Deep learning to detect musculoskeletal problems after breast cancer surgery
AI-Powered Deep Learning Models for Prospective Prediction of Musculoskeletal Complications After Breast Cancer Surgery: Focus on Lymphedema, Axillary Web Syndrome, Neuropathy, and Pain
Ankara Etlik City Hospital · NCT07236658
This project will test a deep-learning tool to detect and track shoulder, lymphatic, and bone problems in adult women having unilateral breast cancer surgery.
Quick facts
| Study type | Observational |
|---|---|
| Enrollment | 133 (estimated) |
| Ages | 18 Years and up |
| Sex | Female |
| Sponsor | Ankara Etlik City Hospital (other gov) |
| Drugs / interventions | chemotherapy |
| Locations | 1 site (Ankara) |
| Trial ID | NCT07236658 on ClinicalTrials.gov |
What this trial studies
This observational project will collect standardized physical-exam and clinical follow-up data from adult women scheduled for unilateral breast cancer surgery at Ankara Etlik City Hospital. Researchers will apply deep learning methods to the collected clinical and examination findings to identify patterns associated with postmastectomy lymphedema, axillary web syndrome, shoulder adhesive capsulitis, surgery-related pain, and secondary osteoporosis. Participants will be followed through postoperative visits to capture onset and progression of musculoskeletal complications. The goal is to create models that help clinicians recognize problems earlier and guide rehabilitation planning.
Who should consider this trial
Good fit: Adult women (age ≥18) scheduled for unilateral breast cancer surgery who can attend follow-up visits at the study site and can provide informed consent.
Not a fit: Men, patients with bilateral breast cancer, pregnant, postpartum, or breastfeeding women, minors, those unable to attend follow-up visits, and legally incapacitated individuals are excluded and would not benefit from participation.
Why it matters
Potential benefit: If successful, the tool could enable earlier detection and more targeted rehabilitation, reducing disability and improving daily function for survivors.
How similar studies have performed: Machine-learning approaches have shown promise in related imaging and outcome-prediction tasks, but applying deep learning to routine physical-exam data for these specific post-breast-cancer musculoskeletal complications is relatively novel.
Eligibility criteria
Show full inclusion / exclusion criteria
Inclusion Criteria: Female sex Age ≥18 years Scheduled for surgery due to unilateral breast cancer Exclusion Criteria: Inability to comply with follow-up visits Bilateral breast cancer Male breast cancer Children (\<18 years) Pregnant women Postpartum women Breastfeeding women Individuals in intensive care Impaired consciousness Legally incapacitated individuals
Where this trial is running
Ankara
- Ankara Etlik City Hospital — Ankara, Turkey (Türkiye) (RECRUITING)
Study contacts
- Study coordinator: Başak Mansız Kaplan
- Email: basakmansiz@hotmail.com
- Phone: +905358582176
How to participate
- Review the eligibility criteria above with your treating physician.
- Visit the official trial page on ClinicalTrials.gov for the most current contact information and recruitment status.
- Contact the listed study coordinator or principal investigator to request pre-screening. Pre-screening is free and never obligates you to enroll.
Conditions: Postmastectomy Lymphedema Syndrome, Breast Cancer Surgery Pain, Osteoporosis Secondary, Shoulder Adhesive Capsulitis, Axillary Web Syndrome