• Breast ultrasound
• Histological subtype
• Long-tailed recognition

• Humans
• Breast Neoplasms/diagnostic imaging
• Breast Neoplasms/pathology
• Breast Neoplasms/classification
• Female
• Ultrasonography, Mammary/methods
• Image Interpretation, Computer-Assisted/methods
• Algorithms
• Artificial Intelligence

• Breast Imaging Reporting and Data System 4 breast nodules (BI-RADS 4 breast nodules)
• artificial intelligence (AI)
• combined diagnosis
• differential diagnosis
• high frame-rate contrast-enhanced ultrasound (HiFR-CEUS)

• Humans
• Breast Neoplasms/diagnostic imaging
• Female
• Image Interpretation, Computer-Assisted/methods
• Algorithms
• Elasticity Imaging Techniques/methods
• Ultrasonography, Mammary/methods
• Breast/diagnostic imaging

• Artificial intelligence
• Breast cancer
• Health technology assessment
• Machine learning

• Humans
• Breast Neoplasms/diagnostic imaging
• Breast Neoplasms/diagnosis
• Female
• Technology Assessment, Biomedical
• Artificial Intelligence
• Machine Learning

• Artificial intelligence
• Breast
• Radiology

• Breast cancer classification
• deep learning
• radiologist comparison

• artificial intelligence
• breast cancer
• deep learning
• image-guided therapy
• mammography
• mri
• radiology
• radiomics
• theranostics
• ultrasound imaging

• Bioinformatics
• Cancer

• artificial intelligence network
• breast cancer
• deep learning
• machine learning
• mammography image
• ultrasound image

• Humans
• Breast Neoplasms/diagnostic imaging
• Artificial Intelligence
• Female
• Mammography/methods
• Breast/diagnostic imaging
• Breast/pathology
• Early Detection of Cancer/methods
• Magnetic Resonance Imaging/methods
• Ultrasonography, Mammary/methods
• Image Interpretation, Computer-Assisted/methods

• Quantitative analysis
• artificial intelligence (AI)
• breast ultrasound
• machine learning

• Artificial intelligence (AI)
• breast imaging
• breast surgery

• B-mode ultrasound
• breast cancer
• reliability
• shear wave elastography

• Humans
• Female
• Breast Neoplasms/diagnostic imaging
• Breast Neoplasms/pathology
• Elasticity Imaging Techniques
• Ultrasonography, Mammary
• Prospective Studies
• Reproducibility of Results
• Breast/diagnostic imaging
• Breast/pathology
• Sensitivity and Specificity
• Diagnosis, Differential

• Humans
• Female
• Artificial Intelligence
• Breast Density
• Breast Neoplasms/diagnostic imaging
• Mammography

Artificial intelligence (AI) has been used in various fields of day-to-day life and its role in medicine is immense. Understanding of oncology has been improved with the introduction of AI which helps in diagnosis, treatment planning, management, prognosis, and follow-up. It also helps to identify high-risk groups who can be subjected to timely screening for early detection of malignant conditions. It is more important in pancreatic cancer as it is one of the major causes of cancer-related deaths worldwide and there are no specific early features (clinical and radiological) for diagnosis. With improvement in imaging modalities (computed tomography, magnetic resonance imaging, endoscopic ultrasound), most often clinicians were being challenged with lesions that were difficult to diagnose with human competence. AI has been used in various other branches of medicine to differentiate such indeterminate lesions including the thyroid gland, breast, lungs, liver, adrenal gland, kidney, etc. In the case of pancreatic cancer, the role of AI has been explored and is still ongoing. This review article will focus on how AI can be used to diagnose pancreatic cancer early or differentiate it from benign pancreatic lesions, therefore, management can be planned at an earlier stage.

• Artificial intelligence
• Indeterminate pancreatic lesion
• Imaging
• Biomarkers
• Diagnosis

• BI-RADS
• artificial intelligence
• breast
• diagnosis
• ultrasound video

• Artificial intelligence
• Breast neoplasms
• Risk assessment.
• Ultrasonography
• mammary

• bronchoscopy
• lung cancer

• Humans
• Artificial Intelligence
• Deep Learning
• Retrospective Studies
• Neural Networks, Computer
• Lung Neoplasms/diagnostic imaging

• National Tsing Hua University
• National Taiwan University Hospital Hsin-Chu Branch

• artificial intelligence
• breast cancer
• breast imaging

• Humans
• Female
• Artificial Intelligence
• Breast Neoplasms/diagnostic imaging
• Breast
• Diagnostic Imaging
• Prognosis

• biophysical ultrasound feature
• breast cancer diagnosis
• machine learning
• multiparametric analysis
• multiparametric imaging
• tissue characterization

• R21 EB025290 NIBIB NIH HHS
• National Institutes of Health

• artificial intelligence
• breast cancer
• deep learning
• histopathology
• imaging modality
• mammography

• Bibliometrix analysis
• artificial intelligence
• breast cancer
• diagnosis and prognosis
• knowledge structures

In today's world, progressively enormous popularity prevails around artificial intelligence (AI). AI is gaining popularity in the identification of various images. Therefore, it has been widely used in the ultrasound of the breast. Furthermore, AI can perform a quantitative evaluation, which further helps maintain the diagnosis's accuracy. Moreover, breast cancer is the most common cancer in women, posing a severe threat to women's health. Hence, its early detection is usually associated with a patient's prognosis. As a result, using AI in breast cancer screening and detection is highly crucial. The concept of AI in the perspective of breast ultrasound has been highlighted in this brief review article. It tends to focus on early AI, i.e., traditional machine learning and deep learning algorithms. Also, the use of AI in ultrasound and the use of it in mammography, magnetic resonance imaging, nuclear medicine imaging, and classification of breast lesions is broadly explained, along with the challenges faced in bringing AI into daily practice.

Endobronchial ultrasound-guided transbronchial aspiration is a minimally invasive and highly accurate modality for the diagnosis of lymph node metastasis and is useful for pre-treatment biomarker test sampling in patients with lung cancer. Endobronchial ultrasound image analysis is useful for predicting nodal metastasis; however, it can only be used as a supplemental method to tissue sampling. In recent years, deep learning-based computer-aided diagnosis using artificial intelligence technology has been introduced in research and clinical medicine. This study investigated the feasibility of computer-aided diagnosis for the prediction of nodal metastasis in lung cancer using endobronchial ultrasound images. The outcome of this study may help improve diagnostic efficiency and reduce invasiveness of the procedure.

Endobronchial ultrasound-guided transbronchial needle aspiration (EBUS-TBNA) is a valid modality for nodal lung cancer staging. The sonographic features of EBUS helps determine suspicious lymph nodes (LNs). To facilitate this use of this method, machine-learning-based computer-aided diagnosis (CAD) of medical imaging has been introduced in clinical practice. This study investigated the feasibility of CAD for the prediction of nodal metastasis in lung cancer using endobronchial ultrasound images. Image data of patients who underwent EBUS-TBNA were collected from a video clip. Xception was used as a convolutional neural network to predict the nodal metastasis of lung cancer. The prediction accuracy of nodal metastasis through deep learning (DL) was evaluated using both the five-fold cross-validation and hold-out methods. Eighty percent of the collected images were used in five-fold cross-validation, and all the images were used for the hold-out method. Ninety-one patients (166 LNs) were enrolled in this study. A total of 5255 and 6444 extracted images from the video clip were analyzed using the five-fold cross-validation and hold-out methods, respectively. The prediction of LN metastasis by CAD using EBUS images showed high diagnostic accuracy with high specificity. CAD during EBUS-TBNA may help improve the diagnostic efficiency and reduce invasiveness of the procedure.

• Artificial intelligence
• Breast cancer
• Machine learning
• Ultrasonography

• breast cancer
• deep convolutional network
• image classification
• semantic segmentation
• ultrasonic imaging

• MOST-108-2314-B-371-008 Ministry of Science and Technology
• 109-CCH-MST-179 Department of Research, Changhua Christian Hospital

