Related Experiment Video
Updated: Jul 26, 2026
Fabrication of the Composite Regenerative Peripheral Nerve Interface C-RPNI in the Adult Rat
Published on: February 25, 2020
Transforming peripheral nerve surgery with artificial intelligence: a review of surgical advances
Metin T Buldu1, M R Ezquerro Cortes1, Anna Panagiotidou1
1Unit of Peripheral Nerve Injury, Royal National Orthopedic Hospital, Stanmore, UK.
Introduction:
Peripheral nerve injuries (PNIs) are challenging to manage due to complex anatomy and variable presentations. Artificial intelligence (AI) techniques are increasingly applied in medicine, and their role in PNI care is emerging. This systematic review aimed to summarize the current applications of AI in the diagnosis, prognosis, and treatment of PNIs.
Evidence Acquisition:
A comprehensive literature search of PubMed, Embase, and Web of Science was conducted (October 2024) for studies applying AI to human PNI diagnosis, prognostication, or treatment. After screening 1000 records, 42 studies met inclusion criteria. Data were extracted by two independent reviewers. Due to heterogeneity of outcomes, a narrative synthesis was performed complying with PRISMA guidelines.
Evidence Synthesis:
Included studies covered diverse AI applications: 20 on carpal tunnel syndrome, 10 on traumatic nerve injuries, 4 on brachial plexus injuries, 3 on nerve sheath tumors, 2 on regional anesthesia nerve blocks, and 1 each on peroneal nerve palsy, thermography-based diagnosis, and hand trauma. AI showed the greatest utility in diagnostics - for example, automating ultrasound and MRI image analysis with accuracy often comparable to experts. Notably, AI models accurately diagnosed carpal tunnel syndrome from ultrasound images and segmented nerves in medical images with high precision. Prognostic uses and intraoperative applications are promising but currently in early stages. Overall, AI tools demonstrated feasibility and improved speed or consistency in many PNI-related tasks, though most are still in validation phases.
Conclusions:
AI is rapidly emerging as a valuable adjunct in PNI management. Diagnostic applications have progressed the most, while predictive modelling and surgical assistance remain nascent. The current evidence, although encouraging, is limited by small sample sizes, heterogeneity and lack of multicenter validation. With further development and proper integration, AI has the potential to enhance diagnostic precision, guide treatment decisions, and ultimately improve outcomes for patients with peripheral nerve injuries.
