An Artificial Intelligence Model for Lhermitte's Sign in Patients With Pediatric-Onset Multiple Sclerosis: a Follow-Up Study

Abstract

Background. Lhermitte's sign (LS) is an important clinical marker for patients with multiple sclerosis (MS). Research on pediatric-onset MS (POMS) and LS is limited. To date, there has been no research conducted on the clinical and artificial intelligence (AI)-based radiological correlation of LS. Objectives. This follow-up study aims to investigate the relationship between LS and clinical findings according to AI-based radiological characteristics of patients with POMS. Materials and methods. Basic descriptive statistics of patients with POMS according to sociodemographic, clinical and radiological findings were collected. Variables were evaluated at a 95% confidence level (95% CI), and a value of p < 0.05 was accepted as statistically significant. The LS in patients with MS was classified according to its presence in the past and at the time of the study screening: group A: absent; group B: positive in the past but absent at screening; group C: present both in the past and at the screening; group D: absent in the past but present at the screening. In addition, patients were grouped according to the duration of their MS, with the following classifications: <10 years and at least 10 years. Results. A total of 1,298 records were identified in the database search. Ninety-two patients who met the inclusion criteria were included in the study. The frequency of upper cervical lesions (C1-4 vertebral segmental levels) was higher in group B and C than in group A (p = 0.017). Among patients with an MS duration of 10-years, C1-4 lesions were least frequent in group A. Conclusions. Spinal imaging with AI-based programs can be used at least as much as brain magnetic resonance imaging (MRI) for early diagnosis, prognosis and treatment response. We have for the first time investigated LS in a large sample of patients with POMS. It is, however, recommended to conduct further multicenter studies to more specifically identify LS in patients with POMS.