Multi-center Study of Deep Learning AI in Breast Mass

Study Results

Results pending

The study team has not published outcome measurements, participant flow, or safety data for this trial yet. Check back later for updates.

Basic Information

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Recruitment Status

UNKNOWN

Total Enrollment

1122 participants

Study Classification

OBSERVATIONAL

Study Start Date

2021-08-12

Study Completion Date

2023-08-31

Brief Summary

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This multi-center study intends to evaluate the value of the detection and differential diagnosis of breast mass using deep learning AI-based real-time ultrasound examination.

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Detailed Description

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As the most common cancer expected to occur all over the world, extensive population screening plays a very important role in the early diagnosis and prognosis of the breast cancer. X-ray and ultrasound are the most commonly used screening methods, and ultrasound is especially important for Asian women with dense breasts. However, ultrasound is greatly affected by the operator's skill and experience, and the diagnostic accuracy varies greatly.

Artificial intelligence (AI) is a new method emerging in recent years, active in many medical fields and can effectively improve the diagnostic efficiency. However, previous researches on the application of AI in ultrasound are focused on single or multi-modality static ultrasound images. This multi-center study intends to evaluate the value of the detection and differential diagnosis of breast mass using deep learning AI-based real-time ultrasound examination.

Conditions

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Breast Neoplasms

Study Design

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Observational Model Type

OTHER

Study Time Perspective

PROSPECTIVE

Interventions

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Yizhun BUSMS

During the breast scanning, Yizhun BUSMS uses different color box to identify the breast lesion, and the box color indicates the risk grade of the lesion.

Intervention Type DEVICE

Eligibility Criteria

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Inclusion Criteria

1. Females who undergo ultrasound examination for a complaint of breast lesion;
2. The breast lesion that will obtain definite pathological diagnosis or follow-up at least two years.