The development of spinal deformity in children with underlying neurodisability can affect their ability to function and impact on their quality of life, as well as compromise provision of nursing care. Patients with neuromuscular spinal deformity are among the most challenging due to the number and complexity of medical comorbidities that increase the risk for severe intraoperative or postoperative complications. A multidisciplinary approach is mandatory at every stage to ensure that all nonoperative measures have been applied, and that the treatment goals have been clearly defined and agreed with the family. This will involve input from multiple specialities, including allied healthcare professionals, such as physiotherapists and wheelchair services. Surgery should be considered when there is significant impact on the patients’ quality of life, which is usually due to poor sitting balance, back or costo-pelvic pain, respiratory complications, or problems with self-care and feeding. Meticulous preoperative assessment is required, along with careful consideration of the nature of the deformity and the problems that it is causing. Surgery can achieve good curve correction and results in high levels of satisfaction from the patients and their caregivers. Modern modular posterior instrumentation systems allow an effective deformity correction. However, the risks of surgery remain high, and involvement of the family at all stages of decision-making is required in order to balance the risks and anticipated gains of the procedure, and to select those patients who can mostly benefit from spinal correction.
In recent years, machine learning (ML) and artificial neural networks (ANNs), a particular subset of ML, have been adopted by various areas of healthcare. A number of diagnostic and prognostic algorithms have been designed and implemented across a range of orthopaedic sub-specialties to date, with many positive results. However, the methodology of many of these studies is flawed, and few compare the use of ML with the current approach in clinical practice. Spinal surgery has advanced rapidly over the past three decades, particularly in the areas of implant technology, advanced surgical techniques, biologics, and enhanced recovery protocols. It is therefore regarded an innovative field. Inevitably, spinal surgeons will wish to incorporate ML into their practice should models prove effective in diagnostic or prognostic terms. The purpose of this article is to review published studies that describe the application of neural networks to spinal surgery and which actively compare ANN models to contemporary clinical standards allowing evaluation of their efficacy, accuracy, and relatability. It also explores some of the limitations of the technology, which act to constrain the widespread adoption of neural networks for diagnostic and prognostic use in spinal care. Finally, it describes the necessary considerations should institutions wish to incorporate ANNs into their practices. In doing so, the aim of this review is to provide a practical approach for spinal surgeons to understand the relevant aspects of neural networks. Cite this article:
