Modelling the interpretation of digital mammography using high order statistics and deep machine learning

Visual search is an inhomogeneous, yet efficient sampling process accomplished by the saccades and the central (foveal) vision. Areas that attract the central vision have been studied for errors in interpretation of medical images. In this study, we extend existing visual search studies to understand features of areas that receive direct visual attention and elicit a mark by the radiologist (True and False Positive decisions) from those that elicit a mark but were captured by the peripheral vision. We also investigate if there are any differences between these areas and those that are never fixated by radiologists. Extending these investigations, we further explore the possibility of modelling radiologists’ search behavior and their interpretation of mammograms using deep machine learning techniques. We demonstrated that energy profiles of foveated (FC), peripherally fixated (PC), and never fixated (NFC) areas are distinct. It was shown that FCs are selected on the basis of being most informative. Never fixated regions were found to be least informative. Evidences that energy profiles and dwell time of these areas influence radiologists’ decisions (and confidence in such decisions) were also shown. High-order features provided additional information to the radiologists, however their effect on decision (and confidence in such decision) was not significant. We also showed that deep-convolution neural network can successfully be used to model radiologists’ attentional level, decisions and confidence in their decisions. High accuracy and high agreement (between true and predicted values) in such predictions can be achieved in modelling attentional level (accuracy: 0.90, kappa: 0.82) and decisions (accuracy: 0.92, kappa: 0.86) of radiologists. Our results indicated that an ensembled model for radiologist’s search behavior and decision can successfully be built. Convolution networks failed to model missed cancers however

Multiparametric Magnetic Resonance Imaging Artificial Intelligence Pipeline for Oropharyngeal Cancer Radiotherapy Treatment Guidance

Oropharyngeal cancer (OPC) is a widespread disease and one of the few domestic cancers that is rising in incidence. Radiographic images are crucial for assessment of OPC and aid in radiotherapy (RT) treatment. However, RT planning with conventional imaging approaches requires operator-dependent tumor segmentation, which is the primary source of treatment error. Further, OPC expresses differential tumor/node mid-RT response (rapid response) rates, resulting in significant differences between planned and delivered RT dose. Finally, clinical outcomes for OPC patients can also be variable, which warrants the investigation of prognostic models. Multiparametric MRI (mpMRI) techniques that incorporate simultaneous anatomical and functional information coupled to artificial intelligence (AI) approaches could improve clinical decision support for OPC by providing immediately actionable clinical rationale for adaptive RT planning. If tumors could be reproducibly segmented, rapid response could be classified, and prognosis could be reliably determined, overall patient outcomes would be optimized to improve the therapeutic index as a function of more risk-adapted RT volumes. Consequently, there is an unmet need for automated and reproducible imaging which can simultaneously segment tumors and provide predictive value for actionable RT adaptation. This dissertation primarily seeks to explore and optimize image processing, tumor segmentation, and patient outcomes in OPC through a combination of advanced imaging techniques and AI algorithms. In the first specific aim of this dissertation, we develop and evaluate mpMRI pre-processing techniques for use in downstream segmentation, response prediction, and outcome prediction pipelines. Various MRI intensity standardization and registration approaches were systematically compared and benchmarked. Moreover, synthetic image algorithms were developed to decrease MRI scan time in an effort to optimize our AI pipelines. We demonstrated that proper intensity standardization and image registration can improve mpMRI quality for use in AI algorithms, and developed a novel method to decrease mpMRI acquisition time. Subsequently, in the second specific aim of this dissertation, we investigated underlying questions regarding the implementation of RT-related auto-segmentation. Firstly, we quantified interobserver variability for an unprecedented large number of observers for various radiotherapy structures in several disease sites (with a particular emphasis on OPC) using a novel crowdsourcing platform. We then trained an AI algorithm on a series of extant matched mpMRI datasets to segment OPC primary tumors. Moreover, we validated and compared our best model\u27s performance to clinical expert observers. We demonstrated that AI-based mpMRI OPC tumor auto-segmentation offers decreased variability and comparable accuracy to clinical experts, and certain mpMRI input channel combinations could further improve performance. Finally, in the third specific aim of this dissertation, we predicted OPC primary tumor mid-therapy (rapid) treatment response and prognostic outcomes. Using co-registered pre-therapy and mid-therapy primary tumor manual segmentations of OPC patients, we generated and characterized treatment sensitive and treatment resistant pre-RT sub-volumes. These sub-volumes were used to train an AI algorithm to predict individual voxel-wise treatment resistance. Additionally, we developed an AI algorithm to predict OPC patient progression free survival using pre-therapy imaging from an international data science competition (ranking 1st place), and then translated these approaches to mpMRI data. We demonstrated AI models could be used to predict rapid response and prognostic outcomes using pre-therapy imaging, which could help guide treatment adaptation, though further work is needed. In summary, the completion of these aims facilitates the development of an image-guided fully automated OPC clinical decision support tool. The resultant deliverables from this project will positively impact patients by enabling optimized therapeutic interventions in OPC. Future work should consider investigating additional imaging timepoints, imaging modalities, uncertainty quantification, perceptual and ethical considerations, and prospective studies for eventual clinical implementation. A dynamic version of this dissertation is publicly available and assigned a digital object identifier through Figshare (doi: 10.6084/m9.figshare.22141871)

A critical review of the current state of forensic science knowledge and its integration in legal systems

