Improving Affect Detection in Game-Based Learning with Multimodal Data Fusion
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ABSTRACT:
SUBMITTER: Bittencourt I
PROVIDER: S-EPMC7334173 | biostudies-literature | 2020 Jun
REPOSITORIES: biostudies-literature
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Project description:PurposeTo develop a multimodal model to automate glaucoma detection DESIGN: Development of a machine-learning glaucoma detection model METHODS: We selected a study cohort from the UK Biobank data set with 1193 eyes of 863 healthy subjects and 1283 eyes of 771 subjects with glaucoma. We trained a multimodal model that combines multiple deep neural nets, trained on macular optical coherence tomography volumes and color fundus photographs, with demographic and clinical data. We performed an interpretability analysis to identify features the model relied on to detect glaucoma. We determined the importance of different features in detecting glaucoma using interpretable machine learning methods. We also evaluated the model on subjects who did not have a diagnosis of glaucoma on the day of imaging but were later diagnosed (progress-to-glaucoma [PTG]).ResultsResults show that a multimodal model that combines imaging with demographic and clinical features is highly accurate (area under the curve 0.97). Interpretation of this model highlights biological features known to be related to the disease, such as age, intraocular pressure, and optic disc morphology. Our model also points to previously unknown or disputed features, such as pulmonary function and retinal outer layers. Accurate prediction in PTG highlights variables that change with progression to glaucoma-age and pulmonary function.ConclusionsThe accuracy of our model suggests distinct sources of information in each imaging modality and in the different clinical and demographic variables. Interpretable machine learning methods elucidate subject-level prediction and help uncover the factors that lead to accurate predictions, pointing to potential disease mechanisms or variables related to the disease.
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Project description:ObjectivesTo investigate the impact of deep learning (DL) on radiologists' detection accuracy and reading efficiency of rib fractures on CT.MethodsBlunt chest trauma patients (n = 198) undergoing thin-slice CT were enrolled. Images were read by two radiologists (R1, R2) in three sessions: S1, unassisted reading; S2, assisted by DL as the concurrent reader; S3, DL as the second reader. The fractures detected by the readers and total reading time were documented. The reference standard for rib fractures was established by an expert panel. The sensitivity and false-positives per scan were calculated and compared among S1, S2, and S3.ResultsThe reference standard identified 865 fractures on 713 ribs (102 patients) The sensitivity of S1, S2, and S3 was 82.8, 88.9, and 88.7% for R1, and 83.9, 88.7, and 88.8% for R2, respectively. The sensitivity of S2 and S3 was significantly higher compared to S1 for both readers (all p < 0.05). The sensitivity between S2 and S3 did not differ significantly (both p > 0.9). The false-positive per scan had no difference between sessions for R1 (p = 0.24) but was lower for S2 and S3 than S1 for R2 (both p < 0.05). Reading time decreased by 36% (R1) and 34% (R2) in S2 compared to S1.ConclusionsUsing DL as a concurrent reader can improve the detection accuracy and reading efficiency for rib fracture.Advances in knowledgeDL can be integrated into the radiology workflow to improve the accuracy and reading efficiency of CT rib fracture detection.
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Project description:BACKGROUND:Reverse engineering of transcriptional regulatory networks (TRN) from genomics data has always represented a computational challenge in System Biology. The major issue is modeling the complex crosstalk among transcription factors (TFs) and their target genes, with a method able to handle both the high number of interacting variables and the noise in the available heterogeneous experimental sources of information. RESULTS:In this work, we propose a data fusion approach that exploits the integration of complementary omics-data as prior knowledge within a Bayesian framework, in order to learn and model large-scale transcriptional networks. We develop a hybrid structure-learning algorithm able to jointly combine TFs ChIP-Sequencing data and gene expression compendia to reconstruct TRNs in a genome-wide perspective. Applying our method to high-throughput data, we verified its ability to deal with the complexity of a genomic TRN, providing a snapshot of the synergistic TFs regulatory activity. Given the noisy nature of data-driven prior knowledge, which potentially contains incorrect information, we also tested the method's robustness to false priors on a benchmark dataset, comparing the proposed approach to other regulatory network reconstruction algorithms. We demonstrated the effectiveness of our framework by evaluating structural commonalities of our learned genomic network with other existing networks inferred by different DNA binding information-based methods. CONCLUSIONS:This Bayesian omics-data fusion based methodology allows to gain a genome-wide picture of the transcriptional interplay, helping to unravel key hierarchical transcriptional interactions, which could be subsequently investigated, and it represents a promising learning approach suitable for multi-layered genomic data integration, given its robustness to noisy sources and its tailored framework for handling high dimensional data.
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