Speech Analysis Using Artificial Intelligence as a Peri-Operative Evaluation: A Case Report of a Patient with Temporal Lobe Epilepsy Secondary to Tuberous Sclerosis Complex Who Underwent Epilepsy Surgery

Abstract

Background: Improved conversational fluency is sometimes identified postoperatively in patients with epilepsy, but improvements can be difficult to assess using tests such as the intelligence quotient (IQ) test. Evaluation of pre- and postoperative differences might be considered subjective at present because of the lack of objective criteria. Artificial intelligence (AI) could possibly be used to make the evaluations more objective. The aim of this case report is thus to analyze the speech of a young female patient with epilepsy before and after surgery. Method: The speech of a nine-year-old girl with epilepsy secondary to tuberous sclerosis complex is recorded during interviews one month before and two months after surgery. The recorded speech is then manually transcribed and annotated, and subsequently automatically analyzed using AI software. IQ testing is also conducted on both occasions. The patient remains seizure-free for at least 13 months postoperatively. Results: There are decreases in total interview time and subjective case markers per second, whereas there are increases in morphemes and objective case markers per second. Postoperatively, IQ scores improve, except for the Perceptual Reasoning Index. Conclusions: AI analysis is able to identify differences in speech before and after epilepsy surgery upon an epilepsy patient with tuberous sclerosis complex.