AI in Medical Imaging Informatics: Current Challenges and Future Directions

This paper reviews state-of-the-art research solutions across the spectrum of medical imaging informatics, discusses clinical translation, and provides future directions for advancing clinical practice. More specifically, it summarizes advances in medical imaging acquisition technologies for different modalities, highlighting the necessity for efficient medical data management strategies in the context of AI in big healthcare data analytics. It then provides a synopsis of contemporary and emerging algorithmic methods for disease classification and organ/ tissue segmentation, focusing on AI and deep learning architectures that have already become the de facto approach. The clinical benefits of in-silico modelling advances linked with evolving 3D reconstruction and visualization applications are further documented. Concluding, integrative analytics approaches driven by associate research branches highlighted in this study promise to revolutionize imaging informatics as known today across the healthcare continuum for both radiology and digital pathology applications. The latter, is projected to enable informed, more accurate diagnosis, timely prognosis, and effective treatment planning, underpinning precision medicine

EXPLORATION OF DOMAIN-SPECIFIC KNOWLEDGE GRAPHS FOR TESTABLE HYPOTHESIS GENERATION

In the span of a decade, we have brought about a fundamental shift in the way we structure, organize, store, and conceptualize biomedical datasets. Data which had previously been siloed has been gathered, organized, and aggregated into central repositories, interlinked with each other by categorizing these vast sums of knowledge into well defined ontologies. These interlinked databases, better known as knowledge graphs, have come to redefine our ability to explore the current state of our knowledge, answer complex questions about how objects relate to each other, and invent novel connections in vastly different research disciplines. With these knowledge graphs, new ideas can be quickly formulated, instead of relying upon the insight of a single scientist or small team of experts, these ideas can be made leveraging the vast historical catalog of research progress that has been captured in biomedical databases. Knowledge graphs can be used to propose hypotheses which narrow the nearly infinite array of possible explorations which can link any pair of ideas to only those which have some historical and practical considerations. In this way, we hope to utilize these knowledge graphs to produce hypotheses, promote those which are viable, and provide them to biomedical experts. In this work, we aim to develop methodologies to produce meaningful hypotheses using these graphs as inputs. We approach this problem by (i) utilizing intrinsic mathematical properties of the intermediate nodes along a pathways, (ii) translating existing biomedical ideas into graphical structures, and (iii) incorporating niche domain-specific biomedical datasets to explore domain problems. We have shown the ability of these methods to produce practical and useful hypotheses and pathways which can be utilized by experts for immediate exploration.Doctor of Philosoph

An Informatics Roadmap Toward a FAIR Understanding of Mitochondrial Biology and Rare Mitochondrial Disease

Mitochondrial biology is integral to our fundamental understanding of human health and many diseases. They exist in every human cell type except for red blood cells and have critical functions in metabolism, oxidative phosphorylation, oxidation-reduction, and as signaling hubs responsible for mediating protective mechanisms. Rare mitochondrial diseases (RMDs) are devastating and complex, affect multiple organ systems, and disproportionately impact young children. Despite copious existing knowledge and increased public interest, the knowledge is fragmented and difficult to access. Clinical case reports (CCRs) on RMDs contain valuable clinical insights, but they are scarce and lack the metadata necessary to facilitate their discovery among the two million CCRs on PubMed. The unstructured text data of CCRs is also ill-suited to computational approaches, limiting our ability to derive the knowledge contained within.To address these issues, I assembled all available informatics tools and resources with mitochondrial components and used them to contribute to Gene Wiki pages that enable easy access to mitochondrial knowledge for researchers, students, clinicians, and patients. Through these efforts, I made mitochondrial gene, protein, and disease knowledge widely accessible with contributions of over 4MB of content across 541 Gene Wiki pages. Concurrently, I used Gene Wiki as an educational platform to train over 50 students in the biosciences and pre-medical studies in mitochondrial biology and disease, as well as instilling effective research and writing methods in biomedicine.To impose structure on CCRs and render them FAIR (Findable, Accessible, Interoperable, Reusable), I developed and applied a standardized metadata template to RMD CCRs and codified patient symptomology with the International Statistical Classification of Disease and Related Health Problems (ICD) system. I created the open-source, cloud-based MitoCases RMD Knowledge Platform (http://mitocases.org/) to house data on 384 RMD CCRs, including 4,561 instances of 952 unique ICD codes. Supplementing CCRs with structured metadata amplifies machine-readable information content and provides a distinct improvement in searching for CCRs as compared to indexing by title and abstract. Finally, I employed these resources to conduct a thorough review of Barth syndrome and characterized the diversity of presentations, range of genetic etiologies, and treatment paradigms

