Prediction of anterior cruciate ligament injury in basketball players using machine learning

Prediction of anterior cruciate ligament (ACL) injuries in basketball players using machine learning algorithms: a prospective study

Status

Active, not recruiting

Phases

Unknown

Study type

Observational

Source

ISRCTN

Registry ID

ISRCTN18009799

Enrollment

120

Registered

2024-08-22

Start date

2022-09-02

Completion date

Unknown

Last updated

2025-11-03

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

Conditions

Prevention of anterior cruciate ligament (ACL) injury in male basketball players Musculoskeletal Diseases

Interventions

In this study, 104 young adult basketball players volunteer to participate. The athletes' profiles, physical functions, basketball-specific skills, biomechanics, and electromyography (EMG) of seven mu

Eligibility

Sex/Gender

Male

Inclusion criteria

Inclusion criteria: 1. Male 2. Age over 18 years 3. Exercising =8 hours per week, 4. Having played basketball for at least 3 years 5. Having a negative Lachman's knee examination

Exclusion criteria

Exclusion criteria: 1. Exercise-related or neurological disorders 2. Recent hip or knee surgery or trauma 3. Incomplete data not being analyzed

Design outcomes

Primary

Measure	Time frame
Measured at baseline and 12 months: 1. The athlete's profile (height, weight, age, level of play, playing position), basketball training record, and self-reported injury history for each participant were recorded 2. Balance testing and joint mobility testing were conducted using YBT and FMS, with a duration of half an hour 3. Biomechanical and synchronized electromyography (EMG) experiments were performed, lasting for two hours 4. Trunk testing was conducted using DLH, strength testing was performed with 1-RM weighted squat and deadlift, explosive strength was assessed using countermovement jump (CMJ), squat jump (SJ), and drop jump (DJ), and agility testing was carried out with the Lane Agility Test lasting for two hours	—

Measure

Time frame

Measured at baseline and 12 months: 1. The athlete's profile (height, weight, age, level of play, playing position), basketball training record, and self-reported injury history for each participant were recorded 2. Balance testing and joint mobility testing were conducted using YBT and FMS, with a duration of half an hour 3. Biomechanical and synchronized electromyography (EMG) experiments were performed, lasting for two hours 4. Trunk testing was conducted using DLH, strength testing was performed with 1-RM weighted squat and deadlift, explosive strength was assessed using countermovement jump (CMJ), squat jump (SJ), and drop jump (DJ), and agility testing was carried out with the Lane Agility Test lasting for two hours

—

Secondary

Measure	Time frame
There are no secondary outcome measures	—

Countries

China

Contacts

Public ContactLongfei Guo

guolongfei0422@student.usm.my+86 15536887299

Outcome results

None listed

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