Studies have demonstrated the effectiveness of innovative screening tools, such as doppler ultrasound, pain drawings, magnetic resonance neurography, and nerve blocks to help identify candidates for surgery. Machine learning has emerged as a powerful tool to predict surgical outcomes. In addition, advances in surgical techniques, including minimally invasive incisions, fat injections, and novel strategies to treat injured nerves (neuromas) have demonstrated promising results. Lastly, improved patient-reported outcome measures are evolving to provide a framework for comparison of conservative and invasive treatment outcomes. Despite these developments, challenges persist, particularly related to appropriate patient selection, insurance coverage, delays in diagnosis and surgical treatment, and the absence of standardized measures to assess and compare treatment impact. Collaboration between medical/procedural and surgical specialties is required to overcome these obstacles.
Keyphrases
- minimally invasive
- patient reported
- magnetic resonance
- machine learning
- affordable care act
- chronic pain
- healthcare
- randomized controlled trial
- magnetic resonance imaging
- adipose tissue
- ultrasound guided
- robot assisted
- pain management
- health insurance
- case report
- coronary artery bypass
- neuropathic pain
- computed tomography
- artificial intelligence
- big data
- combination therapy
- case control
- acute coronary syndrome
- spinal cord injury
- surgical site infection
- smoking cessation
- contrast enhanced ultrasound