Conventional deep learning (DL) algorithm requires full supervision of annotating the region of interest (ROI) that is laborious and often biased. We aimed to develop a weakly-supervised DL algorithm that diagnosis breast cancer at ultrasound without image annotation. Weakly-supervised DL algorithms were implemented with three networks (VGG16, ResNet34, and GoogLeNet) and trained using 1000 unannotated US images (500 benign and 500 malignant masses). Two sets of 200 images (100 benign and 100 malignant masses) were used for internal and external validation sets. For comparison with fully-supervised algorithms, ROI annotation was performed manually and automatically. Diagnostic performances were calculated as the area under the receiver operating characteristic curve (AUC). Using the class activation map, we determined how accurately the weakly-supervised DL algorithms localized the breast masses. For internal validation sets, the weakly-supervised DL algorithms achieved excellent diagnostic performances, with AUC values of 0.92–0.96, which were not statistically different (all Ps > 0.05) from those of fully-supervised DL algorithms with either manual or automated ROI annotation (AUC, 0.92–0.96). For external validation sets, the weakly-supervised DL algorithms achieved AUC values of 0.86–0.90, which were not statistically different (Ps > 0.05) or higher (P = 0.04, VGG16 with automated ROI annotation) from those of fully-supervised DL algorithms (AUC, 0.84–0.92). In internal and external validation sets, weakly-supervised algorithms could localize 100% of malignant masses, except for ResNet34 (98%). The weakly-supervised DL algorithms developed in the present study were feasible for US diagnosis of breast cancer with well-performing localization and differential diagnosis.

Ultrasonography (US) is the primary diagnostic tool for thyroid nodules, while the accuracy is operator-dependent. It is widely used not only by radiologists but also by physicians with different levels of experience. The aim of this study was to investigate whether US with computer-aided diagnosis (CAD) has assisting roles to physicians in the diagnosis of thyroid nodules. 451 thyroid nodules evaluated by fine-needle aspiration cytology following surgery were included. 300 (66.5%) of them were diagnosed as malignancy. Physicians with US experience less than 1 year (inexperienced, n = 10), or more than 5 years (experienced, n = 3) reviewed the US images of thyroid nodules with or without CAD assistance. The diagnostic performance of CAD was comparable to that of the experienced group, and better than those of the inexperienced group. The AUC of the CAD for conventional PTC was higher than that for FTC and follicular variant PTC (0.925 vs. 0.499), independent of tumor size. CAD assistance significantly improved diagnostic performance in the inexperienced group, but not in the experienced groups. In conclusion, the CAD system showed good performance in the diagnosis of conventional PTC. CAD assistance improved the diagnostic performance of less experienced physicians in US, especially in diagnosis of conventional PTC.

This study was performed to evaluate the diagnostic performance of deep learning systems using ultrasonography (USG) images of the submandibular glands (SMGs) in three different conditions: obstructive sialoadenitis, Sjögren’s syndrome (SjS), and normal glands. Fifty USG images with a confirmed diagnosis of obstructive sialoadenitis, 50 USG images with a confirmed diagnosis of SjS, and 50 USG images with no SMG abnormalities were included in the study. The training group comprised 40 obstructive sialoadenitis images, 40 SjS images, and 40 control images, and the test group comprised 10 obstructive sialoadenitis images, 10 SjS images, and 10 control images for deep learning analysis. The performance of the deep learning system was calculated and compared between two experienced radiologists. The sensitivity of the deep learning system in the obstructive sialoadenitis group, SjS group, and control group was 55.0%, 83.0%, and 73.0%, respectively, and the total accuracy was 70.3%. The sensitivity of the two radiologists was 64.0%, 72.0%, and 86.0%, respectively, and the total accuracy was 74.0%. This study revealed that the deep learning system was more sensitive than experienced radiologists in diagnosing SjS in USG images of two case groups and a group of healthy subjects in inflammation of SMGs.

• Sjögren’s syndrome
• deep learning
• obstructive sialoadenitis
• submandibular gland
• ultrasonography

• medical imaging
• translational research
• breast cancer
• computer science

• Artificial Intelligence
• Breast neoplasms
• Deep learning
• Magnetic resonance imaging
• Mammography
• Ultrasound

• Artificial Intelligence
• Breast/diagnostic imaging
• Breast Neoplasms/diagnostic imaging
• Female
• Humans
• Image Processing, Computer-Assisted
• Mammography

• P30 CA008748 NCI NIH HHS

• Breast cancer
• Computer prediction model
• Convolutional neural network
• Diagnosis
• Ultrasound

