{'fraction_non_alphanumeric': 0.016976127320954906, 'fraction_numerical': 0.010786914235190096, 'mean_word_length': 5.355056179775281, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 1, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
Machine Learning and the Prediction of Hydrocephalus: Can Quantitative Image Analysis Assist the Clinician?

The advanced computational analysis of large data sets holds great promise for the field of neurosurgery and for medicine at large. Machine learning algorithms help uncover the contribution of nonlinear variables in complex environments, with applications such as predicting tumor growth or identifying surgical candidacy.1 Hydrocephalus is an area that may benefit from automated image analysis and predictive modeling. Although the cerebral ventricular system may be thought of in simple terms as a balloon that expands when fluid pressure is high, the more detailed reality is of an interconnected series of fluid compartments with regional compliance that can vary with symmetry relationships that change by individual and predisposing conditions. Given this reality, modern analysis software can isolate the ventricles through automated segmentation and can detect structural associations that potentially highlight unrecognized aspects of hydrocephalus. In this issue of JAMA Pediatrics, Pisapia and colleagues2 investigate cerebral ventriculomegaly by using the structural characteristics of the ventricles observed on a single fetal magnetic resonance image to forecast the need for postnatal shunt placement. They extract 77 imaging features, including primary measurements of lateral ventricle length, width, and volume as well as calculated values, such as the length to volume ratio. The authors use a machine learning algorithm known as support vector machines3 to compare the ventricular features of 25 children who needed shunts and 25 children who did not. They test their model on a second data set of 24 children, 4 of whom received shunts. The model predicted the need for shunting in 3 of the 4 children (75% sensitivity), with 95% specificity. These results suggest a future of surgical decision making in which family counseling may be performed far in advance of birth and clinical assessment is supplemented by highly accurate “intelligent” software. In considering these positive implications for the future of pediatric neurosurgery, it is worthwhile to inspect the role of statistical modeling as it pertains to the study by Pisapia et al. Excitement regarding the use of machine learning must be tempered by its judicious use and comparison with conventional modeling techniques (eg, multivariate logistic regression). Much simpler models have been shown to predict a need for shunting with reasonable accuracy. For example, an atrial diameter of 20 mm or more on prenatal imaging alone predicted 75% of needed shunts in cases of isolated ventriculomegaly4—the same sensitivity as that reported by Pisapia and colleagues. In the case of supervised learning algorithms such as support vector machines, the mathematical model is trained on a data set with known results to find optimal variable weightings. The well-trained algorithm then gets a test-drive on additional validation sets of data to determine the success of its predictions. Machine learning algorithms, like more traditional statistical methods, remain dependent on the quality of input data, the inherent biases that those data contain, and the independence of the chosen features. Because it is difficult to extract a fit equation from support vector machines, the use of this model makes analyzing its worth a “black box” for clinicians who are used to making judgments based on classic statistics. Most features of lateral ventricular size—whether occipital horn area, ventricular area, ventricular perimeter, ventricular volume, or the like—are highly likely to vary in tandem with one another. The use of 77 features, then, to fit data with no estimated covariance and therefore no supplied measure of variable orthogonality is problematic. In a separate study published in 2017 using the same 2008 to 2014 data set,5 Pisapia and colleagues provided evidence that atrial diameter and normalized ventricular volume vary nearly linearly with each other (r > 0.9). For fetal magnetic resonance imaging performed after 24 weeks’ gestation, fronto-occipital horn ratio (FOHR) and volume also have a colinear association (r = 0.924). Questions arise as to whether the large number of measurements provide any benefit to predictive modeling over simply 1 or a handful of measurements within the same data set. Although such a comparison among multiple modeling choices is not performed by the authors,2 it would be important in future efforts to quantify how machine learning benefits from the addition of features and to provide a direct comparison with simpler modeling techniques and their resulting covariances. Pisapia and colleagues describe 4 features with the highest predictive value for shunt requirement. Three of these features are directly associated with the size of the smaller occipital horn; the fourth is the ratio between total ventricular area and intracranial area. From a neurosurgical standpoint, the occipital horn size and atrial diameter have been previously recognized as reasonable surrogates for overall ventricular size.5-7 Whether due to changes in surrounding brain compliance or ependymal elasticity, the atrium and occipital horn often dilate prior to other ventricular regions.8 A normal fetal atrial diameter has been estimated at between 6 and 8 mm,6 whereas mild ventriculomegaly has been said to begin near an atrial diameter threshold of approximately 10 mm.7,9 The fronto-occipital horn ratio is widely used in the field as a proxy for prenatal and postnatal ventricular sizes,10 but in the Related article Opinion