Fleming Lure's research while affiliated with Georgia Institute of Technology and other places

Drug resistance in tuberculosis bacteria has always been one of the puzzles to be solved in the treatment of pulmonary tuberculosis. Clinically, methods for detecting drug resistance of tubercle bacillus include mainly the phenotypic drug susceptibility test and drug-resistance gene mutation detection. Among them, phenotypic methods include traditional drug susceptibility detection, microscopic observation of drug susceptibility, and the detection based on the metabolic reaction of tubercle bacillus. The traditional drug susceptibility test is the gold standard for detecting drug-resistant tubercle bacillus, but it is usually time-consuming with high cost, and the operation requires accurate bacterial quantification to match the drug concentration of the medium. The use of this method in hospitals either domestic or abroad is limited by its availability, reliability, accuracy, repeatability, and standardization, and its results have limited guidance on clinical diagnosis and treatment. Therefore, further improvement is required for it. The determination of susceptibility of Mycobacterium tuberculosis to drugs based on its characteristic microscopic observation is characterized by high sensitivity and high specificity. However, the high requirements on personnel engaged in the detection limit the generalized clinical application of the microscopic method. Metabolic reaction of Mycobacterium tuberculosis is a commonly used method for cultivating rapidly Mycobacterium tuberculosis and identifying drug susceptibility. Due to its high automation and low pollution rate, it has been approved by WHO as a recommended method for determining drug resistance in Mycobacterium tuberculosis.

Ordinal learning (OL) is a type of machine learning models with broad utility in health care applications such as diagnosis of different grades of a disease (e.g., mild, modest, severe) and prediction of the speed of disease progression (e.g., very fast, fast, moderate, slow). This paper aims to tackle a situation when precisely labeled samples are limited in the training set due to cost or availability constraints, whereas there could be an abundance of samples with imprecise labels. We focus on imprecise labels that are intervals, i.e., one can know that the a sample belongs to an interval of labels but cannot know which unique label it has. This situation is quite common in health care datasets due to limitations of the diagnostic instrument, sparse clinical visits, or/and patient dropout. Limited research has been done to develop OL models with imprecise/interval labels. We propose a new Hybrid Ordinal Learner (HOL) to integrate samples with both precise and interval labels to train a robust OL model. We also develop a tractable and efficient optimization algorithm to solve the HOL formulation. We compare HOL with several recently developed OL methods on four benchmarking datasets, which demonstrate the superior performance of HOL. Finally, we apply HOL to a real-world dataset for predicting the speed of progressing to Alzheimer’s Disease (AD) for individuals with Mild Cognitive Impairment (MCI) based on a combination of multi-modality neuroimaging and demographic/clinical datasets. HOL achieves high accuracy in the prediction and outperforms existing methods. The capability of accurately predicting the speed of progression to AD for each individual with MCI has the potential for helping facilitate more individually-optimized interventional strategies. Note to Practitioners —Machine learning (ML) algorithms have been widely adopted to support disease diagnosis and prognosis. In some situations, the outcome variable of interest is on an ordinal scale, i.e., it includes several classes with a natural order. For example, the variable of interest can be the grade of a disease as mild, moderate, or severe; or it can be the progression speed of a disease as very fast, fast, moderate, or slow. Ordinal learning (OL) is the type of ML algorithms for ordinal variable prediction. Most existing OL algorithms can only include samples with precise labels in training. However, it is common to have samples with imprecise/interval labels, i.e., we know that a sample belongs to a range of classes/labels but do not know which specific class/label it belongs to. This situation can happen due to a variety of different reasons such as use of less accurate diagnostic instrument under cost or availability constraints, sparse clinical assessment, and patient dropout. We propose a Hybrid Ordinal Learner (HOL) to integrate samples with both precise and interval labels to train a robust OL model. HOL is evaluated using four public benchmarking datasets and shows superior performance compared to existing methods. Also, we apply HOL to a real-world dataset for predicting the speed of progressing to Alzheimer’s Disease (AD) for individuals with Mild Cognitive Impairment (MCI). MCI is the prodromal stage of AD. Individuals with MCI show noticeable signs of memory loss and cognitive declines, but these symptoms are not severe enough to interfere their independent living. HOL achieves high accuracy in predicting the speed of progressing to AD for each MCI subject (e.g., the speed of ‘very fast’‘, fast’‘, moderate’, or ‘slow), which could potentially help facilitate the development of more individually-optimized interventional strategies.

Background Prediction of Alzheimer’s disease (AD) risk for individuals with mild cognitive impairment (MCI) provides an opportunity for early intervention. Neuroimaging of different types/modalities has shown promise, but not every patient has all the modalities due to the cost and accessibility constraints. To integrate incomplete multi‐modality datasets, we previously developed a machine learning (ML) model called incomplete‐modality transfer learning (IMTL). We extended the capacity of IMTL to handle high‐dimensional feature sets, namely, HD‐IMTL, to further improve accuracy and robustness. Method Our dataset included 1319 T1‐MRI scans from MCI patients in ADNI; among them, 1002 had FDG‐PET and 612 had amyloid‐PET. 156 regional volumetric and thickness features were computed from MRI and 83 and 83 regional SUVR features from FDG‐PET and amyloid‐PET, respectively. The dataset is randomly split into training and test sets. The goal of HD‐IMTL was to jointly train 4 ML models to predict MCI conversion to AD in 36 months, with each model based on a certain combination of available modalities, namely, MRI, MRI+FDG, MRI+amyloid, and MRI+FDG+amyloid. These correspond to patient sub‐cohorts that differ in their access to imaging modalities. To handle high‐dimensional features, we employed feature screening to remove uninformative features, performed modality‐wise partial least squares (PLS) to condense remaining features into PLS components, and used correlation tests to select components. To jointly train the 4 ML prediction models, IMTL was used, which is a generative model that uses expectation‐maximization (EM) in joint parameter estimation to facilitate transfer learning. To account for sample imbalance in training, the Synthetic Minority Over‐sampling Technique (SMOTE) was used. The trained models were applied to the test set. 20 training/test splits were repeated and AUCs on the test set were averaged. For comparison, three existing ML models for incomplete‐modality fusion were applied to the same dataset. Result The AUCs by HD‐IMTL were 0.802, 0.840, 0.868, and 0.880 for sub‐cohorts with MRI, MRI+FDG, MRI+amyloid, and MRI+FDG+amyloid, respectively. The AUCs by existing methods were lower, with ranges of 0.749‐0.793, 0.769‐0.826, 0.816‐0.863, and 0.832‐0.868. Conclusion HD‐IMTL demonstrated high accuracy in predicting MCI conversion to AD for patients with varying access/availability of imaging modalities.

