Objective. Magnetoencephalography (MEG) is a powerful non-invasive diagnostic modality for presurgical epilepsy evaluation. However, the clinical utility of MEG mapping for localising epileptic foci is limited by its low efficiency, high labour requirements, and considerable interoperator variability. To address these obstacles, we proposed a novel artificial intelligence–based automated magnetic source imaging (AMSI) pipeline for automated detection and localisation of epileptic sources from MEG data. Approach. To expedite the analysis of clinical MEG data from patients with epilepsy and reduce human bias, we developed an autolabelling method, a deep-learning model based on convolutional neural networks and a hierarchical clustering method based on a perceptual hash algorithm, to enable the coregistration of MEG and magnetic resonance imaging, the detection and clustering of epileptic activity, and the localisation of epileptic sources in a highly automated manner. We tested the capability of the AMSI pipeline by assessing MEG data from 48 epilepsy patients. Main results. The AMSI pipeline was able to rapidly detect interictal epileptiform discharges with 93.31% ± 3.87% precision based on a 35-patient dataset (with sevenfold patientwise cross-validation) and robustly rendered accurate localisation of epileptic activity with a lobar concordance of 87.18% against interictal and ictal stereo-electroencephalography findings in a 13-patient dataset. We also showed that the AMSI pipeline accomplishes the necessary processes and delivers objective results within a much shorter time frame (∼12 min) than traditional manual processes (∼4 h). Significance. The AMSI pipeline promises to facilitate increased utilisation of MEG data in the clinical analysis of patients with epilepsy.

This study presents a quantitative approach to exploring the spatio-functional characteristics of historic Chinese towns (HCTs) from a heritage tourism perspective. In recent years, HCTs have evolved from being resident-oriented to being more tourist-oriented, in part due to their heritage significance for attracting tourists. Spatio-functional qualities of a historic town are essential elements of the town’s urban morphology and of great concern for preservation. Previous studies that discussed this issue often used qualitative descriptions, and only limited studies have systematically explored the spatio-functional qualities of HCTs. Thus, there is presently a lack of understanding around this issue, especially based on rigorous quantitative approaches. This study examines the spatio-functional qualities of HCTs using space syntax, a commonly used method in urban studies that enables measurement of spatial characteristics through mathematical means. Four HCTs with heritage significance, Pingyao, Lijiang, Kulangsu, and Wuzhen, were selected as case studies. The study has examined the role of heritage tourism and the results show that tourist-focused functions tend to distribute and aggregate in the urban core of HCTs. By contrast, cultural relics are freely distributed and not expanded over time. Spatio-functional patterns of the four HCTs were theorised, and the paper concludes with suggestions regarding future land-use optimisation for the four HCTs concerning heritage tourism.