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Accurate measurement of frontomaxillary facial (FMF) angles in prenatal ultrasound (US) scans plays a pivotal role in the screening of trisomy 21. Nevertheless, this intricate procedure heavily relies on the proficiency of the ultrasonographer and tends to be a time-intensive task. Furthermore, FMF angles are subjective when measured manually. To address this challenge, we propose a deep learning-based assisted examination framework for automatically measuring FMF angles on 2D ultrasound images. Firstly, we trained a deep learning network using 1549 fetal ultrasound images to achieve automatic and accurate segmentation of critical areas. Subsequently, a key point detection network was employed to predict the coordinates of the requisite points for calculating FMF angles. Finally, FMF angles were obtained through computational means. We employed Pearson correlation coefficients and Bland–Altman plots to assess the correlation and consistency between the model's predictions and manual measurements. Notably, our method exhibited a mean absolute error of 2.354°, outperforming the typical standards of the junior expert. This indicates the high degree of accuracy and reliability achieved by our approach. [ABSTRACT FROM AUTHOR]

Background: Developing a combined artificial intelligence (AI) and ultrasound imaging to provide an accurate, objective, and efficient adjunctive diagnostic approach for fetal heart ventricular septal defects (VSD). Methods: 1,451 fetal heart ultrasound images from 500 pregnant women were comprehensively analyzed between January 2016 and June 2022. The fetal heart region was manually labeled and the presence of VSD was discriminated by experts. The principle of five-fold cross-validation was followed in the training set to develop the AI model to assist in the diagnosis of VSD. The model was evaluated in the test set using metrics such as mAP@0.5, precision, recall, and F1 score. The diagnostic accuracy and inference time were also compared with junior doctors, intermediate doctors, and senior doctors. Results: The mAP@0.5, precision, recall, and F1 scores for the AI model diagnosis of VSD were 0.926, 0.879, 0.873, and 0.88, respectively. The accuracy of junior doctors and intermediate doctors improved by 6.7% and 2.8%, respectively, with the assistance of this system. Conclusions: This study reports an AI-assisted diagnostic method for VSD that has a high agreement with manual recognition. It also has a low number of parameters and computational complexity, which can also improve the diagnostic accuracy and speed of some physicians for VSD. [ABSTRACT FROM AUTHOR]

Objectives: Ultrasound screening during early pregnancy is vital in preventing congenital disabilities. For example, nuchal translucency (NT) thickening is associated with fetal chromosomal abnormalities, particularly trisomy 21 and fetal heart malformations. Obtaining accurate ultrasound standard planes of a fetal face during early pregnancy is the key to subsequent biometry and disease diagnosis. Therefore, we propose a lightweight target detection network for early pregnancy fetal facial ultrasound standard plane recognition and quality assessment. Methods: First, a clinical control protocol was developed by ultrasound experts. Second, we constructed a YOLOv4 target detection algorithm based on the backbone network as GhostNet and added attention mechanisms CBAM and CA to the backbone and neck structure. Finally, key anatomical structures in the image were automatically scored according to a clinical control protocol to determine whether they were standard planes. Results: We reviewed other detection techniques and found that the proposed method performed well. The average recognition accuracy for six structures was 94.16%, the detection speed was 51 FPS, and the model size was 43.2 MB, and a reduction of 83% compared with the original YOLOv4 model was obtained. The precision for the standard median sagittal plane was 97.20%, and the accuracy for the standard retro‐nasal triangle view was 99.07%. Conclusions: The proposed method can better identify standard or non‐standard planes from ultrasound image data, providing a theoretical basis for automatic acquisition of standard planes in the prenatal diagnosis of early pregnancy fetuses. [ABSTRACT FROM AUTHOR]

