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Using AI Based Model to Detect Cancer Related Changes from Medical Images and Identify Fake or Altered Medical Images

Digital Biopsy based Non Invasive Clinical Decision Support for Precision Medicine and Deepfake Resilience for Trustworthy AI in Radiology - NIL

Status
Active, not recruiting
Phases
Unknown
Study type
Observational
Source
CTRI
Registry ID
CTRI/2026/07/114024
Enrollment
1000
Registered
2026-07-15
Start date
Unknown
Completion date
Unknown
Last updated
2026-08-03

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

Conditions

Health Condition 1: C30-C39- Malignant neoplasms of respiratory and intrathoracic organs Health Condition 2: C50-C50- Malignant neoplasms of breast Health Condition 3: C69-C72- Malignant neoplasms of eye, brain and other parts of central nervous system Health Condition 4: C43-C44- Melanoma and other malignant neoplasms of skin Health Condition 5: C81-C96- Malignant neoplasms of lymphoid, hematopoietic and related tissue Health Condition 6: C45-C49- Malignant neoplasms of mesothelial and soft tis

Interventions

Intervention1: NIL: NIL Intervention2: Nil: Nil Control Intervention1: NIL: NIL

Sponsors

Indian Institute of Science
Lead Sponsor
PRAZIM Trading and Investment Company Private Limited
Collaborator

Eligibility

Inclusion criteria

Inclusion criteria: Patient with histopathologically confirmed Stage 3 or Stage 4 cancers or Grade 4 tumors. Availability of baseline medical imaging like MRI,CT,PET or any other relevant imaging modalities performed as a part of routine diagnostic care. Availability of complete and verifiable medical electronic records, including molecular or genomic status confirmed by standard tissue biopsy.

Exclusion criteria

Exclusion criteria: Incomplete or unavailable electronic medical records, including molecular or genomic status. Poor quality medical images that are unsuitable for radiomics analysis. Presence of conditions that interfere with accurate tumor assessment on imaging like extensive infection or inflammation or concurrent lesions. Previous surgeriesin the region of interest prior to baseline scans that affect radiomics feature extraction. Presence of metallic implants, pacemakers or other foreign bodies that can cause severe imaging artifacts. Unconfirmed or unverified histopathological diagnosis.

Design outcomes

Primary

MeasureTime frame
To develop and validate Artifical Intelligence based radiomics model for non invasive prediction of cancer related genetic changes from medical imagesTimepoint: 1 year—

Secondary

MeasureTime frame
To develop and validate AI based model for deepfake detection of medical imagesTimepoint: 1 year—

Countries

India

Contacts

Public ContactPhaneendra Kumar Yalavarthy

Indian Institute of Science

yalavarthy@iisc.ac.in9448087978

Outcome results

None listed

Source: CTRI (via WHO ICTRP) · Data processed: Aug 10, 2026