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1. Statistical Applications to Cognitive Diagnostic Testing

   https://doi.org/10.1146/annurev-statistics-033021-111803  

   Zhang, Susu; Liu, Jingchen; Ying, Zhiliang (March 2023, Annual Review of Statistics and Its Application)

   Diagnostic classification tests are designed to assess examinees’ discrete mastery status on a set of skills or attributes. Such tests have gained increasing attention in educational and psychological measurement. We review diagnostic classification models and their applications to testing and learning, discuss their statistical and machine learning connections and related challenges, and introduce some contemporary and future extensions. 

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2. A Multimodal Deep Learning Approach for Automated Detection and Characterization of Distinctly Salient Features of Alzheimers Disease

   https://doi.org/10.1109/ISBI53787.2023.10230525  

   Batta, Ishaan; Abrol, Anees; Calhoun, Vince (April 2023, IEEE)

   Neurological disorders generally involve multiple kinds of changes in the functional and structural properties of the brain. In this study, we develop a CNN-based multimodal deep learning pipeline by exploiting both functional and structural neuroimaging features to generate full-brain maps that encode significant differences between patient groups and between modalities in terms of their distinctive contribution towards diagnostic classification of Alzheimer’s disease. Through a repeated cross-validation procedure and robust statistical analysis, we show that our approach can be used to encode highly discriminative and abstract information from full-brain data, while also retaining the ability to identify and categorize significantly contributing voxel-level features based on their salient strength in various diagnostic and modality-related contexts. Our results on an Alzheimer’s disease classification task show that such approaches can be used for creating more elaborately defined biomarkers for brain disorders. 

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3. IIoT Deployment of a Physics-Informed Deep Learning Model for Online Bearing Fault Diagnostics

   Lu, Hao; Allen, Cade; Nemani, Venkat; Hu, Chao; Zimmerman, Andrew (January 2023, Proceedings of the 2022 International Symposium on Flexible Automation)

   Early fault detection in rolling element bearings is pivotal for the effective predictive maintenance of rotating machinery. Deep Learning (DL) methods have been widely studied for vibration-based bearing fault diagnostics largely because of their capability to automatically extract fault-related features from raw or processed vibration data. Although most DL models in the current literature can provide fairly accurate classification outputs, the typical diagnostic procedure is performed in an offline environment utilizing powerful computers. This centralized approach can lead to unacceptable delays in safety-critical applications and can prohibit cost-sensitive wireless data collection. Meanwhile, very few studies have reported on deploying DL models on microprocessor-based Industrial Internet of Things (IIoT) devices, where edge computing can give users a real-time evaluation of bearing health without requiring expensive computational infrastructure. This paper demonstrates an IIoT deployment of a physics-informed DL model inside a commercially available wireless vibration sensor for online health classification. The diagnostic model here is developed and trained offline, and the trained model is then deployed inside the embedded system for online prediction. We demonstrate the model’s online diagnostic performance by imitating bearing vibration signals on a vibration shaker and by performing edge computing on the embedded system mounted on the shaker. 

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4. Radiomics at a Glance: A Few Lessons Learned from Learning Approaches

   https://doi.org/10.3390/cancers12092453  

   Capobianco, Enrico; Deng, Jun (September 2020, Cancers)

   null (Ed.)

   Processing and modeling medical images have traditionally represented complex tasks requiring multidisciplinary collaboration. The advent of radiomics has assigned a central role to quantitative data analytics targeting medical image features algorithmically extracted from large volumes of images. Apart from the ultimate goal of supporting diagnostic, prognostic, and therapeutic decisions, radiomics is computationally attractive due to specific strengths: scalability, efficiency, and precision. Optimization is achieved by highly sophisticated statistical and machine learning algorithms, but it is especially deep learning that stands out as the leading inference approach. Various types of hybrid learning can be considered when building complex integrative approaches aimed to deliver gains in accuracy for both classification and prediction tasks. This perspective reviews some selected learning methods by focusing on both their significance for radiomics and their unveiled potential. 

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5. Lyophilization of premixed COVID‐19 diagnostic RT‐qPCR reactions enables stable long‐term storage at elevated temperature

   https://doi.org/10.1002/biot.202000572  

   Hammerling, Michael J.; Warfel, Katherine F.; Jewett, Michael C. (May 2021, Biotechnology Journal)

   null (Ed.)

   Background Reverse transcriptase-quantitative polymerase chain reaction (RT-qPCR) diagnostic tests for SARS-CoV-2 are the cornerstone of the global testing infrastructure. However, these tests require cold-chain shipping to distribute, and the labor of skilled technicians to assemble reactions and interpret the results. Strategies to reduce shipping and labor costs at the point-of-care could aid in diagnostic testing scale-up and response to the COVID-19 outbreak, as well as in future outbreaks. Methods In this study we test both lab-developed and commercial SARS-CoV-2 diagnostic RT-qPCR mixes for the ability to be stabilized against elevated temperature by lyophilization. Fully assembled reactions were lyophilized and stored for up to a month at ambient or elevated temperature and were subsequently assayed for their ability to detect dilutions of synthetic SARS-CoV-2 RNA. Results Of the mixes tested, we show that one commercial mix can maintain activity and sensitivity after storage for at least 30 days at ambient temperature after lyophilization. We also demonstrate that lyoprotectants such as disaccharides can stabilize freeze-dried diagnostic reactions against elevated temperatures (up to 50°C) for at least 30 days. Conclusion We anticipate that the incorporation of these methods into SARS-CoV-2 diagnostic testing will improve testing pipelines by reducing labor at the testing facility and eliminating the need for cold-chain shipping. 

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