Artificial intelligence system reduces false-positive findings in the interpretation of breast ultrasound exams

Abstract

Though consistently shown to detect mammographically occult cancers, breast ultrasound has been noted to have high false-positive rates. In this work, we present an AI system that achieves radiologist-level accuracy in identifying breast cancer in ultrasound images. Developed on 288,767 exams, consisting of 5,442,907 B-mode and Color Doppler images, the AI achieves an area under the receiver operating characteristic curve (AUROC) of 0.976 on a test set consisting of 44,755 exams. In a retrospective reader study, the AI achieves a higher AUROC than the average of ten board-certified breast radiologists (AUROC: 0.962 AI, 0.924 ± 0.02 radiologists). With the help of the AI, radiologists decrease their false positive rates by 37.3% and reduce requested biopsies by 27.8%, while maintaining the same level of sensitivity. This highlights the potential of AI in improving the accuracy, consistency, and efficiency of breast ultrasound diagnosis.

Ultrasound for the Diagnosis of Biliary Atresia: From Conventional Ultrasound to Artificial Intelligence

“…AI, powered by advances in computation and immense amounts of datasets, has been shown superior or comparable to human experts in many US data analysis tasks, such the diagnosis of thyroid nodules [ 61 , 62 ], breast cancer [ 63 , 64 ], congenital heart disease [ 65 ] and fetal brain abnormalities [ 66 ]. Driven by the recent advances in deep learning technologies, AI may have the potential to revolutionize BA diagnosis from US images particularly in rural area without relevant expertise.…”

Deep Learning Using CT Images to Grade Clear Cell Renal Cell Carcinoma: Development and Validation of a Prediction Model

“…Thus, given sufficient data, the accuracy of a deep-learning-enabled diagnosis system often matches or even surpasses the level of expert physicians [ 9 , 10 ]. A myriad of studies have validated the utility of DL in various clinical settings through various experiments, including the reduction of false-positive findings in the interpretation of breast ultrasound exams [ 11 ], the detection of intensive care unit patient mobilization activities [ 12 ], and the improvement of medical technology [ 13 ]. In the same way, DL enables the ability to non-invasively and automatically assess the pathological grade for ccRCC, monitor patients’ conditions and construct personalized subsequent treatment strategies.…”

Quantifying Trustworthiness of Explainability in Medical AI

“…Explainability is a pillar in supporting applications of artificial intelligence and machine learning (AI/ML) in medicine [1][2][3][4] . Understanding how and why AI models make particular decisions is critical for building trust in AI-driven applications 1,2,[5][6][7][8][9] .…”

“…Explainability is a pillar in supporting applications of artificial intelligence and machine learning (AI/ML) in medicine [1][2][3][4] . Understanding how and why AI models make particular decisions is critical for building trust in AI-driven applications 1,2,[5][6][7][8][9] . The United States Food and Drug Administration (FDA), the federal agency responsible for clearing medical AI devices for clinical use in the United States, stresses trustworthiness as a key factor for adopting and evolving medical AI/ML devices, a viewpoint internationally endorsed 10 .…”