Diagnostic support in pediatric craniopharyngioma using deep learning.

Abstract

We studied a pediatric group of patients with sellar-suprasellar tumors, aiming to develop a convolutional deep learning algorithm for radiological assistance to classify them into their respective cohort. T1w and T2w preoperative magnetic resonance images of226 Chilean patientswere collected at theInstitute of Neurosurgery Dr. Alfonso Asenjo (INCA), which were divided into three classes: healthy control (68subjects), craniopharyngioma (58 subjects) and differential sellar/suprasellar tumors (100 subjects). The PPV among classes was 0.828±0.039, and the NPVwas 0.919±0.063. Also explainable artificial intelligence (XAI) was used, finding thatstructures that are relevantduring diagnosis and radiological evaluationhighly influence the decision-making process of the machine. This is the first experience of this kind of study in our institution, and it led to promising results on the task of radiological diagnostic support based on explainable artificial intelligence (AI) and deep learning models.