Articles That Use Artificial Intelligence for Ultrasound: A Reader's Guide

Handheld Ultrasound Bladder Volume Assessment Compared to Standard Technique

Urinary retention is a common complaint encountered in the emergency department (ED). Current tools for the assessment of urinary retention are either bladder volume estimation with a bladder scanner performed by nursing staff or direct visualization and measurement via bedside ultrasound performed by an emergency physician. Newer handheld ultrasound devices such as the Butterfly iQ have been brought to the market to bring ultrasound more conveniently to the bedside. A recently released handheld auto-calculation tool produces a 3D image of the bladder and instant bladder volume measurement in milliliters. However, there is a paucity of data assessing the validity of the new Butterfly iQ at the bedside. This study sought to compare the diagnostic accuracy and rated user convenience of the nursing bladder scanner, the cart-based ultrasound machine, and the Butterfly iQ auto-bladder volume tool. ED patients were prospectively enrolled and underwent bladder measurements in a randomized, pre-determined order with each modality. Measurements were subsequently compared to the gold standard of catheterization. Cart-based ultrasound had the highest agreement to catheterization when compared to the RN scanner and the Butterfly iQ. However, the Butterfly iQ and RN scanner were both considered more convenient measurement modalities than the cart-based ultrasound. The Butterfly iQ serves as a cost-effective alternative to cart-based ultrasound while providing greater general utility compared to bladder scanners.

Prospective study of AI-assisted prediction of breast malignancies in physical health examinations: role of off-the-shelf AI software and comparison to radiologist performance

Objective

In physical health examinations, breast sonography is a commonly used imaging method, but it can lead to repeated exams and unnecessary biopsy due to discrepancies among radiologists and health centers. This study explores the role of off-the-shelf artificial intelligence (AI) software in assisting radiologists to classify incidentally found breast masses in two health centers.

Methods

Female patients undergoing breast ultrasound examinations with incidentally discovered breast masses were categorized according to the 5th edition of the Breast Imaging Reporting and Data System (BI-RADS), with categories 3 to 5 included in this study. The examinations were conducted at two municipal health centers from May 2021 to May 2023.The final pathological results from surgical resection or biopsy served as the gold standard for comparison. Ultrasonographic images were obtained in longitudinal and transverse sections, and two junior radiologists and one senior radiologist independently assessed the images without knowing the pathological findings. The BI-RADS classification was adjusted following AI assistance, and diagnostic performance was compared using receiver operating characteristic curves.

Results

A total of 196 patients with 202 breast masses were included in the study, with pathological results confirming 107 benign and 95 malignant masses. The receiver operating characteristic curve showed that experienced breast radiologists had higher diagnostic performance in BI-RADS classification than junior radiologists, similar to AI classification (AUC = 0.936, 0.806, 0.896, and 0.950, p < 0.05). The AI software improved the accuracy, sensitivity, and negative predictive value of the adjusted BI-RADS classification for the junior radiologists' group (p< 0.05), while no difference was observed in the senior radiologist group. Furthermore, AI increased the negative predictive value for BI-RADS 4a masses and the positive predictive value for 4b masses among radiologists (p < 0.05). AI enhances the sensitivity of invasive breast cancer detection more effectively than ductal carcinoma in situ and rare subtypes of breast cancer.

Conclusions

The AI software enhances diagnostic efficiency for breast masses, reducing the performance gap between junior and senior radiologists, particularly for BI-RADS 4a and 4b masses. This improvement reduces unnecessary repeat examinations and biopsies, optimizing medical resource utilization and enhancing overall diagnostic effectiveness.

LN-Net: Perfusion Pattern-Guided Deep Learning for Lymph Node Metastasis Diagnosis Based on Contrast-Enhanced Ultrasound Videos

OBJECTIVE

The blood flow in lymph nodes reflects important pathological features. However, most intelligent diagnosis based on contrast-enhanced ultrasound (CEUS) video focuses only on CEUS images, ignoring the process of extracting blood flow information. In the work described here, a parametric imaging method for describing blood perfusion pattern was proposed and a multimodal network (LN-Net) to predict lymph node metastasis was designed.

METHODS

First, the commercially available artificial intelligence object detection model YOLOv5 was improved to detect the lymph node region. Then the correlation and inflection point matching algorithms were combined to calculate the parameters of the perfusion pattern. Finally, the Inception-V3 architecture was used to extract the image features of each modality, with the blood perfusion pattern taken as the guiding factor in fusing the features with CEUS by sub-network weighting.

DISCUSSION

The average precision of the improved YOLOv5s algorithm compared with baseline was improved by 5.8%. LN-Net predicted lymph node metastasis with 84.9% accuracy, 83.7% precision and 80.3% recall. Compared with the model without blood flow feature guidance, accuracy was improved by 2.6%. The intelligent diagnosis method has good clinical interpretability.

CONCLUSION

A static parametric imaging map could describe a dynamic blood flow perfusion pattern, and as a guiding factor, it could improve the classification ability of the model with respect to lymph node metastasis.

Deep learning based on ultrasound images assists breast lesion diagnosis in China: a multicenter diagnostic study

BACKGROUND

Studies on deep learning (DL)-based models in breast ultrasound (US) remain at the early stage due to a lack of large datasets for training and independent test sets for verification. We aimed to develop a DL model for differentiating benign from malignant breast lesions on US using a large multicenter dataset and explore the model's ability to assist the radiologists.

METHODS

A total of 14,043 US images from 5012 women were prospectively collected from 32 hospitals. To develop the DL model, the patients from 30 hospitals were randomly divided into a training cohort (n = 4149) and an internal test cohort (n = 466). The remaining 2 hospitals (n = 397) were used as the external test cohorts (ETC). We compared the model with the prospective Breast Imaging Reporting and Data System assessment and five radiologists. We also explored the model's ability to assist the radiologists using two different methods.

RESULTS

The model demonstrated excellent diagnostic performance with the ETC, with a high area under the receiver operating characteristic curve (AUC, 0.913), sensitivity (88.84%), specificity (83.77%), and accuracy (86.40%). In the comparison set, the AUC was similar to that of the expert (p = 0.5629) and one experienced radiologist (p = 0.2112) and significantly higher than that of three inexperienced radiologists (p < 0.01). After model assistance, the accuracies and specificities of the radiologists were substantially improved without loss in sensitivities.

CONCLUSIONS

The DL model yielded satisfactory predictions in distinguishing benign from malignant breast lesions. The model showed the potential value in improving the diagnosis of breast lesions by radiologists.

Interdisciplinary Collaboration Opportunities, Challenges and Solutions for Artificial Intelligence in Ultrasound

Ultrasound is one of the most widely utilized imaging tools in clinical practice with the advantages of noninvasive nature and ease of use. However, ultrasound examinations have low reproducibility and considerable heterogeneity due to the variability of operators, scanners, and patients. In recent years, Artificial Intelligence (AI) -assisted ultrasound has matured and moved closer to routine clinical uses. The combination of AI with ultrasound has opened up a world of possibilities for increasing work productivity and precision diagnostics. In this article, we describe AI strategies in ultrasound, from current opportunities, constraints to potential options for AI-assisted ultrasound.