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Design and Development of decision support system for detection of breast cancer from Mammograms

Design and Development of Deep Learning based system for detection of breast cancer from Digital Mammograms

Status
Active, not recruiting
Phases
Unknown
Study type
Observational
Source
CTRI
Registry ID
CTRI/2022/11/047369
Enrollment
5500
Registered
2022-11-16
Start date
Unknown
Completion date
Unknown
Last updated
2022-11-21

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

Conditions

Health Condition 1: D249- Benign neoplasm of unspecified breast Health Condition 2: C509- Malignant neoplasm of breast of unspecified site Health Condition 3: C508- Malignant neoplasm of overlappingsites of breast

Interventions

Intervention1: Nil: Nil

Sponsors

Ashwini Amin
Lead Sponsor
Medical Superintendent
Collaborator

Eligibility

Inclusion criteria

Inclusion criteria: 1. Patients who undergo bilateral mammography 2. Consenting individuals 3. Patients undergoing FNAC, biopsy or surgical excision for confirmation

Exclusion criteria

Exclusion criteria: 1. Patients below 18 years and above 99 years 2. Male Patients 3. Patients who have undergone only unilateral mammography 4. Patients who do not have cytological or histopathological confirmation of diagnosis

Design outcomes

Primary

MeasureTime frame
Positive identification of malignant and benign lesions in the mammogram using deep learning modelTimepoint: 30 days—

Secondary

MeasureTime frame
Identification of ideal parameters to incorporate into the CAD designTimepoint: 30 days—

Countries

India

Contacts

Public ContactDr Stanley Mathew

Manipal Institute of Technology

dinesh.acharya@manipal.edu9449367822

Outcome results

None listed

Source: CTRI (via WHO ICTRP) · Data processed: Feb 4, 2026