Artificial intelligence (AI) has invaded our daily lives, and in the last decade, there have been very promising applications of AI in the field of medicine, including medical imaging, in vitro diagnosis, intelligent rehabilitation, and prognosis. Breast cancer is one of the common malignant tumors in women and seriously threatens women’s physical and mental health. Early screening for breast cancer via mammography, ultrasound and magnetic resonance imaging (MRI) can significantly improve the prognosis of patients. AI has shown excellent performance in image recognition tasks and has been widely studied in breast cancer screening. This paper introduces the background of AI and its application in breast medical imaging (mammography, ultrasound and MRI), such as in the identification, segmentation and classification of lesions; breast density assessment; and breast cancer risk assessment. In addition, we also discuss the challenges and future perspectives of the application of AI in medical imaging of the breast.

• artificial intelligence
• breast
• deep learning
• imaging
• machine learning

• Retina
• artificial intelligence
• convolutional neural network
• deep learning
• machine learning
• optical coherence tomography angiography
• retinopathy

We aimed to assess the performance of radiomics and machine learning (ML) for classification of non-cystic benign and malignant breast lesions on ultrasound images, compare ML’s accuracy with that of a breast radiologist, and verify if the radiologist’s performance is improved by using ML.

Our retrospective study included patients from two institutions. A total of 135 lesions from Institution 1 were used to train and test the ML model with cross-validation. Radiomic features were extracted from manually annotated images and underwent a multistep feature selection process. Not reproducible, low variance, and highly intercorrelated features were removed from the dataset. Then, 66 lesions from Institution 2 were used as an external test set for ML and to assess the performance of a radiologist without and with the aid of ML, using McNemar’s test.

After feature selection, 10 of the 520 features extracted were employed to train a random forest algorithm. Its accuracy in the training set was 82% (standard deviation, SD, ± 6%), with an AUC of 0.90 (SD ± 0.06), while the performance on the test set was 82% (95% confidence intervals (CI) = 70–90%) with an AUC of 0.82 (95% CI = 0.70–0.93). It resulted in being significantly better than the baseline reference (p = 0.0098), but not different from the radiologist (79.4%, p = 0.815). The radiologist’s performance improved when using ML (80.2%), but not significantly (p = 0.508).

A radiomic analysis combined with ML showed promising results to differentiate benign from malignant breast lesions on ultrasound images.

• Machine learning showed good accuracy in discriminating benign from malignant breast lesions

• The machine learning classifier’s performance was comparable to that of a breast radiologist

• The radiologist’s accuracy improved with machine learning, but not significantly

The online version contains supplementary material available at 10.1007/s00330-021-08009-2.

• Breast cancer
• Machine learning
• Ultrasound

• whole body imaging
• translational research

• MC_PC_18032 Medical Research Council

Extracorporeal shock wave lithotripsy (ESWL) of kidney stones is losing ground to more expensive and invasive endoscopic treatments.

This proof-of-concept project was initiated to develop artificial intelligence (AI)-augmented ESWL and to investigate the potential for machine learning to improve the efficacy of ESWL.

Two-dimensional ultrasound videos were captured during ESWL treatments from an inline ultrasound device with a video grabber. An observer annotated 23 212 images from 11 patients as either in or out of focus. The median hit rate was calculated on a patient level via bootstrapping. A convolutional neural network with U-Net architecture was trained on 57 ultrasound images with delineated kidney stones from the same patients annotated by a second observer. We tested U-Net on the ultrasound images annotated by the first observer. Cross-validation with a training set of nine patients, a validation set of one patient, and a test set of one patient was performed.

Classical metrics describing classifier performance were calculated, together with an estimation of how the algorithm would affect shock wave hit rate.

The median hit rate for standard ESWL was 55.2% (95% confidence interval [CI] 43.2–67.3%). The performance metrics for U-Net were accuracy 63.9%, sensitivity 56.0%, specificity 74.7%, positive predictive value 75.3%, negative predictive value 55.2%, Youden’s J statistic 30.7%, no-information rate 58.0%, and Cohen’s κ 0.2931. The algorithm reduced total mishits by 67.1%. The main limitation is that this is a proof-of-concept study involving only 11 patients.

