Introduction: Pain is the unpleasant sensation and emotional experience that leads to poor quality of life for millions of people worldwide. Considering the complexity in understanding the principles of pain and its significant impact on individuals and society, research focuses to deliver innovative pain relief methods and techniques. This review explores the clinical uses of machine learning (ML) for the diagnosis, classification, and management of pain. Methods: A systematic review of the current literature was conducted using the PubMed database library.Results: Twenty-six papers related to pain and ML research were included. Most of the studies used ML for effectively classifying the patients' level of pain, followed by use of ML for the prediction of manifestation of pain and for pain management. A less common reason for performing ML analysis was for the diagnosis of pain. The different approaches are thoroughly discussed. Conclusion: ML is increasingly used in pain medicine and appears to be more effective compared to traditional statistical approaches in the diagnosis, classification, and management of pain.

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A Guideline Group (GG) was convened from multiple specialties and patients to develop the first comprehensive schwannomatosis guideline. The GG undertook thorough literature review and wrote recommendations for treatment and surveillance. A modified Delphi process was used to gain approval for recommendations which were further altered for maximal consensus. Schwannomatosis is a tumour predisposition syndrome leading to development of multiple benign nerve-sheath non-intra-cutaneous schwannomas that infrequently affect the vestibulocochlear nerves. Two definitive genes (SMARCB1/LZTR1) have been identified on chromosome 22q centromeric to NF2 that cause schwannoma development by a 3-event, 4-hit mechanism leading to complete inactivation of each gene plus NF2. These genes together account for 70–85% of familial schwannomatosis and 30–40% of isolated cases in which there is considerable overlap with mosaic NF2. Craniospinal MRI is generally recommended from symptomatic diagnosis or from age 12–14 if molecularly confirmed in asymptomatic individuals whose relative has schwannomas. Whole-body MRI may also be deployed and can alternate with craniospinal MRI. Ultrasound scans are useful in limbs where typical pain is not associated with palpable lumps. Malignant-Peripheral-Nerve-Sheath-Tumour-MPNST should be suspected in anyone with rapidly growing tumours and/or functional loss especially with SMARCB1-related schwannomatosis. Pain (often intractable to medication) is the most frequent symptom. Surgical removal, the most effective treatment, must be balanced against potential loss of function of adjacent nerves. Assessment of patients’ psychosocial needs should be assessed annually as well as review of pain/pain medication. Genetic diagnosis and counselling should be guided ideally by both blood and tumour molecular testing.

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