Recent advancements in artificial intelligence (AI) and the vast data generated by modern clinical systems have driven the development of AI solutions in medical imaging, encompassing image reconstruction, segmentation, diagnosis, and treatment planning. Despite these successes and potential, many stakeholders worry about the risks and ethical implications of imaging AI, viewing it as complex, opaque, and challenging to understand, use, and trust in critical clinical applications. The FUTURE-AI guideline for trustworthy AI in healthcare was established based on six guiding principles: Fairness, Universality, Traceability, Usability, Robustness, and Explainability. Through international consensus, a set of recommendations was defined, covering the entire lifecycle of medical AI tools, from design, development, and validation to regulation, deployment, and monitoring. In this paper, we describe how these specific recommendations can be instantiated in the domain of medical imaging, providing an overview of current best practices along with guidelines and concrete metrics on how those recommendations could be met, offering a valuable resource to the international medical imaging community.

The identification of the location of prostate cancer is of paramount importance for improved treatment. This process is strictly bonded with the accurate segmentation of the prostate gland and its zones, on MR images. In the present study, an ensemble of 3 deep learning models along with a Meta-learner module able to refine the outcomes of the models, is proposed (Transi-Net) to segment the prostate's transition zone. A method to quantify the model's uncertainty is introduced to measure the confidence of an architecture with respect to its final decision. The backbone of Transi-Net consist the original U-net, Dense2U-net and Bridged U-net models. The proposed model showcased significant improvement in comparison with its base components as well as an independent model, the USE-Net, while it was proven more confident about its decision. The proposed model resulted in an improvement of 5%, 3%, 3% and 4% for Sensitivity, Balanced Accuracy, Dice Score and Rand Error Index respectively, compared to the second best, USE-Net.

The aim of this work is to present two different algorithmic pipelines for human activity recognition (HAR) in real time, exploiting inertial measurement unit (IMU) sensors. Various learning classifiers have been developed and tested across different datasets. The experimental results provide a comparative performance analysis based on accuracy and latency during fine-tuning, training and prediction. The overall accuracy of the proposed pipeline reaches 66 % in the publicly available dataset and 90% in the in-house one.

The paper describes experiences from building and cloudification of the in-silico research platform SilicoFCM, an innovative in-silico clinical trials' solution for the design and functional optimization of whole heart performance and monitoring effectiveness of pharmacological treatment, with the aim to reduce the animal studies and the human clinical trials. The primary aim of cloudification was to prove portability, improve scalability and reduce long-term infrastructure costs. The most computationally expensive part of the platform, the scientific workflow manager, was successfully ported to Amazon Web Services. We benchmarked the performance on three distinct research workflows, each of them having different resource requirements and execution time. The first benchmark was pure performance of running workflow sequentially. The aim of the second test was to stress-test the underlying infrastructure by submitting multiple workflows simultaneously. The benchmark results are promising, painting the infrastructure launching overhead almost negligible in this kind of heavy computational use-case.

Cognitive workload is a critical feature in related psychology, ergonomics, and human factors for understanding performance. However, it still is difficult to describe and thus, to measure it. Since there is no single sensor that can give a full understanding of workload, extended research has been conducted in order to present robust biomarkers. During the last years, machine learning techniques have been used to predict cognitive workload based on various features. Gaze extracted features, such as pupil size, blink activity and saccadic measures, have been used as predictors. The aim of this study is to use gaze extracted features as the only predictors of cognitive workload. Two factors were investigated: time pressure and multi tasking. The findings of this study showed that eye and gaze features are useful indicators of cognitive workload levels, reaching up to 88% accuracy.

In the last years, many studies have been investigating emotional arousal and valence. Most of them have focused on the use of physiological signals such as EEG or EMG, cardiovascular measures or skin conductance. However, eye related features have proven to be very helpful and easy to use metrics, especially pupil size and blink activity. The aim of this study is to predict emotional arousal and valence levels which are induced during emotionally charged situations from eye related features. For this reason, we performed an experimental study where the participants watched emotion-eliciting videos and self-assessed their emotions, while their eye movements were being recorded. In this work, several classifiers such as KNN, SVM, Naive Bayes, Trees and Ensemble methods were trained and tested. Finally, emotional arousal and valence levels were predicted with 85 and 91% efficiency, respectively.

Automatic segmentation of the prostate peripheral zone on Magnetic Resonance Images (MRI) is a necessary but challenging step for accurate prostate cancer diagnosis. Deep learning (DL) based methods, such as U-Net, have recently been developed to segment the prostate and its' sub-regions. Nevertheless, the presence of class imbalance in the image labels, where the background pixels dominate over the region to be segmented, may severely hamper the segmentation performance. In the present work, we propose a DL-based preprocessing pipeline for segmenting the peripheral zone of the prostate by cropping unnecessary information without making a priori assumptions regarding the location of the region of interest. The effect of DL-cropping for improving the segmentation performance was compared to the standard center-cropping using three state-of-the-art DL networks, namely U-net, Bridged U-net and Dense U-net. The proposed method achieved an improvement of 24%, 12% and 15% for the U-net, Bridged U-net and Dense U-net, respectively, in terms of Dice score.

The revolution in modelling and simulation methodologies accompanied with the recent events in the high-performance computing (HPC) helped the development of in silico clinical platforms which integrate advanced and individualized simulation models to support drug development. These platforms incorporate patient specific models to create and generate virtual patients (VPs). A parametric methodology for resampling and generating VPs is the multivariate normal distribution which in the current work is optimized through an iterative pipeline by the Kolmogorov-Smirnov goodness-of-fit test. The proposed VP generator is integrated in the multi-repository VP model of SILICOFCM which is a multi-modular, innovative in silico clinical trials solution for the design and functional optimization of the whole heart performance and monitoring effectiveness of pharmacological treatment for familial cardiomyopathies, with aim to reduce the animal studies and the human clinical trials.

Coronary stents are expandable scaffolds that are used to widen occluded diseased arteries and restore blood flow. Because of the strain they are exposed to and forces they must resist as well as the importance of surface interactions, material properties are dominant. Indeed, a common differentiating factors amongst commercially available stents is their material. Several performance requirements relate to stent materials including radial strength for adequate arterial support post-deployment. This study investigated the effect of the stent material in three finite element models using different stents made of: (i) Cobalt-Chromium (CoCr), (ii) Stainless Steel (SS316L), and (iii) Platinum Chromium (PtCr). Deployment was investigated in a patient specific arterial geometry, created based on a fusion of angiographic data and intravascular ultrasound images. In silico results show that: (i) the maximum von Mises stress occurs for the CoCr, however the curved areas of the stent links present higher stresses compared to the straight stent segments for all stents, (ii) more areas of high inner arterial stress exist in the case of the CoCr stent deployment, (iii) there is no significant difference in the percentage of arterial stress volume distribution among all models.

The aim of this study is to investigate the effect of mallear and incudal ligaments to the tympanic membrane and the stapes footplate displacement in a finite element model of the middle ear. Three cases were simulated: one without the ligaments, one including the posterior incudal and the anterior mallear ligaments and one including in addition the superior mallear and incudal ligaments. A maximum stapes footplate displacement 0.023 μm was observed at a frequency 1024 Hz by exciting the tympanic membrane at a sinusoidal sound pressure level (SPL) of 90 dB. The computational results were validated with experimental measurements from the literature. Concluding our results show that the superior ligaments are most beneficial for an accurate representation of the middle ear frequency response. Excellent agreement is observed between our results and human temporal bone experimental data and other finite element studies.