Diagnostic accuracy of S-Detect to breast cancer on ultrasonography: A meta-analysis (PRISMA)

Diagnostic performance of deep learning in ultrasound diagnosis of breast cancer: a systematic review

Deep learning (DL) has been widely investigated in breast ultrasound (US) for distinguishing between benign and malignant breast masses. This systematic review of test diagnosis aims to examine the accuracy of DL, compared to human readers, for the diagnosis of breast cancer in the US under clinical settings. Our literature search included records from databases including PubMed, Embase, Scopus, and Cochrane Library. Test accuracy outcomes were synthesized to compare the diagnostic performance of DL and human readers as well as to evaluate the assistive role of DL to human readers. A total of 16 studies involving 9238 female participants were included. There were no prospective studies comparing the test accuracy of DL versus human readers in clinical workflows. Diagnostic test results varied across the included studies. In 14 studies employing standalone DL systems, DL showed significantly lower sensitivities in 5 studies with comparable specificities and outperformed human readers at higher specificities in another 4 studies; in the remaining studies, DL models and human readers showed equivalent test outcomes. In 12 studies that assessed assistive DL systems, no studies proved the assistive role of DL in the overall diagnostic performance of human readers. Current evidence is insufficient to conclude that DL outperforms human readers or enhances the accuracy of diagnostic breast US in a clinical setting. Standardization of study methodologies is required to improve the reproducibility and generalizability of DL research, which will aid in clinical translation and application.

Artificial intelligence in breast ultrasound: application in clinical practice

Ultrasound (US) is a widely accessible and extensively used tool for breast imaging. It is commonly used as an additional screening tool, especially for women with dense breast tissue. Advances in artificial intelligence (AI) have led to the development of various AI systems that assist radiologists in identifying and diagnosing breast lesions using US. This article provides an overview of the background and supporting evidence for the use of AI in hand held breast US. It discusses the impact of AI on clinical workflow, covering breast cancer detection, diagnosis, prediction of molecular subtypes, evaluation of axillary lymph node status, and response to neoadjuvant chemotherapy. Additionally, the article highlights the potential significance of AI in breast US for low and middle income countries.

Artificial intelligence in interventional pulmonology

PURPOSE OF REVIEW

In recent years, there has been remarkable progress in the field of artificial intelligence technology. Artificial intelligence applications have been extensively researched and actively implemented across various domains within healthcare. This study reviews the current state of artificial intelligence research in interventional pulmonology and engages in a discussion to comprehend its capabilities and implications.

RECENT FINDINGS

Deep learning, a subset of artificial intelligence, has found extensive applications in recent years, enabling highly accurate identification and labeling of bronchial segments solely from intraluminal bronchial images. Furthermore, research has explored the use of artificial intelligence for the analysis of endobronchial ultrasound images, achieving a high degree of accuracy in distinguishing between benign and malignant targets within ultrasound images. These advancements have become possible due to the increased computational power of modern systems and the utilization of vast datasets, facilitating detections and predictions with greater precision and speed.

SUMMARY

Artificial intelligence integration into interventional pulmonology has the potential to enhance diagnostic accuracy and patient safety, ultimately leading to improved patient outcomes. However, the clinical impacts of artificial intelligence enhanced procedures remain unassessed. Additional research is necessary to evaluate both the advantages and disadvantages of artificial intelligence in the field of interventional pulmonology.