Forensic science has a significant historical and contemporary relationship with the criminal justice system. It is a relationship between two disciplines whose origins stem from different backgrounds. It is trite that effective communication assist in resolving underlying problems in any given context. However, a lack of communication continues to characterise the intersection between law and science. As recently as 2019, a six-part symposium on the use of forensic science in the criminal justice system again posed the question on how the justice system could ensure the reliability of forensic science evidence presented during trials. As the law demands finality, science is always evolving and can never be considered finite or final. Legal systems do not always adapt to the nature of scientific knowledge, and are not willing to abandon finality when that scientific knowledge shifts. Advocacy plays an important role in the promotion of forensic science, particularly advocacy to the broader scientific community for financial support, much needed research and more testing. However, despite its important function, advocacy should not be conflated with science. The foundation of advocacy is a cause; whereas the foundation of science is fact. The objective of this research was to conduct a qualitative literature review of the field of forensic science; to identify gaps in the knowledge of forensic science and its integration in the criminal justice system. The literature review will provide researchers within the field of forensic science with suggested research topics requiring further examination and research. To achieve its objective, the study critically analysed the historical development of, and evaluated the use of forensic science evidence in legal systems generally, including its role regarding the admissibility or inadmissibility of the evidence in the courtroom. In conclusion, it was determined that the breadth of forensic scientific knowledge is comprehensive but scattered. The foundational underpinning of the four disciplines, discussed in this dissertation, has been put to the legal test on countless occasions. Some gaps still remain that require further research in order to strengthen the foundation of the disciplines. Human influence will always be present in examinations and interpretations and will lean towards subjective decision making.JurisprudenceD. Phil

Craniofacial Growth Series Volume 56

https://deepblue.lib.umich.edu/bitstream/2027.42/153991/1/56th volume CF growth series FINAL 02262020.pdfDescription of 56th volume CF growth series FINAL 02262020.pdf : Proceedings of the 46th Annual Moyers Symposium and 44th Moyers Presymposiu

Minimally Invasive Expeditionary Surgical Care Using Human-Inspired Robots

This technical report serves as an updated collection of subject matter experts on surgical care using human-inspired robotics for human exploration. It is a summary of the Blue Sky Meeting, organized by the Florida Institute for Human and Machine Cognition (IHMC), Pensacola, Florida, and held on October 2-3, 2018. It contains an executive summary, the final report, all of the presentation materials, and an updated reference list

Space Science Opportunities Augmented by Exploration Telepresence

Since the end of the Apollo missions to the lunar surface in December 1972, humanity has exclusively conducted scientific studies on distant planetary surfaces using teleprogrammed robots. Operations and science return for all of these missions are constrained by two issues related to the great distances between terrestrial scientists and their exploration targets: high communication latencies and limited data bandwidth. Despite the proven successes of in-situ science being conducted using teleprogrammed robotic assets such as Spirit, Opportunity, and Curiosity rovers on the surface of Mars, future planetary field research may substantially overcome latency and bandwidth constraints by employing a variety of alternative strategies that could involve: 1) placing scientists/astronauts directly on planetary surfaces, as was done in the Apollo era; 2) developing fully autonomous robotic systems capable of conducting in-situ field science research; or 3) teleoperation of robotic assets by humans sufficiently proximal to the exploration targets to drastically reduce latencies and significantly increase bandwidth, thereby achieving effective human telepresence. This third strategy has been the focus of experts in telerobotics, telepresence, planetary science, and human spaceflight during two workshops held from October 3–7, 2016, and July 7–13, 2017, at the Keck Institute for Space Studies (KISS). Based on findings from these workshops, this document describes the conceptual and practical foundations of low-latency telepresence (LLT), opportunities for using derivative approaches for scientific exploration of planetary surfaces, and circumstances under which employing telepresence would be especially productive for planetary science. An important finding of these workshops is the conclusion that there has been limited study of the advantages of planetary science via LLT. A major recommendation from these workshops is that space agencies such as NASA should substantially increase science return with greater investments in this promising strategy for human conduct at distant exploration sites

Space Science Opportunities Augmented by Exploration Telepresence

Since the end of the Apollo missions to the lunar surface in December 1972, humanity has exclusively conducted scientific studies on distant planetary surfaces using teleprogrammed robots. Operations and science return for all of these missions are constrained by two issues related to the great distances between terrestrial scientists and their exploration targets: high communication latencies and limited data bandwidth. Despite the proven successes of in-situ science being conducted using teleprogrammed robotic assets such as Spirit, Opportunity, and Curiosity rovers on the surface of Mars, future planetary field research may substantially overcome latency and bandwidth constraints by employing a variety of alternative strategies that could involve: 1) placing scientists/astronauts directly on planetary surfaces, as was done in the Apollo era; 2) developing fully autonomous robotic systems capable of conducting in-situ field science research; or 3) teleoperation of robotic assets by humans sufficiently proximal to the exploration targets to drastically reduce latencies and significantly increase bandwidth, thereby achieving effective human telepresence. This third strategy has been the focus of experts in telerobotics, telepresence, planetary science, and human spaceflight during two workshops held from October 3–7, 2016, and July 7–13, 2017, at the Keck Institute for Space Studies (KISS). Based on findings from these workshops, this document describes the conceptual and practical foundations of low-latency telepresence (LLT), opportunities for using derivative approaches for scientific exploration of planetary surfaces, and circumstances under which employing telepresence would be especially productive for planetary science. An important finding of these workshops is the conclusion that there has been limited study of the advantages of planetary science via LLT. A major recommendation from these workshops is that space agencies such as NASA should substantially increase science return with greater investments in this promising strategy for human conduct at distant exploration sites