The technical combination of artificial intelligence (AI) and thoracic surgery is increasingly close, especially in the field of image recognition and pathology diagnosis. Additionally, robotic surgery, as a representative of high-end technology in minimally invasive surgery is flourishing. What progress has been or will be made in robotic surgery in the era of AI? This article aims to summarize the application status of AI in thoracic surgery and progress in robotic surgery, and looks ahead the future.
Objective To summarize and analyze the clinical and video-EEG (VEEG) characteristics of adult sleep-related epilepsy, so as to provide evidence for clinical diagnosis, differential diagnosis and treatment. Methods The clinical data, routine EEG and long-term VEEG of 187 adult patients with sleep-related epilepsy treated in Department of Neurology, Xiangya Hospital, Central South University from January 2017 to December 2017 were retrospectively analyzed by χ2 test. Results Clinical manifestations: The duration of sleep-related epilepsy in 187 adults was concentrated in 1～10 years (101 cases, 54.01%); the frequency of seizures was mainly from several to dozens of times a year (99 cases, 52.94%); 119 cases (63.64%) had two or more types of seizures. Among the patients, 121 cases (39.29%) had focal origin, 152 cases (49.35%) had bilateral tonic clonus and 110 cases (58.82%) were treated with two or more drugs. EEG results: ① The detection rate of epileptiform discharges in routine EEG was 22.78%, and that in long-term video EEG was 80.43%. There was significant difference between the two methods (P< 0.01); ② Eighteen epileptiform discharges were monitored by routine EEG during interparoxysmal period and 111 epileptiform discharges were monitored by video EEG; and ③ Fifty-six epileptic events were monitored and all occurred in the process of long-term VEEG monitoring, 50 of them occurred in sleep (89.29%) and 6 in awake (10.71%); 45 cases (80.36%) were diagnosed as epileptic seizures, 9 cases (16.07%) were diagnosed as non-epileptic seizures, and 2 cases (3.57%) could not be determined. ④ The detection rate of epileptic discharges during sleep was higher than that during awake period in long-term VEEG monitoring (P< 0.01). The detection rate of epileptiform discharges in NREM stage I–II was the highest in sleep stage. Conclusion Sleep-related epilepsy in adults has certain clinical features and EEG manifestations. Compared with conventional EEG, long-term video-EEG can improve the detection rate of epileptiform discharges, provide diagnostic basis for the qualitative analysis of sleep-related seizures, and reflect the relationship between epileptiform discharges and sleep, and provide basis for the clinical diagnosis and treatment of sleep-related epilepsy in adults.