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Machine Learning model to predict high risk individuals for ACL reinjury post reconstruction using Clinical Biomechanical and Demographic factors

A Machine Learning Based Predictive model to identify individuals at high risk of Anterior Cruciate Ligament Reinjury after reconstruction based on Clinical, Biomechanical and Demographic factors: A Retrospective Study - NIL

Status
Active, not recruiting
Phases
Unknown
Study type
Observational
Source
CTRI
Registry ID
CTRI/2025/03/083532
Enrollment
120
Registered
2025-03-26
Start date
Unknown
Completion date
Unknown
Last updated
2026-04-27

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

Conditions

Health Condition 1: M708- Other soft tissue disorders related to use, overuse and pressure

Interventions

Intervention1: NIL: NIL Control Intervention1: NIL: NIL

Sponsors

NIL
Lead Sponsor

Eligibility

Inclusion criteria

Inclusion criteria: Control group Patients who have undergone Primary ACL reconstruction and concomitant injury to medial/lateral meniscus and medial and lateral collateral ligament Study Group Patients who have undergone revision ACL reconstruction and concomitant injury to medial/lateral meniscus and medial/lateral collateral ligament

Exclusion criteria

Exclusion criteria: Patients with missing essential data Patients with avulsion fracture Road traffic Accident related ACL reconstruction surgery

Design outcomes

Primary

MeasureTime frame
1) Beightons score 2) MMT (Manual Muscle Testing) of quadriceps and hamstringsTimepoint: Baseline, following the injury and prior to the surgery—

Secondary

MeasureTime frame
NILTimepoint: NIL—

Countries

India

Contacts

Public ContactDr Ashish John Prabhakar

Kasturba Medical College, Mangalore, MAHE

sneha.mchpmlr2024@learner.manipal.edu7708106751

Outcome results

None listed

Source: CTRI (via WHO ICTRP) · Data processed: May 1, 2026