Early diagnosis of Alzheimer’s disease (AD) is an important task that facilitates the development of treatment and prevention strategies, and may potentially improve patient outcomes. Neuroimaging has shown great promise, including the amyloid-PET, which measures the accumulation of amyloid plaques in the brain—a hallmark of AD. It is desirable to train end-to-end deep learning models to predict the progression of AD for individuals at early stages based on 3D amyloid-PET. However, commonly used models are trained in a fully supervised learning manner, and they are inevitably biased toward the given label information. To this end, we propose a selfsupervised contrastive learning method to accurately predict the conversion to AD for individuals with mild cognitive impairment (MCI) with 3D amyloid-PET. The proposed method, SMoCo, uses both labeled and unlabeled data to capture general semantic representations underlying the images. As the downstream task is given as classification of converters vs. non-converters, unlike the general self-supervised learning problem that aims to generate task-agnostic representations, SMoCo additionally utilizes the label information in the pre-training. To demonstrate the performance of our method, we conducted experiments on the Alzheimer’s Disease Neuroimaging Initiative (ADNI) dataset. The results confirmed that the proposed method is capable of providing appropriate data representations, resulting in accurate classification. SMoCo showed the best classification performance over the existing methods, with AUROC = 85.17%, accuracy = 81.09%, sensitivity = 77.39%, and specificity = 82.17%. While SSL has demonstrated great success in other application domains of computer vision, this study provided the initial investigation of using a proposed self-supervised contrastive learning model, SMoCo, to effectively predict MCI conversion to AD based on 3D amyloid-PET.

Early detection of Alzheimer's Disease (AD) is crucial to ensure timely interventions and optimize treatment outcomes for patients. While integrating multi-modal neuroimages, such as MRI and PET, has shown great promise, limited research has been done to effectively handle incomplete multi-modal image datasets in the integration. To this end, we propose a deep learning-based framework that employs Mutual Knowledge Distillation (MKD) to jointly model different sub-cohorts based on their respective available image modalities. In MKD, the model with more modalities (e.g., MRI and PET) is considered a teacher while the model with fewer modalities (e.g., only MRI) is considered a student. Our proposed MKD framework includes three key components: First, we design a teacher model that is student-oriented, namely the Student-oriented Multi-modal Teacher (SMT), through multi-modal information disentanglement. Second, we train the student model by not only minimizing its classification errors but also learning from the SMT teacher. Third, we update the teacher model by transfer learning from the student's feature extractor because the student model is trained with more samples. Evaluations on Alzheimer's Disease Neuroimaging Initiative (ADNI) datasets highlight the effectiveness of our method. Our work demonstrates the potential of using AI for addressing the challenges of incomplete multi-modal neuroimage datasets, opening new avenues for advancing early AD detection and treatment strategies.

In the above article [1] , unfortunately, there were some typos. These errors are highlighted in this article. In the Abstract and Introduction, the abbreviation ARDR should be corrected to “ADRD. ” The hyphen (“-”) is lost in many places in the above article. We highlight the corrected text here to avoid any potential misunderstanding. They are “ $H=1.60-1.80 \text {m}$ ,” “ $D_{c}=(1.346/{(Rv)}^{1/2})\approx 1.54\,-1.63 \text {s}$ ,” “ $D_{s}=0.762\ast D_{c}- 0.143\approx 1.02-1.09 \text {s}$ ,” “ $D_{\text {ds}}=0.252\ast D_{c}-0.143\approx 0.25-0.27 \text {s}$ ,” “sub-band,” “micro-motions,” “re-split,” “elderly-specific,” and “multi-subjects.”

Abnormal gait is a significant non-cognitive biomarker for Alzheimer’s disease (AD) and AD-related dementia (ADRD). Micro-Doppler radar, a non-wearable technology, can capture human gait movements for potential early ADRD risk assessment. In this research, we propose to design STRIDE integrating micro-Doppler radar sensors with advanced artificial intelligence (AI) technologies. STRIDE embeds a new deep learning (DL) classification framework. As a proof of concept, we develop a “digital-twin” of STRIDE, consisting of a human walking simulation model and a micro-Doppler radar simulation model, to generate a gait signature dataset. Taking established human walking parameters, the walking model simulates individuals with ADRD under various conditions. The radar model based on electromagnetic scattering and the Doppler frequency shift model is employed to generate micro-Doppler signatures from different moving body parts (e.g., foot, limb, joint, torso, shoulder, etc.). A band-dependent DL framework is developed to predict ADRD risks. The experimental results demonstrate the effectiveness and feasibility of STRIDE for evaluating ADRD risk.