Fully-Automated Spike Detection and Dipole Analysis of Epileptic MEG Using Deep Learning.

Magnetoencephalography (MEG) is a useful tool for clinically evaluating the localization of interictal spikes. Neurophysiologists visually identify spikes from the MEG waveforms and estimate the equivalent current dipoles (ECD). However, presently, these analyses are manually performed by neurophysiologists and are time-consuming. Another problem is that spike identification from MEG waveforms largely depends on neurophysiologists' skills and experiences. These problems cause poor cost-effectiveness in clinical MEG examination. To overcome these problems, we fully automated spike identification and ECD estimation using a deep learning approach fully automated AI-based MEG interictal epileptiform discharge identification and ECD estimation (FAMED). We applied a semantic segmentation method, which is an image processing technique, to identify the appropriate times between spike onset and peak and to select appropriate sensors for ECD estimation. FAMED was trained and evaluated using clinical MEG data acquired from 375 patients. FAMED training was performed in two stages: in the first stage, a classification network was learned, and in the second stage, a segmentation network that extended the classification network was learned. The classification network had a mean AUC of 0.9868 (10-fold patient-wise cross-validation); the sensitivity and specificity were 0.7952 and 0.9971, respectively. The median distance between the ECDs estimated by the neurophysiologists and those using FAMED was 0.63 cm. Thus, the performance of FAMED is comparable to that of neurophysiologists, and it can contribute to the efficiency and consistency of MEG ECD analysis.

Development of MRI Projection Mapping System for Breast-Conserving Surgery in the Operating Room: Preliminary Clinical Results in Invasive Breast Cancer.

AIM: To evaluate the feasibility of a newly developed prototype MRI projection mapping (PM) system for localization of invasive breast cancer before breast-conserving surgery. METHODS: This prospective study enrolled 10 women with invasive breast cancer. MRI was performed in both prone and supine positions. The tumor location was drawn on the breast skin using palpation and sonography while referring to the prone MRI (i.e., a conventional method). A maximum intensity projection image generated from the supine MRI was projected using our PM system, and the tumor location was drawn. The PM system consisted of a projector and a camera and was used to measure the shape of the breast surface using the structured light method. Breast-conserving surgery was performed based on the conventional method. We compared the tumor size and location between the PM and conventional methods or pathology. RESULTS: There were no significant differences in the maximum diameters of invasive cancers between the PM system and the conventional method or pathology. The maximum discrepancy in tumor location between the PM and conventional method was 3-8 mm. CONCLUSIONS: This PM system may support breast-conserving surgery by showing the tumor size and location on the breast surface.