Computer-aided diagnosis system for breast ultrasound images using deep learning

Computer-aided diagnosis system for breast ultrasound images using deep learning - Computer-aided diagnosis system for breast ultrasound images using deep learning

Status

Active, not recruiting

Phases

Unknown

Study type

Observational

Source

JPRN

Registry ID

JPRN-UMIN000031548

Enrollment

1543

Registered

2018-03-03

Start date

2018-02-05

Completion date

Unknown

Last updated

2026-06-29

For informational purposes only — not medical advice. Sourced from public registries and may not reflect the latest updates. Terms

Conditions

Breast cancer

Interventions

None listed

Eligibility

Sex/Gender

All

Inclusion criteria

Inclusion criteria: Patients who were within the image database managed by Japan Association of Breast and Thyroid Sonology, and received a breast ultrasound examination from November 2011 to December 2015. And satisfied the following rules. 1.Mass lesions had already been assessed. 2.Associating with definitive diagnosis or category diagnosed by B-mode

Exclusion criteria

Exclusion criteria: 1.Typical cysts 2. Mass lesions >= 4.5cm diameter

Design outcomes

Primary

Measure	Time frame
Accuracy, sensitivity, specificity and area under the curve of the algorithm which classifying lesion as benign or malignant.	—

Secondary

Measure	Time frame
Accuracy and sensitivity of the algorithm which classifying lesion as 3 categories by.	—

Countries

Japan

Contacts

Public ContactTakuhiro Yamaguchi

Tohoku University Graduate School of Medicine Biostatistics

yamaguchi@med.tohoku.ac.jp022-717-7659

Outcome results