Front-Line Physicians' Satisfaction with Information Systems in Hospitals

Day-to-day operations management in hospital units is difficult due to continuously varying situations, several actors involved and a vast number of information systems in use. The aim of this study was to describe front-line physicians' satisfaction with existing information systems needed to support the day-to-day operations management in hospitals. A cross-sectional survey was used and data chosen with stratified random sampling were collected in nine hospitals. Data were analyzed with descriptive and inferential statistical methods. The response rate was 65 % (n = 111). The physicians reported that information systems support their decision making to some extent, but they do not improve access to information nor are they tailored for physicians. The respondents also reported that they need to use several information systems to support decision making and that they would prefer one information system to access important information. Improved information access would better support physicians' decision making and has the potential to improve the quality of decisions and speed up the decision making process.Peer reviewe

Quantitative imaging in radiation oncology

Artificially intelligent eyes, built on machine and deep learning technologies, can empower our capability of analysing patients’ images. By revealing information invisible at our eyes, we can build decision aids that help our clinicians to provide more effective treatment, while reducing side effects. The power of these decision aids is to be based on patient tumour biologically unique properties, referred to as biomarkers. To fully translate this technology into the clinic we need to overcome barriers related to the reliability of image-derived biomarkers, trustiness in AI algorithms and privacy-related issues that hamper the validation of the biomarkers. This thesis developed methodologies to solve the presented issues, defining a road map for the responsible usage of quantitative imaging into the clinic as decision support system for better patient care

Computational Pathology: A Survey Review and The Way Forward

Computational Pathology CPath is an interdisciplinary science that augments developments of computational approaches to analyze and model medical histopathology images. The main objective for CPath is to develop infrastructure and workflows of digital diagnostics as an assistive CAD system for clinical pathology, facilitating transformational changes in the diagnosis and treatment of cancer that are mainly address by CPath tools. With evergrowing developments in deep learning and computer vision algorithms, and the ease of the data flow from digital pathology, currently CPath is witnessing a paradigm shift. Despite the sheer volume of engineering and scientific works being introduced for cancer image analysis, there is still a considerable gap of adopting and integrating these algorithms in clinical practice. This raises a significant question regarding the direction and trends that are undertaken in CPath. In this article we provide a comprehensive review of more than 800 papers to address the challenges faced in problem design all-the-way to the application and implementation viewpoints. We have catalogued each paper into a model-card by examining the key works and challenges faced to layout the current landscape in CPath. We hope this helps the community to locate relevant works and facilitate understanding of the field's future directions. In a nutshell, we oversee the CPath developments in cycle of stages which are required to be cohesively linked together to address the challenges associated with such multidisciplinary science. We overview this cycle from different perspectives of data-centric, model-centric, and application-centric problems. We finally sketch remaining challenges and provide directions for future technical developments and clinical integration of CPath (https://github.com/AtlasAnalyticsLab/CPath_Survey).Comment: Accepted in Elsevier Journal of Pathology Informatics (JPI) 202

Integrative approaches to high-throughput data in lymphoid leukemias (on transcriptomes, the whole-genome mutational landscape, flow cytometry and gene copy-number alterations)

Within this thesis I developed a new approach for the analysis and integration of heterogeneous leukemic data sets applicable to any high-throughput analysis including basic research. All layers are stored in a semantic graph which facilitates modifications by just adding edges (relationships/attributes) and nodes (values/results) as well as calculating biological consensus and clinical correlation. The front-end is accessible through a GUI (graphical user interface) on a Java-based Semantic Web server. I used this framework to describe the genomic landscape of T-PLL (T-cell prolymphocytic leukemia), which is a rare (~0.6/million) mature T-cell malignancy with aggressive clinical course, notorious treatment resistance, and generally low overall survival. We have conducted gene expression and copy-number profiling as well as NGS (next-generation sequencing) analyses on a cohort comprising 94 T-PLL cases. TCL1A (T-cell leukemia/lymphoma 1A) overexpression and ATM (Ataxia Telangiectasia Mutated) impairment represent central hallmarks of T-PLL, predictive for patient survival, T-cell function and proper DNA damage responses. We identified new chromosomal lesions, including a gain of AGO2 (Argonaute 2, RISC Catalytic Component; 57.14% of cases), which is decisive for the chromosome 8q lesion. While we found significant enrichments of truncating mutations in ATM mut/no del (p=0.01365), as well as FAT (FAT Atypical Cadherin) domain mutations in ATM mut/del (p=0.01156), JAK3 (Janus Kinase 3) mut/ATM del cases may represent another tumor lineage. Using whole-transcriptome sequencing, we identified novel structural variants affecting chromosome 14 that lead to the expression of a TCL1A-TCR (T-cell receptor) fusion transcript and a likely degradated TCL1A protein. Two clustering approaches of normal T-cell subsets vs. leukemia gene expression profiles, as well as immunophenotyping-based agglomerative clustering and TCR repertoire reconstruction further revealed a restricted, memory-like T-cell phenotype. This is to date the most comprehensive, multi-level, integrative study on T-PLL and it led to an evolutionary disease model and a histone deacetylase-inhibiting / double strand break-inducing treatment that performs better than the current standard of chemoimmunotherapy in preclinical testing