracy.

Insight into breast cancer detection: new hybrid feature selection method

Breast cancer, which is also the leading cause of death among women, is one of the most common forms of the disease that affects females all over the world. The discovery of breast cancer at an early stage is extremely important because it allows selecting appropriate treatment protocol and thus, stops the development of cancer cells. In this paper, a new patients detection strategy has been presented to identify patients with the disease earlier. The proposed strategy composes of two parts which are data preprocessing phase and patient detection phase (PDP). The purpose of this study is to introduce a feature selection methodology for determining the most efficient and significant features for identifying breast cancer patients. This method is known as new hybrid feature selection method (NHFSM). NHFSM is made up of two modules which are quick selection module that uses information gain, and feature selection module that uses hybrid bat algorithm and particle swarm optimization. Consequently, NHFSM is a hybrid method that combines the advantages of bat algorithm and particle swarm optimization based on filter method to eliminate many drawbacks such as being stuck in a local optimal solution and having unbalanced exploitation. The preprocessed data are then used during PDP in order to enable a quick and accurate detection of patients. Based on experimental results, the proposed NHFSM improves the efficiency of patients’ classification in comparison with state-of-the-art feature selection approaches by roughly 0.97, 0.76, 0.75, and 0.716 in terms of accuracy, precision, sensitivity/recall, and F-measure. In contrast, it has the lowest error rate value of 0.03.

Application of a Remotely Controlled Artificial Intelligence Analgesic Pump Device in Painless Treatment of Children

In order to effectively improve the application of analgesic pump devices in the treatment of children, a method based on remote control artificial intelligence is proposed. 100 children with dental pulpitis who were treated in a hospital from December 2018 to December 2020 were selected as the research subjects; they were randomly divided into control group and observation group by an equidistant sampling method, with 50 cases in each group. Children in the control group were given articaine and adrenaline anesthesia, and the observation group was treated with articaine and adrenaline combined with a computer-controlled anesthesia system, the anesthesia pain degree and satisfaction degree of the two groups of children were observed and compared. The results showed that the pain score in anesthesia and intraoperative pain score in the observation group was significantly lower than that in the control group, and the differences were statistically significant (P < 0.05). The total satisfaction of 96.6% patients in the observation group was significantly higher than that in the control group (84.7%) and the difference was statistically significant (P < 0.05). There were no serious complications in both groups. The application of the computer anesthesia system combined with articaine adrenaline in the painless treatment of children's dental pulp proved to have better effects, the treatment compliance is higher, and it is worthy of clinical promotion.

Models of Artificial Intelligence-Assisted Diagnosis of Lung Cancer Pathology Based on Deep Learning Algorithms

In this article, in order to explore the application of a diagnosis system for lung cancer, we use an auxiliary diagnostic system to predict and diagnose the good and evil attributes of chest CT pulmonary nodules. This research improves the new diagnosis method based on the convolutional neural network (CNN) and the recurrent neural network (RNN) and combines the dual effects of the two algorithms to process the classification of benign and malignant nodules. By collecting H-E-stained pathological slices of 652 patients' lung lesions from two hospitals between January 2018 and January 2019, the output results of the improved 3D U-net system and the consistent results of two-person reading were compared. This article analyzes the sensitivity, specificity, positive flammability rate, and negative flammability rate of different lung nodule detection methods. In addition, the artificial intelligence system's and the radiologist's judgment results of benign and malignant pulmonary nodules are used to draw ROC curves for further analysis. The improved model has an accuracy rate of 92.3% for predicting malignant lung nodules and an accuracy rate of 82.8% for benign lung nodules. The new diagnostic method using the convolutional neural network and the recurrent neural network can be very effective for improving the accuracy of predicting lung cancer diagnosis. It can play a very effective role in the disease prediction of lung cancer patients, thereby improving the treatment effect.

Evaluation of Cardiac Space-Occupying Lesions by Myocardial Contrast Echocardiography and Transesophageal Echocardiography

Heart space-occupying lesions are a disease that occurs frequently in clinical setting, and therefore, it is important to diagnose and treat this type of pathologies properly. Angiographic echocardiography and transesophageal sonogram are widely used for clinical diagnosis. Their application provides a guarantee for the diagnosis of cardiac space-occupying lesions. In this paper, the application of cardiac contrast echocardiography and transesophageal echocardiography in cardiac space-occupying lesions was studied. Prediction of cardiac lesions can accurately determine the nature of cardiac occupancies and provide a basis for clinical diagnosis and management judgments. The results of pathological analysis and experimental comparison showed that myocardial contrast echocardiography can accurately distinguish tumor and thrombus and make contribution to patients taking appropriate medical measures. At the same time, it can compare conventional transthoracic echocardiography and transesophageal echocardiography. The results showed that TEE could clearly show the cardiac lesions. The experimental data of 76.9% confirmed cases showed that the diagnostic accuracy is greatly improved. TEE can also clearly show small thrombus that TTE cannot, in which 2DTEE can clearly show the boundary between the space-occupying and surrounding tissues, and whether there is a clear boundary between the space-occupying and surrounding tissues is an important distinguishing point of benign and malignant tumors. In addition, the TEE probe can also be used for large angle imaging and multiangle rotation, so as to determine the tumor boundary and the spatial position relationship between the tumor and the surrounding tissue. All in all, myocardial contrast echocardiography and transesophageal echocardiography have better clinical application effect on cardiac space-occupying lesions.