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Machine Learning using CT Images in Diagnosis and Staging of Laryngeal Cancer

Machine Learning for the Characterization of Laryngeal Cancer in Computed Tomography and Exploring its Predictive Capabilities in Identifying Clinically Relevant Disease Characteristics - NIL

Status
Active, not recruiting
Phases
Unknown
Study type
Observational
Source
CTRI
Registry ID
CTRI/2024/06/069023
Enrollment
100
Registered
2024-06-18
Start date
Unknown
Completion date
Unknown
Last updated
2024-06-24

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

Conditions

Health Condition 1: C329- Malignant neoplasm of larynx, unspecified

Interventions

Control Intervention1: NIL: NIL

Sponsors

Nivea Roy
Lead Sponsor

Eligibility

Inclusion criteria

Inclusion criteria: Patients undergoing Head and Neck CT Imaging.

Exclusion criteria

Exclusion criteria: Patients with other malignancies of head and neck except laryngeal cancer

Design outcomes

Primary

MeasureTime frame
A machine learning model for the diagnosis and staging of laryngeal cancer, with Accuracy greater than or equal to 95 percentage.Timepoint: 3 years from the start of study.—

Secondary

MeasureTime frame
Machine learning model to segment the laryngeal subsites accurately with an accuracy greater than or equal to 90 percentage.Timepoint: Two years from the start of the study.—

Countries

India

Contacts

Public ContactNIVEA ROY

Kasturba Medical College Manipal

devaraja.k@manipal.edu9999662597

Outcome results

None listed

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