# A Machine Learning Approach to Support Treatment Identification for Chari I Malformation
## 1 Introduction
From a genetic point of view, a series of mutations have been described involving gene CDXI, FLT1, and RARG [9], especially in syndromic patients [9; 10], but any specific mutation has not yet been identified. According to the \"International Consensus Document in the Diagnosis and Treatment of Chiari Malformation I in Children\", surgery is indicated for symptomatic patients or asymptomatic patients with syringomyelia larger than 5-8 mm and/or progressively enlarging. Asymptomatic pediatric patients with an incidental diagnosis of CM-I should have periodic clinical and radiological follow-up until the end of their growth because progression is possible despite rare [11]. The aim of the surgery for CM-I is to increase the volume of posterior fossa to restore the CSF circulation [12]. The technique consists either in a simple bony decompression by suboccipital craniectomy and C1 laminectomy, or duraplasty with or without araboidolysis and resection of cerebellar tonsils by opening of the dura mater finally closed with autologous or not-autologous graft [7]. Regarding the post-surgical outcome, bone decompression alone has a lower complication rate, but a lower probability to reduce the syringomyelia; on the other hand, duraplasty provides better reduction of syringomyelia, but has higher rate of complications [13; 14; 15; 16]. Consequently, the first is suggested in children without syringomyelia and without severe symptoms, taking into account a possible risk of recurrence of symptoms,while decompression with duraplasty is to be reserved for those patients with concomitant syringomyelia and marked symptoms. However, no robust consensus exists about the ideal surgical technique and there are controversies in the scientific literature on the choice of the optimal surgical treatment [17; 18]. Some studies have investigated new parameters alternative to the dimension of the hernia which correlate with CM-I and can provide information on the optimal intervention [19]. Promising parameters have been proposed based on morphometric skull analysis from MRI [20; 21; 22; 23]. However, manual measurements of morphometrics were usually taken, introducing possible bias due to subjectivity and a dependence on the expertise of the operator. In order to remove this subjectivity, automatic methods for the segmentation of MRIs of the brain can be used [24; 25]. Machine learning algorithms have recently been applied also to diagnose CM-I [26; 27; 28]. However, manual measurements of morphometrics have been used in [26; 28]; a fully automated approach (based on deep learning) is instead discussed in [27], but it focuses only on discriminating controls and CM-I patients. Recent works from our group proposed fully automated methods, registering the sagittal MRI and extracting the morphometrics of interest, among which the ratios of the areas of brain regions accounting for possible overcrowding of posterior fossa [29; 30]. Associated to a machine learning algorithm, a prediction of the optimal surgery could be obtained [30], thus not only discriminating healthy controls and patients, but also identifying the severity of the pathology (useful to select the optimal treatment). However, only few patients were available to the mentioned study, so that limited performances in discriminating their severity were achieved. Moreover, both adults and pediatric patients were included to enlarge the database. Hence, this study aims at deepening the previous approach proposed in [30] by optimizing and testing a machine learning method on a larger database of pediatric patients requiring surgery, that after one year from the intervention either improved or not. The objective of our algorithm is to predict the outcome, by identifying the patients with higher risk of syringomyelia, thus recommending a more aggressive surgery (i.e., bony decompression plus duraplasty and resection of cerebellar tonsils) only for them. This algorithm could support the surgeon in refining the clinical picture pre-intervention, in order to choose the most efficacious treatment.