More Like this

1. Ensemble Machine Learning for Alzheimer’s disease Classification from Retinal Vasculature

   Lin, Hely ; Fang, Ruogu ( October 2021 , Biomedical Engineering Society Annual Meeting)

   null (Ed.)

   Introduction: Alzheimer’s disease (AD) causes progressive irreversible cognitive decline and is the leading cause of dementia. Therefore, a timely diagnosis is imperative to maximize neurological preservation. However, current treatments are either too costly or limited in availability. In this project, we explored using retinal vasculature as a potential biomarker for early AD diagnosis. This project focuses on stage 3 of a three-stage modular machine learning pipeline which consisted of image quality selection, vessel map generation, and classification [1]. The previous model only used support vector machine (SVM) to classify AD labels which limited its accuracy to 82%. In this project, random forest and gradient boosting were added and, along with SVM, combined into an ensemble classifier, raising the classification accuracy to 89%. Materials and Methods: Subjects classified as AD were those who were diagnosed with dementia in “Dementia Outcome: Alzheimer’s disease” from the UK Biobank Electronic Health Records. Five control groups were chosen with a 5:1 ratio of control to AD patients where the control patients had the same age, gender, and eye side image as the AD patient. In total, 122 vessel images from each group (AD and control) were used. The vessel maps were then segmented from fundus images through U-net. A t-test feature selection was first done on the training folds and the selected features was fed into the classifiers with a p-value threshold of 0.01. Next, 20 repetitions of 5-fold cross validation were performed where the hyperparameters were solely tuned on the training data. An ensemble classifier consisting of SVM, gradient boosting tree, and random forests was built and the final prediction was made through majority voting and evaluated on the test set. Results and Discussion: Through ensemble classification, accuracy increased by 4-12% relative to the individual classifiers, precision by 9-15%, sensitivity by 2-9%, specificity by at least 9-16%, and F1 score by 712%. Conclusions: Overall, a relatively high classification accuracy was achieved using machine learning ensemble classification with SVM, random forest, and gradient boosting. Although the results are very promising, a limitation of this study is that the requirement of needing images of sufficient quality decreased the amount of control parameters that can be implemented. However, through retinal vasculature analysis, this project shows machine learning’s high potential to be an efficient, more cost-effective alternative to diagnosing Alzheimer’s disease. Clinical Application: Using machine learning for AD diagnosis through retinal images will make screening available for a broader population by being more accessible and cost-efficient. Mobile device based screening can also be enabled at primary screening in resource-deprived regions. It can provide a pathway for future understanding of the association between biomarkers in the eye and brain. 

   more » « less
2. Multi‐scale, domain knowledge‐guided attention + random forest: a two‐stage deep learning‐based multi‐scale guided attention models to diagnose idiopathic pulmonary fibrosis from computed tomography images

   https://doi.org/10.1002/mp.16053  

   Yu, Wenxi ; Zhou, Hua ; Choi, Youngwon ; Goldin, Jonathan G. ; Teng, Pangyu ; Wong, Weng Kee ; McNitt‐Gray, Michael F. ; Brown, Matthew S. ; Kim, Grace Hyun J. ( November 2022 , Medical Physics)

   Idiopathic pulmonary fibrosis (IPF) is a progressive, irreversible, and usually fatal lung disease of unknown reasons, generally affecting the elderly population. Early diagnosis of IPF is crucial for triaging patients’ treatment planning into anti‐fibrotic treatment or treatments for other causes of pulmonary fibrosis. However, current IPF diagnosis workflow is complicated and time‐consuming, which involves collaborative efforts from radiologists, pathologists, and clinicians and it is largely subject to inter‐observer variability.

   The purpose of this work is to develop a deep learning‐based automated system that can diagnose subjects with IPF among subjects with interstitial lung disease (ILD) using an axial chest computed tomography (CT) scan. This work can potentially enable timely diagnosis decisions and reduce inter‐observer variability.

   Our dataset contains CT scans from 349 IPF patients and 529 non‐IPF ILD patients. We used 80% of the dataset for training and validation purposes and 20% as the holdout test set. We proposed a two‐stage model: at stage one, we built a multi‐scale, domain knowledge‐guided attention model (MSGA) that encouraged the model to focus on specific areas of interest to enhance model explainability, including both high‐ and medium‐resolution attentions; at stage two, we collected the output from MSGA and constructed a random forest (RF) classifier for patient‐level diagnosis, to further boost model accuracy. RF classifier is utilized as a final decision stage since it is interpretable, computationally fast, and can handle correlated variables. Model utility was examined by (1) accuracy, represented by the area under the receiver operating characteristic curve (AUC) with standard deviation (SD), and (2) explainability, illustrated by the visual examination of the estimated attention maps which showed the important areas for model diagnostics.

   During the training and validation stage, we observe that when we provide no guidance from domain knowledge, the IPF diagnosis model reaches acceptable performance (AUC±SD = 0.93±0.07), but lacks explainability; when including only guided high‐ or medium‐resolution attention, the learned attention maps are not satisfactory; when including both high‐ and medium‐resolution attention, under certain hyperparameter settings, the model reaches the highest AUC among all experiments (AUC±SD = 0.99±0.01) and the estimated attention maps concentrate on the regions of interests for this task. Three best‐performing hyperparameter selections according to MSGA were applied to the holdout test set and reached comparable model performance to that of the validation set.

   Our results suggest that, for a task with only scan‐level labels available, MSGA+RF can utilize the population‐level domain knowledge to guide the training of the network, which increases both model accuracy and explainability.