The liver ultrasound standard planes (LUSP) have significant diagnostic significance during ultrasonic liver diagnosis. However, the location and acquisition of LUSP could be a time-consuming and complicated mission and requires the relevant operator to have comprehensive knowledge of ultrasound diagnosis. Therefore, this study puts forward an automatic classification approach for eight types of LUSP based on a pre-trained CNN(Convolutional Neural Network). With the comparison to classification methods on the basis of conventional hand-craft characteristics, the method proposed by us can automatically catch the appearance in LUSP and classify the LUSP. The proposed model is consisted of 13 convolutional layers with little 3×3 size kernels and three completely connected layers. To address the limitation of data, we adopt the transfer learning strategy, which pre-trains the weight of convolutional layers and fine-tune the weight of fully connected layers. These extensive experiments show that the accuracy of the suggested method reaches 92.31%, as well as the performance of the suggested means outperforms previous ways, which demonstrates the suitability and effectiveness of CNN to classify LUSP for clinical diagnosis. [ABSTRACT FROM AUTHOR]

Automatic classification of benign and malignant breast ultrasound images is an important and challenging task to improve the efficiency and accuracy of clinical diagnosis of breast tumors and reduce the rate of missed and misdiagnosis. The task often requires a large amount of data to train. However, it is difficult to obtain medical images, which contradicts the large amount of data needed to obtain good diagnostic models for training. In this paper, a novel classification model for the classification of breast tumors is proposed to improve the performance of diagnosis models trained by small datasets. The method integrates three features from medical features extracted from segmented images, features selected from the pre-trained ResNet101 output by principal component analysis (PCA), and texture features. Among the medical features that are used to train the naive Bayes (NB) classifier, and the PCA-selected features are used to train the support vector machine (SVM) classifier. Subsequently, the final results of boosting are obtained by weighting the classifiers. A five-fold cross-validation experiment yields an average accuracy of 89.17%, an average precision of 90.00%, and an average AUC value of 0.95. According to the experimental results, the proposed method has better classification accuracy compared to the accuracy obtained by other models trained on only small datasets. This approach can serve as a reliable second opinion for radiologists, and it can also provide useful advice for junior radiologists who do not have sufficient clinical experience. [ABSTRACT FROM AUTHOR]

Ultrasound is one of the critical methods for diagnosis and treatment in thyroid examination. In clinical application, many reasons, such as large outpatient traffic, time-consuming training of sonographers, and uneven professional level of physicians, often cause irregularities during the ultrasonic examination, leading to misdiagnosis or missed diagnosis. In order to standardize the thyroid ultrasound examination process, this paper proposes using a deep learning method based on residual network to recognize the Thyroid Ultrasound Standard Plane (TUSP). At first, referring to multiple relevant guidelines, eight TUSP were determined with the advice of clinical ultrasound experts. A total of 5,500 TUSP images of 8 categories were collected with the approval and review of the Ethics Committee and the patient's informed consent. Then, after desensitizing and filling the images, the 18-layer residual network model (ResNet-18) was trained for TUSP image recognition, and five-fold cross-validation was performed. Finally, through indicators like accuracy rate, we compared the recognition effect of other mainstream deep convolutional neural network models. Experimental results showed that ResNet-18 has the best recognition effect on TUSP images with an average accuracy rate of 91.07%. The average macro precision, average macro recall, and average macro F1-score are 91.39%, 91.34%, and 91.30%, respectively. It proves that the deep learning method based on residual network can effectively recognize TUSP images, which is expected to standardize clinical thyroid ultrasound examination and reduce misdiagnosis and missed diagnosis. [ABSTRACT FROM AUTHOR]

