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Clinical Trial Summary

The accuracy of breast examinations and ultrasonography performed clinically to detect breast mass varies greatly depending on the physician's skill level, and the accuracy of breast examinations by non-experts is particularly low. In this study, we aimed to validate whether the concurrent use of ultrasound sensor technology is an efficient strategy for the purpose of improving the sensitivity of detecting breast masses through breast examination.


Clinical Trial Description

[Background] This research team would like to conduct this study based on the idea that the sensitivity of breast palpation can be improved by moving away from traditional breast palpation, which is simply performed by hand, and using auxiliary examination equipment based on ultrasonic sensor technology. In particular, our research team focused on the waveform of the ultrasound itself rather than the visual images obtained through the ultrasound device. In the existing breast ultrasound, the medical staff reads images created through ultrasound from multiple sensors to confirm the possibility of breast cancer, and this is read based on the medical staff's very subjective opinions. However, ultrasonic waveforms acquired through ultrasound can store information about the waveform as data and thus be implemented as objective values. [Study design] Prospective, multi-institutional [Study protocol] ① Preoperative ultrasound sensor-based diagnostic equipment was applied to 200 patients with breast mass among patients admitted to the breast surgery department, and prospectively obtained ultrasound echo signal data generated by the mass. ② For this purpose, the researcher uses equipment containing a single ultrasound sensor to manually scan the mass lesion area and no evidence disease area. ③ Diagnostic performance (judgment for presence or absence of a tumor) of diagnostic tool based on ultrasound sensor technology through an artificial intelligence algorithm designed based on ultrasound wavelength and frequency optimized for mass detection. [Objectives] 1. Primary endpoint Sensitivity/specificity/predictive value/accuracy/positive & negative predictive of diagnostic performance 2. Secondary endpoint Artificial intelligence algorithm efficacy ;


Study Design


Related Conditions & MeSH terms


NCT number NCT06255808
Study type Observational
Source Seoul National University Hospital
Contact Hyeong-Gon Moon, MD
Phone 02-2072-2634
Email moonhgsurgi@gmail.com
Status Recruiting
Phase
Start date October 5, 2022
Completion date January 5, 2025

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