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Deep Learning and Radiomic Features in The Classification of T2 Weighted MRI Images in Patients with and without low Backpain

Deep Learning and Radiomic Features in The Classification of T2 Weighted MRI Images in Patients with and without low Backpain - nil

Status
Active, not recruiting
Phases
Unknown
Study type
Observational
Source
CTRI
Registry ID
CTRI/2023/08/056954
Enrollment
264
Registered
2023-08-25
Start date
Unknown
Completion date
Unknown
Last updated
2023-09-18

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

Conditions

Health Condition 1: M959- Acquired deformity of musculoskeletal system, unspecified

Interventions

None listed

Sponsors

NIL
Lead Sponsor

Eligibility

Inclusion criteria

Inclusion criteria: Patients referred for MRI Whole spine screening 18 years and above 1)For symptomatic cases: Low back pain over a period of 12 months 2)For assymptomatic cases: No current back pain

Exclusion criteria

Exclusion criteria: Tumour,Uncontrolledhypertension,Severe osteoporosis and Previous spine surgery

Design outcomes

Primary

MeasureTime frame
To investigate the accuracy of deep learning methods in classification of Lumbar Spine MRI images into symptomatic & asymptomatic cases.Timepoint: 15 months—

Secondary

MeasureTime frame
To identify the significant MRI radiomic features in patients with & without back pain. Timepoint: 15 months—

Countries

India

Contacts

Public ContactDr Saikiran P

Manipal College of Health Professions, MAHE, Manipal

saikiran.p@manipal.edu8320126806

Outcome results

None listed

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