    

   more » « less
3. Composition and Functional Potential of the Human Mammary Microbiota Prior to and Following Breast Tumor Diagnosis

   https://doi.org/10.1128/msystems.01489-21  

   Hoskinson, Courtney ; Zheng, Kelly ; Gabel, Jaelyn ; Kump, Annie ; German, Rana ; Podicheti, Ram ; Marino, Natascia ; Stiemsma, Leah T. ( June 2022 , mSystems)

   Gibbons, Sean M. (Ed.)

   ABSTRACT Microbiota studies have reported changes in the microbial composition of the breast upon cancer development. However, results are inconsistent and limited to the later phases of cancer development (after diagnosis). We analyzed and compared the resident bacterial taxa of histologically normal breast tissue (healthy, H, n  = 49) with those of tissues donated prior to (prediagnostic, PD, n  = 15) and after (adjacent normal, AN, n  = 49, and tumor, T, n  = 46) breast cancer diagnosis ( n total = 159). DNA was isolated from tissue samples and submitted for Illumina MiSeq paired-end sequencing of the V3-V4 region of the 16S gene. To infer bacterial function in breast cancer, we predicted the functional bacteriome from the 16S sequencing data using PICRUSt2. Bacterial compositional analysis revealed an intermediary taxonomic signature in the PD tissue relative to that of the H tissue, represented by shifts in Bacillaceae , Burkholderiaceae , Corynebacteriaceae , Streptococcaceae , and Staphylococcaceae . This compositional signature was enhanced in the AN and T tissues. We also identified significant metabolic reprogramming of the microbiota of the PD, AN, and T tissue compared with the H tissue. Further, preliminary correlation analysis between host transcriptome profiling and microbial taxa and genes in H and PD tissues identified altered associations between the human host and mammary microbiota in PD tissue compared with H tissue. These findings suggest that compositional shifts in bacterial abundance and metabolic reprogramming of the breast tissue microbiota are early events in breast cancer development that are potentially linked with cancer susceptibility. IMPORTANCE The goal of this study was to determine the role of resident breast tissue bacteria in breast cancer development. We analyzed breast tissue bacteria in healthy breast tissue and breast tissue donated prior to (precancerous) and after (postcancerous) breast cancer diagnosis. Compared to healthy tissue, the precancerous and postcancerous breast tissues demonstrated differences in the amounts of breast tissue bacteria. In addition, breast tissue bacteria exhibit different functions in pre-cancerous and post-cancerous breast tissues relative to healthy tissue. These differences in function are further emphasized by altered associations of the breast tissue bacteria with gene expression in the human host prior to cancer development. Collectively, these analyses identified shifts in bacterial abundance and metabolic function (dysbiosis) prior to breast tumor diagnosis. This dysbiosis may serve as a therapeutic target in breast cancer prevention. 

   more » « less
4. Auto-ASD-Network: A Technique Based on Deep Learning and Support Vector Machines for Diagnosing Autism Spectrum Disorder using fMRI Data

   https://doi.org/10.1145/3307339.3343482  

   Eslami, Taban ; Saeed, Fahad ( January 2019 , Proceedings of the 10th ACM International Conference on Bioinformatics, Computational Biology and Health Informatics)

   Quantitative analysis of brain disorders such as Autism Spectrum Disorder (ASD) is an ongoing field of research. Machine learning and deep learning techniques have been playing an important role in automating the diagnosis of brain disorders by extracting discriminative features from the brain data. In this study, we propose a model called Auto-ASD-Network in order to classify subjects with Autism disorder from healthy subjects using only fMRI data. Our model consists of a multilayer perceptron (MLP) with two hidden layers. We use an algorithm called SMOTE for performing data augmentation in order to generate artificial data and avoid overfitting, which helps increase the classification accuracy. We further investigate the discriminative power of features extracted using MLP by feeding them to an SVM classifier. In order to optimize the hyperparameters of SVM, we use a technique called Auto Tune Models (ATM) which searches over the hyperparameter space to find the best values of SVM hyperparameters. Our model achieves more than 70% classification accuracy for 4 fMRI datasets with the highest accuracy of 80%. It improves the performance of SVM by 26%, the stand-alone MLP by 16% and the state of the art method in ASD classification by 14%. The implemented code will be available as GPL license on GitHub portal of our lab (https://github.com/PCDS). 

   more » « less
5. Overview of cell motility-based sickle cell disease diagnostic system in shearing digital holographic microscopy

   https://doi.org/10.1088/2515-7647/ab8a58  

   O’Connor, Timothy ; Anand, Arun ; Andemariam, Biree ; Javidi, Bahram ( May 2020 , Journal of Physics: Photonics)

   We overview a previously reported low-cost, compact, and 3D-printed shearing interferometer system for automated diagnosis of sickle cell disease based on red blood cell (RBC) bio-physical parameters and membrane fluctuations measured via digital holographic microscopy. The portable quantitative phase microscope is used to distinguish between healthy RBCs and those affected by sickle cell disease. Video holograms of RBCs are recorded, then each video hologram frame is computationally reconstructed to retrieve the time-varying phase profile of the cell distribution under study. The dynamic behavior of the cells is captured by creating a spatio-temporal data cube from which features regarding membrane fluctuations are extracted. Furthermore, the Optical Flow algorithm was used to capture lateral motility information of the cells. The motility-based features are combined with physical, morphology-based cell features and inputted into a random forest classifier which outputs the health state of the cell. Classification is performed with high accuracy at both the cell level and patient level.

    

   more » « less