Our calculated ESWL hit rate of 55.2% (95% CI 43.2–67.3%) supports findings from earlier research. We have demonstrated that a machine learning algorithm trained on just 11 patients increases the hit rate to 75.3% and reduces mishits by 67.1%. When U-Net is trained on more and higher-quality annotations, even better results can be expected.

Kidney stones can be treated by applying shockwaves to the outside of the body. Ultrasound scans of the kidney are used to guide the machine delivering the shockwaves, but the shockwaves can still miss the stone. We used artificial intelligence to improve the accuracy in hitting the stone being treated.

• Artificial intelligence
• Extracorporeal shockwave lithotripsy
• Kidney stones
• Machine learning
• Neural network

• Artificial intelligence
• Deep learning
• Machine learning
• Medical imaging

• Algorithms
• Artificial Intelligence
• Machine Learning
• Radiology
• Technology

• K08 EB026500 NIBIB NIH HHS
• R01 CA237269 NCI NIH HHS

This study investigated the effects of 1 year of training on imaging diagnosis, using static ultrasonography (US) salivary gland images of Sjögren syndrome patients.

This study involved 3 inexperienced radiologists with different levels of experience, who received training 1 or 2 days a week under the supervision of experienced radiologists. The training program included collecting patient histories and performing physical and imaging examinations for various maxillofacial diseases. The 3 radiologists (observers A, B, and C) evaluated 400 static US images of salivary glands twice at a 1-year interval. To compare their performance, 2 experienced radiologists evaluated the same images. Diagnostic performance was compared between the 2 evaluations using the area under the receiver operating characteristic curve (AUC).

Observer A, who was participating in the training program for the second year, exhibited no significant difference in AUC between the first and second evaluations, with results consistently comparable to those of experienced radiologists. After 1 year of training, observer B showed significantly higher AUCs than before training. The diagnostic performance of observer B reached the level of experienced radiologists for parotid gland assessment, but differed for submandibular gland assessment. For observer C, who did not complete the training, there was no significant difference in the AUC between the first and second evaluations, both of which showed significant differences from those of the experienced radiologists.

These preliminary results suggest that the training program effectively helped inexperienced radiologists reach the level of experienced radiologists for US examinations.

• Education
• Sjogren's Syndrome
• Ultrasonography

• Artificial intelligence
• Breast magnetic resonance imaging
• Breast ultrasonography
• Deep learning
• Digital mammography

• Artificial Intelligence
• Breast
• Deep Learning
• Humans
• Prospective Studies
• Radiology

Traditional computer-aided diagnosis (CAD) processes include feature extraction, selection, and classification. Effective feature extraction in CAD is important in improving the classification’s performance. We introduce a machine-learning method and have designed an analysis procedure of benign and malignant breast tumour classification in ultrasound (US) images without a need for a priori tumour region-selection processing, thereby decreasing clinical diagnosis efforts while maintaining high classification performance. Our dataset constituted 677 US images (benign: 312, malignant: 365). Regarding two-dimensional US images, the oriented gradient descriptors’ histogram pyramid was extracted and utilised to obtain feature vectors. The correlation-based feature selection method was used to evaluate and select significant feature sets for further classification. Sequential minimal optimisation—combining local weight learning—was utilised for classification and performance enhancement. The image dataset’s classification performance showed an 81.64% sensitivity and 87.76% specificity for malignant images (area under the curve = 0.847). The positive and negative predictive values were 84.1 and 85.8%, respectively. Here, a new workflow, utilising machine learning to recognise malignant US images was proposed. Comparison of physician diagnoses and the automatic classifications made using machine learning yielded similar outcomes. This indicates the potential applicability of machine learning in clinical diagnoses.

Although breast ultrasonography is the mainstay modality for differentiating between benign and malignant breast masses, it has intrinsic problems with false positives and substantial interobserver variability. Artificial intelligence (AI), particularly with deep learning models, is expected to improve workflow efficiency and serve as a second opinion. AI is highly useful for performing three main clinical tasks in breast ultrasonography: detection (localization/segmentation), differential diagnosis (classification), and prognostication (prediction). This article provides a current overview of AI applications in breast ultrasonography, with a discussion of methodological considerations in the development of AI models and an up-to-date literature review of potential clinical applications.

• Artificial intelligence
• Breast diseases
• Breast neoplasm
• Convolutional neural network
• Ultrasonography