At present, prenatal ultrasound is one of the important means for screening fetal malformations. In the process of prenatal ultrasound diagnosis, the accurate recognition of fetal facial ultrasound standard plane is crucial for facial malformation detection and disease screening. Due to the dense distribution of fetal facial images, no obvious structure contour boundary, small structure area, and large area overlap in the middle of the structure detection frame, this paper regards the fetal facial standard plane and its structure recognition as a universal target detection task for the first time, and applies real‐time YOLO v5s to the fetal facial ultrasound standard plane structure detection and classification task. First, we detect the structure of a single slice, and take the structure of a slice class as the recognition object. Second, we carry out structural detection experiments on three standard planes; then, on the basis of the previous stage, the images of all parts included in the ultrasound examination of multiple fetuses were collected. In the single‐class structure detection experiment and the structure detection and classification experiment of three types of standard planes, the overall recognition effect of Precision and Recall index data is better, with Precision being 98.3% and 98.1%, and Recall being 99.3% and 98.2%, respectively. The experimental results show that the model has the ability to identify fetal facial anatomy and standard sections in different data, which can help the physician to automatically and quickly screen out the standard sections of each fetal facial ultrasound. Synopsis: By automatically identifying key anatomical structures within ultrasound images, it provides a theoretical basis for doctors to choose standard planes. [ABSTRACT FROM AUTHOR]

Prostate cancer is one of the most common cancers in men. Early detection of prostate cancer is the key to successful treatment. Ultrasound imaging is one of the most suitable methods for the early detection of prostate cancer. Although ultrasound images can show cancer lesions, subjective interpretation is not accurate. Therefore, this paper proposes a transrectal ultrasound image analysis method, aiming at characterizing prostate tissue through image processing to evaluate the possibility of malignant tumours. Firstly, the input image is preprocessed by optical density conversion. Then, local binarization and Gaussian Markov random fields are used to extract texture features, and the linear combination is performed. Finally, the fused texture features are provided to SVM classifier for classification. The method has been applied to data set of 342 transrectal ultrasound images obtained from hospitals with an accuracy of 70.93%, sensitivity of 70.00%, and specificity of 71.74%. The experimental results show that it is possible to distinguish cancerous tissues from noncancerous tissues to some extent. [ABSTRACT FROM AUTHOR]

Uterine smooth muscle hyperplasia causes a tumor called a uterine fibroid. With an incidence of up to 30%, it is one of the most prevalent tumors in women and has the third highest prevalence of all gynecological illnesses. Although uterine fibroids are usually not accompanied by symptoms, there are physical effects, such as impairment of the ability to conceive. To reduce morbidity, early detection and treatment are crucial. Ultrasound imaging is a common method used for pre-operative guidance and interventional therapy. Many applications of object detection are performing well with the advancement of deep learning in the field of medical image analysis. To ensure accuracy, computer-assisted detection can further solve the subjective problem generated by different doctors when they read images. Our research provides an improved YOLOv3 that combines the properties of EfficientNet and YOLOv3, which use a convolutional neural network to extract features, to detect uterine fibroid ultrasound images. Our approach attained an F1 score of 95% and an average precision of 98.38% and reached a detection speed of 0.28 s per image. We reviewed and analyzed several detection techniques and identified potential future research hotpots. This technique offers enough supplementary diagnostic tools for amateur or expert ultrasonologists and sets a solid foundation for future medical care and surgical excision. [ABSTRACT FROM AUTHOR]

Acquisition of a standard section is a prerequisite for ultrasound diagnosis. For a long time, there has been a lack of clear definitions of standard liver views because of physician experience. The accurate automated scanning of standard liver sections, however, remains one of ultrasonography medicine's most important issues. In this article, we enrich and expand the classification criteria of liver ultrasound standard sections from clinical practice and propose an Ultra-Attention structured perception strategy to automate the recognition of these sections. Inspired by the attention mechanism in natural language processing, the standard liver ultrasound views will participate in the global attention algorithm as modular local images in computer vision of ultrasound images, which will significantly amplify small features that would otherwise go unnoticed. In addition to using the dropout mechanism, we also use a Part-Transfer Learning training approach to fine-tune the model's rate of convergence to increase its robustness. The proposed Ultra-Attention model outperforms various traditional convolutional neural network-based techniques, achieving the best known performance in the field with a classification accuracy of 93.2%. As part of the feature extraction procedure, we also illustrate and compare the convolutional structure and the Ultra-Attention approach. This analysis provides a reasonable view for future research on local modular feature capture in ultrasound images. By developing a standard scan guideline for liver ultrasound-based illness diagnosis, this work will advance the research on automated disease diagnosis that is directed by standard sections of liver ultrasound. [ABSTRACT FROM AUTHOR]

