An artificial intelligence algorithm for analyzing globus pallidus necrosis after carbon monoxide intoxication

Globus pallidus necrosis (GPN) is one of typical neurological imaging features in patients with carbon monoxide (CO) poisoning. Current clinical guideline recommends neurological imaging examination for CO-intoxicated patients with conscious disturbance rather than routine screening, which may lead to undiagnosed GPN. We aimed to develop an artificial intelligence algorithm for predicting GPN in CO intoxication patients. We included CO intoxication patients with neurological images between 2000 and 2019 in Chang Gung Memorial Hospital. We collected 41 clinical and laboratory parameters on the first day of admission for algorithm development. We used fivefold cross validation and applied several machine learning algorithms. Random forest classifier (RFC) provided the best predictive performance in our cohort. Among the 261 patients with CO intoxication, 52 patients presented with GPN. The artificial intelligence algorithm using the RFC-based AI model achieved an accuracy = 79.2 ± 2.6%, sensitivity = 77.7%, precision score = 81.9 ± 3.4%, and F1 score = 73.2 ± 1.8%. The area under receiver operating characteristic was approximately 0.64. Top five weighted variables were Platelet count, carboxyhemoglobin, Glasgow Coma scale, creatinine, and hemoglobin. Our RFC-based algorithm is the first to predict GPN in patients with CO intoxication and provides fair predictive ability. Further studies are needed to validate our findings.