Intelligent noninvasive meningioma grading with a fully automatic segmentation using interpretable multiparametric deep learning.

OBJECTIVES: To establish a robust interpretable multiparametric deep learning (DL) model for automatic noninvasive grading of meningiomas along with segmentation. METHODS: In total, 257 patients with pathologically confirmed meningiomas (162 low-grade, 95 high-grade) who underwent a preoperative brain MRI, including T2-weighted (T2) and contrast-enhanced T1-weighted images (T1C), were included in the institutional training set. A two-stage DL grading model was constructed for segmentation and classification based on multiparametric three-dimensional U-net and ResNet. The models were validated in the external validation set consisting of 61 patients with meningiomas (46 low-grade, 15 high-grade). Relevance-weighted Class Activation Mapping (RCAM) method was used to interpret the DL features contributing to the prediction of the DL grading model. RESULTS: On external validation, the combined T1C and T2 model showed a Dice coefficient of 0.910 in segmentation and the highest performance for meningioma grading compared to the T2 or T1C only models, with an area under the curve (AUC) of 0.770 (95% confidence interval: 0.644-0.895) and accuracy, sensitivity, and specificity of 72.1%, 73.3%, and 71.7%, respectively. The AUC and accuracy of the combined DL grading model were higher than those of the human readers (AUCs of 0.675-0.690 and accuracies of 65.6-68.9%, respectively). The RCAM of the DL grading model showed activated maps at the surface regions of meningiomas indicating that the model recognized the features at the tumor margin for grading. CONCLUSIONS: An interpretable multiparametric DL model combining T1C and T2 can enable fully automatic grading of meningiomas along with segmentation. KEY POINTS: • The multiparametric DL model showed robustness in grading and segmentation on external validation. • The diagnostic performance of the combined DL grading model was higher than that of the human readers. • The RCAM interpreted that DL grading model recognized the meaningful features at the tumor margin for grading.

Ultrathin crystalline-silicon-based strain gauges with deep learning algorithms for silent speech interfaces.

A wearable silent speech interface (SSI) is a promising platform that enables verbal communication without vocalization. The most widely studied methodology for SSI focuses on surface electromyography (sEMG). However, sEMG suffers from low scalability because of signal quality-related issues, including signal-to-noise ratio and interelectrode interference. Hence, here, we present a novel SSI by utilizing crystalline-silicon-based strain sensors combined with a 3D convolutional deep learning algorithm. Two perpendicularly placed strain gauges with minimized cell dimension (<0.1 mm2) could effectively capture the biaxial strain information with high reliability. We attached four strain sensors near the subject's mouths and collected strain data of unprecedently large wordsets (100 words), which our SSI can classify at a high accuracy rate (87.53%). Several analysis methods were demonstrated to verify the system's reliability, as well as the performance comparison with another SSI using sEMG electrodes with the same dimension, which exhibited a relatively low accuracy rate (42.60%).

Quantitative analysis of the mouth opening movement of temporomandibular joint disorder patients according to disc position using computer vision: a pilot study.

Background: Temporomandibular joint disorder (TMD), which is a broad category encompassing disc displacement, is a common condition with an increasing prevalence. This study aimed to develop an automated movement tracing algorithm for mouth opening and closing videos, and to quantitatively analyze the relationship between the results obtained using this developed system and disc position on magnetic resonance imaging (MRI). Methods: Mouth opening and closing videos were obtained with a digital camera from 91 subjects, who underwent MRI. Before video acquisition, an 8.0-mm-diameter circular sticker was attached to the center of the subject's upper and lower lips. The automated mouth opening tracing system based on computer vision was developed in two parts: (I) automated landmark detection of the upper and lower lips in acquired videos, and (II) graphical presentation of the tracing results for detected landmarks and an automatically calculated graph height (mouth opening length) and width (sideways values). The graph paths were divided into three types: straight, sideways-skewed, and limited-straight line graphs. All traced results were evaluated according to disc position groups determined using MRI. Graph height and width were compared between groups using analysis of variance (SPSS version 25.0; IBM Corp., Armonk, NY, USA). Results: Subjects with a normal disc position predominantly (85.72%) showed straight line graphs. The other two types (sideways-skewed or limited-straight line graphs) were found in 85.0% and 89.47% in the anterior disc displacement with reduction group and in the anterior disc displacement without reduction group, respectively, reflecting a statistically significant correlation (χ2=38.113, P<0.001). A statistically significant difference in graph height was found between the normal group and the anterior disc displacement without reduction group, 44.90±9.61 and 35.78±10.24 mm, respectively (P<0.05). Conclusions: The developed mouth opening tracing system was reliable. It presented objective and quantitative information about different trajectories from those associated with a normal disc position in mouth opening and closing movements. This system will be helpful to clinicians when it is difficult to obtain information through MRI.