Magnetic Resonance Imaging (MRI) Based on Machine Learning Algorithms for the Diagnosis in Efficacy of Dexmedetomidine along with Modified Electroconvulsive Therapy Nursing on First Episode Schizophrenia

Abstract

This study was aimed to explore the efficacy of dexmedetomidine combined with modified electroconvulsive therapy (MECT) and continuous nursing on first episode schizophrenia (FES), as well as the continuous monitoring effect of magnetic resonance imaging (MRI) on the FES patients under machine learning algorithms. In this study, 48 schizophrenia patients who received the combined treatment of MECT and dexmedetomidine were selected, and they were divided into the routine group (RG) and observation group (OG), with routine nursing and continuous nursing, respectively. Besides, another 30 healthy people were included as the control group (Ctrl group). Based on MRI characteristics, the machine learning algorithm designed for FES patients was used to analyze the changes in the amplitude low-frequency fluctuations (ALFF) of the static state brain MRI; the positive and negative score scale (PANSS) was used to evaluate the mental symptoms of patients. The Schizophrenia Quality of Life Scale (SQLS) score, treatment compliance rate, follow-up rate, and recurrence rate of patients were also compared. The results showed that the image quality of the weakened and enhanced areas of brain MRI was significantly improved under the machine learning algorithm. After treatment, the total score of PANSS and the scores of all dimensions were greatly reduced (P<0.05). For the FES patients, the ALFF value of the frontal right lower lobe before treatment was higher than that of the Ctrl group (P<0.05), while the ALFF value of the right insula and right superior frontal gyrus after treatment was lower than that of the Ctrl group (P<0.01), and the ALFF value of the left inferior frontal gyrus opercular part was lower than that of the Ctrl group (P