Diagnostic Performance of an Artificial Intelligence System in Breast Ultrasound

Abstract

Objectives We study the performance of an artificial intelligence (AI) program designed to assist radiologists in the diagnosis of breast cancer, relative to measures obtained from conventional readings by radiologists. Methods A total of 10 radiologists read a curated, anonymized group of 299 breast ultrasound images that contained at least one suspicious lesion and for which a final diagnosis was independently determined. Separately, the AI program was initialized by a lead radiologist and the computed results compared against those of the radiologists. Results The AI program's diagnoses of breast lesions had concordance with the 10 radiologists' readings across a number of BI‐RADS descriptors. The sensitivity, specificity, and accuracy of the AI program's diagnosis of benign versus malignant was above 0.8, in agreement with the highest performing radiologists and commensurate with recent studies. Conclusion The trained AI program can contribute to accuracy of breast cancer diagnoses with ultrasound.

The Use of Artificial Intelligence in Automation in the Fields of Gynaecology and Obstetrics – an Assessment of the State of Play

“…OʼConnell et al have published similarly promising data on AI-assisted evaluation of breast sonography findings. They demonstrated on the basis of 300 patients that, using a commercial diagnostic tool (S-Detect), automated detection of breast lesions using a set of BI-RADS descriptors was in agreement with the results obtained by ten radiologists with appropriate expertise (sensitivity, specificity > 0.8) 17 .…”

“…OʼConnell et al publizierten ähnlich erfolgversprechende Daten zur KI-unterstützten Auswertung von Mammasonografie-Befunden. Sie konnten anhand von 300 Patientinnen zeigen, dass mithilfe eines kommerziellen Diagnosetools (S-Detect) die automatisierte Detektion von Brustläsionen unter Anwendung einer Reihe von BI-RADS-Deskriptoren mit den Resultaten von 10 Radiologen mit entsprechender Expertise übereinstimmte (Sensitivität, Spezifität > 0,8) 17 .…”

Design and Analysis Methods for Trials with AI-Based Diagnostic Devices for Breast Cancer

“…Unlike concordance rate, however, it is not clear how similar two kappa values should be to conclude similarity of two different groups of raters. The proposed methods were successfully used by O'Connell et al [8] to design and analyze a device trial. Appendix A.2.…”

“…The earlier approaches to breast ultrasound technology concentrated on the extraction of features of lesions such as size, shape, texture, and boundaries within a clustering or classification or rule-based decision making algorithms [3][4][5][6]. More recent developments in AI, machine learning, and deep learning systems have utilized layers of convolution neural network models, a variety of approaches and extensive training sets to produce differentiated output classifications [7,8].…”

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“…The network was able to classify malignant lesions in a short time with an accuracy of 90% and therefore was proposed to work together with radiologists to improve breast cancer diagnosis. Several groups have investigated these AI models in multi-reader studies [37][38][39][40]. Becker et al [37] retrospectively evaluated the performance of a generic deep learning software for the classification of 637 breast lesions on US exams and compared it to radiologists with varying levels of expertise.…”