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Multicentric Study for External Validation of a Deep Learning Model for Mammographic Breast Density Categorization

NCT05021055 · Status: UNKNOWN · Type: OBSERVATIONAL · Enrollment: 277

Last updated 2021-08-25

No results posted yet for this study

Summary

The correct categorization of breast density is essential to adapt the diagnostic examination to the needs of each patient. Assessment of breast density is performed visually by radiologists. Some authors have detected that this method involves considerable intra and interobserver variability. On the other hand, automated systems for measuring breast density are becoming more and more frequent. Machine learning is a domain of Artificial Intelligence, which comprises the process of developing systems with the ability to learn and make predictions using data. These systems are designed to aid healthcare professional decision making. In the present work, the multicenter study of external validation of a tool based on deep learning for the categorization of mammographic breast density is proposed.

Conditions

Sponsors & Collaborators

  • Hospital Italiano de Buenos Aires

    lead OTHER

Principal Investigators

  • Daniel R Luna, MD · Hospital Italiano de Buenos Aires

Eligibility

Sex
ALL
Healthy Volunteers
Yes

Timeline & Regulatory

Start
2021-09-30
Primary Completion
2022-04-30
Completion
2022-07-31

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Diseases

Read the full study record

This page highlights key information. For complete eligibility criteria, study locations, investigator contacts, and the full protocol, visit the original record on ClinicalTrials.gov.

View NCT05021055 on ClinicalTrials.gov