In the process of prenatal ultrasound diagnosis, accurate identification of fetal facial ultrasound standard plane (FFUSP) is essential for accurate facial deformity detection and disease screening, such as cleft lip and palate detection and Down syndrome screening check. However, the traditional method of obtaining standard planes is manual screening by doctors. Due to different levels of doctors, this method often leads to large errors in the results. Therefore, in this study, we propose a texture feature fusion method (LH-SVM) for automatic recognition and classification of FFUSP. First, extract image's texture features, including Local Binary Pattern (LBP) and Histogram of Oriented Gradient (HOG), then perform feature fusion, and finally adopt Support Vector Machine (SVM) for predictive classification. In our study, we used fetal facial ultrasound images from 20 to 24 weeks of gestation as experimental data for a total of 943 standard plane images (221 ocular axial planes, 298 median sagittal planes, 424 nasolabial coronal planes, and 350 nonstandard planes, OAP, MSP, NCP, N-SP). Based on this data set, we performed five-fold cross-validation. The final test results show that the accuracy rate of the proposed method for FFUSP classification is 94.67%, the average precision rate is 94.27%, the average recall rate is 93.88%, and the average F 1 score is 94.08%. The experimental results indicate that the texture feature fusion method can effectively predict and classify FFUSP, which provides an essential basis for clinical research on the automatic detection method of FFUSP. [ABSTRACT FROM AUTHOR]

Although liver ultrasound (US) is quick and convenient, it presents challenges due to patient variations. Previous research has predominantly focused on computer-aided diagnosis (CAD), particularly for disease analysis. However, characterizing liver US images is complex due to structural diversity and a limited number of samples. Normal liver US images are crucial, especially for standard section diagnosis. This study explicitly addresses Liver US standard sections (LUSS) and involves detailed labeling of eight anatomical structures. We propose SEG-LUS, a US image segmentation model for the liver and its accessory structures. In SEG-LUS, we have adopted the shifted windows feature encoder combined with the cross-attention mechanism to adapt to capturing image information at different scales and resolutions and address context mismatch and sample imbalance in the segmentation task. By introducing the UUF module, we achieve the perfect fusion of shallow and deep information, making the information retained by the network in the feature extraction process more comprehensive. We have improved the Focal Loss to tackle the imbalance of pixel-level distribution. The results show that the SEG-LUS model exhibits significant performance improvement, with mPA, mDice, mIOU, and mASD reaching 85.05%, 82.60%, 74.92%, and 0.31, respectively. Compared with seven state-of-the-art semantic segmentation methods, the mPA improves by 5.32%. SEG-LUS is positioned to serve as a crucial reference for research in computer-aided modeling using liver US images, thereby advancing the field of US medicine research. • We propose SEG-LUS, a semantic segmentation model for liver ultrasound diagnostic and therapeutic processes. It is the sole model applicable to LUSS recognition, effectively representing key anatomical structures during clinical scanning. • Introducing a hybrid attention mechanism, Cross Shift-window MSA (CSW-MSA), combined with UUF for liver ultrasound analysis, achieves top performance on an LUSS dataset with eight key anatomical structures. • Compared with seven other leading segmentation methods, we achieve a 4.5% lead over the baseline average. Results include mPA 85.05%, mDice 82.60%, mIOU 74.92%, and mASD 0.31. This provides an effective design reference for automated computer-aided modeling based on liver ultrasound data. [ABSTRACT FROM AUTHOR]