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1. Statistical Applications to Cognitive Diagnostic Testing

   Zhang, Susu; Liu, Jingchen; Ying, Zhiliang (March 2023, Annual review of statistics and its application)

   Reid, Nancy (Ed.)

   Diagnostic classification tests are designed to assess examinees’ discrete mastery status on a set of skills or attributes. Such tests have gained increas- ing 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. Fluorescence excitation-scanning hyperspectral imaging with scalable 2D–3D deep learning framework for colorectal cancer detection

   https://doi.org/10.1038/s41598-024-64917-5  

   Oswald, Willaim; Browning, Craig; Yasmin, Ruthba; Deal, Joshua; Rich, Thomas C; Leavesley, Silas J; Gong, Na (December 2024, Scientific Reports)

   Abstract Colorectal cancer is one of the top contributors to cancer-related deaths in the United States, with over 100,000 estimated cases in 2020 and over 50,000 deaths. The most common screening technique is minimally invasive colonoscopy using either reflected white light endoscopy or narrow-band imaging. However, current imaging modalities have only moderate sensitivity and specificity for lesion detection. We have developed a novel fluorescence excitation-scanning hyperspectral imaging (HSI) approach to sample image and spectroscopic data simultaneously on microscope and endoscope platforms for enhanced diagnostic potential. Unfortunately, fluorescence excitation-scanning HSI datasets pose major challenges for data processing, interpretability, and classification due to their high dimensionality. Here, we present an end-to-end scalable Artificial Intelligence (AI) framework built for classification of excitation-scanning HSI microscopy data that provides accurate image classification and interpretability of the AI decision-making process. The developed AI framework is able to perform real-time HSI classification with different speed/classification performance trade-offs by tailoring the dimensionality of the dataset, supporting different dimensions of deep learning models, and varying the architecture of deep learning models. We have also incorporated tools to visualize the exact location of the lesion detected by the AI decision-making process and to provide heatmap-based pixel-by-pixel interpretability. In addition, our deep learning framework provides wavelength-dependent impact as a heatmap, which allows visualization of the contributions of HSI wavelength bands during the AI decision-making process. This framework is well-suited for HSI microscope and endoscope platforms, where real-time analysis and visualization of classification results are required by clinicians. 

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4. 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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5. Employing Raman Spectroscopy and Machine Learning for the Identification of Breast Cancer

   https://doi.org/10.1186/s12575-024-00255-0  

   Zhang, Ya; Li, Zheng; Li, Zhongqiang; Wang, Huaizhi; Regmi, Dinkar; Zhang, Jian; Feng, Jiming; Yao, Shaomian; Xu, Jian (December 2024, Biological Procedures Online)

   Abstract BackgroundBreast cancer poses a significant health risk to women worldwide, with approximately 30% being diagnosed annually in the United States. The identification of cancerous mammary tissues from non-cancerous ones during surgery is crucial for the complete removal of tumors. ResultsOur study innovatively utilized machine learning techniques (Random Forest (RF), Support Vector Machine (SVM), and Convolutional Neural Network (CNN)) alongside Raman spectroscopy to streamline and hasten the differentiation of normal and late-stage cancerous mammary tissues in mice. The classification accuracy rates achieved by these models were 94.47% for RF, 96.76% for SVM, and 97.58% for CNN, respectively. To our best knowledge, this study was the first effort in comparing the effectiveness of these three machine-learning techniques in classifying breast cancer tissues based on their Raman spectra. Moreover, we innovatively identified specific spectral peaks that contribute to the molecular characteristics of the murine cancerous and non-cancerous tissues. ConclusionsConsequently, our integrated approach of machine learning and Raman spectroscopy presents a non-invasive, swift diagnostic tool for breast cancer, offering promising applications in intraoperative settings. 

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