| Example of normal contrast-enhanced abdominal CT scan in human patient; L, liver; K, kidney; P, pancreas; St, stomach; Sp, spleen.

| Example of normal contrast-enhanced abdominal CT scan in human patient; L, liver; K, kidney; P, pancreas; St, stomach; Sp, spleen.

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Clinical drug trials for oncology have resulted in universal protocols for medical imaging in order to standardize protocols for image procurement, radiologic interpretation, and therapeutic response assessment. In recent years, there has been increasing interest in using large animal models to study oncologic disease, though few standards currentl...

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... in 1972, CT was the first non-invasive radiologic imaging technique allowing for tomographic imaging without superimposition of neighboring anatomic structures onto one another. This imaging technology operates through the acquisition of x-ray images spanning different angles across a single axis of rotation, and uses computer algorithms to reconstruct these planar projection images into cross-sectional slices (Figure 1). The x-ray imaging technology which this imaging modality is based upon measures the attenuation of high energy photon beams transmitted through a subject. ...
Context 2
... has resulted in a customized and tested clinical imaging workflow (68). The developed CT (Figure 9) and MR imaging (Figure 10) protocols demonstrate consistent and reproducible, high-resolution radiologic depiction of the liver which parallels human patient imaging. The protocols support the capability to use advanced radiological imaging for diagnostic surveillance and therapeutic outcomes analysis. ...

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... This includes the strong need to ensure reliability and reproducibility in the acquisition of diagnostic and prognostic imaging, especially with an increasingly broad range of imaging biomarkers that may be structural/morphological, textural, or functional. Thus, many endeavours have been made to provide general imaging modality harmonization from an image acquisition perspective across multicentre studies [5][6][7]. This includes specific clinical indications such as brain diffusion MRI [8], general neurological MRI [9], PET oncology trials [10,11] and lung CT [12] to name but a few. ...
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Introduction: The roles and responsibilities of medical physicists (MPs) are growing together with the evolving science and technology. The complexity of today’s clinical trials requires the skills and knowledge of MPs for their safe and efficient implementation. However, it is unclear to what extent the skillsets offered by MPs are being exploited in clinical trials across Europe. Methods: The EFOMP Working Group on the role of Medical Physics Experts in Clinical Trials has designed a survey that targeted all 36 current National Member Organisations, receiving a response from 31 countries. The survey included both quantitative and qualitative queries regarding the involvement of MPs in trial design, setup, and coordination, either as trial team members or principal investigators. Results: The extent of MPs involvement in clinical trials greatly varies across European countries. The results showed disparities between the roles played by MPs in trial design, conduct or data processing. Similarly, differences among the 31 European countries that responded to the survey were found regarding the existence of national bodies responsible for trials or the available training offered to MPs. The role of principal investigator or co-investigator was reported by 12 countries (39%), a sign of efficient collaboration with medical doctors in designing and implementing clinical studies. Conclusion: Organisation of specific training courses and guideline development for clinical trial design and conduct would encourage the involvement of a larger number of MPs in all stages of trials across Europe, leading to a better standardisation of clinical practice.
... In breast cancer, medical imaging systems allow the end-user to diagnose several modalities, such as MammoGraphy (MG), UltraSound (US) or Magnetic Resonance Imaging (MRI), from the retrieval of medical imaging data [58,127]. A wide range of Clinical Decision Support Systems (CDSS) exist, providing clinicians with the knowledge to enhance the clinical workflow [28,55,64], from systems that supply potential information for medical decision-making to those that make diagnostic decisions [77,111]. ...
Article
In this paper, we developed BreastScreening-AI within two scenarios for the classification of multimodal beast images: (1) Clinician-Only; and (2) Clinician-AI. The novelty relies on the introduction of a deep learning method into a real clinical workflow for medical imaging diagnosis. We attempt to address three high-level goals in the two above scenarios. Concretely, how clinicians: i) accept and interact with these systems, revealing whether are explanations and functionalities required; ii) are receptive to the introduction of AI-assisted systems, by providing benefits from mitigating the clinical error; and iii) are affected by the AI assistance. We conduct an extensive evaluation embracing the following experimental stages: (a) patient selection with different severities, (b) qualitative and quantitative analysis for the chosen patients under the two different scenarios. We address the high-level goals through a real-world case study of 45 clinicians from nine institutions. We compare the diagnostic and observe the superiority of the Clinician-AI scenario, as we obtained a decrease of 27% for False-Positives and 4% for False-Negatives. Through an extensive experimental study, we conclude that the proposed design techniques positively impact the expectations and perceptive satisfaction of 91% clinicians, while decreasing the time-to-diagnose by 3 min per patient.
... Medical imaging systems allow the end-user to diagnose several imaging modalities, such as Computed Tomography (CT), UltraSound (US) or Magnetic Resonance Imaging (MRI), from the retrieval of medical imaging data [34,88]. Bringing those modalities together offers new possibilities for quantitative and qualitative imaging and diagnosis but also requires specialized data handling, post-processing and novel visualization methods [45,78,94]. ...
... From the retrieval of medical imaging data [170,171], medical imaging systems allow the end-user to diagnose several modalities. In fact, by bringing several modalities together, it offers new possibilities for quantitative and qualitative imaging and diagnosis but also requires specialized data handling, postprocessing and novel visualization methods [172][173][174]. ...
Research Proposal
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The goal of this thesis is the study, design, and development, as well as evaluation of novel AI-based visual representations supported by intelligent agents for medical imaging diagnosis. For this purpose, recent achievements are built on the accuracy of intelligent agents. To address the effects of varied visual representations, the intelligent agents are applied for the breast cancer domain across different levels of medical expertise and multiple clinical workflows. Despite the promise of assisting clinicians in the decision-making process, there are two initial challenges that this thesis aims to cover: (i) the lack of available and curated medical data to be consumed by the AI algorithms; and (ii) the fact that medical professionals often find it challenging to understand how an AI system transform their initial input into a final decision. The goal is to evaluate the impact of the introduction of several techniques using an intelligent agent. Specifically, by focusing on how multimodality and AI-assistance could add value to the medical workflow in a User-Centered Design process. Indeed, this thesis provides a clinicians' evaluation and results for several high-level goals, namely: (a) understand clinicians' response to the AI-assistance; (b) find how they interact (and accept) with these systems; (c) discover how AI-assistive affects the UX of clinicians. A clinically oriented intelligent agent system can be achieved by mimicking the medical diagnosis and look for the presence of patient-relevant clues in breast cancer images. This approach (i.e., HAII that brings Human and AI together), tend to be easier for clinicians and leading to better patient results. In the end, the thesis discuss the importance, as well as pros-and-cons for the introduction of intelligent agents in the medical imaging workflow. Nevertheless, work is still in progress and some goals have not been accomplished yet which will be addressed as future work.
Article
Artificial intelligence has the potential to transform many application domains fundamentally. One notable example is clinical radiology. A growing number of decision-making support systems are available for lesion detection and segmentation, two fundamental steps to accomplish diagnosis and treatment planning. This paper proposes a model based on the unified theory of acceptance and use of technology to study the determinants for the adoption of intelligent agents across the medical imaging workflow. We tested the model via confirmatory factor analysis and structural equation modeling using clinicians’ data from an international evaluation of healthcare practitioners. Results show an increased understanding of the vital role of security, risk, and trust in the usage intention of intelligent agents. These empirical findings provide valuable theoretical contributions to researchers by explaining the reasons behind the adoption and usage of intelligent agents in the medical imaging workflow.