- Browse by Author
Browsing by Author "McNutt, Andrew T."
Now showing 1 - 2 of 2
Results Per Page
Sort Options
Item Comparison of Supervised Machine Learning and Probabilistic Approaches for Record Linkage(AMIA Informatics summit 2019 Conference Proceedings., 2020-03-25) McNutt, Andrew T.; Grannis, Shaun J.; Bo, Na; Xu, Huiping; Kasthurirathne, Suranga N.Record linkage is vital to prevent fragmentation of patient data. Machine learning approaches present considerable potential for record linkage. We compared the performance of three machine learning algorithms to an established probabilistic record linkage technique. Machine learning approaches exhibited results that were comparable, or statistically superior to the established probabilistic approach. It is unclear if the cost of manually reviewing datasets for supervised learning is justified by the performance improvements they yield.Item In Situ Classification of Cell Types in Human Kidney Tissue Using 3D Nuclear Staining(Wiley, 2021) Woloshuk, Andre; Khochare, Suraj; Almulhim, Aljohara F.; McNutt, Andrew T.; Dean, Dawson; Barwinska, Daria; Ferkowicz, Michael J.; Eadon, Michael T.; Kelly, Katherine J.; Dunn, Kenneth W.; Hasan, Mohammad A.; El-Achkar, Tarek M.; Winfree, Seth; Medicine, School of MedicineTo understand the physiology and pathology of disease, capturing the heterogeneity of cell types within their tissue environment is fundamental. In such an endeavor, the human kidney presents a formidable challenge because its complex organizational structure is tightly linked to key physiological functions. Advances in imaging-based cell classification may be limited by the need to incorporate specific markers that can link classification to function. Multiplex imaging can mitigate these limitations, but requires cumulative incorporation of markers, which may lead to tissue exhaustion. Furthermore, the application of such strategies in large scale 3-dimensional (3D) imaging is challenging. Here, we propose that 3D nuclear signatures from a DNA stain, DAPI, which could be incorporated in most experimental imaging, can be used for classifying cells in intact human kidney tissue. We developed an unsupervised approach that uses 3D tissue cytometry to generate a large training dataset of nuclei images (NephNuc), where each nucleus is associated with a cell type label. We then devised various supervised machine learning approaches for kidney cell classification and demonstrated that a deep learning approach outperforms classical machine learning or shape-based classifiers. Specifically, a custom 3D convolutional neural network (NephNet3D) trained on nuclei image volumes achieved a balanced accuracy of 80.26%. Importantly, integrating NephNet3D classification with tissue cytometry allowed in situ visualization of cell type classifications in kidney tissue. In conclusion, we present a tissue cytometry and deep learning approach for in situ classification of cell types in human kidney tissue using only a DNA stain. This methodology is generalizable to other tissues and has potential advantages on tissue economy and non-exhaustive classification of different cell types.