Expert microscopic analysis of cells obtained from frequent heart biopsies is vital for early detection of pediatric heart transplant rejection to prevent heart failure. Detection of this rare condition is prone to low levels of expert agreement due to the difficulty of identifying subtle rejection signs within biopsy samples. The rarity of pediatric heart transplant rejection also means that very few gold-standard images are available for developing machine learning models. To solve this urgent clinical challenge, we developed a deep learning model to automatically quantify rejection risk within digital images of biopsied tissue using an explainable synthetic data augmentation approach. We developed this explainable AI framework to illustrate how our progressive and inspirational generative adversarial network models distinguish between normal tissue images and those containing cellular rejection signs. To quantify biopsy-level rejection risk, we first detect local rejection features using a binary image classifier trained with expert-annotated and synthetic examples. We converted these local predictions into a biopsy-wide rejection score via an interpretable histogram-based approach. Our model significantly improves upon prior works with the same dataset with an area under the receiver operating curve (AUROC) of 98.84% for the local rejection detection task and 95.56% for the biopsy-rejection prediction task. A biopsy-level sensitivity of 83.33% makes our approach suitable for early screening of biopsies to prioritize expert analysis. Our framework provides a solution to rare medical imaging challenges currently limited by small datasets.

According to the World Health Organization, Artificial Intelligence (AI) technology may assist in COVID-19 management. However, existing image segmentation using AI suffers from a lack of accuracy and explainability, which prevents its adoption in actual clinical practice. In this paper, we investigated an attention-based image segmentation method for COVID-19 CT imaging with enhanced interpretation capabilities. Specifically, we developed U-Net architecture-based for segmentation with attention coefficients to produce a salient feature map. We use the DICE score and accuracy to perform a comprehensive model evaluation. We compared to other well-known methods such as Light U-Net, COPLE-Net, and Res U-Net and demonstrated that attention U-Net is superior for COVID-19 segmentation tasks in terms of performance and explainability. We also developed the tool as a web-application with a graphic user interface with the goal to translate this AI-driven clinical decision-support system for real-world clinical use.

This paper reports an interpretable automated grading system for diabetic retinopathy using color fundus images. First, we develop shallow learners as baselines. Second, we pre-train deep neural networks to extract high-dimensional features and complex patterns from fundus images and utilize ensemble models to do automatic grading. Then we develop several explainable artificial intelligence models to visualize the extracted deep features and to interpret the predicted outcomes. We investigate the robustness of our system over two publicly available diabetic retinopathy fundus imaging datasets. In addition, we displayed both local and global explainable results to further illustrate the clinical decision-making process with deep models. The innovations of our work include (1) using ensemble models to boost the performance of diabetic retinopathy grading system, and (2) providing transparency of ensemble models using explainable artificial intelligence. The result has shown the potential to improve the effectiveness and accessibility of diabetic retinopathy screening in clinical practice and research settings.

Alzheimer's Disease (AD) is an irreversible and progressive neurodegenerative disorder with three stages: cognitively normal (CN), mild cognitive impairment (MCI), and clinical dementia. Progression and stage prediction of dementia plays an important role in prognosis and treatment. In this work, we developed a multi-modal AD progress prediction model that integrates magnetic resonance imaging (MRI) and electronic health record (EHR) to classify patients into three stages: CN, MCI, and AD. We trained deep auto-encoder to extract features from EHR data, and ResNet and 3D U-Net for MRI imaging data. We developed an entropy-based weighted sum classification method to integrate the classification results from each individual modality to generate final prediction. We experimented on Alzheimer's Disease Neuroimaging Initiative (ADNI) data to demonstrate that the multi-modality integration model outperforms single modality models in accuracy, precision, recall, and F1 scores. In addition, our model achieves competitive performance in comparison with other state-of-the-art multi-modality integration methods on AD progression prediction.

There is a perennial need to identify novel, effective therapeutic agents to combat rising infections. Recently, prediction of therapeutic targets to decrease the impact of COVID-19 has posed an urgent challenge requiring innovative solutions. Successful identification of novel drug-target combinations may greatly facilitate drug development. To meet this need, we developed a COVID-19 drug target prediction model using machine learning approaches to quickly identify drug candidates for 18 COVID-19 protein targets. Specifically, we analyzed the performance of three prediction models to predict drug-target docking scores, which represents the strength of interactions between ligands and proteins. Docking scores were predicted for 300,457 molecules on 18 different COVID-19 related protein docking targets. Our proposed approach achieved a competitive performance with $\mathrm{R}^{2}$=0.69,MAE=0.285, MSE=0.627. In addition, we identify chemical structures associated with stronger binding affinities across target binding sites. We believe our work could potentially save pharmaceutical companies significant resources, especially during the early stages of drug development.

Scoliosis is a spinal curvature that most frequently affects adolescents. Posterior spinal fusion surgery is required to correct the deformity in patients with severe scoliosis. Surgeons frequently use radiographic measurements and patient reported outcomes to aid in surgical treatment and monitor patient rehabilitation. Shriners Hospitals for Children is a large healthcare system caring for a significant percentage of pediatric patients with scoliosis. Surgeons from SHC-Greenville and SHC-Lexington have recorded data from more than 1,000 individual scoliosis patients. However, these collected data are usually dispersed across individual healthcare sites, necessitating the development of an integrated clinical data repository for data sharing and management. In this paper, we established a standardized research data repository with FHIR resources to harmonize multi-modal patient data from multiple clinical sites. Additionally, a FHIR-compliant application with a web-based user interface was prototyped to enable clinicians and researchers to access scoliosis patient data within our integrated and standardized research repository. Patient cohort definitions can be used to search these records using the same FHIR application. This standardized data-sharing framework and healthcare information system can be applied to multi-site and multimodality studies for clinical and research purposes, with the ultimate goal of improving the quality of patient care.