RESUMO
PURPOSE: To develop and validate a system that could perform automated diagnosis of common and rare neurologic diseases involving deep gray matter on clinical brain MRI studies. MATERIALS AND METHODS: In this retrospective study, multimodal brain MRI scans from 212 patients (mean age, 55 years ± 17 [standard deviation]; 113 women) with 35 neurologic diseases and normal brain MRI scans obtained between January 2008 and January 2018 were included (110 patients in the training set, 102 patients in the test set). MRI scans from 178 patients (mean age, 48 years ± 17; 106 women) were used to supplement training of the neural networks. Three-dimensional convolutional neural networks and atlas-based image processing were used for extraction of 11 imaging features. Expert-derived Bayesian networks incorporating domain knowledge were used for differential diagnosis generation. The performance of the artificial intelligence (AI) system was assessed by comparing diagnostic accuracy with that of radiologists of varying levels of specialization by using the generalized estimating equation with robust variance estimator for the top three differential diagnoses (T3DDx) and the correct top diagnosis (TDx), as well as with receiver operating characteristic analyses. RESULTS: In the held-out test set, the imaging pipeline detected 11 key features on brain MRI scans with 89% accuracy (sensitivity, 81%; specificity, 95%) relative to academic neuroradiologists. The Bayesian network, integrating imaging features with clinical information, had an accuracy of 85% for T3DDx and 64% for TDx, which was better than that of radiology residents (n = 4; 56% for T3DDx, 36% for TDx; P < .001 for both) and general radiologists (n = 2; 53% for T3DDx, 31% for TDx; P < .001 for both). The accuracy of the Bayesian network was better than that of neuroradiology fellows (n = 2) for T3DDx (72%; P = .003) but not for TDx (59%; P = .19) and was not different from that of academic neuroradiologists (n = 2; 84% T3DDx, 65% TDx; P > .09 for both). CONCLUSION: A hybrid AI system was developed that simultaneously provides a quantitative assessment of disease burden, explainable intermediate imaging features, and a probabilistic differential diagnosis that performed at the level of academic neuroradiologists. This type of approach has the potential to improve clinical decision making for common and rare diseases.Supplemental material is available for this article.© RSNA, 2020.
RESUMO
OBJECTIVE: Brain-computer interfaces (BCI) use measures of brain activity to convey a user's intent without the need for muscle movement. Hybrid designs, which use multiple measures of brain activity, have been shown to increase the accuracy of BCIs, including those based on EEG signals reflecting covert attention. Our study examined whether incorporating a measure of the P3 response improved the performance of a previously reported attention-based BCI design that incorporates measures of steady-state visual evoked potentials (SSVEP) and alpha band modulations. APPROACH: Subjects viewed stimuli consisting of two bi-laterally located flashing white boxes on a black background. Streams of letters were presented sequentially within the boxes, in random order. Subjects were cued to attend to one of the boxes without moving their eyes, and they were tasked with counting the number of target-letters that appeared within. P3 components evoked by target appearance, SSVEPs evoked by the flashing boxes, and power in the alpha band are modulated by covert attention, and the modulations can be used to classify trials as left-attended or right-attended. MAIN RESULTS: We showed that classification accuracy was improved by including a P3 feature along with the SSVEP and alpha features (the inclusion of a P3 feature lead to a 9% increase in accuracy compared to the use of SSVEP and Alpha features alone). We also showed that the design improves the robustness of BCI performance to individual subject differences. SIGNIFICANCE: These results demonstrate that incorporating multiple neurophysiological indices of covert attention can improve performance in a gaze-independent BCI.