# A Machine Learning Approach to Support Treatment Identification for Chari I Malformation
## 4 Discussion  
According to the preliminary results of our study, a decisional machine learning-based algorithm may be useful to select the most efficacious surgery for patients harboring CM-I with and without syringomyelia. The proper selection of surgical technique for CM-I patients is very difficult since there are not current guidelines, but only recommendations and multicentric series [1, 2, 5, 13, 46]. Good and similar surgical outcomes have been reported both by simple bony decompression of posterior fossa [13, 14] and duraplasty [1, 18]. However, after surgery about 35% of patients' clinical complaints may relapse [46]. Moreover, despite the applied type of surgery, the development of new syringomyelia and its worsening if pre-existent may occur [47]. Given the significant incidence of long-term patients' deterioration, many literature reviews and case series have tried to select the \"best\" surgery procedure focusing on the opening of the dura mater with cerebellar tonsils coagulation as compared to simple bony decompression [1, 48]. Thus, developing an objective and precise method to support the surgeon in selecting the surgery treatment of CM-I patients could be a great asset for neurosurgeons. Both clinical and radiological information could be useful to select the most efficacious surgical technique in term of lower risk of post-operative syringomyelia [19, 20, 21]. On this basis, we proposed an automatic method to delineate important brain regions [33], from which morphometric measurements can be extracted to predict the right surgical approach [30]. Preliminary results have been obtained for the discrimination of healthy controls and patients and for the identification of the severity of the pathology [30]. However, only few patients (i.e., 10 mild and 8 severe) were available in our previous study. Based on a new database of 58 pediatric patients, in this work, the processing method was optimized: the bilateral filter was used instead of the median filter; the active contour method was improved adding more terms in the functional to be minimized. Different classifiers were then implemented to predict the outcome of the intervention, either considering patients with/without syringomyelia at first diagnosis or grouping all patients together. Both general patients properties (i.e., age and clinical symptoms) and morphometrics were used as possible predictors. The morphometrics were measured either manually by expert neurosurgeons or automatically by our algorithm. The single features allow a better linear discrimination of different patient groups when measured manually (Figure 5). Two important parameters which show quite a large difference between manual and automatic measurement are the area of posterior fossa and the length of the hernia (Table 1). Notice that the estimation of posterior fossa shows also the largest variability when considering two different manual delineations. An error in a measurement can also have negative  
Figure 7: F1-score (**a**) and false positive rate (**b**) of SVM classifier. For both (**a**,**b**) we can observe from left to right patients with syringomyelia, without syringomyelia and all together. The corresponding side-by-side bars, differentiated by textures and colors, provide the two metrics respectively for the SVM fed with features automatically obtained from our methodology (light gray, dashed bar) and with medical-based features (dark gray, solid). The error bars provide the standard error among the Leave-One-Out cross-validation folds. Both the metrics are “macro-weighted”, namely averaged among the two classes. impact on the estimation of other parameters. For example, ratios of region areas are affected by errors in each of the two areas involved, as even small mistakes at the numerator and at the denominator could result in a large inaccuracy in the ratio (e.g., if the numerator is estimated larger and the denominator smaller).